Tourism forecasting using modified empirical mode decomposition and group method of data handling

NASA Astrophysics Data System (ADS)

Yahya, N. A.; Samsudin, R.; Shabri, A.

2017-09-01

In this study, a hybrid model using modified Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) model is proposed for tourism forecasting. This approach reconstructs intrinsic mode functions (IMFs) produced by EMD using trial and error method. The new component and the remaining IMFs is then predicted respectively using GMDH model. Finally, the forecasted results for each component are aggregated to construct an ensemble forecast. The data used in this experiment are monthly time series data of tourist arrivals from China, Thailand and India to Malaysia from year 2000 to 2016. The performance of the model is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) where conventional GMDH model and EMD-GMDH model are used as benchmark models. Empirical results proved that the proposed model performed better forecasts than the benchmarked models.

Forecasting stochastic neural network based on financial empirical mode decomposition.

PubMed

Wang, Jie; Wang, Jun

2017-06-01

In an attempt to improve the forecasting accuracy of stock price fluctuations, a new one-step-ahead model is developed in this paper which combines empirical mode decomposition (EMD) with stochastic time strength neural network (STNN). The EMD is a processing technique introduced to extract all the oscillatory modes embedded in a series, and the STNN model is established for considering the weight of occurrence time of the historical data. The linear regression performs the predictive availability of the proposed model, and the effectiveness of EMD-STNN is revealed clearly through comparing the predicted results with the traditional models. Moreover, a new evaluated method (q-order multiscale complexity invariant distance) is applied to measure the predicted results of real stock index series, and the empirical results show that the proposed model indeed displays a good performance in forecasting stock market fluctuations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Gravity Tides Extracted from Relative Gravimeter Data by Combining Empirical Mode Decomposition and Independent Component Analysis

NASA Astrophysics Data System (ADS)

Yu, Hongjuan; Guo, Jinyun; Kong, Qiaoli; Chen, Xiaodong

2018-04-01

The static observation data from a relative gravimeter contain noise and signals such as gravity tides. This paper focuses on the extraction of the gravity tides from the static relative gravimeter data for the first time applying the combined method of empirical mode decomposition (EMD) and independent component analysis (ICA), called the EMD-ICA method. The experimental results from the CG-5 gravimeter (SCINTREX Limited Ontario Canada) data show that the gravity tides time series derived by EMD-ICA are consistent with the theoretical reference (Longman formula) and the RMS of their differences only reaches 4.4 μGal. The time series of the gravity tides derived by EMD-ICA have a strong correlation with the theoretical time series and the correlation coefficient is greater than 0.997. The accuracy of the gravity tides estimated by EMD-ICA is comparable to the theoretical model and is slightly higher than that of independent component analysis (ICA). EMD-ICA could overcome the limitation of ICA having to process multiple observations and slightly improve the extraction accuracy and reliability of gravity tides from relative gravimeter data compared to that estimated with ICA.

Detecting intrinsic dynamics of traffic flow with recurrence analysis and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Xiong, Hui; Shang, Pengjian; Bian, Songhan

2017-05-01

In this paper, we apply the empirical mode decomposition (EMD) method to the recurrence plot (RP) and recurrence quantification analysis (RQA), to evaluate the frequency- and time-evolving dynamics of the traffic flow. Based on the cumulative intrinsic mode functions extracted by the EMD, the frequency-evolving RP regarding different oscillation of modes suggests that apparent dynamics of the data considered are mainly dominated by its components of medium- and low-frequencies while severely affected by fast oscillated noises contained in the signal. Noises are then eliminated to analyze the intrinsic dynamics and consequently, the denoised time-evolving RQA diversely characterizes the properties of the signal and marks crucial points more accurately where white bands in the RP occur, whereas a strongly qualitative agreement exists between all the non-denoised RQA measures. Generally, the EMD combining with the recurrence analysis sheds more reliable, abundant and inherent lights into the traffic flow, which is meaningful to the empirical analysis of complex systems.

Detection of the ice assertion on aircraft using empirical mode decomposition enhanced by multi-objective optimization

NASA Astrophysics Data System (ADS)

Bagherzadeh, Seyed Amin; Asadi, Davood

2017-05-01

In search of a precise method for analyzing nonlinear and non-stationary flight data of an aircraft in the icing condition, an Empirical Mode Decomposition (EMD) algorithm enhanced by multi-objective optimization is introduced. In the proposed method, dissimilar IMF definitions are considered by the Genetic Algorithm (GA) in order to find the best decision parameters of the signal trend. To resolve disadvantages of the classical algorithm caused by the envelope concept, the signal trend is estimated directly in the proposed method. Furthermore, in order to simplify the performance and understanding of the EMD algorithm, the proposed method obviates the need for a repeated sifting process. The proposed enhanced EMD algorithm is verified by some benchmark signals. Afterwards, the enhanced algorithm is applied to simulated flight data in the icing condition in order to detect the ice assertion on the aircraft. The results demonstrate the effectiveness of the proposed EMD algorithm in aircraft ice detection by providing a figure of merit for the icing severity.

Research on Ship-Radiated Noise Denoising Using Secondary Variational Mode Decomposition and Correlation Coefficient.

PubMed

Li, Yuxing; Li, Yaan; Chen, Xiao; Yu, Jing

2017-12-26

As the sound signal of ships obtained by sensors contains other many significant characteristics of ships and called ship-radiated noise (SN), research into a denoising algorithm and its application has obtained great significance. Using the advantage of variational mode decomposition (VMD) combined with the correlation coefficient for denoising, a hybrid secondary denoising algorithm is proposed using secondary VMD combined with a correlation coefficient (CC). First, different kinds of simulation signals are decomposed into several bandwidth-limited intrinsic mode functions (IMFs) using VMD, where the decomposition number by VMD is equal to the number by empirical mode decomposition (EMD); then, the CCs between the IMFs and the simulation signal are calculated respectively. The noise IMFs are identified by the CC threshold and the rest of the IMFs are reconstructed in order to realize the first denoising process. Finally, secondary denoising of the simulation signal can be accomplished by repeating the above steps of decomposition, screening and reconstruction. The final denoising result is determined according to the CC threshold. The denoising effect is compared under the different signal-to-noise ratio and the time of decomposition by VMD. Experimental results show the validity of the proposed denoising algorithm using secondary VMD (2VMD) combined with CC compared to EMD denoising, ensemble EMD (EEMD) denoising, VMD denoising and cubic VMD (3VMD) denoising, as well as two denoising algorithms presented recently. The proposed denoising algorithm is applied to feature extraction and classification for SN signals, which can effectively improve the recognition rate of different kinds of ships.

A novel hybrid ensemble learning paradigm for tourism forecasting

NASA Astrophysics Data System (ADS)

Shabri, Ani

2015-02-01

In this paper, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) is proposed to forecast tourism demand. This methodology first decomposes the original visitor arrival series into several Intrinsic Model Function (IMFs) components and one residual component by EMD technique. Then, IMFs components and the residual components is forecasted respectively using GMDH model whose input variables are selected by using Partial Autocorrelation Function (PACF). The final forecasted result for tourism series is produced by aggregating all the forecasted results. For evaluating the performance of the proposed EMD-GMDH methodologies, the monthly data of tourist arrivals from Singapore to Malaysia are used as an illustrative example. Empirical results show that the proposed EMD-GMDH model outperforms the EMD-ARIMA as well as the GMDH and ARIMA (Autoregressive Integrated Moving Average) models without time series decomposition.

Noise-assisted data processing with empirical mode decomposition in biomedical signals.

PubMed

Karagiannis, Alexandros; Constantinou, Philip

2011-01-01

In this paper, a methodology is described in order to investigate the performance of empirical mode decomposition (EMD) in biomedical signals, and especially in the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with white Gaussian noise are employed and time series of various lengths are processed with EMD in order to extract the intrinsic mode functions (IMFs). A statistical significance test is implemented for the identification of IMFs with high-level noise components and their exclusion from denoising procedures. Simulation campaign results reveal that a decrease of processing time is accomplished with the introduction of preprocessing stage, prior to the application of EMD in biomedical time series. Furthermore, the variation in the number of IMFs according to the type of the preprocessing stage is studied as a function of SNR and time-series length. The application of the methodology in MIT-BIH ECG records is also presented in order to verify the findings in real ECG signals.

Empirical mode decomposition-based facial pose estimation inside video sequences

NASA Astrophysics Data System (ADS)

Qing, Chunmei; Jiang, Jianmin; Yang, Zhijing

2010-03-01

We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions.

Tissue artifact removal from respiratory signals based on empirical mode decomposition.

PubMed

Liu, Shaopeng; Gao, Robert X; John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-05-01

On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the rib cage and abdomen of the test object. Affected by the movement of body tissues, respiratory signals typically have a low signal-to-noise ratio. Removing tissue artifacts therefore is critical to ensuring effective respiration analysis. This paper presents a signal decomposition technique for tissue artifact removal from respiratory signals, based on the empirical mode decomposition (EMD). An algorithm based on the mutual information and power criteria was devised to automatically select appropriate intrinsic mode functions for tissue artifact removal and respiratory signal reconstruction. Performance of the EMD-algorithm was evaluated through simulations and real-life experiments (N = 105). Comparison with low-pass filtering that has been conventionally applied confirmed the effectiveness of the technique in tissue artifacts removal.

An improved EMD method for modal identification and a combined static-dynamic method for damage detection

NASA Astrophysics Data System (ADS)

Yang, Jinping; Li, Peizhen; Yang, Youfa; Xu, Dian

2018-04-01

Empirical mode decomposition (EMD) is a highly adaptable signal processing method. However, the EMD approach has certain drawbacks, including distortions from end effects and mode mixing. In the present study, these two problems are addressed using an end extension method based on the support vector regression machine (SVRM) and a modal decomposition method based on the characteristics of the Hilbert transform. The algorithm includes two steps: using the SVRM, the time series data are extended at both endpoints to reduce the end effects, and then, a modified EMD method using the characteristics of the Hilbert transform is performed on the resulting signal to reduce mode mixing. A new combined static-dynamic method for identifying structural damage is presented. This method combines the static and dynamic information in an equilibrium equation that can be solved using the Moore-Penrose generalized matrix inverse. The combination method uses the differences in displacements of the structure with and without damage and variations in the modal force vector. Tests on a four-story, steel-frame structure were conducted to obtain static and dynamic responses of the structure. The modal parameters are identified using data from the dynamic tests and improved EMD method. The new method is shown to be more accurate and effective than the traditional EMD method. Through tests with a shear-type test frame, the higher performance of the proposed static-dynamic damage detection approach, which can detect both single and multiple damage locations and the degree of the damage, is demonstrated. For structures with multiple damage, the combined approach is more effective than either the static or dynamic method. The proposed EMD method and static-dynamic damage detection method offer improved modal identification and damage detection, respectively, in structures.

Acoustical Applications of the HHT Method

NASA Technical Reports Server (NTRS)

Huang, Norden E.

2003-01-01

A document discusses applications of a method based on the Huang-Hilbert transform (HHT). The method was described, without the HHT name, in Analyzing Time Series Using EMD and Hilbert Spectra (GSC-13817), NASA Tech Briefs, Vol. 24, No. 10 (October 2000), page 63. To recapitulate: The method is especially suitable for analyzing time-series data that represent nonstationary and nonlinear physical phenomena. The method involves the empirical mode decomposition (EMD), in which a complicated signal is decomposed into a finite number of functions, called intrinsic mode functions (IMFs), that admit well-behaved Hilbert transforms. The HHT consists of the combination of EMD and Hilbert spectral analysis.

[An EMD based time-frequency distribution and its application in EEG analysis].

PubMed

Li, Xiaobing; Chu, Meng; Qiu, Tianshuang; Bao, Haiping

2007-10-01

Hilbert-Huang transform (HHT) is a new time-frequency analytic method to analyze the nonlinear and the non-stationary signals. The key step of this method is the empirical mode decomposition (EMD), with which any complicated signal can be decomposed into a finite and small number of intrinsic mode functions (IMF). In this paper, a new EMD based method for suppressing the cross-term of Wigner-Ville distribution (WVD) is developed and is applied to analyze the epileptic EEG signals. The simulation data and analysis results show that the new method suppresses the cross-term of the WVD effectively with an excellent resolution.

Dynamic correlations at different time-scales with empirical mode decomposition

NASA Astrophysics Data System (ADS)

Nava, Noemi; Di Matteo, T.; Aste, Tomaso

2018-07-01

We introduce a simple approach which combines Empirical Mode Decomposition (EMD) and Pearson's cross-correlations over rolling windows to quantify dynamic dependency at different time scales. The EMD is a tool to separate time series into implicit components which oscillate at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S&P 500 (USA), the IPC (Mexico) and the VIX (volatility index USA), obtaining time-varying multidimensional cross-correlations at different time-scales. The correlations computed over a rolling window are compared across the three indices, across the components at different time-scales and across different time lags. We uncover a rich heterogeneity of interactions, which depends on the time-scale and has important lead-lag relations that could have practical use for portfolio management, risk estimation and investment decisions.

Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification

NASA Astrophysics Data System (ADS)

He, Zhi; Liu, Lin

2016-11-01

Empirical mode decomposition (EMD) and its variants have recently been applied for hyperspectral image (HSI) classification due to their ability to extract useful features from the original HSI. However, it remains a challenging task to effectively exploit the spectral-spatial information by the traditional vector or image-based methods. In this paper, a three-dimensional (3D) extension of EMD (3D-EMD) is proposed to naturally treat the HSI as a cube and decompose the HSI into varying oscillations (i.e. 3D intrinsic mode functions (3D-IMFs)). To achieve fast 3D-EMD implementation, 3D Delaunay triangulation (3D-DT) is utilized to determine the distances of extrema, while separable filters are adopted to generate the envelopes. Taking the extracted 3D-IMFs as features of different tasks, robust multitask learning (RMTL) is further proposed for HSI classification. In RMTL, pairs of low-rank and sparse structures are formulated by trace-norm and l1,2 -norm to capture task relatedness and specificity, respectively. Moreover, the optimization problems of RMTL can be efficiently solved by the inexact augmented Lagrangian method (IALM). Compared with several state-of-the-art feature extraction and classification methods, the experimental results conducted on three benchmark data sets demonstrate the superiority of the proposed methods.

Multivariate EMD and full spectrum based condition monitoring for rotating machinery

NASA Astrophysics Data System (ADS)

Zhao, Xiaomin; Patel, Tejas H.; Zuo, Ming J.

2012-02-01

Early assessment of machinery health condition is of paramount importance today. A sensor network with sensors in multiple directions and locations is usually employed for monitoring the condition of rotating machinery. Extraction of health condition information from these sensors for effective fault detection and fault tracking is always challenging. Empirical mode decomposition (EMD) is an advanced signal processing technology that has been widely used for this purpose. Standard EMD has the limitation in that it works only for a single real-valued signal. When dealing with data from multiple sensors and multiple health conditions, standard EMD faces two problems. First, because of the local and self-adaptive nature of standard EMD, the decomposition of signals from different sources may not match in either number or frequency content. Second, it may not be possible to express the joint information between different sensors. The present study proposes a method of extracting fault information by employing multivariate EMD and full spectrum. Multivariate EMD can overcome the limitations of standard EMD when dealing with data from multiple sources. It is used to extract the intrinsic mode functions (IMFs) embedded in raw multivariate signals. A criterion based on mutual information is proposed for selecting a sensitive IMF. A full spectral feature is then extracted from the selected fault-sensitive IMF to capture the joint information between signals measured from two orthogonal directions. The proposed method is first explained using simple simulated data, and then is tested for the condition monitoring of rotating machinery applications. The effectiveness of the proposed method is demonstrated through monitoring damage on the vane trailing edge of an impeller and rotor-stator rub in an experimental rotor rig.

Trend extraction using empirical mode decomposition and statistical empirical mode decomposition: Case study: Kuala Lumpur stock market

NASA Astrophysics Data System (ADS)

Jaber, Abobaker M.

2014-12-01

Two nonparametric methods for prediction and modeling of financial time series signals are proposed. The proposed techniques are designed to handle non-stationary and non-linearity behave and to extract meaningful signals for reliable prediction. Due to Fourier Transform (FT), the methods select significant decomposed signals that will be employed for signal prediction. The proposed techniques developed by coupling Holt-winter method with Empirical Mode Decomposition (EMD) and it is Extending the scope of empirical mode decomposition by smoothing (SEMD). To show performance of proposed techniques, we analyze daily closed price of Kuala Lumpur stock market index.

A New Strategy for ECG Baseline Wander Elimination Using Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Shahbakhti, Mohammad; Bagheri, Hamed; Shekarchi, Babak; Mohammadi, Somayeh; Naji, Mohsen

2016-06-01

Electrocardiogram (ECG) signals might be affected by various artifacts and noises that have biological and external sources. Baseline wander (BW) is a low-frequency artifact that may be caused by breathing, body movements and loose sensor contact. In this paper, a novel method based on empirical mode decomposition (EMD) for removal of baseline noise from ECG is presented. When compared to other EMD-based methods, the novelty of this research is to reach the optimized number of decomposed levels for ECG BW de-noising using mean power frequency (MPF), while the reduction of processing time is considered. To evaluate the performance of the proposed method, a fifth-order Butterworth high pass filtering (BHPF) with cut-off frequency at 0.5Hz and wavelet approach are applied. Three performance indices, signal-to-noise ratio (SNR), mean square error (MSE) and correlation coefficient (CC), between pure and filtered signals have been utilized for qualification of presented techniques. Results suggest that the EMD-based method outperforms the other filtering method.

Seismic random noise attenuation method based on empirical mode decomposition of Hausdorff dimension

NASA Astrophysics Data System (ADS)

Yan, Z.; Luan, X.

2017-12-01

Introduction Empirical mode decomposition (EMD) is a noise suppression algorithm by using wave field separation, which is based on the scale differences between effective signal and noise. However, since the complexity of the real seismic wave field results in serious aliasing modes, it is not ideal and effective to denoise with this method alone. Based on the multi-scale decomposition characteristics of the signal EMD algorithm, combining with Hausdorff dimension constraints, we propose a new method for seismic random noise attenuation. First of all, We apply EMD algorithm adaptive decomposition of seismic data and obtain a series of intrinsic mode function (IMF)with different scales. Based on the difference of Hausdorff dimension between effectively signals and random noise, we identify IMF component mixed with random noise. Then we use threshold correlation filtering process to separate the valid signal and random noise effectively. Compared with traditional EMD method, the results show that the new method of seismic random noise attenuation has a better suppression effect. The implementation process The EMD algorithm is used to decompose seismic signals into IMF sets and analyze its spectrum. Since most of the random noise is high frequency noise, the IMF sets can be divided into three categories: the first category is the effective wave composition of the larger scale; the second category is the noise part of the smaller scale; the third category is the IMF component containing random noise. Then, the third kind of IMF component is processed by the Hausdorff dimension algorithm, and the appropriate time window size, initial step and increment amount are selected to calculate the Hausdorff instantaneous dimension of each component. The dimension of the random noise is between 1.0 and 1.05, while the dimension of the effective wave is between 1.05 and 2.0. On the basis of the previous steps, according to the dimension difference between the random noise and effective signal, we extracted the sample points, whose fractal dimension value is less than or equal to 1.05 for the each IMF components, to separate the residual noise. Using the IMF components after dimension filtering processing and the effective wave IMF components after the first selection for reconstruction, we can obtained the results of de-noising.

An epileptic seizures detection algorithm based on the empirical mode decomposition of EEG.

PubMed

Orosco, Lorena; Laciar, Eric; Correa, Agustina Garces; Torres, Abel; Graffigna, Juan P

2009-01-01

Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates the energy of each IMF and performs the detection based on an energy threshold and a minimum duration decision. The algorithm was tested in 9 invasive EEG records provided and validated by the Epilepsy Center of the University Hospital of Freiburg. In 90 segments analyzed (39 with epileptic seizures) the sensitivity and specificity obtained with the method were of 56.41% and 75.86% respectively. It could be concluded that EMD is a promissory method for epileptic seizure detection in EEG records.

Telephone-quality pathological speech classification using empirical mode decomposition.

PubMed

Kaleem, M F; Ghoraani, B; Guergachi, A; Krishnan, S

2011-01-01

This paper presents a computationally simple and effective methodology based on empirical mode decomposition (EMD) for classification of telephone quality normal and pathological speech signals. EMD is used to decompose continuous normal and pathological speech signals into intrinsic mode functions, which are analyzed to extract physically meaningful and unique temporal and spectral features. Using continuous speech samples from a database of 51 normal and 161 pathological speakers, which has been modified to simulate telephone quality speech under different levels of noise, a linear classifier is used with the feature vector thus obtained to obtain a high classification accuracy, thereby demonstrating the effectiveness of the methodology. The classification accuracy reported in this paper (89.7% for signal-to-noise ratio 30 dB) is a significant improvement over previously reported results for the same task, and demonstrates the utility of our methodology for cost-effective remote voice pathology assessment over telephone channels.

Dynamic characterization of a damaged beam using empirical mode decomposition and Hilbert spectrum method

NASA Astrophysics Data System (ADS)

Chang, Chih-Chen; Poon, Chun-Wing

2004-07-01

Recently, the empirical mode decomposition (EMD) in combination with the Hilbert spectrum method has been proposed to identify the dynamic characteristics of linear structures. In this study, this EMD and Hilbert spectrum method is used to analyze the dynamic characteristics of a damaged reinforced concrete (RC) beam in the laboratory. The RC beam is 4m long with a cross section of 200mm X 250mm. The beam is sequentially subjected to a concentrated load of different magnitudes at the mid-span to produce different degrees of damage. An impact load is applied around the mid-span to excite the beam. Responses of the beam are recorded by four accelerometers. Results indicate that the EMD and Hilbert spectrum method can reveal the variation of the dynamic characteristics in the time domain. These results are also compared with those obtained using the Fourier analysis. In general, it is found that the two sets of results correlate quite well in terms of mode counts and frequency values. Some differences, however, can be seen in the damping values, which perhaps can be attributed to the linear assumption of the Fourier transform.

Temporal structure of neuronal population oscillations with empirical model decomposition

NASA Astrophysics Data System (ADS)

Li, Xiaoli

2006-08-01

Frequency analysis of neuronal oscillation is very important for understanding the neural information processing and mechanism of disorder in the brain. This Letter addresses a new method to analyze the neuronal population oscillations with empirical mode decomposition (EMD). Following EMD of neuronal oscillation, a series of intrinsic mode functions (IMFs) are obtained, then Hilbert transform of IMFs can be used to extract the instantaneous time frequency structure of neuronal oscillation. The method is applied to analyze the neuronal oscillation in the hippocampus of epileptic rats in vivo, the results show the neuronal oscillations have different descriptions during the pre-ictal, seizure onset and ictal periods of the epileptic EEG at the different frequency band. This new method is very helpful to provide a view for the temporal structure of neural oscillation.

Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain.

PubMed

Zhuang, Ning; Zeng, Ying; Tong, Li; Zhang, Chi; Zhang, Hanming; Yan, Bin

2017-01-01

This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy. The performance of the proposed method is verified on a publicly available emotional database. The results show that the three features are effective for emotion recognition. The role of each IMF is inquired and we find that high frequency component IMF1 has significant effect on different emotional states detection. The informative electrodes based on EMD strategy are analyzed. In addition, the classification accuracy of the proposed method is compared with several classical techniques, including fractal dimension (FD), sample entropy, differential entropy, and discrete wavelet transform (DWT). Experiment results on DEAP datasets demonstrate that our method can improve emotion recognition performance.

Adaptive variational mode decomposition method for signal processing based on mode characteristic

NASA Astrophysics Data System (ADS)

Lian, Jijian; Liu, Zhuo; Wang, Haijun; Dong, Xiaofeng

2018-07-01

Variational mode decomposition is a completely non-recursive decomposition model, where all the modes are extracted concurrently. However, the model requires a preset mode number, which limits the adaptability of the method since a large deviation in the number of mode set will cause the discard or mixing of the mode. Hence, a method called Adaptive Variational Mode Decomposition (AVMD) was proposed to automatically determine the mode number based on the characteristic of intrinsic mode function. The method was used to analyze the simulation signals and the measured signals in the hydropower plant. Comparisons have also been conducted to evaluate the performance by using VMD, EMD and EWT. It is indicated that the proposed method has strong adaptability and is robust to noise. It can determine the mode number appropriately without modulation even when the signal frequencies are relatively close.

A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis

NASA Astrophysics Data System (ADS)

Grasso, M.; Chatterton, S.; Pennacchi, P.; Colosimo, B. M.

2016-12-01

Health condition analysis and diagnostics of rotating machinery requires the capability of properly characterizing the information content of sensor signals in order to detect and identify possible fault features. Time-frequency analysis plays a fundamental role, as it allows determining both the existence and the causes of a fault. The separation of components belonging to different time-frequency scales, either associated to healthy or faulty conditions, represents a challenge that motivates the development of effective methodologies for multi-scale signal decomposition. In this framework, the Empirical Mode Decomposition (EMD) is a flexible tool, thanks to its data-driven and adaptive nature. However, the EMD usually yields an over-decomposition of the original signals into a large number of intrinsic mode functions (IMFs). The selection of most relevant IMFs is a challenging task, and the reference literature lacks automated methods to achieve a synthetic decomposition into few physically meaningful modes by avoiding the generation of spurious or meaningless modes. The paper proposes a novel automated approach aimed at generating a decomposition into a minimal number of relevant modes, called Combined Mode Functions (CMFs), each consisting in a sum of adjacent IMFs that share similar properties. The final number of CMFs is selected in a fully data driven way, leading to an enhanced characterization of the signal content without any information loss. A novel criterion to assess the dissimilarity between adjacent CMFs is proposed, based on probability density functions of frequency spectra. The method is suitable to analyze vibration signals that may be periodically acquired within the operating life of rotating machineries. A rolling element bearing fault analysis based on experimental data is presented to demonstrate the performances of the method and the provided benefits.

Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition.

PubMed

Wang, Fu-Tai; Chan, Hsiao-Lung; Wang, Chun-Li; Jian, Hung-Ming; Lin, Sheng-Hsiung

2015-07-07

Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring. The empirical mode decomposition (EMD) can decompose a signal into several intrinsic mode functions (IMFs) that disclose nonstationary components as well as stationary components and, similarly, capture respiratory episodes from thoracic impedance. However, upper-body movements usually produce motion artifacts that are not easily removed by digital filtering. Moreover, large motion artifacts disable the EMD to decompose respiratory components. In this paper, motion artifacts are detected and replaced by the data mirrored from the prior and the posterior before EMD processing. A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation. Based on the experiments performing a series of static and dynamic physical activates, our results showed the proposed method had higher cross correlations between respiratory frequencies estimated from thoracic impedance and those from oronasal airflow based on small window size compared to the Fourier transform-based method.

Instantaneous Respiratory Estimation from Thoracic Impedance by Empirical Mode Decomposition

PubMed Central

Wang, Fu-Tai; Chan, Hsiao-Lung; Wang, Chun-Li; Jian, Hung-Ming; Lin, Sheng-Hsiung

2015-01-01

Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, thereby reducing the need for adjustments as body position changes and making it suitable for long-term or ambulatory monitoring. The empirical mode decomposition (EMD) can decompose a signal into several intrinsic mode functions (IMFs) that disclose nonstationary components as well as stationary components and, similarly, capture respiratory episodes from thoracic impedance. However, upper-body movements usually produce motion artifacts that are not easily removed by digital filtering. Moreover, large motion artifacts disable the EMD to decompose respiratory components. In this paper, motion artifacts are detected and replaced by the data mirrored from the prior and the posterior before EMD processing. A novel intrinsic respiratory reconstruction index that considers both global and local properties of IMFs is proposed to define respiration-related IMFs for respiration reconstruction and instantaneous respiratory estimation. Based on the experiments performing a series of static and dynamic physical activates, our results showed the proposed method had higher cross correlations between respiratory frequencies estimated from thoracic impedance and those from oronasal airflow based on small window size compared to the Fourier transform-based method. PMID:26198231

Modified complementary ensemble empirical mode decomposition and intrinsic mode functions evaluation index for high-speed train gearbox fault diagnosis

NASA Astrophysics Data System (ADS)

Chen, Dongyue; Lin, Jianhui; Li, Yanping

2018-06-01

Complementary ensemble empirical mode decomposition (CEEMD) has been developed for the mode-mixing problem in Empirical Mode Decomposition (EMD) method. Compared to the ensemble empirical mode decomposition (EEMD), the CEEMD method reduces residue noise in the signal reconstruction. Both CEEMD and EEMD need enough ensemble number to reduce the residue noise, and hence it would be too much computation cost. Moreover, the selection of intrinsic mode functions (IMFs) for further analysis usually depends on experience. A modified CEEMD method and IMFs evaluation index are proposed with the aim of reducing the computational cost and select IMFs automatically. A simulated signal and in-service high-speed train gearbox vibration signals are employed to validate the proposed method in this paper. The results demonstrate that the modified CEEMD can decompose the signal efficiently with less computation cost, and the IMFs evaluation index can select the meaningful IMFs automatically.

Hybrid empirical mode decomposition- ARIMA for forecasting exchange rates

NASA Astrophysics Data System (ADS)

Abadan, Siti Sarah; Shabri, Ani; Ismail, Shuhaida

2015-02-01

This paper studied the forecasting of monthly Malaysian Ringgit (MYR)/ United State Dollar (USD) exchange rates using the hybrid of two methods which are the empirical model decomposition (EMD) and the autoregressive integrated moving average (ARIMA). MYR is pegged to USD during the Asian financial crisis causing the exchange rates are fixed to 3.800 from 2nd of September 1998 until 21st of July 2005. Thus, the chosen data in this paper is the post-July 2005 data, starting from August 2005 to July 2010. The comparative study using root mean square error (RMSE) and mean absolute error (MAE) showed that the EMD-ARIMA outperformed the single-ARIMA and the random walk benchmark model.

Analyzing Tropical Waves Using the Parallel Ensemble Empirical Model Decomposition Method: Preliminary Results from Hurricane Sandy

NASA Technical Reports Server (NTRS)

Shen, Bo-Wen; Cheung, Samson; Li, Jui-Lin F.; Wu, Yu-ling

2013-01-01

In this study, we discuss the performance of the parallel ensemble empirical mode decomposition (EMD) in the analysis of tropical waves that are associated with tropical cyclone (TC) formation. To efficiently analyze high-resolution, global, multiple-dimensional data sets, we first implement multilevel parallelism into the ensemble EMD (EEMD) and obtain a parallel speedup of 720 using 200 eight-core processors. We then apply the parallel EEMD (PEEMD) to extract the intrinsic mode functions (IMFs) from preselected data sets that represent (1) idealized tropical waves and (2) large-scale environmental flows associated with Hurricane Sandy (2012). Results indicate that the PEEMD is efficient and effective in revealing the major wave characteristics of the data, such as wavelengths and periods, by sifting out the dominant (wave) components. This approach has a potential for hurricane climate study by examining the statistical relationship between tropical waves and TC formation.

Gyroscope-driven mouse pointer with an EMOTIV® EEG headset and data analysis based on Empirical Mode Decomposition.

PubMed

Rosas-Cholula, Gerardo; Ramirez-Cortes, Juan Manuel; Alarcon-Aquino, Vicente; Gomez-Gil, Pilar; Rangel-Magdaleno, Jose de Jesus; Reyes-Garcia, Carlos

2013-08-14

This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user's blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented.

Gyroscope-Driven Mouse Pointer with an EMOTIV® EEG Headset and Data Analysis Based on Empirical Mode Decomposition

PubMed Central

Rosas-Cholula, Gerardo; Ramirez-Cortes, Juan Manuel; Alarcon-Aquino, Vicente; Gomez-Gil, Pilar; Rangel-Magdaleno, Jose de Jesus; Reyes-Garcia, Carlos

2013-01-01

This paper presents a project on the development of a cursor control emulating the typical operations of a computer-mouse, using gyroscope and eye-blinking electromyographic signals which are obtained through a commercial 16-electrode wireless headset, recently released by Emotiv. The cursor position is controlled using information from a gyroscope included in the headset. The clicks are generated through the user's blinking with an adequate detection procedure based on the spectral-like technique called Empirical Mode Decomposition (EMD). EMD is proposed as a simple and quick computational tool, yet effective, aimed to artifact reduction from head movements as well as a method to detect blinking signals for mouse control. Kalman filter is used as state estimator for mouse position control and jitter removal. The detection rate obtained in average was 94.9%. Experimental setup and some obtained results are presented. PMID:23948873

EMD-WVD time-frequency distribution for analysis of multi-component signals

NASA Astrophysics Data System (ADS)

Chai, Yunzi; Zhang, Xudong

2016-10-01

Time-frequency distribution (TFD) is two-dimensional function that indicates the time-varying frequency content of one-dimensional signals. And The Wigner-Ville distribution (WVD) is an important and effective time-frequency analysis method. The WVD can efficiently show the characteristic of a mono-component signal. However, a major drawback is the extra cross-terms when multi-component signals are analyzed by WVD. In order to eliminating the cross-terms, we decompose signals into single frequency components - Intrinsic Mode Function (IMF) - by using the Empirical Mode decomposition (EMD) first, then use WVD to analyze each single IMF. In this paper, we define this new time-frequency distribution as EMD-WVD. And the experiment results show that the proposed time-frequency method can solve the cross-terms problem effectively and improve the accuracy of WVD time-frequency analysis.

On the Hilbert-Huang Transform Theoretical Developments

NASA Technical Reports Server (NTRS)

Kizhner, Semion; Blank, Karin; Flatley, Thomas; Huang, Norden E.; Patrick, David; Hestnes, Phyllis

2005-01-01

One of the main heritage tools used in scientific and engineering data spectrum analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). Both carry strong a-priori assumptions about the source data, such as linearity, of being stationary, and of satisfying the Dirichlet conditions. A recent development at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), known as the Hilbert-Huang Transform (HHT), proposes a novel approach to the solution for the nonlinear class of spectrum analysis problems. Using a-posteriori data processing based on the Empirical Mode Decomposition (EMD) sifting process (algorithm), followed by the normalized Hilbert Transform of the decomposition data, the HHT allows spectrum analysis of nonlinear and nonstationary data. The EMD sifting process results in a non-constrained decomposition of a source real value data vector into a finite set of Intrinsic Mode Functions (IMF). These functions form a near orthogonal adaptive basis, a basis that is derived from the data. The IMFs can be further analyzed for spectrum interpretation by the classical Hilbert Transform. A new engineering spectrum analysis tool using HHT has been developed at NASA GSFC, the HHT Data Processing System (HHT-DPS). As the HHT-DPS has been successfully used and commercialized, new applications post additional questions about the theoretical basis behind the HHT and EMD algorithms. Why is the fastest changing component of a composite signal being sifted out first in the EMD sifting process? Why does the EMD sifting process seemingly converge and why does it converge rapidly? Does an IMF have a distinctive structure? Why are the IMFs near orthogonal? We address these questions and develop the initial theoretical background for the HHT. This will contribute to the developments of new HHT processing options, such as real-time and 2-D processing using Field Programmable Array (FPGA) computational resources, enhanced HHT synthesis, and broaden the scope of HHT applications for signal processing.

A Signal Processing Approach with a Smooth Empirical Mode Decomposition to Reveal Hidden Trace of Corrosion in Highly Contaminated Guided Wave Signals for Concrete-Covered Pipes

PubMed Central

Rostami, Javad; Chen, Jingming; Tse, Peter W.

2017-01-01

Ultrasonic guided waves have been extensively applied for non-destructive testing of plate-like structures particularly pipes in past two decades. In this regard, if a structure has a simple geometry, obtained guided waves’ signals are easy to explain. However, any small degree of complexity in the geometry such as contacting with other materials may cause an extra amount of complication in the interpretation of guided wave signals. The problem deepens if defects have irregular shapes such as natural corrosion. Signal processing techniques that have been proposed for guided wave signals’ analysis are generally good for simple signals obtained in a highly controlled experimental environment. In fact, guided wave signals in a real situation such as the existence of natural corrosion in wall-covered pipes are much more complicated. Considering pipes in residential buildings that pass through concrete walls, in this paper we introduced Smooth Empirical Mode Decomposition (SEMD) to efficiently separate overlapped guided waves. As empirical mode decomposition (EMD) which is a good candidate for analyzing non-stationary signals, suffers from some shortcomings, wavelet transform was adopted in the sifting stage of EMD to improve its outcome in SEMD. However, selection of mother wavelet that suits best for our purpose plays an important role. Since in guided wave inspection, the incident waves are well known and are usually tone-burst signals, we tailored a complex tone-burst signal to be used as our mother wavelet. In the sifting stage of EMD, wavelet de-noising was applied to eliminate unwanted frequency components from each IMF. SEMD greatly enhances the performance of EMD in guided wave analysis for highly contaminated signals. In our experiment on concrete covered pipes with natural corrosion, this method not only separates the concrete wall indication clearly in time domain signal, a natural corrosion with complex geometry that was hidden and located inside the concrete section was successfully exposed. PMID:28178220

A Signal Processing Approach with a Smooth Empirical Mode Decomposition to Reveal Hidden Trace of Corrosion in Highly Contaminated Guided Wave Signals for Concrete-Covered Pipes.

PubMed

Rostami, Javad; Chen, Jingming; Tse, Peter W

2017-02-07

Ultrasonic guided waves have been extensively applied for non-destructive testing of plate-like structures particularly pipes in past two decades. In this regard, if a structure has a simple geometry, obtained guided waves' signals are easy to explain. However, any small degree of complexity in the geometry such as contacting with other materials may cause an extra amount of complication in the interpretation of guided wave signals. The problem deepens if defects have irregular shapes such as natural corrosion. Signal processing techniques that have been proposed for guided wave signals' analysis are generally good for simple signals obtained in a highly controlled experimental environment. In fact, guided wave signals in a real situation such as the existence of natural corrosion in wall-covered pipes are much more complicated. Considering pipes in residential buildings that pass through concrete walls, in this paper we introduced Smooth Empirical Mode Decomposition (SEMD) to efficiently separate overlapped guided waves. As empirical mode decomposition (EMD) which is a good candidate for analyzing non-stationary signals, suffers from some shortcomings, wavelet transform was adopted in the sifting stage of EMD to improve its outcome in SEMD. However, selection of mother wavelet that suits best for our purpose plays an important role. Since in guided wave inspection, the incident waves are well known and are usually tone-burst signals, we tailored a complex tone-burst signal to be used as our mother wavelet. In the sifting stage of EMD, wavelet de-noising was applied to eliminate unwanted frequency components from each IMF. SEMD greatly enhances the performance of EMD in guided wave analysis for highly contaminated signals. In our experiment on concrete covered pipes with natural corrosion, this method not only separates the concrete wall indication clearly in time domain signal, a natural corrosion with complex geometry that was hidden and located inside the concrete section was successfully exposed.

A hybrid approach EMD-HW for short-term forecasting of daily stock market time series data

NASA Astrophysics Data System (ADS)

Awajan, Ahmad Mohd; Ismail, Mohd Tahir

2017-08-01

Recently, forecasting time series has attracted considerable attention in the field of analyzing financial time series data, specifically within the stock market index. Moreover, stock market forecasting is a challenging area of financial time-series forecasting. In this study, a hybrid methodology between Empirical Mode Decomposition with the Holt-Winter method (EMD-HW) is used to improve forecasting performances in financial time series. The strength of this EMD-HW lies in its ability to forecast non-stationary and non-linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy and offers a new forecasting method in time series. The daily stock market time series data of 11 countries is applied to show the forecasting performance of the proposed EMD-HW. Based on the three forecast accuracy measures, the results indicate that EMD-HW forecasting performance is superior to traditional Holt-Winter forecasting method.

Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis.

PubMed

Saidi, Lotfi; Ali, Jaouher Ben; Fnaiech, Farhat

2014-09-01

Empirical mode decomposition (EMD) has been widely applied to analyze vibration signals behavior for bearing failures detection. Vibration signals are almost always non-stationary since bearings are inherently dynamic (e.g., speed and load condition change over time). By using EMD, the complicated non-stationary vibration signal is decomposed into a number of stationary intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal. Bi-spectrum, a third-order statistic, helps to identify phase coupling effects, the bi-spectrum is theoretically zero for Gaussian noise and it is flat for non-Gaussian white noise, consequently the bi-spectrum analysis is insensitive to random noise, which are useful for detecting faults in induction machines. Utilizing the advantages of EMD and bi-spectrum, this article proposes a joint method for detecting such faults, called bi-spectrum based EMD (BSEMD). First, original vibration signals collected from accelerometers are decomposed by EMD and a set of IMFs is produced. Then, the IMF signals are analyzed via bi-spectrum to detect outer race bearing defects. The procedure is illustrated with the experimental bearing vibration data. The experimental results show that BSEMD techniques can effectively diagnosis bearing failures. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Recognizing of stereotypic patterns in epileptic EEG using empirical modes and wavelets

NASA Astrophysics Data System (ADS)

Grubov, V. V.; Sitnikova, E.; Pavlov, A. N.; Koronovskii, A. A.; Hramov, A. E.

2017-11-01

Epileptic activity in the form of spike-wave discharges (SWD) appears in the electroencephalogram (EEG) during absence seizures. This paper evaluates two approaches for detecting stereotypic rhythmic activities in EEG, i.e., the continuous wavelet transform (CWT) and the empirical mode decomposition (EMD). The CWT is a well-known method of time-frequency analysis of EEG, whereas EMD is a relatively novel approach for extracting signal's waveforms. A new method for pattern recognition based on combination of CWT and EMD is proposed. It was found that this combined approach resulted to the sensitivity of 86.5% and specificity of 92.9% for sleep spindles and 97.6% and 93.2% for SWD, correspondingly. Considering strong within- and between-subjects variability of sleep spindles, the obtained efficiency in their detection was high in comparison with other methods based on CWT. It is concluded that the combination of a wavelet-based approach and empirical modes increases the quality of automatic detection of stereotypic patterns in rat's EEG.

A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

PubMed Central

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782

A four-stage hybrid model for hydrological time series forecasting.

PubMed

Di, Chongli; Yang, Xiaohua; Wang, Xiaochao

2014-01-01

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models.

Benchmarking of a T-wave alternans detection method based on empirical mode decomposition.

PubMed

Blanco-Velasco, Manuel; Goya-Esteban, Rebeca; Cruz-Roldán, Fernando; García-Alberola, Arcadi; Rojo-Álvarez, José Luis

2017-07-01

T-wave alternans (TWA) is a fluctuation of the ST-T complex occurring on an every-other-beat basis of the surface electrocardiogram (ECG). It has been shown to be an informative risk stratifier for sudden cardiac death, though the lack of gold standard to benchmark detection methods has promoted the use of synthetic signals. This work proposes a novel signal model to study the performance of a TWA detection. Additionally, the methodological validation of a denoising technique based on empirical mode decomposition (EMD), which is used here along with the spectral method, is also tackled. The proposed test bed system is based on the following guidelines: (1) use of open source databases to enable experimental replication; (2) use of real ECG signals and physiological noise; (3) inclusion of randomized TWA episodes. Both sensitivity (Se) and specificity (Sp) are separately analyzed. Also a nonparametric hypothesis test, based on Bootstrap resampling, is used to determine whether the presence of the EMD block actually improves the performance. The results show an outstanding specificity when the EMD block is used, even in very noisy conditions (0.96 compared to 0.72 for SNR = 8 dB), being always superior than that of the conventional SM alone. Regarding the sensitivity, using the EMD method also outperforms in noisy conditions (0.57 compared to 0.46 for SNR=8 dB), while it decreases in noiseless conditions. The proposed test setting designed to analyze the performance guarantees that the actual physiological variability of the cardiac system is reproduced. The use of the EMD-based block in noisy environment enables the identification of most patients with fatal arrhythmias. Copyright © 2017 Elsevier B.V. All rights reserved.

Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis

PubMed Central

2018-01-01

Objective To study the performance of multifocal-visual-evoked-potential (mfVEP) signals filtered using empirical mode decomposition (EMD) in discriminating, based on amplitude, between control and multiple sclerosis (MS) patient groups, and to reduce variability in interocular latency in control subjects. Methods MfVEP signals were obtained from controls, clinically definitive MS and MS-risk progression patients (radiologically isolated syndrome (RIS) and clinically isolated syndrome (CIS)). The conventional method of processing mfVEPs consists of using a 1–35 Hz bandpass frequency filter (XDFT). The EMD algorithm was used to decompose the XDFT signals into several intrinsic mode functions (IMFs). This signal processing was assessed by computing the amplitudes and latencies of the XDFT and IMF signals (XEMD). The amplitudes from the full visual field and from ring 5 (9.8–15° eccentricity) were studied. The discrimination index was calculated between controls and patients. Interocular latency values were computed from the XDFT and XEMD signals in a control database to study variability. Results Using the amplitude of the mfVEP signals filtered with EMD (XEMD) obtains higher discrimination index values than the conventional method when control, MS-risk progression (RIS and CIS) and MS subjects are studied. The lowest variability in interocular latency computations from the control patient database was obtained by comparing the XEMD signals with the XDFT signals. Even better results (amplitude discrimination and latency variability) were obtained in ring 5 (9.8–15° eccentricity of the visual field). Conclusions Filtering mfVEP signals using the EMD algorithm will result in better identification of subjects at risk of developing MS and better accuracy in latency studies. This could be applied to assess visual cortex activity in MS diagnosis and evolution studies. PMID:29677200

Prediction of mean monthly river discharges in Colombia through Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Carmona, A. M.; Poveda, G.

2015-04-01

The hydro-climatology of Colombia exhibits strong natural variability at a broad range of time scales including: inter-decadal, decadal, inter-annual, annual, intra-annual, intra-seasonal, and diurnal. Diverse applied sectors rely on quantitative predictions of river discharges for operational purposes including hydropower generation, agriculture, human health, fluvial navigation, territorial planning and management, risk preparedness and mitigation, among others. Various methodologies have been used to predict monthly mean river discharges that are based on "Predictive Analytics", an area of statistical analysis that studies the extraction of information from historical data to infer future trends and patterns. Our study couples the Empirical Mode Decomposition (EMD) with traditional methods, e.g. Autoregressive Model of Order 1 (AR1) and Neural Networks (NN), to predict mean monthly river discharges in Colombia, South America. The EMD allows us to decompose the historical time series of river discharges into a finite number of intrinsic mode functions (IMF) that capture the different oscillatory modes of different frequencies associated with the inherent time scales coexisting simultaneously in the signal (Huang et al. 1998, Huang and Wu 2008, Rao and Hsu, 2008). Our predictive method states that it is easier and simpler to predict each IMF at a time and then add them up together to obtain the predicted river discharge for a certain month, than predicting the full signal. This method is applied to 10 series of monthly mean river discharges in Colombia, using calibration periods of more than 25 years, and validation periods of about 12 years. Predictions are performed for time horizons spanning from 1 to 12 months. Our results show that predictions obtained through the traditional methods improve when the EMD is used as a previous step, since errors decrease by up to 13% when the AR1 model is used, and by up to 18% when using Neural Networks is combined with the EMD.

Patient-Specific Seizure Detection in Long-Term EEG Using Signal-Derived Empirical Mode Decomposition (EMD)-based Dictionary Approach.

PubMed

Kaleem, Muhammad; Gurve, Dharmendra; Guergachi, Aziz; Krishnan, Sridhar

2018-06-25

The objective of the work described in this paper is development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings. Approach: A novel patient-specific seizure detection approach based on signal-derived Empirical Mode Decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches. Main results: The performance of the proposed approach is validated using the CHB-MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability of the approach for seizure detection in long-term multi-channel EEG recordings is discussed. Significance: The proposed approach describes a computationally efficient method for automatic seizure detection in long-term multi-channel EEG recordings. The method does not rely on hand-engineered features, as are required in traditional approaches. Furthermore, the approach is suitable for scenarios where the dictionary once formed and trained can be used for automatic seizure detection of newly recorded data, making the approach suitable for long-term multi-channel EEG recordings. © 2018 IOP Publishing Ltd.

EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal

NASA Astrophysics Data System (ADS)

Chen, Yong; Wu, Chun-ting; Liu, Huan-lin

2017-07-01

Noise may reduce the demodulation accuracy of fiber Bragg grating (FBG) sensing signal so as to affect the quality of sensing detection. Thus, the recovery of a signal from observed noisy data is necessary. In this paper, a precise self-adaptive algorithm of selecting relevant modes is proposed to remove the noise of signal. Empirical mode decomposition (EMD) is first used to decompose a signal into a set of modes. The pseudo modes cancellation is introduced to identify and eliminate false modes, and then the Mutual Information (MI) of partial modes is calculated. MI is used to estimate the critical point of high and low frequency components. Simulation results show that the proposed algorithm estimates the critical point more accurately than the traditional algorithms for FBG spectral signal. While, compared to the similar algorithms, the signal noise ratio of the signal can be improved more than 10 dB after processing by the proposed algorithm, and correlation coefficient can be increased by 0.5, so it demonstrates better de-noising effect.

Improving prediction accuracy of cooling load using EMD, PSR and RBFNN

NASA Astrophysics Data System (ADS)

Shen, Limin; Wen, Yuanmei; Li, Xiaohong

2017-08-01

To increase the accuracy for the prediction of cooling load demand, this work presents an EMD (empirical mode decomposition)-PSR (phase space reconstruction) based RBFNN (radial basis function neural networks) method. Firstly, analyzed the chaotic nature of the real cooling load demand, transformed the non-stationary cooling load historical data into several stationary intrinsic mode functions (IMFs) by using EMD. Secondly, compared the RBFNN prediction accuracies of each IMFs and proposed an IMF combining scheme that is combine the lower-frequency components (called IMF4-IMF6 combined) while keep the higher frequency component (IMF1, IMF2, IMF3) and the residual unchanged. Thirdly, reconstruct phase space for each combined components separately, process the highest frequency component (IMF1) by differential method and predict with RBFNN in the reconstructed phase spaces. Real cooling load data of a centralized ice storage cooling systems in Guangzhou are used for simulation. The results show that the proposed hybrid method outperforms the traditional methods.

Empirical mode decomposition for analyzing acoustical signals

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2005-01-01

The present invention discloses a computer implemented signal analysis method through the Hilbert-Huang Transformation (HHT) for analyzing acoustical signals, which are assumed to be nonlinear and nonstationary. The Empirical Decomposition Method (EMD) and the Hilbert Spectral Analysis (HSA) are used to obtain the HHT. Essentially, the acoustical signal will be decomposed into the Intrinsic Mode Function Components (IMFs). Once the invention decomposes the acoustic signal into its constituting components, all operations such as analyzing, identifying, and removing unwanted signals can be performed on these components. Upon transforming the IMFs into Hilbert spectrum, the acoustical signal may be compared with other acoustical signals.

Defects diagnosis in laser brazing using near-infrared signals based on empirical mode decomposition

NASA Astrophysics Data System (ADS)

Cheng, Liyong; Mi, Gaoyang; Li, Shuo; Wang, Chunming; Hu, Xiyuan

2018-03-01

Real-time monitoring of laser welding plays a very important role in the modern automated production and online defects diagnosis is necessary to be implemented. In this study, the status of laser brazing was monitored in real time using an infrared photoelectric sensor. Four kinds of braze seams (including healthy weld, unfilled weld, hole weld and rough surface weld) along with corresponding near-infrared signals were obtained. Further, a new method called Empirical Mode Decomposition (EMD) was proposed to analyze the near-infrared signals. The results showed that the EMD method had a good performance in eliminating the noise on the near-infrared signals. And then, the correlation coefficient was developed for selecting the Intrinsic Mode Function (IMF) more sensitive to the weld defects. A more accurate signal was reconstructed with the selected IMF components. Simultaneously, the spectrum of selected IMF components was solved using fast Fourier transform, and the frequency characteristics were clearly revealed. The frequency energy of different frequency bands was computed to diagnose the defects. There was a significant difference in four types of weld defects. This approach has been proved to be an effective and efficient method for monitoring laser brazing defects.

Pathological speech signal analysis and classification using empirical mode decomposition.

PubMed

Kaleem, Muhammad; Ghoraani, Behnaz; Guergachi, Aziz; Krishnan, Sridhar

2013-07-01

Automated classification of normal and pathological speech signals can provide an objective and accurate mechanism for pathological speech diagnosis, and is an active area of research. A large part of this research is based on analysis of acoustic measures extracted from sustained vowels. However, sustained vowels do not reflect real-world attributes of voice as effectively as continuous speech, which can take into account important attributes of speech such as rapid voice onset and termination, changes in voice frequency and amplitude, and sudden discontinuities in speech. This paper presents a methodology based on empirical mode decomposition (EMD) for classification of continuous normal and pathological speech signals obtained from a well-known database. EMD is used to decompose randomly chosen portions of speech signals into intrinsic mode functions, which are then analyzed to extract meaningful temporal and spectral features, including true instantaneous features which can capture discriminative information in signals hidden at local time-scales. A total of six features are extracted, and a linear classifier is used with the feature vector to classify continuous speech portions obtained from a database consisting of 51 normal and 161 pathological speakers. A classification accuracy of 95.7 % is obtained, thus demonstrating the effectiveness of the methodology.

Iterative filtering decomposition based on local spectral evolution kernel

PubMed Central

Wang, Yang; Wei, Guo-Wei; Yang, Siyang

2011-01-01

The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and analysis. Recently, an iterative filtering decomposition (IFD) has been introduced to address the stability and efficiency problems of the EMD. Another data analysis technique is the local spectral evolution kernel (LSEK), which provides a near prefect low pass filter with desirable time-frequency localizations. The present work utilizes the LSEK to further stabilize the IFD, and offers an efficient, flexible and robust scheme for information extraction, complexity reduction, and signal and image understanding. The performance of the present LSEK based IFD is intensively validated over a wide range of data processing tasks, including mode decomposition, analysis of time-varying data, information extraction from nonlinear dynamic systems, etc. The utility, robustness and usefulness of the proposed LESK based IFD are demonstrated via a large number of applications, such as the analysis of stock market data, the decomposition of ocean wave magnitudes, the understanding of physiologic signals and information recovery from noisy images. The performance of the proposed method is compared with that of existing methods in the literature. Our results indicate that the LSEK based IFD improves both the efficiency and the stability of conventional EMD algorithms. PMID:22350559

Identification of significant intrinsic mode functions for the diagnosis of induction motor fault.

PubMed

Cho, Sangjin; Shahriar, Md Rifat; Chong, Uipil

2014-08-01

For the analysis of non-stationary signals generated by a non-linear process like fault of an induction motor, empirical mode decomposition (EMD) is the best choice as it decomposes the signal into its natural oscillatory modes known as intrinsic mode functions (IMFs). However, some of these oscillatory modes obtained from a fault signal are not significant as they do not bear any fault signature and can cause misclassification of the fault instance. To solve this issue, a novel IMF selection algorithm is proposed in this work.

Using Empirical Mode Decomposition to process Marine Magnetotelluric Data

NASA Astrophysics Data System (ADS)

Chen, J.; Jegen, M. D.; Heincke, B. H.; Moorkamp, M.

2014-12-01

The magnetotelluric (MT) data always exhibits nonstationarities due to variations of source mechanisms causing MT variations on different time and spatial scales. An additional non-stationary component is introduced through noise, which is particularly pronounced in marine MT data through motion induced noise caused by time-varying wave motion and currents. We present a new heuristic method for dealing with the non-stationarity of MT time series based on Empirical Mode Decomposition (EMD). The EMD method is used in combination with the derived instantaneous spectra to determine impedance estimates. The procedure is tested on synthetic and field MT data. In synthetic tests the reliability of impedance estimates from EMD-based method is compared to the synthetic responses of a 1D layered model. To examine how estimates are affected by noise, stochastic stationary and non-stationary noise are added on the time series. Comparisons reveal that estimates by the EMD-based method are generally more stable than those by simple Fourier analysis. Furthermore, the results are compared to those derived by a commonly used Fourier-based MT data processing software (BIRRP), which incorporates additional sophisticated robust estimations to deal with noise issues. It is revealed that the results from both methods are already comparable, even though no robust estimate procedures are implemented in the EMD approach at present stage. The processing scheme is then applied to marine MT field data. Testing is performed on short, relatively quiet segments of several data sets, as well as on long segments of data with many non-stationary noise packages. Compared to BIRRP, the new method gives comparable or better impedance estimates, furthermore, the estimates are extended to lower frequencies and less noise biased estimates with smaller error bars are obtained at high frequencies. The new processing methodology represents an important step towards deriving a better resolved Earth model to greater depth underneath the seafloor.

On Certain Theoretical Developments Underlying the Hilbert-Huang Transform

NASA Technical Reports Server (NTRS)

Kizhner, Semion; Blank, Karin; Flatley, Thomas; Huang, Norden E.; Petrick, David; Hestness, Phyllis

2006-01-01

One of the main traditional tools used in scientific and engineering data spectral analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). Both carry strong a-priori assumptions about the source data, such as being linear and stationary, and of satisfying the Dirichlet conditions. A recent development at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), known as the Hilbert-Huang Transform (HHT), proposes a novel approach to the solution for the nonlinear class of spectral analysis problems. Using a-posteriori data processing based on the Empirical Mode Decomposition (EMD) sifting process (algorithm), followed by the normalized Hilbert Transform of the decomposed data, the HHT allows spectral analysis of nonlinear and nonstationary data. The EMD sifting process results in a non-constrained decomposition of a source real-value data vector into a finite set of Intrinsic Mode Functions (IMF). These functions form a nearly orthogonal derived from the data (adaptive) basis. The IMFs can be further analyzed for spectrum content by using the classical Hilbert Transform. A new engineering spectral analysis tool using HHT has been developed at NASA GSFC, the HHT Data Processing System (HHT-DPS). As the HHT-DPS has been successfully used and commercialized, new applications pose additional questions about the theoretical basis behind the HHT and EMD algorithms. Why is the fastest changing component of a composite signal being sifted out first in the EMD sifting process? Why does the EMD sifting process seemingly converge and why does it converge rapidly? Does an IMF have a distinctive structure? Why are the IMFs nearly orthogonal? We address these questions and develop the initial theoretical background for the HHT. This will contribute to the development of new HHT processing options, such as real-time and 2-D processing using Field Programmable Gate Array (FPGA) computational resources,

Temporal Associations between Weather and Headache: Analysis by Empirical Mode Decomposition

PubMed Central

Yang, Albert C.; Fuh, Jong-Ling; Huang, Norden E.; Shia, Ben-Chang; Peng, Chung-Kang; Wang, Shuu-Jiun

2011-01-01

Background Patients frequently report that weather changes trigger headache or worsen existing headache symptoms. Recently, the method of empirical mode decomposition (EMD) has been used to delineate temporal relationships in certain diseases, and we applied this technique to identify intrinsic weather components associated with headache incidence data derived from a large-scale epidemiological survey of headache in the Greater Taipei area. Methodology/Principal Findings The study sample consisted of 52 randomly selected headache patients. The weather time-series parameters were detrended by the EMD method into a set of embedded oscillatory components, i.e. intrinsic mode functions (IMFs). Multiple linear regression models with forward stepwise methods were used to analyze the temporal associations between weather and headaches. We found no associations between the raw time series of weather variables and headache incidence. For decomposed intrinsic weather IMFs, temperature, sunshine duration, humidity, pressure, and maximal wind speed were associated with headache incidence during the cold period, whereas only maximal wind speed was associated during the warm period. In analyses examining all significant weather variables, IMFs derived from temperature and sunshine duration data accounted for up to 33.3% of the variance in headache incidence during the cold period. The association of headache incidence and weather IMFs in the cold period coincided with the cold fronts. Conclusions/Significance Using EMD analysis, we found a significant association between headache and intrinsic weather components, which was not detected by direct comparisons of raw weather data. Contributing weather parameters may vary in different geographic regions and different seasons. PMID:21297940

Monte Carlo study for physiological interference reduction in near-infrared spectroscopy based on empirical mode decomposition

NASA Astrophysics Data System (ADS)

Zhang, Yan; Sun, JinWei; Rolfe, Peter

2010-12-01

Near-infrared spectroscopy (NIRS) can be used as the basis of non-invasive neuroimaging that may allow the measurement of haemodynamic changes in the human brain evoked by applied stimuli. Since this technique is very sensitive, physiological interference arising from the cardiac cycle and breathing can significantly affect the signal quality. Such interference is difficult to remove by conventional techniques because it occurs not only in the extracerebral layer but also in the brain tissue itself. Previous work on this problem employing temporal filtering, spatial filtering, and adaptive filtering have exhibited good performance for recovering brain activity data in evoked response studies. However, in this study, we present a time-frequency adaptive method for physiological interference reduction based on the combination of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). Monte Carlo simulations based on a five-layered slab model of a human adult head were implemented to evaluate our methodology. We applied an EMD algorithm to decompose the NIRS time series derived from Monte Carlo simulations into a series of intrinsic mode functions (IMFs). In order to identify the IMFs associated with symmetric interference, the extracted components were then Hilbert transformed from which the instantaneous frequencies could be acquired. By reconstructing the NIRS signal by properly selecting IMFs, we determined that the evoked brain response is effectively filtered out with even higher signal-to-noise ratio (SNR). The results obtained demonstrated that EMD, combined with HSA, can effectively separate, identify and remove the contamination from the evoked brain response obtained with NIRS using a simple single source-detector pair.

Seismic facies analysis based on self-organizing map and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Du, Hao-kun; Cao, Jun-xing; Xue, Ya-juan; Wang, Xing-jian

2015-01-01

Seismic facies analysis plays an important role in seismic interpretation and reservoir model building by offering an effective way to identify the changes in geofacies inter wells. The selections of input seismic attributes and their time window have an obvious effect on the validity of classification and require iterative experimentation and prior knowledge. In general, it is sensitive to noise when waveform serves as the input data to cluster analysis, especially with a narrow window. To conquer this limitation, the Empirical Mode Decomposition (EMD) method is introduced into waveform classification based on SOM. We first de-noise the seismic data using EMD and then cluster the data using 1D grid SOM. The main advantages of this method are resolution enhancement and noise reduction. 3D seismic data from the western Sichuan basin, China, are collected for validation. The application results show that seismic facies analysis can be improved and better help the interpretation. The powerful tolerance for noise makes the proposed method to be a better seismic facies analysis tool than classical 1D grid SOM method, especially for waveform cluster with a narrow window.

Optical diagnosis of cervical cancer by intrinsic mode functions

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Pratiher, Sawon; Pratiher, Souvik; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-03-01

In this paper, we make use of the empirical mode decomposition (EMD) to discriminate the cervical cancer tissues from normal ones based on elastic scattering spectroscopy. The phase space has been reconstructed through decomposing the optical signal into a finite set of bandlimited signals known as intrinsic mode functions (IMFs). It has been shown that the area measure of the analytic IMFs provides a good discrimination performance. Simulation results validate the efficacy of the IMFs followed by SVM based classification.

A novel spatial-temporal detection method of dim infrared moving small target

NASA Astrophysics Data System (ADS)

Chen, Zhong; Deng, Tao; Gao, Lei; Zhou, Heng; Luo, Song

2014-09-01

Moving small target detection under complex background in infrared image sequence is one of the major challenges of modern military in Early Warning Systems (EWS) and the use of Long-Range Strike (LRS). However, because of the low SNR and undulating background, the infrared moving small target detection is a difficult problem in a long time. To solve this problem, a novel spatial-temporal detection method based on bi-dimensional empirical mode decomposition (EMD) and time-domain difference is proposed in this paper. This method is downright self-data decomposition and do not rely on any transition kernel function, so it has a strong adaptive capacity. Firstly, we generalized the 1D EMD algorithm to the 2D case. In this process, the project has solved serial issues in 2D EMD, such as large amount of data operations, define and identify extrema in 2D case, and two-dimensional signal boundary corrosion. The EMD algorithm studied in this project can be well adapted to the automatic detection of small targets under low SNR and complex background. Secondly, considering the characteristics of moving target, we proposed an improved filtering method based on three-frame difference on basis of the original difference filtering in time-domain, which greatly improves the ability of anti-jamming algorithm. Finally, we proposed a new time-space fusion method based on a combined processing of 2D EMD and improved time-domain differential filtering. And, experimental results show that this method works well in infrared small moving target detection under low SNR and complex background.

Identification of sudden stiffness changes in the acceleration response of a bridge to moving loads using ensemble empirical mode decomposition

NASA Astrophysics Data System (ADS)

Aied, H.; González, A.; Cantero, D.

2016-01-01

The growth of heavy traffic together with aggressive environmental loads poses a threat to the safety of an aging bridge stock. Often, damage is only detected via visual inspection at a point when repairing costs can be quite significant. Ideally, bridge managers would want to identify a stiffness change as soon as possible, i.e., as it is occurring, to plan for prompt measures before reaching a prohibitive cost. Recent developments in signal processing techniques such as wavelet analysis and empirical mode decomposition (EMD) have aimed to address this need by identifying a stiffness change from a localised feature in the structural response to traffic. However, the effectiveness of these techniques is limited by the roughness of the road profile, the vehicle speed and the noise level. In this paper, ensemble empirical mode decomposition (EEMD) is applied by the first time to the acceleration response of a bridge model to a moving load with the purpose of capturing sudden stiffness changes. EEMD is more adaptive and appears to be better suited to non-linear signals than wavelets, and it reduces the mode mixing problem present in EMD. EEMD is tested in a variety of theoretical 3D vehicle-bridge interaction scenarios. Stiffness changes are successfully identified, even for small affected regions, relatively poor profiles, high vehicle speeds and significant noise. The latter is due to the ability of EEMD to separate high frequency components associated to sudden stiffness changes from other frequency components associated to the vehicle-bridge interaction system.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples

NASA Astrophysics Data System (ADS)

Zhang, Meijun; Tang, Jian; Zhang, Xiaoming; Zhang, Jiaojiao

2016-03-01

The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD(empirical mode decomposition) and EEMD(ensemble empirical mode decomposition) are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM(support vector machine), extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD+SVM method is compared with the EEMD+SVM and EMD+SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.

High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples.

PubMed

Bian, Xihui; Li, Shujuan; Lin, Ligang; Tan, Xiaoyao; Fan, Qingjie; Li, Ming

2016-06-21

Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples. Copyright © 2016 Elsevier B.V. All rights reserved.

On Hilbert-Huang Transform Based Synthesis of a Signal Contaminated by Radio Frequency Interference or Fringes

NASA Technical Reports Server (NTRS)

Kizhner, Semion; Shiri, Ron S.; Vootukuru, Meg; Coletti, Alessandro

2015-01-01

Norden E. Huang et al. had proposed and published the Hilbert-Huang Transform (HHT) concept correspondently in 1996, 1998. The HHT is a novel method for adaptive spectral analysis of non-linear and non-stationary signals. The HHT comprises two components: - the Huang Empirical Mode Decomposition (EMD), resulting in an adaptive data-derived basis of Intrinsic Mode functions (IMFs), and the Hilbert Spectral Analysis (HSA1) based on the Hilbert Transform for 1-dimension (1D) applied to the EMD IMF's outcome. Although paper describes the HHT concept in great depth, it does not contain all needed methodology to implement the HHT computer code. In 2004, Semion Kizhner and Karin Blank implemented the reference digital HHT real-time data processing system for 1D (HHT-DPS Version 1.4). The case for 2-Dimension (2D) (HHT2) proved to be difficult due to the computational complexity of EMD for 2D (EMD2) and absence of a suitable Hilbert Transform for 2D spectral analysis (HSA2). The real-time EMD2 and HSA2 comprise the real-time HHT2. Kizhner completed the real-time EMD2 and the HSA2 reference digital implementations respectively in 2013 & 2014. Still, the HHT2 outcome synthesis remains an active research area. This paper presents the initial concepts and preliminary results of HHT2-based synthesis and its application to processing of signals contaminated by Radio-Frequency Interference (RFI), as well as optical systems' fringe detection and mitigation at design stage. The Soil Moisture Active Passive (SMAP mission (SMAP) carries a radiometer instrument that measures Earth soil moisture at L1 frequency (1.4 GHz polarimetric - H, V, 3rd and 4th Stokes parameters). There is abundant RFI at L1 and because soil moisture is a strategic parameter, it is important to be able to recover the RFI-contaminated measurement samples (15% of telemetry). State-of-the-art only allows RFI detection and removes RFI-contaminated measurements. The HHT-based analysis and synthesis facilitates recovery of measurements contaminated by all kinds of RFI, including jamming [7-8]. The fringes are inherent in optical systems and multi-layer complex contour expensive coatings are employed to remove the unwanted fringes. HHT2-based analysis allows test image decomposition to analyze and detect fringes, and HHT2-based synthesis of useful image.

Analyzing nonstationary financial time series via hilbert-huang transform (HHT)

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2008-01-01

An apparatus, computer program product and method of analyzing non-stationary time varying phenomena. A representation of a non-stationary time varying phenomenon is recursively sifted using Empirical Mode Decomposition (EMD) to extract intrinsic mode functions (IMFs). The representation is filtered to extract intrinsic trends by combining a number of IMFs. The intrinsic trend is inherent in the data and identifies an IMF indicating the variability of the phenomena. The trend also may be used to detrend the data.

Image fusion method based on regional feature and improved bidimensional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Qin, Xinqiang; Hu, Gang; Hu, Kai

2018-01-01

The decomposition of multiple source images using bidimensional empirical mode decomposition (BEMD) often produces mismatched bidimensional intrinsic mode functions, either by their number or their frequency, making image fusion difficult. A solution to this problem is proposed using a fixed number of iterations and a union operation in the sifting process. By combining the local regional features of the images, an image fusion method has been developed. First, the source images are decomposed using the proposed BEMD to produce the first intrinsic mode function (IMF) and residue component. Second, for the IMF component, a selection and weighted average strategy based on local area energy is used to obtain a high-frequency fusion component. Third, for the residue component, a selection and weighted average strategy based on local average gray difference is used to obtain a low-frequency fusion component. Finally, the fused image is obtained by applying the inverse BEMD transform. Experimental results show that the proposed algorithm provides superior performance over methods based on wavelet transform, line and column-based EMD, and complex empirical mode decomposition, both in terms of visual quality and objective evaluation criteria.

A New Approach of evaluating the damage in simply-supported reinforced concrete beam by Local mean decomposition (LMD)

NASA Astrophysics Data System (ADS)

Zhang, Xuebing; Liu, Ning; Xi, Jiaxin; Zhang, Yunqi; Zhang, Wenchun; Yang, Peipei

2017-08-01

How to analyze the nonstationary response signals and obtain vibration characters is extremely important in the vibration-based structural diagnosis methods. In this work, we introduce a more reasonable time-frequency decomposition method termed local mean decomposition (LMD) to instead the widely-used empirical mode decomposition (EMD). By employing the LMD method, one can derive a group of component signals, each of which is more stationary, and then analyze the vibration state and make the assessment of structural damage of a construction or building. We illustrated the effectiveness of LMD by a synthetic data and an experimental data recorded in a simply-supported reinforced concrete beam. Then based on the decomposition results, an elementary method of damage diagnosis was proposed.

Enhancing micro-seismic P-phase arrival picking: EMD-cosine function-based denoising with an application to the AIC picker

NASA Astrophysics Data System (ADS)

Shang, Xueyi; Li, Xibing; Morales-Esteban, A.; Dong, Longjun

2018-03-01

Micro-seismic P-phase arrival picking is an elementary step into seismic event location, source mechanism analysis, and seismic tomography. However, a micro-seismic signal is often mixed with high frequency noises and power frequency noises (50 Hz), which could considerably reduce P-phase picking accuracy. To solve this problem, an Empirical Mode Decomposition (EMD)-cosine function denoising-based Akaike Information Criterion (AIC) picker (ECD-AIC picker) is proposed for picking the P-phase arrival time. Unlike traditional low pass filters which are ineffective when seismic data and noise bandwidths overlap, the EMD adaptively separates the seismic data and the noise into different Intrinsic Mode Functions (IMFs). Furthermore, the EMD-cosine function-based denoising retains the P-phase arrival amplitude and phase spectrum more reliably than any traditional low pass filter. The ECD-AIC picker was tested on 1938 sets of micro-seismic waveforms randomly selected from the Institute of Mine Seismology (IMS) database of the Chinese Yongshaba mine. The results have shown that the EMD-cosine function denoising can effectively estimate high frequency and power frequency noises and can be easily adapted to perform on signals with different shapes and forms. Qualitative and quantitative comparisons show that the combined ECD-AIC picker provides better picking results than both the ED-AIC picker and the AIC picker, and the comparisons also show more reliable source localization results when the ECD-AIC picker is applied, thus showing the potential of this combined P-phase picking technique.

CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system

NASA Astrophysics Data System (ADS)

Zhang, Hongbo; Singh, Vijay P.; Wang, Bin; Yu, Yinghao

2016-09-01

Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition-prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD-RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD-RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD-based forecasting, and results showed that removing high-frequency component is an effective measure to improve forecasting precision and is suggested for use with the CEREF model for better performance. Finally, the study concluded that the CEREF model can be used to forecast non-stationary annual streamflow change as a co-evolution of hydrologic and social systems with better accuracy. Also, the modification about removing high-frequency can further improve the performance of the CEREF model. It should be noted that the CEREF model is beneficial for data-driven hydrologic forecasting in complex socio-hydrologic systems, and as a simple data-driven socio-hydrologic forecasting model, deserves more attention.

Signatures of the seismic source in EMD-based characterization of the 1994 Northridge, California, earthquake recordings

USGS Publications Warehouse

Zhang, R.R.; Ma, S.; Hartzell, S.

2003-01-01

In this article we use empirical mode decomposition (EMD) to characterize the 1994 Northridge, California, earthquake records and investigate the signatures carried over from the source rupture process. Comparison of the current study results with existing source inverse solutions that use traditional data processing suggests that the EMD-based characterization contains information that sheds light on aspects of the earthquake rupture process. We first summarize the fundamentals of the EMD and illustrate its features through the analysis of a hypothetical and a real record. Typically, the Northridge strong-motion records are decomposed into eight or nine intrinsic mode functions (IMF's), each of which emphasizes a different oscillation mode with different amplitude and frequency content. The first IMF has the highest-frequency content; frequency content decreases with an increase in IMF component. With the aid of a finite-fault inversion method, we then examine aspects of the source of the 1994 Northridge earthquake that are reflected in the second to fifth IMF components. This study shows that the second IMF is predominantly wave motion generated near the hypocenter, with high-frequency content that might be related to a large stress drop associated with the initiation of the earthquake. As one progresses from the second to the fifth IMF component, there is a general migration of the source region away from the hypocenter with associated longer-period signals as the rupture propagates. This study suggests that the different IMF components carry information on the earthquake rupture process that is expressed in their different frequency bands.

Application of the Hilbert-Huang Transform to Financial Data

NASA Technical Reports Server (NTRS)

Huang, Norden

2005-01-01

A paper discusses the application of the Hilbert-Huang transform (HHT) method to time-series financial-market data. The method was described, variously without and with the HHT name, in several prior NASA Tech Briefs articles and supporting documents. To recapitulate: The method is especially suitable for analyzing time-series data that represent nonstationary and nonlinear phenomena including physical phenomena and, in the present case, financial-market processes. The method involves the empirical mode decomposition (EMD), in which a complicated signal is decomposed into a finite number of functions, called "intrinsic mode functions" (IMFs), that admit well-behaved Hilbert transforms. The HHT consists of the combination of EMD and Hilbert spectral analysis. The local energies and the instantaneous frequencies derived from the IMFs through Hilbert transforms can be used to construct an energy-frequency-time distribution, denoted a Hilbert spectrum. The instant paper begins with a discussion of prior approaches to quantification of market volatility, summarizes the HHT method, then describes the application of the method in performing time-frequency analysis of mortgage-market data from the years 1972 through 2000. Filtering by use of the EMD is shown to be useful for quantifying market volatility.

Hilbert-Huang transform analysis of dynamic and earthquake motion recordings

USGS Publications Warehouse

Zhang, R.R.; Ma, S.; Safak, E.; Hartzell, S.

2003-01-01

This study examines the rationale of Hilbert-Huang transform (HHT) for analyzing dynamic and earthquake motion recordings in studies of seismology and engineering. In particular, this paper first provides the fundamentals of the HHT method, which consist of the empirical mode decomposition (EMD) and the Hilbert spectral analysis. It then uses the HHT to analyze recordings of hypothetical and real wave motion, the results of which are compared with the results obtained by the Fourier data processing technique. The analysis of the two recordings indicates that the HHT method is able to extract some motion characteristics useful in studies of seismology and engineering, which might not be exposed effectively and efficiently by Fourier data processing technique. Specifically, the study indicates that the decomposed components in EMD of HHT, namely, the intrinsic mode function (IMF) components, contain observable, physical information inherent to the original data. It also shows that the grouped IMF components, namely, the EMD-based low- and high-frequency components, can faithfully capture low-frequency pulse-like as well as high-frequency wave signals. Finally, the study illustrates that the HHT-based Hilbert spectra are able to reveal the temporal-frequency energy distribution for motion recordings precisely and clearly.

Daily air quality index forecasting with hybrid models: A case in China.

PubMed

Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing

2017-12-01

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the proposed hybrid models can be used as effective and simple tools for air pollution forecasting and warning as well as for management. Copyright © 2017 Elsevier Ltd. All rights reserved.

Anomalous volatility scaling in high frequency financial data

NASA Astrophysics Data System (ADS)

Nava, Noemi; Di Matteo, T.; Aste, Tomaso

2016-04-01

Volatility of intra-day stock market indices computed at various time horizons exhibits a scaling behaviour that differs from what would be expected from fractional Brownian motion (fBm). We investigate this anomalous scaling by using empirical mode decomposition (EMD), a method which separates time series into a set of cyclical components at different time-scales. By applying the EMD to fBm, we retrieve a scaling law that relates the variance of the components to a power law of the oscillating period. In contrast, when analysing 22 different stock market indices, we observe deviations from the fBm and Brownian motion scaling behaviour. We discuss and quantify these deviations, associating them to the characteristics of financial markets, with larger deviations corresponding to less developed markets.

EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals.

PubMed

Zhao, Jiaduo; Gong, Weiguo; Tang, Yuzhen; Li, Weihong

2016-01-20

In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD)-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector's false alarms.

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

PubMed

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P; McDonald-Maier, Klaus D

2015-05-08

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

PubMed Central

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P.; McDonald-Maier, Klaus D.

2015-01-01

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences. PMID:26007714

Photoacoustic imaging optimization with raw signal deconvolution and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Guo, Chengwen; Wang, Jing; Qin, Yu; Zhan, Hongchen; Yuan, Jie; Cheng, Qian; Wang, Xueding

2018-02-01

Photoacoustic (PA) signal of an ideal optical absorb particle is a single N-shape wave. PA signals of a complicated biological tissue can be considered as the combination of individual N-shape waves. However, the N-shape wave basis not only complicates the subsequent work, but also results in aliasing between adjacent micro-structures, which deteriorates the quality of the final PA images. In this paper, we propose a method to improve PA image quality through signal processing method directly working on raw signals, which including deconvolution and empirical mode decomposition (EMD). During the deconvolution procedure, the raw PA signals are de-convolved with a system dependent point spread function (PSF) which is measured in advance. Then, EMD is adopted to adaptively re-shape the PA signals with two constraints, positive polarity and spectrum consistence. With our proposed method, the built PA images can yield more detail structural information. Micro-structures are clearly separated and revealed. To validate the effectiveness of this method, we present numerical simulations and phantom studies consist of a densely distributed point sources model and a blood vessel model. In the future, our study might hold the potential for clinical PA imaging as it can help to distinguish micro-structures from the optimized images and even measure the size of objects from deconvolved signals.

Trackside acoustic diagnosis of axle box bearing based on kurtosis-optimization wavelet denoising

NASA Astrophysics Data System (ADS)

Peng, Chaoyong; Gao, Xiaorong; Peng, Jianping; Wang, Ai

2018-04-01

As one of the key components of railway vehicles, the operation condition of the axle box bearing has a significant effect on traffic safety. The acoustic diagnosis is more suitable than vibration diagnosis for trackside monitoring. The acoustic signal generated by the train axle box bearing is an amplitude modulation and frequency modulation signal with complex train running noise. Although empirical mode decomposition (EMD) and some improved time-frequency algorithms have proved to be useful in bearing vibration signal processing, it is hard to extract the bearing fault signal from serious trackside acoustic background noises by using those algorithms. Therefore, a kurtosis-optimization-based wavelet packet (KWP) denoising algorithm is proposed, as the kurtosis is the key indicator of bearing fault signal in time domain. Firstly, the geometry based Doppler correction is applied to signals of each sensor, and with the signal superposition of multiple sensors, random noises and impulse noises, which are the interference of the kurtosis indicator, are suppressed. Then, the KWP is conducted. At last, the EMD and Hilbert transform is applied to extract the fault feature. Experiment results indicate that the proposed method consisting of KWP and EMD is superior to the EMD.

A novel technique for phase synchrony measurement from the complex motor imaginary potential of combined body and limb action

NASA Astrophysics Data System (ADS)

Zhou, Zhong-xing; Wan, Bai-kun; Ming, Dong; Qi, Hong-zhi

2010-08-01

In this study, we proposed and evaluated the use of the empirical mode decomposition (EMD) technique combined with phase synchronization analysis to investigate the human brain synchrony of the supplementary motor area (SMA) and primary motor area (M1) during complex motor imagination of combined body and limb action. We separated the EEG data of the SMA and M1 into intrinsic mode functions (IMFs) using the EMD method and determined the characteristic IMFs by power spectral density (PSD) analysis. Thereafter, the instantaneous phases of the characteristic IMFs were obtained by the Hilbert transformation, and the single-trial phase-locking value (PLV) features for brain synchrony measurement between the SMA and M1 were investigated separately. The classification performance suggests that the proposed approach is effective for phase synchronization analysis and is promising for the application of a brain-computer interface in motor nerve reconstruction of the lower limbs.

Denoising of chaotic signal using independent component analysis and empirical mode decomposition with circulate translating

NASA Astrophysics Data System (ADS)

Wen-Bo, Wang; Xiao-Dong, Zhang; Yuchan, Chang; Xiang-Li, Wang; Zhao, Wang; Xi, Chen; Lei, Zheng

2016-01-01

In this paper, a new method to reduce noises within chaotic signals based on ICA (independent component analysis) and EMD (empirical mode decomposition) is proposed. The basic idea is decomposing chaotic signals and constructing multidimensional input vectors, firstly, on the base of EMD and its translation invariance. Secondly, it makes the independent component analysis on the input vectors, which means that a self adapting denoising is carried out for the intrinsic mode functions (IMFs) of chaotic signals. Finally, all IMFs compose the new denoised chaotic signal. Experiments on the Lorenz chaotic signal composed of different Gaussian noises and the monthly observed chaotic sequence on sunspots were put into practice. The results proved that the method proposed in this paper is effective in denoising of chaotic signals. Moreover, it can correct the center point in the phase space effectively, which makes it approach the real track of the chaotic attractor. Project supported by the National Science and Technology, China (Grant No. 2012BAJ15B04), the National Natural Science Foundation of China (Grant Nos. 41071270 and 61473213), the Natural Science Foundation of Hubei Province, China (Grant No. 2015CFB424), the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics, China (Grant No. SOED1405), the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science, China (Grant No. Z201303), and the Hubei Key Laboratory Foundation of Transportation Internet of Things, Wuhan University of Technology, China (Grant No.2015III015-B02).

Qualitative Investigation of the Earthquake Precuesors Prior to the March 14,2012 Earthquake in Japan

NASA Astrophysics Data System (ADS)

Raghuwanshi, Shailesh Kumar; Gwal, Ashok Kumar

Abstract: In this study we have used the Empirical Mode Decomposition (EMD) method in conjunction with the Cross Correlation analysis to analyze ionospheric foF2 parameter Japan earthquake with magnitude M = 6.9. The data are collected from Kokubunji (35.70N, 139.50E) and Yamakawa (31.20N, 130.60E) ionospheric stations. The EMD method was used for removing the geophysical noise from the foF2 data and then to calculate the correlation coefficient between them. It was found that the ionospheric foF2 parameter shows anomalous change few days before the earthquake. The results are in agreement with the theoretical model evidencing ionospheric modification prior to Japan earthquake in a certain area around the epicenter.

GPR random noise reduction using BPD and EMD

NASA Astrophysics Data System (ADS)

Ostoori, Roya; Goudarzi, Alireza; Oskooi, Behrooz

2018-04-01

Ground-penetrating radar (GPR) exploration is a new high-frequency technology that explores near-surface objects and structures accurately. The high-frequency antenna of the GPR system makes it a high-resolution method compared to other geophysical methods. The frequency range of recorded GPR is so wide that random noise recording is inevitable due to acquisition. This kind of noise comes from unknown sources and its correlation to the adjacent traces is nearly zero. This characteristic of random noise along with the higher accuracy of GPR system makes denoising very important for interpretable results. The main objective of this paper is to reduce GPR random noise based on pursuing denoising using empirical mode decomposition. Our results showed that empirical mode decomposition in combination with basis pursuit denoising (BPD) provides satisfactory outputs due to the sifting process compared to the time-domain implementation of the BPD method on both synthetic and real examples. Our results demonstrate that because of the high computational costs, the BPD-empirical mode decomposition technique should only be used for heavily noisy signals.

Compact Empirical Mode Decomposition: An Algorithm to Reduce Mode Mixing, End Effect, and Detrend Uncertainty

DTIC Science & Technology

2012-01-01

2, . . . , L), G1 = F1(x (ext) 1 , x (ext) 2 , . . . , x (ext) L ). (18) Similarly, GN is a function of (x (ext) l , l = M , M − 1, . . . , M − L+ 1...EMD and EEMD. Since the observational data contain errors, four time series sm(ti) ( m = 1, 2, 3) are constructed each by a signal [components of (25...three-point non-uniform combined compact difference scheme. J. Comput. Phys., 148: 663–674. Huang, N. E., Shen, Z., Long, S . R., Wu, M . C., Shih, H. H

Determination of knock characteristics in spark ignition engines: an approach based on ensemble empirical mode decomposition

NASA Astrophysics Data System (ADS)

Li, Ning; Yang, Jianguo; Zhou, Rui; Liang, Caiping

2016-04-01

Knock is one of the major constraints to improve the performance and thermal efficiency of spark ignition (SI) engines. It can also result in severe permanent engine damage under certain operating conditions. Based on the ensemble empirical mode decomposition (EEMD), this paper proposes a new approach to determine the knock characteristics in SI engines. By adding a uniformly distributed and finite white Gaussian noise, the EEMD can preserve signal continuity in different scales and therefore alleviates the mode-mixing problem occurring in the classic empirical mode decomposition (EMD). The feasibilities of applying the EEMD to detect the knock signatures of a test SI engine via the pressure signal measured from combustion chamber and the vibration signal measured from cylinder head are investigated. Experimental results show that the EEMD-based method is able to detect the knock signatures from both the pressure signal and vibration signal, even in initial stage of knock. Finally, by comparing the application results with those obtained by short-time Fourier transform (STFT), Wigner-Ville distribution (WVD) and discrete wavelet transform (DWT), the superiority of the EEMD method in determining knock characteristics is demonstrated.

An imbalance fault detection method based on data normalization and EMD for marine current turbines.

PubMed

Zhang, Milu; Wang, Tianzhen; Tang, Tianhao; Benbouzid, Mohamed; Diallo, Demba

2017-05-01

This paper proposes an imbalance fault detection method based on data normalization and Empirical Mode Decomposition (EMD) for variable speed direct-drive Marine Current Turbine (MCT) system. The method is based on the MCT stator current under the condition of wave and turbulence. The goal of this method is to extract blade imbalance fault feature, which is concealed by the supply frequency and the environment noise. First, a Generalized Likelihood Ratio Test (GLRT) detector is developed and the monitoring variable is selected by analyzing the relationship between the variables. Then, the selected monitoring variable is converted into a time series through data normalization, which makes the imbalance fault characteristic frequency into a constant. At the end, the monitoring variable is filtered out by EMD method to eliminate the effect of turbulence. The experiments show that the proposed method is robust against turbulence through comparing the different fault severities and the different turbulence intensities. Comparison with other methods, the experimental results indicate the feasibility and efficacy of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

The recognition of ocean red tide with hyper-spectral-image based on EMD

NASA Astrophysics Data System (ADS)

Zhao, Wencang; Wei, Hongli; Shi, Changjiang; Ji, Guangrong

2008-05-01

A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition (EMD), from an artificial red tide experiment in the East China Sea in 2002. A set of characteristic parameters that describe absorbing crest and reflecting crest of the red tide and its recognition methods are put forward based on general picture data, with which the spectral information of certain non-dominant alga species of a red tide occurrence is analyzed for establishing the foundation to estimate the species. Comparative experiments have proved that the method is effective. Meanwhile, the transitional area between red-tide zone and non-red-tide zone can be detected with the information of thickness of algae influence, with which a red tide can be forecast.

Volatility behavior of visibility graph EMD financial time series from Ising interacting system

NASA Astrophysics Data System (ADS)

Zhang, Bo; Wang, Jun; Fang, Wen

2015-08-01

A financial market dynamics model is developed and investigated by stochastic Ising system, where the Ising model is the most popular ferromagnetic model in statistical physics systems. Applying two graph based analysis and multiscale entropy method, we investigate and compare the statistical volatility behavior of return time series and the corresponding IMF series derived from the empirical mode decomposition (EMD) method. And the real stock market indices are considered to be comparatively studied with the simulation data of the proposed model. Further, we find that the degree distribution of visibility graph for the simulation series has the power law tails, and the assortative network exhibits the mixing pattern property. All these features are in agreement with the real market data, the research confirms that the financial model established by the Ising system is reasonable.

On the Hilbert-Huang Transform Data Processing System Development

NASA Technical Reports Server (NTRS)

Kizhner, Semion; Flatley, Thomas P.; Huang, Norden E.; Cornwell, Evette; Smith, Darell

2003-01-01

One of the main heritage tools used in scientific and engineering data spectrum analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). The Fourier view of nonlinear mechanics that had existed for a long time, and the associated FFT (fairly recent development), carry strong a-priori assumptions about the source data, such as linearity and of being stationary. Natural phenomena measurements are essentially nonlinear and nonstationary. A very recent development at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), known as the Hilbert-Huang Transform (HHT) proposes a novel approach to the solution for the nonlinear class of spectrum analysis problems. Using the Empirical Mode Decomposition (EMD) followed by the Hilbert Transform of the empirical decomposition data (HT), the HHT allows spectrum analysis of nonlinear and nonstationary data by using an engineering a-posteriori data processing, based on the EMD algorithm. This results in a non-constrained decomposition of a source real value data vector into a finite set of Intrinsic Mode Functions (IMF) that can be further analyzed for spectrum interpretation by the classical Hilbert Transform. This paper describes phase one of the development of a new engineering tool, the HHT Data Processing System (HHTDPS). The HHTDPS allows applying the "T to a data vector in a fashion similar to the heritage FFT. It is a generic, low cost, high performance personal computer (PC) based system that implements the HHT computational algorithms in a user friendly, file driven environment. This paper also presents a quantitative analysis for a complex waveform data sample, a summary of technology commercialization efforts and the lessons learned from this new technology development.

Fault Detection of a Roller-Bearing System through the EMD of a Wavelet Denoised Signal

PubMed Central

Ahn, Jong-Hyo; Kwak, Dae-Ho; Koh, Bong-Hwan

2014-01-01

This paper investigates fault detection of a roller bearing system using a wavelet denoising scheme and proper orthogonal value (POV) of an intrinsic mode function (IMF) covariance matrix. The IMF of the bearing vibration signal is obtained through empirical mode decomposition (EMD). The signal screening process in the wavelet domain eliminates noise-corrupted portions that may lead to inaccurate prognosis of bearing conditions. We segmented the denoised bearing signal into several intervals, and decomposed each of them into IMFs. The first IMF of each segment is collected to become a covariance matrix for calculating the POV. We show that covariance matrices from healthy and damaged bearings exhibit different POV profiles, which can be a damage-sensitive feature. We also illustrate the conventional approach of feature extraction, of observing the kurtosis value of the measured signal, to compare the functionality of the proposed technique. The study demonstrates the feasibility of wavelet-based de-noising, and shows through laboratory experiments that tracking the proper orthogonal values of the covariance matrix of the IMF can be an effective and reliable measure for monitoring bearing fault. PMID:25196008

Fault Detection of Roller-Bearings Using Signal Processing and Optimization Algorithms

PubMed Central

Kwak, Dae-Ho; Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

2014-01-01

This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system. PMID:24368701

Appropriate IMFs associated with cepstrum and envelope analysis for ball-bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Tsao, Wen-Chang; Pan, Min-Chun

2014-03-01

The traditional envelope analysis is an effective method for the fault detection of rolling bearings. However, all the resonant frequency bands must be examined during the bearing-fault detection process. To handle the above deficiency, this paper proposes using the empirical mode decomposition (EMD) to select a proper intrinsic mode function (IMF) for the subsequent detection tools; here both envelope analysis and cepstrum analysis are employed and compared. By virtue of the band-pass filtering nature of EMD, the resonant frequency bands of structure to be measured are captured in the IMFs. As impulses arising from rolling elements striking bearing faults modulate with structure resonance, proper IMFs potentially enable to characterize fault signatures. In the study, faulty ball bearings are used to justify the proposed method, and comparisons with the traditional envelope analysis are made. Post the use of IMFs highlighting faultybearing features, the performance of using envelope analysis and cepstrum analysis to single out bearing faults is objectively compared and addressed; it is noted that generally envelope analysis offers better performance.

Health monitoring of pipeline girth weld using empirical mode decomposition

NASA Astrophysics Data System (ADS)

Rezaei, Davood; Taheri, Farid

2010-05-01

In the present paper the Hilbert-Huang transform (HHT), as a time-series analysis technique, has been combined with a local diagnostic approach in an effort to identify flaws in pipeline girth welds. This method is based on monitoring the free vibration signals of the pipe at its healthy and flawed states, and processing the signals through the HHT and its associated signal decomposition technique, known as empirical mode decomposition (EMD). The EMD method decomposes the vibration signals into a collection of intrinsic mode functions (IMFs). The deviations in structural integrity, measured from a healthy-state baseline, are subsequently evaluated by two damage sensitive parameters. The first is a damage index, referred to as the EM-EDI, which is established based on an energy comparison of the first or second IMF of the vibration signals, before and after occurrence of damage. The second parameter is the evaluation of the lag in instantaneous phase, a quantity derived from the HHT. In the developed methodologies, the pipe's free vibration is monitored by piezoceramic sensors and a laser Doppler vibrometer. The effectiveness of the proposed techniques is demonstrated through a set of numerical and experimental studies on a steel pipe with a mid-span girth weld, for both pressurized and nonpressurized conditions. To simulate a crack, a narrow notch is cut on one side of the girth weld. Several damage scenarios, including notches of different depths and at various locations on the pipe, are investigated. Results from both numerical and experimental studies reveal that in all damage cases the sensor located at the notch vicinity could successfully detect the notch and qualitatively predict its severity. The effect of internal pressure on the damage identification method is also monitored. Overall, the results are encouraging and promise the effectiveness of the proposed approaches as inexpensive systems for structural health monitoring purposes.

Segmentation of ECG from Surface EMG Using DWT and EMD: A Comparison Study

NASA Astrophysics Data System (ADS)

Shahbakhti, Mohammad; Heydari, Elnaz; Luu, Gia Thien

2014-10-01

The electrocardiographic (ECG) signal is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact from SEMG is investigated. The focus of this research is to reach the optimized number of decomposed levels using mean power frequency (MPF) by both techniques. In order to implement the proposed methods, ten simulated and three real ECG contaminated SEMG signals have been tested. Signal-to-noise ratio (SNR) and mean square error (MSE) between the filtered and the pure signals are applied as the performance indexes of this research. The obtained results suggest both techniques could remove ECG artifact from SEMG signals fair enough, however, DWT performs much better and faster in real data.

Time distortion associated with smartphone addiction: Identifying smartphone addiction via a mobile application (App).

PubMed

Lin, Yu-Hsuan; Lin, Yu-Cheng; Lee, Yang-Han; Lin, Po-Hsien; Lin, Sheng-Hsuan; Chang, Li-Ren; Tseng, Hsien-Wei; Yen, Liang-Yu; Yang, Cheryl C H; Kuo, Terry B J

2015-06-01

Global smartphone penetration has brought about unprecedented addictive behaviors. We report a proposed diagnostic criteria and the designing of a mobile application (App) to identify smartphone addiction. We used a novel empirical mode decomposition (EMD) to delineate the trend in smartphone use over one month. The daily use count and the trend of this frequency are associated with smartphone addiction. We quantify excessive use by daily use duration and frequency, as well as the relationship between the tolerance symptoms and the trend for the median duration of a use epoch. The psychiatrists' assisted self-reporting use time is significant lower than and the recorded total smartphone use time via the App and the degree of underestimation was positively correlated with actual smartphone use. Our study suggests the identification of smartphone addiction by diagnostic interview and via the App-generated parameters with EMD analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality

NASA Astrophysics Data System (ADS)

Masselot, Pierre; Chebana, Fateh; Bélanger, Diane; St-Hilaire, André; Abdous, Belkacem; Gosselin, Pierre; Ouarda, Taha B. M. J.

2018-01-01

In a number of environmental studies, relationships between natural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship.

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

PubMed Central

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-01-01

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijiang stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting. PMID:29883381

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

PubMed

Zhang, Xike; Zhang, Qiuwen; Zhang, Gui; Nie, Zhiping; Gui, Zifan; Que, Huafei

2018-05-21

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

Online Condition Monitoring of Gripper Cylinder in TBM Based on EMD Method

NASA Astrophysics Data System (ADS)

Li, Lin; Tao, Jian-Feng; Yu, Hai-Dong; Huang, Yi-Xiang; Liu, Cheng-Liang

2017-11-01

The gripper cylinder that provides braced force for Tunnel Boring Machine (TBM) might fail due to severe vibration when the TBM excavates in the tunnel. Early fault diagnosis of the gripper cylinder is important for the safety and efficiency of the whole tunneling project. In this paper, an online condition monitoring system based on the Empirical Mode Decomposition (EMD) method is established for fault diagnosis of the gripper cylinder while TBM is working. Firstly, the lumped mass parameter model of the gripper cylinder is established considering the influence of the variable stiffness at the rock interface, the equivalent stiffness of the oil, the seals, and the copper guide sleeve. The dynamic performance of the gripper cylinder is investigated to provide basis for its health condition evaluation. Then, the EMD method is applied to identify the characteristic frequencies of the gripper cylinder for fault diagnosis and a field test is used to verify the accuracy of the EMD method for detection of the characteristic frequencies. Furthermore, the contact stiffness at the interface between the barrel and the rod is calculated with Hertz theory and the relationship between the natural frequency and the stiffness varying with the health condition of the cylinder is simulated based on the dynamic model. The simulation shows that the characteristic frequencies decrease with the increasing clearance between the barrel and the rod, thus the defects could be indicated by monitoring the natural frequency. Finally, a health condition management system of the gripper cylinder based on the vibration signal and the EMD method is established, which could ensure the safety of TBM.

Empirical Mode Decomposition of Geophysical Well-log Data of Bombay Offshore Basin, Mumbai, India

NASA Astrophysics Data System (ADS)

Siddharth Gairola, Gaurav; Chandrasekhar, Enamundram

2016-04-01

Geophysical well-log data manifest the nonlinear behaviour of their respective physical properties of the heterogeneous subsurface layers as a function of depth. Therefore, nonlinear data analysis techniques must be implemented, to quantify the degree of heterogeneity in the subsurface lithologies. One such nonlinear data adaptive technique is empirical mode decomposition (EMD) technique, which facilitates to decompose the data into oscillatory signals of different wavelengths called intrinsic mode functions (IMF). In the present study EMD has been applied to gamma-ray log and neutron porosity log of two different wells: Well B and Well C located in the western offshore basin of India to perform heterogeneity analysis and compare the results with those obtained by multifractal studies of the same data sets. By establishing a relationship between the IMF number (m) and the mean wavelength associated with each IMF (Im), a heterogeneity index (ρ) associated with subsurface layers can be determined using the relation, Im=kρm, where 'k' is a constant. The ρ values bear an inverse relation with the heterogeneity of the subsurface: smaller ρ values designate higher heterogeneity and vice-versa. The ρ values estimated for different limestone payzones identified in the wells clearly show that Well C has higher degree of heterogeneity than Well B. This correlates well with the estimated Vshale values for the limestone reservoir zone showing higher shale content in Well C than Well B. The ρ values determined for different payzones of both wells will be used to quantify the degree of heterogeneity in different wells. The multifractal behaviour of each IMF of both the logs of both the wells will be compared with one another and discussed on the lines of their heterogeneity indices.

Natural periodicities and Northern Hemisphere-Southern Hemisphere connection of temperature changes during the last glacial period: EPICA and NGRIP data sets revisited

NASA Astrophysics Data System (ADS)

Alberti, Tommaso; Lepreti, Fabio; Vecchio, Antonio; Bevacqua, Emanuele; Capparelli, Vincenzo; Carbone, Vincenzo

2015-04-01

We investigate both the European Project for Ice Coring in Antarctica Dronning Maud Land (EDML) and North Greenland Ice-Core Project (NGRIP) δ18O data sets to study both the time evolution of the so-called Dansgaard-Oeschger events and the dynamics at longer timescales during the last glacial period, considering the interval 20 - 120 kyr B.P., since this is the interval in which significant temperature changes, that are the focus of the present work, are observed. To identify the main periodicities and their amplitudes, we applied the Empirical Mode Decomposition (EMD), a technique designed to investigate non-stationary data, by which both the δ18O time series are decomposed into a finite number m of oscillating intrinsic mode functions (IMFs) as 18 mΣ-1 δ O = Cj(t)+ rm(t) j=0 (1) where Cj(t) are the IMFs and rm(t) is a residue which provides the mean trend. We extract the proper modes of both the data sets confirming that natural cycles of abrupt climate changes exist and their occurrence cannot be due to random fluctuations in time. It is shown that the time behavior at the typical timescales of Dansgaard-Oeschger events is captured through signal reconstructions obtained by summing five EMD modes for NGRIP and four EMD modes for EDML. The reconstructions obtained by summing the successive modes can be used to describe the climate evolution at longer timescales, characterized by intervals in which Dansgaard-Oeschger events happen and intervals when these are not observed. Using EMD signal reconstructions and a simple model based on the one-dimensional Langevin equation, it is argued that the occurrence of a Dansgaard-Oeschger event can be described as an excitation of the climate system within the same state, while the longer timescale behavior appears to be due to transitions between different climate states. Finally, on the basis of a cross correlation analysis performed to investigate the North-South asynchrony, it is found that the clearest correlation occurs between the long-scale reconstructions at a lag of ≃ 3.05 kyr, which supports the view according to which the Antarctic climate changes lead that of Greenland, but on a longer time-scale than previously reported. The novelty introduced by this work is represented by the fact that we use EMD reconstructions to investigate the climate dynamics at different timescales and to highlight the behaviour of the climate system in order to describe transitions between two different stable states. We also suggest that the results of correlation analysis could be explained in the framework of seesaw models but building up a model which take into account our EMD filtered long timescales series. The results presented could be also useful for theoretical modeling of the climate evolution in order to study which kind of mechanisms are involved and to clarify the role of the ocean into coupling mechanism between the two hemispheres.

Natural periodicities and Northern Hemisphere-Southern Hemisphere connection of temperature changes during the last glacial period: EPICA and NGRIP data sets revisited

NASA Astrophysics Data System (ADS)

Alberti, Tommaso; Lepreti, Fabio; Vecchio, Antonio; Carbone, Vincenzo

2016-04-01

We investigate both the European Project for Ice Coring in Antarctica Dronning Maud Land (EDML) and North Greenland Ice-Core Project (NGRIP) δ18O data sets to study both the time evolution of the so-called Dansgaard-Oeschger events and the dynamics at longer timescales during the last glacial period, considering the interval 20 - 120 kyr B.P., since this is the interval in which significant temperature changes, that are the focus of the present work, are observed. To identify the main periodicities and their amplitudes, we applied the Empirical Mode Decomposition (EMD), a technique designed to investigate non-stationary data, by which both the δ18O time series are decomposed into a finite number m of oscillating intrinsic mode functions (IMFs) as 18 m∑-1 δ O = Cj(t)+ rm(t) j=0 (1) where Cj(t) are the IMFs and rm(t) is a residue which provides the mean trend. We extract the proper modes of both the data sets confirming that natural cycles of abrupt climate changes exist and their occurrence cannot be due to random fluctuations in time. It is shown that the time behavior at the typical timescales of Dansgaard-Oeschger events is captured through signal reconstructions obtained by summing five EMD modes for NGRIP and four EMD modes for EDML. The reconstructions obtained by summing the successive modes can be used to describe the climate evolution at longer timescales, characterized by intervals in which Dansgaard-Oeschger events happen and intervals when these are not observed. Using EMD signal reconstructions and a simple model based on the one-dimensional Langevin equation, it is argued that the occurrence of a Dansgaard-Oeschger event can be described as an excitation of the climate system within the same state, while the longer timescale behavior appears to be due to transitions between different climate states. Finally, on the basis of a cross correlation analysis performed to investigate the North-South asynchrony, it is found that the clearest correlation occurs between the long-scale reconstructions at a lag of ≃ 3.05 kyr, which supports the view according to which the Antarctic climate changes lead that of Greenland, but on a longer time-scale than previously reported. The novelty introduced by this work is represented by the fact that we use EMD reconstructions to investigate the climate dynamics at different timescales and to highlight the behaviour of the climate system in order to describe transitions between two different stable states. We also suggest that the results of correlation analysis could be explained in the framework of seesaw models but building up a model which take into account our EMD filtered long timescales series. The results presented could be also useful for theoretical modeling of the climate evolution in order to study which kind of mechanisms are involved and to clarify the role of the ocean into coupling mechanism between the two hemispheres.

Investigation of Kelvin wave periods during Hai-Tang typhoon using Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Kishore, P.; Jayalakshmi, J.; Lin, Pay-Liam; Velicogna, Isabella; Sutterley, Tyler C.; Ciracì, Enrico; Mohajerani, Yara; Kumar, S. Balaji

2017-11-01

Equatorial Kelvin waves (KWs) are fundamental components of the tropical climate system. In this study, we investigate Kelvin waves (KWs) during the Hai-Tang typhoon of 2005 using Empirical Mode Decomposition (EMD) of regional precipitation, zonal and meridional winds. For the analysis, we use daily precipitation datasets from the Global Precipitation Climatology Project (GPCP) and wind datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-analysis (ERA-Interim). As an additional measurement, we use in-situ precipitation datasets from rain-gauges over the Taiwan region. The maximum accumulated precipitation was approximately 2400 mm during the period July 17-21, 2005 over the southwestern region of Taiwan. The spectral analysis using the wind speed at 950 hPa found in the 2nd, 3rd, and 4th intrinsic mode functions (IMFs) reveals prevailing Kelvin wave periods of ∼3 days, ∼4-6 days, and ∼6-10 days, respectively. From our analysis of precipitation datasets, we found the Kelvin waves oscillated with periods between ∼8 and 20 days.

Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation

PubMed Central

2015-01-01

This paper presents a data-driven multiscale entropy measure to reveal the scale dependent information quantity of electroencephalogram (EEG) recordings. This work is motivated by the previous observations on the nonlinear and nonstationary nature of EEG over multiple time scales. Here, a new framework of entropy measures considering changing dynamics over multiple oscillatory scales is presented. First, to deal with nonstationarity over multiple scales, EEG recording is decomposed by applying the empirical mode decomposition (EMD) which is known to be effective for extracting the constituent narrowband components without a predetermined basis. Following calculation of Renyi entropy of the probability distributions of the intrinsic mode functions extracted by EMD leads to a data-driven multiscale Renyi entropy. To validate the performance of the proposed entropy measure, actual EEG recordings from rats (n = 9) experiencing 7 min cardiac arrest followed by resuscitation were analyzed. Simulation and experimental results demonstrate that the use of the multiscale Renyi entropy leads to better discriminative capability of the injury levels and improved correlations with the neurological deficit evaluation after 72 hours after cardiac arrest, thus suggesting an effective diagnostic and prognostic tool. PMID:26380297

Multiscale Characterization of PM2.5 in Southern Taiwan based on Noise-assisted Multivariate Empirical Mode Decomposition and Time-dependent Intrinsic Correlation

NASA Astrophysics Data System (ADS)

Hsiao, Y. R.; Tsai, C.

2017-12-01

As the WHO Air Quality Guideline indicates, ambient air pollution exposes world populations under threat of fatal symptoms (e.g. heart disease, lung cancer, asthma etc.), raising concerns of air pollution sources and relative factors. This study presents a novel approach to investigating the multiscale variations of PM2.5 in southern Taiwan over the past decade, with four meteorological influencing factors (Temperature, relative humidity, precipitation and wind speed),based on Noise-assisted Multivariate Empirical Mode Decomposition(NAMEMD) algorithm, Hilbert Spectral Analysis(HSA) and Time-dependent Intrinsic Correlation(TDIC) method. NAMEMD algorithm is a fully data-driven approach designed for nonlinear and nonstationary multivariate signals, and is performed to decompose multivariate signals into a collection of channels of Intrinsic Mode Functions (IMFs). TDIC method is an EMD-based method using a set of sliding window sizes to quantify localized correlation coefficients for multiscale signals. With the alignment property and quasi-dyadic filter bank of NAMEMD algorithm, one is able to produce same number of IMFs for all variables and estimates the cross correlation in a more accurate way. The performance of spectral representation of NAMEMD-HSA method is compared with Complementary Empirical Mode Decomposition/ Hilbert Spectral Analysis (CEEMD-HSA) and Wavelet Analysis. The nature of NAMAMD-based TDICC analysis is then compared with CEEMD-based TDIC analysis and the traditional correlation analysis.

Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine

PubMed Central

2011-01-01

Background Cardiotocography (CTG) is the most widely used tool for fetal surveillance. The visual analysis of fetal heart rate (FHR) traces largely depends on the expertise and experience of the clinician involved. Several approaches have been proposed for the effective interpretation of FHR. In this paper, a new approach for FHR feature extraction based on empirical mode decomposition (EMD) is proposed, which was used along with support vector machine (SVM) for the classification of FHR recordings as 'normal' or 'at risk'. Methods The FHR were recorded from 15 subjects at a sampling rate of 4 Hz and a dataset consisting of 90 randomly selected records of 20 minutes duration was formed from these. All records were labelled as 'normal' or 'at risk' by two experienced obstetricians. A training set was formed by 60 records, the remaining 30 left as the testing set. The standard deviations of the EMD components are input as features to a support vector machine (SVM) to classify FHR samples. Results For the training set, a five-fold cross validation test resulted in an accuracy of 86% whereas the overall geometric mean of sensitivity and specificity was 94.8%. The Kappa value for the training set was .923. Application of the proposed method to the testing set (30 records) resulted in a geometric mean of 81.5%. The Kappa value for the testing set was .684. Conclusions Based on the overall performance of the system it can be stated that the proposed methodology is a promising new approach for the feature extraction and classification of FHR signals. PMID:21244712

Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering

NASA Astrophysics Data System (ADS)

Rai, Akhand; Upadhyay, S. H.

2017-09-01

Bearing is the most critical component in rotating machinery since it is more susceptible to failure. The monitoring of degradation in bearings becomes of great concern for averting the sudden machinery breakdown. In this study, a novel method for bearing performance degradation assessment (PDA) based on an amalgamation of empirical mode decomposition (EMD) and k-medoids clustering is encouraged. The fault features are extracted from the bearing signals using the EMD process. The extracted features are then subjected to k-medoids based clustering for obtaining the normal state and failure state cluster centres. A confidence value (CV) curve based on dissimilarity of the test data object to the normal state is obtained and employed as the degradation indicator for assessing the health of bearings. The proposed outlook is applied on the vibration signals collected in run-to-failure tests of bearings to assess its effectiveness in bearing PDA. To validate the superiority of the suggested approach, it is compared with commonly used time-domain features RMS and kurtosis, well-known fault diagnosis method envelope analysis (EA) and existing PDA classifiers i.e. self-organizing maps (SOM) and Fuzzy c-means (FCM). The results demonstrate that the recommended method outperforms the time-domain features, SOM and FCM based PDA in detecting the early stage degradation more precisely. Moreover, EA can be used as an accompanying method to confirm the early stage defect detected by the proposed bearing PDA approach. The study shows the potential application of k-medoids clustering as an effective tool for PDA of bearings.

Empirical mode decomposition of the ECG signal for noise removal

NASA Astrophysics Data System (ADS)

Khan, Jesmin; Bhuiyan, Sharif; Murphy, Gregory; Alam, Mohammad

2011-04-01

Electrocardiography is a diagnostic procedure for the detection and diagnosis of heart abnormalities. The electrocardiogram (ECG) signal contains important information that is utilized by physicians for the diagnosis and analysis of heart diseases. So good quality ECG signal plays a vital role for the interpretation and identification of pathological, anatomical and physiological aspects of the whole cardiac muscle. However, the ECG signals are corrupted by noise which severely limit the utility of the recorded ECG signal for medical evaluation. The most common noise presents in the ECG signal is the high frequency noise caused by the forces acting on the electrodes. In this paper, we propose a new ECG denoising method based on the empirical mode decomposition (EMD). The proposed method is able to enhance the ECG signal upon removing the noise with minimum signal distortion. Simulation is done on the MIT-BIH database to verify the efficacy of the proposed algorithm. Experiments show that the presented method offers very good results to remove noise from the ECG signal.

Determining temporal scales of the soil moisture variations by Empirical Mode Decompositions and wavelet methods and its use for validation of SMOS data

NASA Astrophysics Data System (ADS)

Usowicz, Jerzy, B.; Marczewski, Wojciech; Usowicz, Boguslaw; Lipiec, Jerzy; Lukowski, Mateusz I.

2010-05-01

This paper presents the results of the time series analysis of the soil moisture observed at two test sites Podlasie, Polesie, in the Cal/Val AO 3275 campaigns in Poland, during the interval 2006-2009. The test sites have been selected on a basis of their contrasted hydrological conditions. The region Podlasie (Trzebieszow) is essentially drier than the wetland region Polesie (Urszulin). It is worthwhile to note that the soil moisture variations can be represented as a non-stationary random process, and therefore appropriate analysis methods are required. The so-called Empirical Mode Decomposition (EMD) method has been chosen, since it is one of the best methods for the analysis of non-stationary and nonlinear time series. To confirm the results obtained by the EMD we have also used the wavelet methods. Firstly, we have used EMD (analyze step) to decompose the original time series into the so-called Intrinsic Mode Functions (IMFs) and then by grouping and addition similar IMFs (synthesize step) to obtain a few signal components with corresponding temporal scales. Such an adaptive procedure enables to decompose the original time series into diurnal, seasonal and trend components. Revealing of all temporal scales which operates in the original time series is our main objective and this approach may prove to be useful in other studies. Secondly, we have analyzed the soil moisture time series from both sites using the cross-wavelet and wavelet coherency. These methods allow us to study the degree of spatial coherence, which may vary in various intervals of time. We hope the obtained results provide some hints and guidelines for the validation of ESA SMOS data. References: B. Usowicz, J.B. Usowicz, Spatial and temporal variation of selected physical and chemical properties of soil, Institute of Agrophysics, Polish Academy of Sciences, Lublin 2004, ISBN 83-87385-96-4 Rao, A.R., Hsu, E.-C., Hilbert-Huang Transform Analysis of Hydrological and Environmental Time Series, Springer, 2008, ISBN: 978-1-4020-6453-1 Acknowledgements. This work was funded in part by the PECS - Programme for European Cooperating States, No. 98084 "SWEX/R - Soil Water and Energy Exchange/Research".

Non-destructive testing of full-length bonded rock bolts based on HHT signal analysis

NASA Astrophysics Data System (ADS)

Shi, Z. M.; Liu, L.; Peng, M.; Liu, C. C.; Tao, F. J.; Liu, C. S.

2018-04-01

Full-length bonded rock bolts are commonly used in mining, tunneling and slope engineering because of their simple design and resistance to corrosion. However, the length of a rock bolt and grouting quality do not often meet the required design standards in practice because of the concealment and complexity of bolt construction. Non-destructive testing is preferred when testing a rock bolt's quality because of the convenience, low cost and wide detection range. In this paper, a signal analysis method for the non-destructive sound wave testing of full-length bonded rock bolts is presented, which is based on the Hilbert-Huang transform (HHT). First, we introduce the HHT analysis method to calculate the bolt length and identify defect locations based on sound wave reflection test signals, which includes decomposing the test signal via empirical mode decomposition (EMD), selecting the intrinsic mode functions (IMF) using the Pearson Correlation Index (PCI) and calculating the instantaneous phase and frequency via the Hilbert transform (HT). Second, six model tests are conducted using different grouting defects and bolt protruding lengths to verify the effectiveness of the HHT analysis method. Lastly, the influence of the bolt protruding length on the test signal, identification of multiple reflections from defects, bolt end and protruding end, and mode mixing from EMD are discussed. The HHT analysis method can identify the bolt length and grouting defect locations from signals that contain noise at multiple reflected interfaces. The reflection from the long protruding end creates an irregular test signal with many frequency peaks on the spectrum. The reflections from defects barely change the original signal because they are low energy, which cannot be adequately resolved using existing methods. The HHT analysis method can identify reflections from the long protruding end of the bolt and multiple reflections from grouting defects based on mutations in the instantaneous frequency, which makes weak reflections more noticeable. The mode mixing phenomenon is observed in several tests, but this does not markedly affect the identification results due to the simple medium in bolt tests. The mode mixing can be reduced by ensemble EMD (EEMD) or complete ensemble EMD with adaptive noise (CEEMDAN), which are powerful tools to used analyze the test signal in a complex medium and may play an important role in future studies. The HHT bolt signal analysis method is a self-adaptive and automatic process, which can be programed as analysis software and will make bolt tests more convenient in practice.

Classification and modeling of human activities using empirical mode decomposition with S-band and millimeter-wave micro-Doppler radars

NASA Astrophysics Data System (ADS)

Fairchild, Dustin P.; Narayanan, Ram M.

2012-06-01

The ability to identify human movements can be an important tool in many different applications such as surveillance, military combat situations, search and rescue operations, and patient monitoring in hospitals. This information can provide soldiers, security personnel, and search and rescue workers with critical knowledge that can be used to potentially save lives and/or avoid a dangerous situation. Most research involving human activity recognition is focused on using the Short-Time Fourier Transform (STFT) as a method of analyzing the micro-Doppler signatures. Because of the time-frequency resolution limitations of the STFT and because Fourier transform-based methods are not well-suited for use with non-stationary and nonlinear signals, we have chosen a different approach. Empirical Mode Decomposition (EMD) has been shown to be a valuable time-frequency method for processing non-stationary and nonlinear data such as micro-Doppler signatures and EMD readily provides a feature vector that can be utilized for classification. For classification, the method of a Support Vector Machine (SVMs) was chosen. SVMs have been widely used as a method of pattern recognition due to their ability to generalize well and also because of their moderately simple implementation. In this paper, we discuss the ability of these methods to accurately identify human movements based on their micro-Doppler signatures obtained from S-band and millimeter-wave radar systems. Comparisons will also be made based on experimental results from each of these radar systems. Furthermore, we will present simulations of micro-Doppler movements for stationary subjects that will enable us to compare our experimental Doppler data to what we would expect from an "ideal" movement.

Steepest Ascent Low/Non-Low-Frequency Ratio in Empirical Mode Decomposition to Separate Deterministic and Stochastic Velocities From a Single Lagrangian Drifter

NASA Astrophysics Data System (ADS)

Chu, Peter C.

2018-03-01

SOund Fixing And Ranging (RAFOS) floats deployed by the Naval Postgraduate School (NPS) in the California Current system from 1992 to 2001 at depth between 150 and 600 m (http://www.oc.nps.edu/npsRAFOS/) are used to study 2-D turbulent characteristics. Each drifter trajectory is adaptively decomposed using the empirical mode decomposition (EMD) into a series of intrinsic mode functions (IMFs) with corresponding specific scale for each IMF. A new steepest ascent low/non-low-frequency ratio is proposed in this paper to separate a Lagrangian trajectory into low-frequency (nondiffusive, i.e., deterministic) and high-frequency (diffusive, i.e., stochastic) components. The 2-D turbulent (or called eddy) diffusion coefficients are calculated on the base of the classical turbulent diffusion with mixing length theory from stochastic component of a single drifter. Statistical characteristics of the calculated 2-D turbulence length scale, strength, and diffusion coefficients from the NPS RAFOS data are presented with the mean values (over the whole drifters) of the 2-D diffusion coefficients comparable to the commonly used diffusivity tensor method.

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain-computer interface

NASA Astrophysics Data System (ADS)

Chen, Yi-Feng; Atal, Kiran; Xie, Sheng-Quan; Liu, Quan

2017-08-01

Objective. Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain-computer interface (BCI) applications. Approach. Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results. We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance. The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI.

On the Hilbert-Huang Transform Theoretical Foundation

NASA Technical Reports Server (NTRS)

Kizhner, Semion; Blank, Karin; Huang, Norden E.

2004-01-01

The Hilbert-Huang Transform [HHT] is a novel empirical method for spectrum analysis of non-linear and non-stationary signals. The HHT is a recent development and much remains to be done to establish the theoretical foundation of the HHT algorithms. This paper develops the theoretical foundation for the convergence of the HHT sifting algorithm and it proves that the finest spectrum scale will always be the first generated by the HHT Empirical Mode Decomposition (EMD) algorithm. The theoretical foundation for cutting an extrema data points set into two parts is also developed. This then allows parallel signal processing for the HHT computationally complex sifting algorithm and its optimization in hardware.

Automatic sleep staging using empirical mode decomposition, discrete wavelet transform, time-domain, and nonlinear dynamics features of heart rate variability signals.

PubMed

Ebrahimi, Farideh; Setarehdan, Seyed-Kamaledin; Ayala-Moyeda, Jose; Nazeran, Homer

2013-10-01

The conventional method for sleep staging is to analyze polysomnograms (PSGs) recorded in a sleep lab. The electroencephalogram (EEG) is one of the most important signals in PSGs but recording and analysis of this signal presents a number of technical challenges, especially at home. Instead, electrocardiograms (ECGs) are much easier to record and may offer an attractive alternative for home sleep monitoring. The heart rate variability (HRV) signal proves suitable for automatic sleep staging. Thirty PSGs from the Sleep Heart Health Study (SHHS) database were used. Three feature sets were extracted from 5- and 0.5-min HRV segments: time-domain features, nonlinear-dynamics features and time-frequency features. The latter was achieved by using empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods. Normalized energies in important frequency bands of HRV signals were computed using time-frequency methods. ANOVA and t-test were used for statistical evaluations. Automatic sleep staging was based on HRV signal features. The ANOVA followed by a post hoc Bonferroni was used for individual feature assessment. Most features were beneficial for sleep staging. A t-test was used to compare the means of extracted features in 5- and 0.5-min HRV segments. The results showed that the extracted features means were statistically similar for a small number of features. A separability measure showed that time-frequency features, especially EMD features, had larger separation than others. There was not a sizable difference in separability of linear features between 5- and 0.5-min HRV segments but separability of nonlinear features, especially EMD features, decreased in 0.5-min HRV segments. HRV signal features were classified by linear discriminant (LD) and quadratic discriminant (QD) methods. Classification results based on features from 5-min segments surpassed those obtained from 0.5-min segments. The best result was obtained from features using 5-min HRV segments classified by the LD classifier. A combination of linear/nonlinear features from HRV signals is effective in automatic sleep staging. Moreover, time-frequency features are more informative than others. In addition, a separability measure and classification results showed that HRV signal features, especially nonlinear features, extracted from 5-min segments are more discriminative than those from 0.5-min segments in automatic sleep staging. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Analysis of turbine-grid interaction of grid-connected wind turbine using HHT

NASA Astrophysics Data System (ADS)

Chen, A.; Wu, W.; Miao, J.; Xie, D.

2018-05-01

This paper processes the output power of the grid-connected wind turbine with the denoising and extracting method based on Hilbert Huang transform (HHT) to discuss the turbine-grid interaction. At first, the detailed Empirical Mode Decomposition (EMD) and the Hilbert Transform (HT) are introduced. Then, on the premise of decomposing the output power of the grid-connected wind turbine into a series of Intrinsic Mode Functions (IMFs), energy ratio and power volatility are calculated to detect the unessential components. Meanwhile, combined with vibration function of turbine-grid interaction, data fitting of instantaneous amplitude and phase of each IMF is implemented to extract characteristic parameters of different interactions. Finally, utilizing measured data of actual parallel-operated wind turbines in China, this work accurately obtains the characteristic parameters of turbine-grid interaction of grid-connected wind turbine.

Assessment of vocal cord nodules: a case study in speech processing by using Hilbert-Huang Transform

NASA Astrophysics Data System (ADS)

Civera, M.; Filosi, C. M.; Pugno, N. M.; Silvestrini, M.; Surace, C.; Worden, K.

2017-05-01

Vocal cord nodules represent a pathological condition for which the growth of unnatural masses on vocal folds affects the patients. Among other effects, changes in the vocal cords’ overall mass and stiffness alter their vibratory behaviour, thus changing the vocal emission generated by them. This causes dysphonia, i.e. abnormalities in the patients’ voice, which can be analysed and inspected via audio signals. However, the evaluation of voice condition through speech processing is not a trivial task, as standard methods based on the Fourier Transform, fail to fit the non-stationary nature of vocal signals. In this study, four audio tracks, provided by a volunteer patient, whose vocal fold nodules have been surgically removed, were analysed using a relatively new technique: the Hilbert-Huang Transform (HHT) via Empirical Mode Decomposition (EMD); specifically, by using the CEEMDAN (Complete Ensemble EMD with Adaptive Noise) algorithm. This method has been applied here to speech signals, which were recorded before removal surgery and during convalescence, to investigate specific trends. Possibilities offered by the HHT are exposed, but also some limitations of decomposing the signals into so-called intrinsic mode functions (IMFs) are highlighted. The results of these preliminary studies are intended to be a basis for the development of new viable alternatives to the softwares currently used for the analysis and evaluation of pathological voice.

Experimental validation of a structural damage detection method based on marginal Hilbert spectrum

NASA Astrophysics Data System (ADS)

Banerji, Srishti; Roy, Timir B.; Sabamehr, Ardalan; Bagchi, Ashutosh

2017-04-01

Structural Health Monitoring (SHM) using dynamic characteristics of structures is crucial for early damage detection. Damage detection can be performed by capturing and assessing structural responses. Instrumented structures are monitored by analyzing the responses recorded by deployed sensors in the form of signals. Signal processing is an important tool for the processing of the collected data to diagnose anomalies in structural behavior. The vibration signature of the structure varies with damage. In order to attain effective damage detection, preservation of non-linear and non-stationary features of real structural responses is important. Decomposition of the signals into Intrinsic Mode Functions (IMF) by Empirical Mode Decomposition (EMD) and application of Hilbert-Huang Transform (HHT) addresses the time-varying instantaneous properties of the structural response. The energy distribution among different vibration modes of the intact and damaged structure depicted by Marginal Hilbert Spectrum (MHS) detects location and severity of the damage. The present work investigates damage detection analytically and experimentally by employing MHS. The testing of this methodology for different damage scenarios of a frame structure resulted in its accurate damage identification. The sensitivity of Hilbert Spectral Analysis (HSA) is assessed with varying frequencies and damage locations by means of calculating Damage Indices (DI) from the Hilbert spectrum curves of the undamaged and damaged structures.

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition.

PubMed

Sengottuvel, S; Khan, Pathan Fayaz; Mariyappa, N; Patel, Rajesh; Saipriya, S; Gireesan, K

2018-06-01

Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

Modal analysis of the thermal conductivity of nanowires: examining unique thermal transport features.

PubMed

Samaraweera, Nalaka; Larkin, Jason M; Chan, Kin L; Mithraratne, Kumar

2018-06-06

In this study, unique thermal transport features of nanowires over bulk materials are investigated using a combined analysis based on lattice dynamics and equilibrium molecular dynamics (EMD). The evaluation of the thermal conductivity (TC) of Lenard-Jones nanowires becomes feasible due to the multi-step normal mode decomposition (NMD) procedure implemented in the study. A convergence issue of the TC of nanowires is addressed by the NMD implementation for two case studies, which employ pristine nanowires (PNW) and superlattice nanowires. Interestingly, mode relaxation times at low frequencies of acoustic branches exhibit signs of approaching constant values, thus indicating the convergence of TC. The TC evaluation procedure is further verified by implementing EMD-based Green-Kubo analysis, which is based on a fundamentally different physical perspective. Having verified the NMD procedure, the non-monotonic trend of the TC of nanowires is addressed. It is shown that the principal cause for the observed trend is due to the competing effects of long wavelength phonons and phonon-surface scatterings as the nanowire's cross-sectional width is changed. A computational procedure is developed to decompose the different modal contribution to the TC of shell alloy nanowires (SANWs) using virtual crystal NMD and the Allen-Feldman theory. Several important conclusions can be drawn from the results. A propagons to non-propagons boundary appeared, resulting in a cut-off frequency (ω cut ); moreover, as alloy atomic mass is increased, ω cut shifts to lower frequencies. The existence of non-propagons partly causes the low TC of SANWs. It can be seen that modes with low frequencies demonstrate a similar behavior to corresponding modes of PNWs. Moreover, lower group velocities associated with higher alloy atomic mass resulted in a lower TC of SANWs.

Modal analysis of the thermal conductivity of nanowires: examining unique thermal transport features

NASA Astrophysics Data System (ADS)

Samaraweera, Nalaka; Larkin, Jason M.; Chan, Kin L.; Mithraratne, Kumar

2018-06-01

In this study, unique thermal transport features of nanowires over bulk materials are investigated using a combined analysis based on lattice dynamics and equilibrium molecular dynamics (EMD). The evaluation of the thermal conductivity (TC) of Lenard–Jones nanowires becomes feasible due to the multi-step normal mode decomposition (NMD) procedure implemented in the study. A convergence issue of the TC of nanowires is addressed by the NMD implementation for two case studies, which employ pristine nanowires (PNW) and superlattice nanowires. Interestingly, mode relaxation times at low frequencies of acoustic branches exhibit signs of approaching constant values, thus indicating the convergence of TC. The TC evaluation procedure is further verified by implementing EMD-based Green–Kubo analysis, which is based on a fundamentally different physical perspective. Having verified the NMD procedure, the non-monotonic trend of the TC of nanowires is addressed. It is shown that the principal cause for the observed trend is due to the competing effects of long wavelength phonons and phonon–surface scatterings as the nanowire’s cross-sectional width is changed. A computational procedure is developed to decompose the different modal contribution to the TC of shell alloy nanowires (SANWs) using virtual crystal NMD and the Allen–Feldman theory. Several important conclusions can be drawn from the results. A propagons to non-propagons boundary appeared, resulting in a cut-off frequency (ω cut); moreover, as alloy atomic mass is increased, ω cut shifts to lower frequencies. The existence of non-propagons partly causes the low TC of SANWs. It can be seen that modes with low frequencies demonstrate a similar behavior to corresponding modes of PNWs. Moreover, lower group velocities associated with higher alloy atomic mass resulted in a lower TC of SANWs.

Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors

PubMed Central

Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus

2014-01-01

Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications. PMID:24678281

Empirical mode decomposition and neural networks on FPGA for fault diagnosis in induction motors.

PubMed

Camarena-Martinez, David; Valtierra-Rodriguez, Martin; Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus

2014-01-01

Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.

Faults Diagnostics of Railway Axle Bearings Based on IMF’s Confidence Index Algorithm for Ensemble EMD

PubMed Central

Yi, Cai; Lin, Jianhui; Zhang, Weihua; Ding, Jianming

2015-01-01

As train loads and travel speeds have increased over time, railway axle bearings have become critical elements which require more efficient non-destructive inspection and fault diagnostics methods. This paper presents a novel and adaptive procedure based on ensemble empirical mode decomposition (EEMD) and Hilbert marginal spectrum for multi-fault diagnostics of axle bearings. EEMD overcomes the limitations that often hypothesize about data and computational efforts that restrict the application of signal processing techniques. The outputs of this adaptive approach are the intrinsic mode functions that are treated with the Hilbert transform in order to obtain the Hilbert instantaneous frequency spectrum and marginal spectrum. Anyhow, not all the IMFs obtained by the decomposition should be considered into Hilbert marginal spectrum. The IMFs’ confidence index arithmetic proposed in this paper is fully autonomous, overcoming the major limit of selection by user with experience, and allows the development of on-line tools. The effectiveness of the improvement is proven by the successful diagnosis of an axle bearing with a single fault or multiple composite faults, e.g., outer ring fault, cage fault and pin roller fault. PMID:25970256

Nonlinear QR code based optical image encryption using spiral phase transform, equal modulus decomposition and singular value decomposition

NASA Astrophysics Data System (ADS)

Kumar, Ravi; Bhaduri, Basanta; Nishchal, Naveen K.

2018-01-01

In this study, we propose a quick response (QR) code based nonlinear optical image encryption technique using spiral phase transform (SPT), equal modulus decomposition (EMD) and singular value decomposition (SVD). First, the primary image is converted into a QR code and then multiplied with a spiral phase mask (SPM). Next, the product is spiral phase transformed with particular spiral phase function, and further, the EMD is performed on the output of SPT, which results into two complex images, Z 1 and Z 2. Among these, Z 1 is further Fresnel propagated with distance d, and Z 2 is reserved as a decryption key. Afterwards, SVD is performed on Fresnel propagated output to get three decomposed matrices i.e. one diagonal matrix and two unitary matrices. The two unitary matrices are modulated with two different SPMs and then, the inverse SVD is performed using the diagonal matrix and modulated unitary matrices to get the final encrypted image. Numerical simulation results confirm the validity and effectiveness of the proposed technique. The proposed technique is robust against noise attack, specific attack, and brutal force attack. Simulation results are presented in support of the proposed idea.

The Multi-Frequency Correlation Between Eua and sCER Futures Prices: Evidence from the Emd Approach

NASA Astrophysics Data System (ADS)

Zhang, Yue-Jun; Huang, Yi-Song

2015-05-01

Currently European Union Allowances (EUA) and secondary Certified Emission Reduction (sCER) have become two dominant carbon trading assets for investors and their linkage attracts much attention from academia and practitioners in recent years. Under this circumstance, we use the empirical mode decomposition (EMD) approach to decompose the two carbon futures contract prices and discuss their correlation from the multi-frequency perspective. The empirical results indicate that, first, the EUA and sCER futures price movements can be divided into those triggered by the long-term, medium-term and short-term market impacts. Second, the price movements in the EUA and sCER futures markets are primarily caused by the long-term impact, while the short-term impact can only explain a small fraction. Finally, the long-term (short-term) effect on EUA prices is statistically uncorrelated with the short-term (long-term) effect of sCER prices, and there is a medium or strong lead-and-lag correlation between the EUA and sCER price components with the same time scales. These results may provide some important insights of price forecast and arbitraging activities for carbon futures market investors, analysts and regulators.

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

NASA Astrophysics Data System (ADS)

Safieddine, Doha; Kachenoura, Amar; Albera, Laurent; Birot, Gwénaël; Karfoul, Ahmad; Pasnicu, Anca; Biraben, Arnaud; Wendling, Fabrice; Senhadji, Lotfi; Merlet, Isabelle

2012-12-01

Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that the performance of muscle artifact correction methods strongly depend on the level of data contamination, and of the source configuration underlying EEG signals. Eventually, some insights into the numerical complexity of these four algorithms are given.

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder.

PubMed

Mumtaz, Wajid; Xia, Likun; Mohd Yasin, Mohd Azhar; Azhar Ali, Syed Saad; Malik, Aamir Saeed

2017-01-01

Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant's treatment outcome may help during antidepressant's selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant's treatment outcome for the MDD patients.

Detection of ionospheric scintillation effects using LMD-DFA

NASA Astrophysics Data System (ADS)

Tadivaka, Raghavendra Vishnu; Paruchuri, Bhanu Priyanka; Miriyala, Sridhar; Koppireddi, Padma Raju; Devanaboyina, Venkata Ratnam

2017-08-01

The performance and measurement accuracy of global navigation satellite system (GNSS) receivers is greatly affected by ionospheric scintillations. Rapid amplitude and phase variations in the received GPS signal, known as ionospheric scintillation, affects the tracking of signals by GNSS receivers. Hence, there is a need to investigate the monitoring of various activities of the ionosphere and to develop a novel approach for mitigation of ionospheric scintillation effects. A method based on Local Mean Decomposition (LMD)-Detrended Fluctuation Analysis (DFA) has been proposed. The GNSS data recorded at Koneru Lakshmaiah (K L) University, Guntur, India were considered for analysis. The carrier to noise ratio (C/N0) of GNSS satellite vehicles were decomposed into several product functions (PF) using LMD to extract the intrinsic features in the signal. Scintillation noise was removed by the DFA algorithm by selecting a suitable threshold. It was observed that the performance of the proposed LMD-DFA was better than that of empirical mode decomposition (EMD)-DFA.

Where is the breathing mode? High voltage Hall effect thruster studies with EMD method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kurzyna, J.; Makowski, K.; Mazouffre, S.

2008-03-19

Discharge current and local plasma oscillations are studied in a high voltage Hall effect thruster PPS registered -X000. Characteristic time scales that appear in different operating conditions are resolved with the use of Hilbert-Huang spectra (HHS) which display time dependenc of instantaneous frequency and power. Sets of intrinsic mode functions (imfs) that are used for HHS calculation result due to application of empirical mode decomposition method (EMD) to nonstationary multicomponent signals. In the experiment the signals are captured in the electric circuit of the thruster as well locally, in the vicinity of the thruster exhaust region. Classical electric probes spacedmore » along the azimuth and/or thruster axis let us study correlations of signals which were captured in different locations. In this way azimuthal and axial propagation of disturbances is inspected. The discharge voltage is varied in the range of 200 divide 900 V while xenon mas flow rate of 5 divide 9 mg/s. LF, MF, and HF characteristic bands that are known from previous studies of PPS registered -100 thruster have been also detected here. However, expanding discharge current onto a set of intrinsic modes we can resolve MF mode more reliably than before. Moreover, for higher discharge voltages, this irregular mode turns into more regular waveform and tends to dominate in the discharge current masking almost completely the breathing mode (LF oscillations of the discharge current). In such a case triggering of HF oscillations is correlated with the phase of MF mode while in the case of PPS registered -100 thruster it was correlated with the appropriate phase of the breathing mode (LF band). Regular HF emission that can be unambiguously interpreted as azimuthal electrostatic wave now is observed only in the specific operating conditions of the thruster. However, even if irregular HF emission is observed the time delay of cross-correlated signals which are captured in different azimuthal locations corresponds to the velocity of azimuthal electron drift in the field of magnetic barrier.« less

Adaptive photoacoustic imaging quality optimization with EMD and reconstruction

NASA Astrophysics Data System (ADS)

Guo, Chengwen; Ding, Yao; Yuan, Jie; Xu, Guan; Wang, Xueding; Carson, Paul L.

2016-10-01

Biomedical photoacoustic (PA) signal is characterized with extremely low signal to noise ratio which will yield significant artifacts in photoacoustic tomography (PAT) images. Since PA signals acquired by ultrasound transducers are non-linear and non-stationary, traditional data analysis methods such as Fourier and wavelet method cannot give useful information for further research. In this paper, we introduce an adaptive method to improve the quality of PA imaging based on empirical mode decomposition (EMD) and reconstruction. Data acquired by ultrasound transducers are adaptively decomposed into several intrinsic mode functions (IMFs) after a sifting pre-process. Since noise is randomly distributed in different IMFs, depressing IMFs with more noise while enhancing IMFs with less noise can effectively enhance the quality of reconstructed PAT images. However, searching optimal parameters by means of brute force searching algorithms will cost too much time, which prevent this method from practical use. To find parameters within reasonable time, heuristic algorithms, which are designed for finding good solutions more efficiently when traditional methods are too slow, are adopted in our method. Two of the heuristic algorithms, Simulated Annealing Algorithm, a probabilistic method to approximate the global optimal solution, and Artificial Bee Colony Algorithm, an optimization method inspired by the foraging behavior of bee swarm, are selected to search optimal parameters of IMFs in this paper. The effectiveness of our proposed method is proved both on simulated data and PA signals from real biomedical tissue, which might bear the potential for future clinical PA imaging de-noising.

A novel time of arrival estimation algorithm using an energy detector receiver in MMW systems

NASA Astrophysics Data System (ADS)

Liang, Xiaolin; Zhang, Hao; Lyu, Tingting; Xiao, Han; Gulliver, T. Aaron

2017-12-01

This paper presents a new time of arrival (TOA) estimation technique using an improved energy detection (ED) receiver based on the empirical mode decomposition (EMD) in an impulse radio (IR) 60 GHz millimeter wave (MMW) system. A threshold is employed via analyzing the characteristics of the received energy values with an extreme learning machine (ELM). The effect of the channel and integration period on the TOA estimation is evaluated. Several well-known ED-based TOA algorithms are used to compare with the proposed technique. It is shown that this ELM-based technique has lower TOA estimation error compared to other approaches and provides robust performance with the IEEE 802.15.3c channel models.

Power independent EMG based gesture recognition for robotics.

PubMed

Li, Ling; Looney, David; Park, Cheolsoo; Rehman, Naveed U; Mandic, Danilo P

2011-01-01

A novel method for detecting muscle contraction is presented. This method is further developed for identifying four different gestures to facilitate a hand gesture controlled robot system. It is achieved based on surface Electromyograph (EMG) measurements of groups of arm muscles. The cross-information is preserved through a simultaneous processing of EMG channels using a recent multivariate extension of Empirical Mode Decomposition (EMD). Next, phase synchrony measures are employed to make the system robust to different power levels due to electrode placements and impedances. The multiple pairwise muscle synchronies are used as features of a discrete gesture space comprising four gestures (flexion, extension, pronation, supination). Simulations on real-time robot control illustrate the enhanced accuracy and robustness of the proposed methodology.

Equivalence of the EMD- and NEMD-based decomposition of thermal conductivity into microscopic building blocks.

PubMed

Matsubara, Hiroki; Kikugawa, Gota; Ishikiriyama, Mamoru; Yamashita, Seiji; Ohara, Taku

2017-09-21

Thermal conductivity of a material can be comprehended as being composed of microscopic building blocks relevant to the energy transfer due to a specific microscopic process or structure. The building block is called the partial thermal conductivity (PTC). The concept of PTC is essential to evaluate the contributions of various molecular mechanisms to heat conduction and has been providing detailed knowledge of the contribution. The PTC can be evaluated by equilibrium molecular dynamics (EMD) and non-equilibrium molecular dynamics (NEMD) in different manners: the EMD evaluation utilizes the autocorrelation of spontaneous heat fluxes in an equilibrium state whereas the NEMD one is based on stationary heat fluxes in a non-equilibrium state. However, it has not been fully discussed whether the two methods give the same PTC or not. In the present study, we formulate a Green-Kubo relation, which is necessary for EMD to calculate the PTCs equivalent to those by NEMD. Unlike the existing theories, our formulation is based on the local equilibrium hypothesis to describe a clear connection between EMD and NEMD simulations. The equivalence of the two derivations of PTCs is confirmed by the numerical results for liquid methane and butane. The present establishment of the EMD-NEMD correspondence makes the MD analysis of PTCs a robust way to clarify the microscopic origins of thermal conductivity.

Asymmetric optical image encryption using Kolmogorov phase screens and equal modulus decomposition

NASA Astrophysics Data System (ADS)

Kumar, Ravi; Bhaduri, Basanta; Quan, Chenggen

2017-11-01

An asymmetric technique for optical image encryption is proposed using Kolmogorov phase screens (KPSs) and equal modulus decomposition (EMD). The KPSs are generated using the power spectral density of Kolmogorov turbulence. The input image is first randomized and then Fresnel propagated with distance d. Further, the output in the Fresnel domain is modulated with a random phase mask, and the gyrator transform (GT) of the modulated image is obtained with an angle α. The EMD is operated on the GT spectrum to get the complex images, Z1 and Z2. Among these, Z2 is reserved as a private key for decryption and Z1 is propagated through a medium consisting of four KPSs, located at specified distances, to get the final encrypted image. The proposed technique provides a large set of security keys and is robust against various potential attacks. Numerical simulation results validate the effectiveness and security of the proposed technique.

Identifying Decadal to Multi-decadal Variability in the Pacific by Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Sommers, L. A.; Hamlington, B.; Cheon, S. H.

2016-12-01

Large scale climate variability in the Pacific Ocean like that associated with ENSO and the Pacific Decadal Oscillation (PDO) has been shown to have a significant impact on climate and sea level across a range of timescales. The changes related to these climate signals have worldwide impacts on fisheries, weather, and precipitation patterns among others. Understanding these inter-annual to multi-decadal oscillations is imperative to longer term climate forecasts and understanding how climate will behave, and its effect on changes in sea level. With a 110-year reconstruction of sea level, we examine decadal to multi-decadal variability seen in the sea level fluctuations in the Pacific Ocean. Using empirical mode decomposition (EMD), we break down regional sea level into a series of intrinsic mode functions (IMFs) and attempt attribution of these IMFs to specific climate modes of variability. In particular, and not unexpectedly, we identify IMFs associated with the PDO, finding correlations between the PDO Index and IMFs in the Pacific Ocean upwards of 0.6-0.8 over the 110-year reconstructed record. Perhaps more significantly, we also find evidence of a longer multi-decadal signal ( 50-60 years) in the higher order IMFs. This lower frequency variability has been suggested in previous literature as influencing GMSL, but here we find a regional pattern associated with this multi-decadal signal. By identifying and separating these periodic climate signals, we can gain a better understanding of how the sea level variability associated with these modes can impact sea level on short timescales and serve to exacerbate the effects of long-term sea level change.

Characterization of large-scale fluctuations and short-term variability of Seine river daily streamflow (France) over the period 1950-2008 by empirical mode decomposition and the Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Massei, N.; Fournier, M.

2010-12-01

Daily Seine river flow from 1950 to 2008 was analyzed using Hilbert-Huang Tranform (HHT). For the last ten years, this method which combines the so-called Empirical Mode Decomposition (EMD) multiresolution analysis and the Hilbert transform has proven its efficiency for the analysis of transient oscillatory signals, although the mathematical definition of the EMD is not totally established yet. HHT also provides an interesting alternative to other time-frequency or time-scale analysis of non-stationary signals, the most famous of which being wavelet-based approaches. In this application of HHT to the analysis of the hydrological variability of the Seine river, we seek to characterize the interannual patterns of daily flow, differenciate them from the short-term dynamics and eventually interpret them in the context of regional climate regime fluctuations. In this aim, HHT is also applied to the North-Atlantic Oscillation (NAO) through the annual winter-months NAO index time series. For both hydrological and climatic signals, dominant variability scales are extracted and their temporal variations analyzed by determination of the intantaneous frequency of each component. When compared to previous ones obtained from continuous wavelet transform (CWT) on the same data, HHT results highlighted the same scales and somewhat the same internal components for each signal. However, HHT allowed the identification and extraction of much more similar features during the 1950-2008 period (e.g., around 7-yr, between NAO and Seine flow than what was obtained from CWT, which comes to say that variability scales in flow likely to originate from climatic regime fluctuations were much properly identified in river flow. In addition, a more accurate determination of singularities in the natural processes analyzed were authorized by HHT compared to CWT, in which case the time-frequency resolution partly depends on the basic properties of the filter (i.e., the reference wavelet chosen initially). Compared to CWT or even to discrete wavelet multiresolution analysis, HHT is auto-adaptive, non-parametric, allows an orthogonal decomposition of the signal analyzed and provides a more accurate estimation of changing variability scales across time for highly transient signals.

Machine fault feature extraction based on intrinsic mode functions

NASA Astrophysics Data System (ADS)

Fan, Xianfeng; Zuo, Ming J.

2008-04-01

This work employs empirical mode decomposition (EMD) to decompose raw vibration signals into intrinsic mode functions (IMFs) that represent the oscillatory modes generated by the components that make up the mechanical systems generating the vibration signals. The motivation here is to develop vibration signal analysis programs that are self-adaptive and that can detect machine faults at the earliest onset of deterioration. The change in velocity of the amplitude of some IMFs over a particular unit time will increase when the vibration is stimulated by a component fault. Therefore, the amplitude acceleration energy in the intrinsic mode functions is proposed as an indicator of the impulsive features that are often associated with mechanical component faults. The periodicity of the amplitude acceleration energy for each IMF is extracted by spectrum analysis. A spectrum amplitude index is introduced as a method to select the optimal result. A comparison study of the method proposed here and some well-established techniques for detecting machinery faults is conducted through the analysis of both gear and bearing vibration signals. The results indicate that the proposed method has superior capability to extract machine fault features from vibration signals.

Fault feature analysis of cracked gear based on LOD and analytical-FE method

NASA Astrophysics Data System (ADS)

Wu, Jiateng; Yang, Yu; Yang, Xingkai; Cheng, Junsheng

2018-01-01

At present, there are two main ideas for gear fault diagnosis. One is the model-based gear dynamic analysis; the other is signal-based gear vibration diagnosis. In this paper, a method for fault feature analysis of gear crack is presented, which combines the advantages of dynamic modeling and signal processing. Firstly, a new time-frequency analysis method called local oscillatory-characteristic decomposition (LOD) is proposed, which has the attractive feature of extracting fault characteristic efficiently and accurately. Secondly, an analytical-finite element (analytical-FE) method which is called assist-stress intensity factor (assist-SIF) gear contact model, is put forward to calculate the time-varying mesh stiffness (TVMS) under different crack states. Based on the dynamic model of the gear system with 6 degrees of freedom, the dynamic simulation response was obtained for different tooth crack depths. For the dynamic model, the corresponding relation between the characteristic parameters and the degree of the tooth crack is established under a specific condition. On the basis of the methods mentioned above, a novel gear tooth root crack diagnosis method which combines the LOD with the analytical-FE is proposed. Furthermore, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) are contrasted with the LOD by gear crack fault vibration signals. The analysis results indicate that the proposed method performs effectively and feasibility for the tooth crack stiffness calculation and the gear tooth crack fault diagnosis.

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

PubMed Central

Xia, Likun; Mohd Yasin, Mohd Azhar; Azhar Ali, Syed Saad

2017-01-01

Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant’s treatment outcome may help during antidepressant’s selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant’s treatment outcome for the MDD patients. PMID:28152063

Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis.

PubMed

Zhang, Chu; Liu, Fei; He, Yong

2018-02-01

Hyperspectral imaging was used to identify and to visualize the coffee bean varieties. Spectral preprocessing of pixel-wise spectra was conducted by different methods, including moving average smoothing (MA), wavelet transform (WT) and empirical mode decomposition (EMD). Meanwhile, spatial preprocessing of the gray-scale image at each wavelength was conducted by median filter (MF). Support vector machine (SVM) models using full sample average spectra and pixel-wise spectra, and the selected optimal wavelengths by second derivative spectra all achieved classification accuracy over 80%. Primarily, the SVM models using pixel-wise spectra were used to predict the sample average spectra, and these models obtained over 80% of the classification accuracy. Secondly, the SVM models using sample average spectra were used to predict pixel-wise spectra, but achieved with lower than 50% of classification accuracy. The results indicated that WT and EMD were suitable for pixel-wise spectra preprocessing. The use of pixel-wise spectra could extend the calibration set, and resulted in the good prediction results for pixel-wise spectra and sample average spectra. The overall results indicated the effectiveness of using spectral preprocessing and the adoption of pixel-wise spectra. The results provided an alternative way of data processing for applications of hyperspectral imaging in food industry.

Hierarchical clustering of EMD based interest points for road sign detection

NASA Astrophysics Data System (ADS)

Khan, Jesmin; Bhuiyan, Sharif; Adhami, Reza

2014-04-01

This paper presents an automatic road traffic signs detection and recognition system based on hierarchical clustering of interest points and joint transform correlation. The proposed algorithm consists of the three following stages: interest points detection, clustering of those points and similarity search. At the first stage, good discriminative, rotation and scale invariant interest points are selected from the image edges based on the 1-D empirical mode decomposition (EMD). We propose a two-step unsupervised clustering technique, which is adaptive and based on two criterion. In this context, the detected points are initially clustered based on the stable local features related to the brightness and color, which are extracted using Gabor filter. Then points belonging to each partition are reclustered depending on the dispersion of the points in the initial cluster using position feature. This two-step hierarchical clustering yields the possible candidate road signs or the region of interests (ROIs). Finally, a fringe-adjusted joint transform correlation (JTC) technique is used for matching the unknown signs with the existing known reference road signs stored in the database. The presented framework provides a novel way to detect a road sign from the natural scenes and the results demonstrate the efficacy of the proposed technique, which yields a very low false hit rate.

Low-pass parabolic FFT filter for airborne and satellite lidar signal processing.

PubMed

Jiao, Zhongke; Liu, Bo; Liu, Enhai; Yue, Yongjian

2015-10-14

In order to reduce random errors of the lidar signal inversion, a low-pass parabolic fast Fourier transform filter (PFFTF) was introduced for noise elimination. A compact airborne Raman lidar system was studied, which applied PFFTF to process lidar signals. Mathematics and simulations of PFFTF along with low pass filters, sliding mean filter (SMF), median filter (MF), empirical mode decomposition (EMD) and wavelet transform (WT) were studied, and the practical engineering value of PFFTF for lidar signal processing has been verified. The method has been tested on real lidar signal from Wyoming Cloud Lidar (WCL). Results show that PFFTF has advantages over the other methods. It keeps the high frequency components well and reduces much of the random noise simultaneously for lidar signal processing.

Enhancement of lung sounds based on empirical mode decomposition and Fourier transform algorithm.

PubMed

Mondal, Ashok; Banerjee, Poulami; Somkuwar, Ajay

2017-02-01

There is always heart sound (HS) signal interfering during the recording of lung sound (LS) signals. This obscures the features of LS signals and creates confusion on pathological states, if any, of the lungs. In this work, a new method is proposed for reduction of heart sound interference which is based on empirical mode decomposition (EMD) technique and prediction algorithm. In this approach, first the mixed signal is split into several components in terms of intrinsic mode functions (IMFs). Thereafter, HS-included segments are localized and removed from them. The missing values of the gap thus produced, is predicted by a new Fast Fourier Transform (FFT) based prediction algorithm and the time domain LS signal is reconstructed by taking an inverse FFT of the estimated missing values. The experiments have been conducted on simulated and recorded HS corrupted LS signals at three different flow rates and various SNR levels. The performance of the proposed method is evaluated by qualitative and quantitative analysis of the results. It is found that the proposed method is superior to the baseline method in terms of quantitative and qualitative measurement. The developed method gives better results compared to baseline method for different SNR levels. Our method gives cross correlation index (CCI) of 0.9488, signal to deviation ratio (SDR) of 9.8262, and normalized maximum amplitude error (NMAE) of 26.94 for 0 dB SNR value. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Multifractal Detrended Fluctuation Analysis of Regional Precipitation Sequences Based on the CEEMDAN-WPT

NASA Astrophysics Data System (ADS)

Liu, Dong; Cheng, Chen; Fu, Qiang; Liu, Chunlei; Li, Mo; Faiz, Muhammad Abrar; Li, Tianxiao; Khan, Muhammad Imran; Cui, Song

2018-03-01

In this paper, the complete ensemble empirical mode decomposition with the adaptive noise (CEEMDAN) algorithm is introduced into the complexity research of precipitation systems to improve the traditional complexity measure method specific to the mode mixing of the Empirical Mode Decomposition (EMD) and incomplete decomposition of the ensemble empirical mode decomposition (EEMD). We combined the CEEMDAN with the wavelet packet transform (WPT) and multifractal detrended fluctuation analysis (MF-DFA) to create the CEEMDAN-WPT-MFDFA, and used it to measure the complexity of the monthly precipitation sequence of 12 sub-regions in Harbin, Heilongjiang Province, China. The results show that there are significant differences in the monthly precipitation complexity of each sub-region in Harbin. The complexity of the northwest area of Harbin is the lowest and its predictability is the best. The complexity and predictability of the middle and Midwest areas of Harbin are about average. The complexity of the southeast area of Harbin is higher than that of the northwest, middle, and Midwest areas of Harbin and its predictability is worse. The complexity of Shuangcheng is the highest and its predictability is the worst of all the studied sub-regions. We used terrain and human activity as factors to analyze the causes of the complexity of the local precipitation. The results showed that the correlations between the precipitation complexity and terrain are obvious, and the correlations between the precipitation complexity and human influence factors vary. The distribution of the precipitation complexity in this area may be generated by the superposition effect of human activities and natural factors such as terrain, general atmospheric circulation, land and sea location, and ocean currents. To evaluate the stability of the algorithm, the CEEMDAN-WPT-MFDFA was compared with the equal probability coarse graining LZC algorithm, fuzzy entropy, and wavelet entropy. The results show that the CEEMDAN-WPT-MFDFA was more stable than 3 contrast methods under the influence of white noise and colored noise, which proves that the CEEMDAN-WPT-MFDFA has a strong robustness under the influence of noise.

A Vibration-Based Strategy for Health Monitoring of Offshore Pipelines' Girth-Welds

PubMed Central

Razi, Pejman; Taheri, Farid

2014-01-01

This study presents numerical simulations and experimental verification of a vibration-based damage detection technique. Health monitoring of a submerged pipe's girth-weld against an advancing notch is attempted. Piezoelectric transducers are bonded on the pipe for sensing or actuation purposes. Vibration of the pipe is excited by two means: (i) an impulsive force; (ii) using one of the piezoelectric transducers as an actuator to propagate chirp waves into the pipe. The methodology adopts the empirical mode decomposition (EMD), which processes vibration data to establish energy-based damage indices. The results obtained from both the numerical and experimental studies confirm the integrity of the approach in identifying the existence, and progression of the advancing notch. The study also discusses and compares the performance of the two vibration excitation means in damage detection. PMID:25225877

Detection of the Vibration Signal from Human Vocal Folds Using a 94-GHz Millimeter-Wave Radar

PubMed Central

Chen, Fuming; Li, Sheng; Zhang, Yang; Wang, Jianqi

2017-01-01

The detection of the vibration signal from human vocal folds provides essential information for studying human phonation and diagnosing voice disorders. Doppler radar technology has enabled the noncontact measurement of the human-vocal-fold vibration. However, existing systems must be placed in close proximity to the human throat and detailed information may be lost because of the low operating frequency. In this paper, a long-distance detection method, involving the use of a 94-GHz millimeter-wave radar sensor, is proposed for detecting the vibration signals from human vocal folds. An algorithm that combines empirical mode decomposition (EMD) and the auto-correlation function (ACF) method is proposed for detecting the signal. First, the EMD method is employed to suppress the noise of the radar-detected signal. Further, the ratio of the energy and entropy is used to detect voice activity in the radar-detected signal, following which, a short-time ACF is employed to extract the vibration signal of the human vocal folds from the processed signal. For validating the method and assessing the performance of the radar system, a vibration measurement sensor and microphone system are additionally employed for comparison. The experimental results obtained from the spectrograms, the vibration frequency of the vocal folds, and coherence analysis demonstrate that the proposed method can effectively detect the vibration of human vocal folds from a long detection distance. PMID:28282892

New procedure for gear fault detection and diagnosis using instantaneous angular speed

NASA Astrophysics Data System (ADS)

Li, Bing; Zhang, Xining; Wu, Jili

2017-02-01

Besides the extreme complexity of gear dynamics, the fault diagnosis results in terms of vibration signal are sometimes easily misled and even distorted by the interference of transmission channel or other components like bearings, bars. Recently, the research field of Instantaneous Angular Speed (IAS) has attracted significant attentions due to its own advantages over conventional vibration analysis. On the basis of IAS signal's advantages, this paper presents a new feature extraction method by combining the Empirical Mode Decomposition (EMD) and Autocorrelation Local Cepstrum (ALC) for fault diagnosis of sophisticated multistage gearbox. Firstly, as a pre-processing step, signal reconstruction is employed to address the oversampled issue caused by the high resolution of the angular sensor and the test speed. Then the adaptive EMD is used to acquire a number of Intrinsic Mode Functions (IMFs). Nevertheless, not all the IMFs are needed for the further analysis since different IMFs have different sensitivities to fault. Hence, the cosine similarity metric is introduced to select the most sensitive IMF. Even though, the sensitive IMF is still insufficient for the gear fault diagnosis due to the weakness of the fault component related to the gear fault. Therefore, as the final step, ALC is used for the purpose of signal de-noising and feature extraction. The effectiveness and robustness of the new approach has been validated experimentally on the basis of two gear test rigs with gears under different working conditions. Diagnosis results show that the new approach is capable of effectively handling the gear fault diagnosis i.e., the highlighted quefrency and its rahmonics corresponding to the rotary period and its multiple are displayed clearly in the cepstrum record of the proposed method.

Enamel matrix derivative enhances tissue formation around scaffolds used for tissue engineering of ligaments.

PubMed

Messenger, Michael P; Raïf, El M; Seedhom, Bahaa B; Brookes, Steven J

2010-02-01

The following in vitro translational study investigated whether enamel matrix derivative (EMD), an approved biomimetic treatment for periodontal disease (Emdogain) and hard-to-heal wounds (Xelma), enhanced synovial cell colonization and protein synthesis around a scaffold used clinically for in situ tissue engineering of the torn anterior cruciate ligament (ACL). Synovial cells were enzymatically extracted from bovine synovium and dynamically seeded onto polyethylene terephthalate (PET) scaffolds. The cells were cultured in low-serum medium (0.5% FBS) for 4 weeks with either a single administration of EMD at the start of the 4 week period or multiple administrations of EMD at regular intervals throughout the 4 weeks. Samples were harvested and evaluated using the Hoechst DNA assay, BCA protein assay, cresolphthalein complexone calcium assay, SDS-PAGE, ELISA and electron microscopy. A significant increase in cell number (DNA) (p < 0.01), protein content (p < 0.01) and TGFbeta1 synthesis (p < 0.01) was observed with multiple administrations of EMD. Additionally, SDS-PAGE showed an increase in high molecular weight proteins, characteristic of the fibril-forming collagens. Electron microscopy supported these findings, showing that scaffolds treated with multiple administrations of EMD were heavily coated with cells and extracellular matrix (ECM) that enveloped the fibres. Multiple administrations of EMD to synovial cell-seeded scaffolds enhanced the formation of tissue in vitro. Additionally, it was shown that EMD enhanced TGFbeta1 synthesis of synovial cells, suggesting a potential mode of action for EMD's capacity to stimulate tissue regeneration.

Mental Task Classification Scheme Utilizing Correlation Coefficient Extracted from Interchannel Intrinsic Mode Function.

PubMed

Rahman, Md Mostafizur; Fattah, Shaikh Anowarul

2017-01-01

In view of recent increase of brain computer interface (BCI) based applications, the importance of efficient classification of various mental tasks has increased prodigiously nowadays. In order to obtain effective classification, efficient feature extraction scheme is necessary, for which, in the proposed method, the interchannel relationship among electroencephalogram (EEG) data is utilized. It is expected that the correlation obtained from different combination of channels will be different for different mental tasks, which can be exploited to extract distinctive feature. The empirical mode decomposition (EMD) technique is employed on a test EEG signal obtained from a channel, which provides a number of intrinsic mode functions (IMFs), and correlation coefficient is extracted from interchannel IMF data. Simultaneously, different statistical features are also obtained from each IMF. Finally, the feature matrix is formed utilizing interchannel correlation features and intrachannel statistical features of the selected IMFs of EEG signal. Different kernels of the support vector machine (SVM) classifier are used to carry out the classification task. An EEG dataset containing ten different combinations of five different mental tasks is utilized to demonstrate the classification performance and a very high level of accuracy is achieved by the proposed scheme compared to existing methods.

A new solar power output prediction based on hybrid forecast engine and decomposition model.

PubMed

Zhang, Weijiang; Dang, Hongshe; Simoes, Rolando

2018-06-12

Regarding to the growing trend of photovoltaic (PV) energy as a clean energy source in electrical networks and its uncertain nature, PV energy prediction has been proposed by researchers in recent decades. This problem is directly effects on operation in power network while, due to high volatility of this signal, an accurate prediction model is demanded. A new prediction model based on Hilbert Huang transform (HHT) and integration of improved empirical mode decomposition (IEMD) with feature selection and forecast engine is presented in this paper. The proposed approach is divided into three main sections. In the first section, the signal is decomposed by the proposed IEMD as an accurate decomposition tool. To increase the accuracy of the proposed method, a new interpolation method has been used instead of cubic spline curve (CSC) fitting in EMD. Then the obtained output is entered into the new feature selection procedure to choose the best candidate inputs. Finally, the signal is predicted by a hybrid forecast engine composed of support vector regression (SVR) based on an intelligent algorithm. The effectiveness of the proposed approach has been verified over a number of real-world engineering test cases in comparison with other well-known models. The obtained results prove the validity of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

The Identification of the Deformation Stage of a Metal Specimen Based on Acoustic Emission Data Analysis

PubMed Central

Zou, Shenao; Yan, Fengying; Yang, Guoan; Sun, Wei

2017-01-01

The acoustic emission (AE) signals of metal materials have been widely used to identify the deformation stage of a pressure vessel. In this work, Q235 steel samples with different propagation distances and geometrical structures are stretched to get the corresponding acoustic emission signals. Then the obtained acoustic emission signals are de-noised by empirical mode decomposition (EMD), and then decomposed into two different frequency ranges, i.e., one mainly corresponding to metal deformation and the other mainly corresponding to friction signals. The ratio of signal energy between two frequency ranges is defined as a new acoustic emission characteristic parameter. Differences can be observed at different deformation stages in both magnitude and data distribution range. Compared with other acoustic emission parameters, the proposed parameter is valid in different setups of the propagation medium and the coupled stiffness. PMID:28387703

Empirical mode decomposition and long-range correlation analysis of sunspot time series

NASA Astrophysics Data System (ADS)

Zhou, Yu; Leung, Yee

2010-12-01

Sunspots, which are the best known and most variable features of the solar surface, affect our planet in many ways. The number of sunspots during a period of time is highly variable and arouses strong research interest. When multifractal detrended fluctuation analysis (MF-DFA) is employed to study the fractal properties and long-range correlation of the sunspot series, some spurious crossover points might appear because of the periodic and quasi-periodic trends in the series. However many cycles of solar activities can be reflected by the sunspot time series. The 11-year cycle is perhaps the most famous cycle of the sunspot activity. These cycles pose problems for the investigation of the scaling behavior of sunspot time series. Using different methods to handle the 11-year cycle generally creates totally different results. Using MF-DFA, Movahed and co-workers employed Fourier truncation to deal with the 11-year cycle and found that the series is long-range anti-correlated with a Hurst exponent, H, of about 0.12. However, Hu and co-workers proposed an adaptive detrending method for the MF-DFA and discovered long-range correlation characterized by H≈0.74. In an attempt to get to the bottom of the problem in the present paper, empirical mode decomposition (EMD), a data-driven adaptive method, is applied to first extract the components with different dominant frequencies. MF-DFA is then employed to study the long-range correlation of the sunspot time series under the influence of these components. On removing the effects of these periods, the natural long-range correlation of the sunspot time series can be revealed. With the removal of the 11-year cycle, a crossover point located at around 60 months is discovered to be a reasonable point separating two different time scale ranges, H≈0.72 and H≈1.49. And on removing all cycles longer than 11 years, we have H≈0.69 and H≈0.28. The three cycle-removing methods—Fourier truncation, adaptive detrending and the proposed EMD-based method—are further compared, and possible reasons for the different results are given. Two numerical experiments are designed for quantitatively evaluating the performances of these three methods in removing periodic trends with inexact/exact cycles and in detecting the possible crossover points.

Quantifying and Reducing Motion Artifacts in Wearable Seismocardiogram Measurements During Walking to Assess Left Ventricular Health.

PubMed

Javaid, Abdul Q; Ashouri, Hazar; Dorier, Alexis; Etemadi, Mozziyar; Heller, J Alex; Roy, Shuvo; Inan, Omer T

2017-06-01

Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subject's preferred pace) and moderately fast (1.34-1.45 m/s) speeds. We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking. The EMD-based denoising approach provides a statistically significant increase in the signal-to-noise ratio of wearable SCG signals and also improves estimation of PEP during walking. The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking. A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high-quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement-aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular system's response to stress (exercise), and thus provide a more holistic assessment of overall health.

A Noise Reduction Method for Dual-Mass Micro-Electromechanical Gyroscopes Based on Sample Entropy Empirical Mode Decomposition and Time-Frequency Peak Filtering

PubMed Central

Shen, Chong; Li, Jie; Zhang, Xiaoming; Shi, Yunbo; Tang, Jun; Cao, Huiliang; Liu, Jun

2016-01-01

The different noise components in a dual-mass micro-electromechanical system (MEMS) gyroscope structure is analyzed in this paper, including mechanical-thermal noise (MTN), electronic-thermal noise (ETN), flicker noise (FN) and Coriolis signal in-phase noise (IPN). The structure equivalent electronic model is established, and an improved white Gaussian noise reduction method for dual-mass MEMS gyroscopes is proposed which is based on sample entropy empirical mode decomposition (SEEMD) and time-frequency peak filtering (TFPF). There is a contradiction in TFPS, i.e., selecting a short window length may lead to good preservation of signal amplitude but bad random noise reduction, whereas selecting a long window length may lead to serious attenuation of the signal amplitude but effective random noise reduction. In order to achieve a good tradeoff between valid signal amplitude preservation and random noise reduction, SEEMD is adopted to improve TFPF. Firstly, the original signal is decomposed into intrinsic mode functions (IMFs) by EMD, and the SE of each IMF is calculated in order to classify the numerous IMFs into three different components; then short window TFPF is employed for low frequency component of IMFs, and long window TFPF is employed for high frequency component of IMFs, and the noise component of IMFs is wiped off directly; at last the final signal is obtained after reconstruction. Rotation experimental and temperature experimental are carried out to verify the proposed SEEMD-TFPF algorithm, the verification and comparison results show that the de-noising performance of SEEMD-TFPF is better than that achievable with the traditional wavelet, Kalman filter and fixed window length TFPF methods. PMID:27258276

A Noise Reduction Method for Dual-Mass Micro-Electromechanical Gyroscopes Based on Sample Entropy Empirical Mode Decomposition and Time-Frequency Peak Filtering.

PubMed

Shen, Chong; Li, Jie; Zhang, Xiaoming; Shi, Yunbo; Tang, Jun; Cao, Huiliang; Liu, Jun

2016-05-31

The different noise components in a dual-mass micro-electromechanical system (MEMS) gyroscope structure is analyzed in this paper, including mechanical-thermal noise (MTN), electronic-thermal noise (ETN), flicker noise (FN) and Coriolis signal in-phase noise (IPN). The structure equivalent electronic model is established, and an improved white Gaussian noise reduction method for dual-mass MEMS gyroscopes is proposed which is based on sample entropy empirical mode decomposition (SEEMD) and time-frequency peak filtering (TFPF). There is a contradiction in TFPS, i.e., selecting a short window length may lead to good preservation of signal amplitude but bad random noise reduction, whereas selecting a long window length may lead to serious attenuation of the signal amplitude but effective random noise reduction. In order to achieve a good tradeoff between valid signal amplitude preservation and random noise reduction, SEEMD is adopted to improve TFPF. Firstly, the original signal is decomposed into intrinsic mode functions (IMFs) by EMD, and the SE of each IMF is calculated in order to classify the numerous IMFs into three different components; then short window TFPF is employed for low frequency component of IMFs, and long window TFPF is employed for high frequency component of IMFs, and the noise component of IMFs is wiped off directly; at last the final signal is obtained after reconstruction. Rotation experimental and temperature experimental are carried out to verify the proposed SEEMD-TFPF algorithm, the verification and comparison results show that the de-noising performance of SEEMD-TFPF is better than that achievable with the traditional wavelet, Kalman filter and fixed window length TFPF methods.

Multiscale characterization and prediction of monsoon rainfall in India using Hilbert-Huang transform and time-dependent intrinsic correlation analysis

NASA Astrophysics Data System (ADS)

Adarsh, S.; Reddy, M. Janga

2017-07-01

In this paper, the Hilbert-Huang transform (HHT) approach is used for the multiscale characterization of All India Summer Monsoon Rainfall (AISMR) time series and monsoon rainfall time series from five homogeneous regions in India. The study employs the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for multiscale decomposition of monsoon rainfall in India and uses the Normalized Hilbert Transform and Direct Quadrature (NHT-DQ) scheme for the time-frequency characterization. The cross-correlation analysis between orthogonal modes of All India monthly monsoon rainfall time series and that of five climate indices such as Quasi Biennial Oscillation (QBO), El Niño Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multi Decadal Oscillation (AMO), and Equatorial Indian Ocean Oscillation (EQUINOO) in the time domain showed that the links of different climate indices with monsoon rainfall are expressed well only for few low-frequency modes and for the trend component. Furthermore, this paper investigated the hydro-climatic teleconnection of ISMR in multiple time scales using the HHT-based running correlation analysis technique called time-dependent intrinsic correlation (TDIC). The results showed that both the strength and nature of association between different climate indices and ISMR vary with time scale. Stemming from this finding, a methodology employing Multivariate extension of EMD and Stepwise Linear Regression (MEMD-SLR) is proposed for prediction of monsoon rainfall in India. The proposed MEMD-SLR method clearly exhibited superior performance over the IMD operational forecast, M5 Model Tree (MT), and multiple linear regression methods in ISMR predictions and displayed excellent predictive skill during 1989-2012 including the four extreme events that have occurred during this period.

Phase space interrogation of the empirical response modes for seismically excited structures

NASA Astrophysics Data System (ADS)

Paul, Bibhas; George, Riya C.; Mishra, Sudib K.

2017-07-01

Conventional Phase Space Interrogation (PSI) for structural damage assessment relies on exciting the structure with low dimensional chaotic waveform, thereby, significantly limiting their applicability to large structures. The PSI technique is presently extended for structure subjected to seismic excitations. The high dimensionality of the phase space for seismic response(s) are overcome by the Empirical Mode Decomposition (EMD), decomposing the responses to a number of intrinsic low dimensional oscillatory modes, referred as Intrinsic Mode Functions (IMFs). Along with their low dimensionality, a few IMFs, retain sufficient information of the system dynamics to reflect the damage induced changes. The mutually conflicting nature of low-dimensionality and the sufficiency of dynamic information are taken care by the optimal choice of the IMF(s), which is shown to be the third/fourth IMFs. The optimal IMF(s) are employed for the reconstruction of the Phase space attractor following Taken's embedding theorem. The widely referred Changes in Phase Space Topology (CPST) feature is then employed on these Phase portrait(s) to derive the damage sensitive feature, referred as the CPST of the IMFs (CPST-IMF). The legitimacy of the CPST-IMF is established as a damage sensitive feature by assessing its variation with a number of damage scenarios benchmarked in the IASC-ASCE building. The damage localization capability, remarkable tolerance to noise contamination and the robustness under different seismic excitations of the feature are demonstrated.

Quantifying Spasticity With Limited Swinging Cycles Using Pendulum Test Based on Phase Amplitude Coupling.

PubMed

Yeh, Chien Hung; Young, Hsu Wen Vincent; Wang, Cheng Yen; Wang, Yung Hung; Lee, Po Lei; Kang, Jiunn Horng; Lo, Men Tzung

2016-10-01

Parameters derived from the goniometer measures in the Pendulum test are insufficient in describing the function of abnormal muscle activity in the spasticity. To explore a quantitative evaluation of muscle activation-movement interaction, we propose a novel index based on phase amplitude coupling (PAC) analysis with the consideration of the relations between movement and surface electromyography (SEMG) activity among 22 hemiplegic stroke patients. To take off trend and noise, we use the empirical mode decomposition (EMD) to obtain intrinsic mode functions (IMFs) of the angular velocity due to its superior decomposing ability in nonlinear oscillations. Shannon entropy based on angular velocity (phase)-envelope of EMG (amplitude) distribution was calculated to demonstrate characteristics of the coupling between SEMG activity and joint movement. We also compare our results with those from traditional methods such as the normalized relaxation index derived from the Pendulum test and the mean root mean square (RMS) of the SEMG signals in the study. Our results show effective discrimination ability between spastic and nonaffected limbs using our method . This study indicates the feasibility of using the novel indices based on the PAC in evaluation the spasticity among the hemiplegic stroke patients with less than three swinging cycles.

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

PubMed Central

Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

2015-01-01

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771

Detection and reconstruction of large scale flow structures in a river by means of empirical mode decomposition combined with Hilbert transform

NASA Astrophysics Data System (ADS)

Franca, Mário J.; Lemmin, Ulrich

2014-05-01

The occurrence of large scale flow structures (LSFS) coherently organized throughout the flow depth has been reported in field and laboratory experiments of flows over gravel beds, especially under low relative submergence conditions. In these, the instantaneous velocity is synchronized over the whole vertical profile oscillating at a low frequency above or below the time-averaged value. The detection of large scale coherently organized regions in the flow field is often difficult since it requires detailed simultaneous observations of the flow velocities at several levels. The present research avoids the detection problem by using an Acoustic Doppler Velocity Profiler (ADVP), which permits measuring three-dimensional velocities quasi-simultaneously over the full water column. Empirical mode decomposition (EMD) combined with the application of the Hilbert transform is then applied to the instantaneous velocity data to detect and isolate LSFS. The present research was carried out in a Swiss river with low relative submergence of 2.9, herein defined as h/D50, (where h is the mean flow depth and D50 the bed grain size diameter for which 50% of the grains have smaller diameters). 3D ADVP instantaneous velocity measurements were made on a 3x5 rectangular horizontal grid (x-y). Fifteen velocity profiles were equally spaced in the spanwise direction with a distance of 10 cm, and in the streamwise direction with a distance of 15 cm. The vertical resolution of the measurements is roughly 0.5 cm. A measuring grid covering a 3D control volume was defined. The instantaneous velocity profiles were measured for 3.5 min with a sampling frequency of 26 Hz. Oscillating LSFS are detected and isolated in the instantaneous velocity signal of the 15 measured profiles. Their 3D cycle geometry is reconstructed and investigated through phase averaging based on the identification of the instantaneous signal phase (related to the Hilbert transform) applied to the original raw signal. Results for all the profiles are consistent and indicate clearly the presence of LSFS throughout the flow depth with impact on the three components of the velocity profile and on the bed friction velocity. A high correlation of the movement is found throughout the flow depth, thus corroborating the hypothesis of large-scale coherent motion evolving over the whole water depth. These latter are characterized in terms of period, horizontal scale and geometry. The high spatial and temporal resolution of our ADVP was crucial for obtaining comprehensive results on coherent structures dynamics. EMD combined with the Hilbert transform have previously been successfully applied to geophysical flow studies. Here we show that this method can also be used for the analysis of river dynamics. In particular, we demonstrate that a clean, well-behaved intrinsic mode function can be obtained from a noisy velocity time series that allowed a precise determination of the vertical structure of the coherent structures. The phase unwrapping of the UMR and the identification of the phase related velocity components brings new insight into the flow dynamics Research supported by the Swiss National Science Foundation (2000-063818). KEY WORDS: large scale flow structures (LSFS); gravel-bed rivers; empirical mode decomposition; Hilbert transform

[An Extraction and Recognition Method of the Distributed Optical Fiber Vibration Signal Based on EMD-AWPP and HOSA-SVM Algorithm].

PubMed

Zhang, Yanjun; Liu, Wen-zhe; Fu, Xing-hu; Bi, Wei-hong

2016-02-01

Given that the traditional signal processing methods can not effectively distinguish the different vibration intrusion signal, a feature extraction and recognition method of the vibration information is proposed based on EMD-AWPP and HOSA-SVM, using for high precision signal recognition of distributed fiber optic intrusion detection system. When dealing with different types of vibration, the method firstly utilizes the adaptive wavelet processing algorithm based on empirical mode decomposition effect to reduce the abnormal value influence of sensing signal and improve the accuracy of signal feature extraction. Not only the low frequency part of the signal is decomposed, but also the high frequency part the details of the signal disposed better by time-frequency localization process. Secondly, it uses the bispectrum and bicoherence spectrum to accurately extract the feature vector which contains different types of intrusion vibration. Finally, based on the BPNN reference model, the recognition parameters of SVM after the implementation of the particle swarm optimization can distinguish signals of different intrusion vibration, which endows the identification model stronger adaptive and self-learning ability. It overcomes the shortcomings, such as easy to fall into local optimum. The simulation experiment results showed that this new method can effectively extract the feature vector of sensing information, eliminate the influence of random noise and reduce the effects of outliers for different types of invasion source. The predicted category identifies with the output category and the accurate rate of vibration identification can reach above 95%. So it is better than BPNN recognition algorithm and improves the accuracy of the information analysis effectively.

Analysis of Human's Motions Based on Local Mean Decomposition in Through-wall Radar Detection

NASA Astrophysics Data System (ADS)

Lu, Qi; Liu, Cai; Zeng, Zhaofa; Li, Jing; Zhang, Xuebing

2016-04-01

Observation of human motions through a wall is an important issue in security applications and search-and rescue. Radar has advantages in looking through walls where other sensors give low performance or cannot be used at all. Ultrawideband (UWB) radar has high spatial resolution as a result of employment of ultranarrow pulses. It has abilities to distinguish the closely positioned targets and provide time-lapse information of targets. Moreover, the UWB radar shows good performance in wall penetration when the inherently short pulses spread their energy over a broad frequency range. Human's motions show periodic features including respiration, swing arms and legs, fluctuations of the torso. Detection of human targets is based on the fact that there is always periodic motion due to breathing or other body movements like walking. The radar can gain the reflections from each human body parts and add the reflections at each time sample. The periodic movements will cause micro-Doppler modulation in the reflected radar signals. Time-frequency analysis methods are consider as the effective tools to analysis and extract micro-Doppler effects caused by the periodic movements in the reflected radar signal, such as short-time Fourier transform (STFT), wavelet transform (WT), and Hilbert-Huang transform (HHT).The local mean decomposition (LMD), initially developed by Smith (2005), is to decomposed amplitude and frequency modulated signals into a small set of product functions (PFs), each of which is the product of an envelope signal and a frequency modulated signal from which a time-vary instantaneous phase and instantaneous frequency can be derived. As bypassing the Hilbert transform, the LMD has no demodulation error coming from window effect and involves no negative frequency without physical sense. Also, the instantaneous attributes obtained by LMD are more stable and precise than those obtained by the empirical mode decomposition (EMD) because LMD uses smoothed local means and local magnitudes that facilitate a more natural decomposition than that using the cubic spline approach of EMD. In this paper, we apply the UWB radar system in through-wall human detections and present a method to characterize human's motions. We start with a walker's motion model and periodic motion features are given the analysis of the experimental data based on the combination of the LMT and fast Fourier Transform (FFT). The characteristics of human's motions including respiration, swing arms and legs, and fluctuations of the torso are extracted. At last, we calculate the actual distance between the human and the wall. This work was supported in part by National Natural Science Foundation of China under Grant 41574109 and 41430322.

Detecting phase-amplitude coupling with high frequency resolution using adaptive decompositions

PubMed Central

Pittman-Polletta, Benjamin; Hsieh, Wan-Hsin; Kaur, Satvinder; Lo, Men-Tzung; Hu, Kun

2014-01-01

Background Phase-amplitude coupling (PAC) – the dependence of the amplitude of one rhythm on the phase of another, lower-frequency rhythm – has recently been used to illuminate cross-frequency coordination in neurophysiological activity. An essential step in measuring PAC is decomposing data to obtain rhythmic components of interest. Current methods of PAC assessment employ narrowband Fourier-based filters, which assume that biological rhythms are stationary, harmonic oscillations. However, biological signals frequently contain irregular and nonstationary features, which may contaminate rhythms of interest and complicate comodulogram interpretation, especially when frequency resolution is limited by short data segments. New method To better account for nonstationarities while maintaining sharp frequency resolution in PAC measurement, even for short data segments, we introduce a new method of PAC assessment which utilizes adaptive and more generally broadband decomposition techniques – such as the empirical mode decomposition (EMD). To obtain high frequency resolution PAC measurements, our method distributes the PAC associated with pairs of broadband oscillations over frequency space according to the time-local frequencies of these oscillations. Comparison with existing methods We compare our novel adaptive approach to a narrowband comodulogram approach on a variety of simulated signals of short duration, studying systematically how different types of nonstationarities affect these methods, as well as on EEG data. Conclusions Our results show: (1) narrowband filtering can lead to poor PAC frequency resolution, and inaccuracy and false negatives in PAC assessment; (2) our adaptive approach attains better PAC frequency resolution and is more resistant to nonstationarities and artifacts than traditional comodulograms. PMID:24452055

Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine.

PubMed

Chen, Lili; Hao, Yaru

2017-01-01

Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA.

PubMed

Javed, Ehtasham; Faye, Ibrahima; Malik, Aamir Saeed; Abdullah, Jafri Malin

2017-11-01

Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact. We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact. The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals. Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy. The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available. Copyright © 2017 Elsevier B.V. All rights reserved.

Spectral Characteristics of Continuous Acoustic Emission (AE) Data from Laboratory Rock Deformation Experiments

NASA Astrophysics Data System (ADS)

Flynn, J. William; Goodfellow, Sebastian; Reyes-Montes, Juan; Nasseri, Farzine; Young, R. Paul

2016-04-01

Continuous acoustic emission (AE) data recorded during rock deformation tests facilitates the monitoring of fracture initiation and propagation due to applied stress changes. Changes in the frequency and energy content of AE waveforms have been previously observed and were associated with microcrack coalescence and the induction or mobilisation of large fractures which are naturally associated with larger amplitude AE events and lower-frequency components. The shift from high to low dominant frequency components during the late stages of the deformation experiment, as the rate of AE events increases and the sample approaches failure, indicates a transition from the micro-cracking to macro-cracking regime, where large cracks generated result in material failure. The objective of this study is to extract information on the fracturing process from the acoustic records around sample failure, where the fast occurrence of AE events does not allow for identification of individual AE events and phase arrivals. Standard AE event processing techniques are not suitable for extracting this information at these stages. Instead the observed changes in the frequency content of the continuous record can be used to characterise and investigate the fracture process at the stage of microcrack coalescence and sample failure. To analyse and characterise these changes, a detailed non-linear and non-stationary time-frequency analysis of the continuous waveform data is required. Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis (HSA) are two of the techniques used in this paper to analyse the acoustic records which provide a high-resolution temporal frequency distribution of the data. In this paper we present the results from our analysis of continuous AE data recorded during a laboratory triaxial deformation experiment using the combined EMD and HSA method.

Characteristics of BeiDou Navigation Satellite System Multipath and Its Mitigation Method Based on Kalman Filter and Rauch-Tung-Striebel Smoother.

PubMed

Zhang, Qiuzhao; Yang, Wei; Zhang, Shubi; Liu, Xin

2018-01-12

Global Navigation Satellite System (GNSS) carrier phase measurement for short baseline meets the requirements of deformation monitoring of large structures. However, the carrier phase multipath effect is the main error source with double difference (DD) processing. There are lots of methods to deal with the multipath errors of Global Position System (GPS) carrier phase data. The BeiDou navigation satellite System (BDS) multipath mitigation is still a research hotspot because the unique constellation design of BDS makes it different to mitigate multipath effects compared to GPS. Multipath error periodically repeats for its strong correlation to geometry of satellites, reflective surface and antenna which is also repetitive. We analyzed the characteristics of orbital periods of BDS satellites which are consistent with multipath repeat periods of corresponding satellites. The results show that the orbital periods and multipath periods for BDS geostationary earth orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites are about one day but the periods of MEO satellites are about seven days. The Kalman filter (KF) and Rauch-Tung-Striebel Smoother (RTSS) was introduced to extract the multipath models from single difference (SD) residuals with traditional sidereal filter (SF). Wavelet filter and Empirical mode decomposition (EMD) were also used to mitigate multipath effects. The experimental results show that the three filters methods all have obvious effect on improvement of baseline accuracy and the performance of KT-RTSS method is slightly better than that of wavelet filter and EMD filter. The baseline vector accuracy on east, north and up (E, N, U) components with KF-RTSS method were improved by 62.8%, 63.6%, 62.5% on day of year 280 and 57.3%, 53.4%, 55.9% on day of year 281, respectively.

Unabated global surface temperature warming: evaluating the evidence

NASA Astrophysics Data System (ADS)

Karl, T. R.; Arguez, A.

2015-12-01

New insights related to time-dependent bias corrections in global surface temperatures have led to higher rates of warming over the past few decades than previously reported in the IPCC Fifth Assessment Report (2014). Record high global temperatures in the past few years have also contributed to larger trends. The combination of these factors and new analyses of the rate of temperature change show unabated global warming since at least the mid-Twentieth Century. New time-dependent bias corrections account for: (1) differences in temperatures measured from ships and drifting buoys; (2) improved corrections to ship measured temperatures; and (3) the larger rates of warming in polar regions (particularly the Arctic). Since 1951, the period over which IPCC (2014) attributes over half of the observed global warming to human causes, it is shown that there has been a remarkably robust and sustained warming, punctuated with inter-annual and decadal variability. This finding is confirmed through simple trend analysis and Empirical Mode Decomposition (EMD). Trend analysis however, especially for decadal trends, is sensitive to selection bias of beginning and ending dates. EMD has no selection bias. Additionally, it can highlight both short- and long-term processes affecting the global temperature times series since it addresses both non-linear and non-stationary processes. For the new NOAA global temperature data set, our analyses do not support the notion of a hiatus or slowing of long-term global warming. However, sub-decadal periods of little (or no warming) and rapid warming can also be found, clearly showing the impact of inter-annual and decadal variability that previously has been attributed to both natural and human-induced non-greenhouse forcings.

Hilbert-Huang spectral analysis for characterizing the intrinsic time-scales of variability in decennial time-series of surface solar radiation

NASA Astrophysics Data System (ADS)

Bengulescu, Marc; Blanc, Philippe; Wald, Lucien

2016-04-01

An analysis of the variability of the surface solar irradiance (SSI) at different local time-scales is presented in this study. Since geophysical signals, such as long-term measurements of the SSI, are often produced by the non-linear interaction of deterministic physical processes that may also be under the influence of non-stationary external forcings, the Hilbert-Huang transform (HHT), an adaptive, noise-assisted, data-driven technique, is employed to extract locally - in time and in space - the embedded intrinsic scales at which a signal oscillates. The transform consists of two distinct steps. First, by means of the Empirical Mode Decomposition (EMD), the time-series is "de-constructed" into a finite number - often small - of zero-mean components that have distinct temporal scales of variability, termed hereinafter the Intrinsic Mode Functions (IMFs). The signal model of the components is an amplitude modulation - frequency modulation (AM - FM) one, and can also be thought of as an extension of a Fourier series having both time varying amplitude and frequency. Following the decomposition, Hilbert spectral analysis is then employed on the IMFs, yielding a time-frequency-energy representation that portrays changes in the spectral contents of the original data, with respect to time. As measurements of surface solar irradiance may possibly be contaminated by the manifestation of different type of stochastic processes (i.e. noise), the identification of real, physical processes from this background of random fluctuations is of interest. To this end, an adaptive background noise null hypothesis is assumed, based on the robust statistical properties of the EMD when applied to time-series of different classes of noise (e.g. white, red or fractional Gaussian). Since the algorithm acts as an efficient constant-Q dyadic, "wavelet-like", filter bank, the different noise inputs are decomposed into components having the same spectral shape, but that are translated to the next lower octave in the spectral domain. Thus, when the sampling step is increased, the spectral shape of IMFs cannot remain at its original position, due to the new lower Nyquist frequency, and is instead pushed toward the lower scaled frequency. Based on these features, the identification of potential signals within the data should become possible without any prior knowledge of the background noises. When applying the above outlined procedure to decennial time-series of surface solar irradiance, only the component that has an annual time-scale of variability is shown to have statistical properties that diverge from those of noise. Nevertheless, the noise-like components are not completely devoid of information, as it is found that their AM components have a non-null rank correlation coefficient with the annual mode, i.e. the background noise intensity seems to be modulated by the seasonal cycle. The findings have possible implications on the modelling and forecast of the surface solar irradiance, by discriminating its deterministic from its quasi-stochastic constituents, at distinct local time-scales.

Effects of intensity on muscle-specific voluntary electromechanical delay and relaxation electromechanical delay.

PubMed

Smith, Cory M; Housh, Terry J; Hill, Ethan C; Keller, Josh L; Johnson, Glen O; Schmidt, Richard J

2018-06-01

The purposes of this study were to examine: 1) the potential muscle-specific differences in voluntary electromechanical delay (EMD) and relaxation electromechanical delay (R-EMD), and 2) the effects of intensity on EMD and R-EMD during step incremental isometric muscle actions from 10 to 100% maximal voluntary isometric contraction (MVIC). EMD and R-EMD measures were calculated from the simultaneous assessments of electromyography, mechanomyography, and force production from the vastus lateralis (VL), vastus medialis (VM), and rectus femoris (RF) during step isometric muscle actions. There were no differences between the VL, VM, and RF for the voluntary EMD E-M (onsets of the electromyographic to mechanomyographic signals), EMD M-F (onsets the mechanomyographic to force production), or EMD E-F (onsets of the electromyographic signal to force production) as well as R-EMD E-M (cessation of electromyographic to mechanomyographic signal), R-EMD M-F (cessation of mechanomyographic signal to force cessation), or R-EMD E-F (cessation of electromyorgraphic signal to force cessation) at any intensity. There were decreases in all EMD and R-EMD measures with increases in intensity. The relative contributions from EMD E-M and EMD M-F to EMD E-F as well as R-EMD E-M and R-EMD M-F to R-EMD E-F remained similar across all intensities. The superficial muscles of the quadriceps femoris shared similar EMD and R-EMD measurements.

Propulsion control experience used in the Highly Integrated Digital Electronic Control (HIDEC) program

NASA Technical Reports Server (NTRS)

Myers, L. P.; Burcham, F. W., Jr.

1984-01-01

The highly integrated digital electronic control (HIDEC) program will integrate the propulsion and flight control systems on an F-15 airplane at NASA Ames Research Center's Dryden Flight Research Facility. Ames-Dryden has conducted several propulsion control programs that have contributed to the HIDEC program. The digital electronic engine control (DEEC) flight evaluation investigated the performance and operability of the F100 engine equipped with a full-authority digital electronic control system. Investigations of nozzle instability, fault detection and accommodation, and augmentor transient capability provided important information for the HIDEC program. The F100 engine model derivative (EMD) was also flown in the F-15 airplane, and airplane performance was significantly improved. A throttle response problem was found and solved with a software fix to the control logic. For the HIDEC program, the F100 EMD engines equipped with DEEC controls will be integrated with the digital flight control system. The control modes to be implemented are an integrated flightpath management mode and an integrated adaptive engine control system mode. The engine control experience that will be used in the HIDEC program is discussed.

Single-Input and Multiple-Output Surface Acoustic Wave Sensing for Damage Quantification in Piezoelectric Sensors.

PubMed

Pamwani, Lavish; Habib, Anowarul; Melandsø, Frank; Ahluwalia, Balpreet Singh; Shelke, Amit

2018-06-22

The main aim of the paper is damage detection at the microscale in the anisotropic piezoelectric sensors using surface acoustic waves (SAWs). A novel technique based on the single input and multiple output of Rayleigh waves is proposed to detect the microscale cracks/flaws in the sensor. A convex-shaped interdigital transducer is fabricated for excitation of divergent SAWs in the sensor. An angularly shaped interdigital transducer (IDT) is fabricated at 0 degrees and ±20 degrees for sensing the convex shape evolution of SAWs. A precalibrated damage was introduced in the piezoelectric sensor material using a micro-indenter in the direction perpendicular to the pointing direction of the SAW. Damage detection algorithms based on empirical mode decomposition (EMD) and principal component analysis (PCA) are implemented to quantify the evolution of damage in piezoelectric sensor material. The evolution of the damage was quantified using a proposed condition indicator (CI) based on normalized Euclidean norm of the change in principal angles, corresponding to pristine and damaged states. The CI indicator provides a robust and accurate metric for detection and quantification of damage.

Asymmetric multiscale detrended fluctuation analysis of California electricity spot price

NASA Astrophysics Data System (ADS)

Fan, Qingju

2016-01-01

In this paper, we develop a new method called asymmetric multiscale detrended fluctuation analysis, which is an extension of asymmetric detrended fluctuation analysis (A-DFA) and can assess the asymmetry correlation properties of series with a variable scale range. We investigate the asymmetric correlations in California 1999-2000 power market after filtering some periodic trends by empirical mode decomposition (EMD). Our findings show the coexistence of symmetric and asymmetric correlations in the price series of 1999 and strong asymmetric correlations in 2000. What is more, we detect subtle correlation properties of the upward and downward price series for most larger scale intervals in 2000. Meanwhile, the fluctuations of Δα(s) (asymmetry) and | Δα(s) | (absolute asymmetry) are more significant in 2000 than that in 1999 for larger scale intervals, and they have similar characteristics for smaller scale intervals. We conclude that the strong asymmetry property and different correlation properties of upward and downward price series for larger scale intervals in 2000 have important implications on the collapse of California power market, and our findings shed a new light on the underlying mechanisms of power price.

Identification of varying time scales in sediment transport using the Hilbert-Huang Transform method

NASA Astrophysics Data System (ADS)

Kuai, Ken Z.; Tsai, Christina W.

2012-02-01

SummarySediment transport processes vary at a variety of time scales - from seconds, hours, days to months and years. Multiple time scales exist in the system of flow, sediment transport and bed elevation change processes. As such, identification and selection of appropriate time scales for flow and sediment processes can assist in formulating a system of flow and sediment governing equations representative of the dynamic interaction of flow and particles at the desired details. Recognizing the importance of different varying time scales in the fluvial processes of sediment transport, we introduce the Hilbert-Huang Transform method (HHT) to the field of sediment transport for the time scale analysis. The HHT uses the Empirical Mode Decomposition (EMD) method to decompose a time series into a collection of the Intrinsic Mode Functions (IMFs), and uses the Hilbert Spectral Analysis (HSA) to obtain instantaneous frequency data. The EMD extracts the variability of data with different time scales, and improves the analysis of data series. The HSA can display the succession of time varying time scales, which cannot be captured by the often-used Fast Fourier Transform (FFT) method. This study is one of the earlier attempts to introduce the state-of-the-art technique for the multiple time sales analysis of sediment transport processes. Three practical applications of the HHT method for data analysis of both suspended sediment and bedload transport time series are presented. The analysis results show the strong impact of flood waves on the variations of flow and sediment time scales at a large sampling time scale, as well as the impact of flow turbulence on those time scales at a smaller sampling time scale. Our analysis reveals that the existence of multiple time scales in sediment transport processes may be attributed to the fractal nature in sediment transport. It can be demonstrated by the HHT analysis that the bedload motion time scale is better represented by the ratio of the water depth to the settling velocity, h/ w. In the final part, HHT results are compared with an available time scale formula in literature.

Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion

NASA Astrophysics Data System (ADS)

Lin, Jinshan; Chen, Qian

2013-07-01

Vibration data of faulty rolling bearings are usually nonstationary and nonlinear, and contain fairly weak fault features. As a result, feature extraction of rolling bearing fault data is always an intractable problem and has attracted considerable attention for a long time. This paper introduces multifractal detrended fluctuation analysis (MF-DFA) to analyze bearing vibration data and proposes a novel method for fault diagnosis of rolling bearings based on MF-DFA and Mahalanobis distance criterion (MDC). MF-DFA, an extension of monofractal DFA, is a powerful tool for uncovering the nonlinear dynamical characteristics buried in nonstationary time series and can capture minor changes of complex system conditions. To begin with, by MF-DFA, multifractality of bearing fault data was quantified with the generalized Hurst exponent, the scaling exponent and the multifractal spectrum. Consequently, controlled by essentially different dynamical mechanisms, the multifractality of four heterogeneous bearing fault data is significantly different; by contrast, controlled by slightly different dynamical mechanisms, the multifractality of homogeneous bearing fault data with different fault diameters is significantly or slightly different depending on different types of bearing faults. Therefore, the multifractal spectrum, as a set of parameters describing multifractality of time series, can be employed to characterize different types and severity of bearing faults. Subsequently, five characteristic parameters sensitive to changes of bearing fault conditions were extracted from the multifractal spectrum and utilized to construct fault features of bearing fault data. Moreover, Hilbert transform based envelope analysis, empirical mode decomposition (EMD) and wavelet transform (WT) were utilized to study the same bearing fault data. Also, the kurtosis and the peak levels of the EMD or the WT component corresponding to the bearing tones in the frequency domain were carefully checked and used as the bearing fault features. Next, MDC was used to classify the bearing fault features extracted by EMD, WT and MF-DFA in the time domain and assess the abilities of the three methods to extract fault features from bearing fault data. The results show that MF-DFA seems to outperform each of envelope analysis, statistical parameters, EMD and WT in feature extraction of bearing fault data and then the proposed method in this paper delivers satisfactory performances in distinguishing different types and severity of bearing faults. Furthermore, to further ascertain the nature causing the multifractality of bearing vibration data, the generalized Hurst exponents of the original bearing vibration data were compared with those of the shuffled and the surrogated data. Consequently, the long-range correlations for small and large fluctuations of data seem to be chiefly responsible for the multifractality of bearing vibration data.

The Subharmonic Behavior and Thresholds of High Frequency Ultrasound Contrast Agents

NASA Astrophysics Data System (ADS)

Allen, John

2016-11-01

Ultrasound contrast agents are encapsulated micro-bubbles used for diagnostic and therapeutic biomedical ultrasound. The agents oscillate nonlinearly about their equilibrium radii upon sufficient acoustic forcing and produce unique acoustic signatures that allow them to be distinguished from scattering from the surrounding tissue. The subharmonic response occurs below the fundamental and is associated with an acoustic pressure threshold. Subharmonic imaging using ultrasound contrast agents has been established for clinical applications at standard diagnostic frequencies typically below 20 MHz. However, for emerging applications of high frequency applications (above 20 MHz) subharmonic imaging is an area of on-going research. The effects of attenuation from tissue are more significant and the characterization of agents is not as well understood. Due to specificity and control production, polymer agents are useful for high frequency applications. In this study, we highlight novel measurement techniques to measure and characterize the mechanical properties of the shell of polymer contrast agents. The definition of the subharmonic threshold is investigated with respect to mono-frequency and chirp forcing waveforms which have been used to achieve optimal subharmonic content in the backscattered signal. Time frequency analysis using the Empirical Mode Decomposition (EMD) and the Hilbert-Huang transform facilitates a more sensitive and robust methodology for characterization of subharmonic content with respect to non-stationary forcing. A new definition of the subharmonic threshold is proposed with respect to the energy content of the associated adaptive basis decomposition. Additional studies with respect to targeted agent behavior and cardiovascular disease are discussed. NIH, ONR.

Dynamic versus isometric electromechanical delay in non-fatigued and fatigued muscle: A combined electromyographic, mechanomyographic, and force approach.

PubMed

Smith, Cory M; Housh, Terry J; Hill, Ethan C; Johnson, Glen O; Schmidt, Richard J

2017-04-01

This study used a combined electromyographic, mechanomyographic, and force approach to identify electromechanical delay (EMD) from the onsets of the electromyographic to force signals (EMD E-F ), onsets of the electromyographic to mechanomyogrpahic signals (EMD E-M ), and onsets of mechanomyographic to force signals (EMD M-F ). The purposes of the current study were to examine: (1) the differences in EMD E-F , EMD E-M , and EMD M-F from the vastus lateralis during maximal, voluntary dynamic (1 repetition maximum [1-RM]) and isometric (maximal voluntary isometric contraction [MVIC]) muscle actions; and (2) the effects of fatigue on EMD E-F , EMD M-F , and EMD E-M . Ten men performed pretest and posttest 1-RM and MVIC leg extension muscle actions. The fatiguing workbout consisted of 70% 1-RM dynamic constant external resistance leg extension muscle actions to failure. The results indicated that there were no significant differences between 1-RM and MVIC EMD E-F , EMD E-M , or EMD M-F. There were, however, significant fatigue-induced increases in EMD E-F (94% and 63%), EMD E-M (107%), and EMD M-F (63%) for both the 1-RM and MVIC measurements. Therefore, these findings demonstrated the effects of fatigue on EMD measures and supported comparisons among studies which examined dynamic or isometric EMD measures from the vastus lateralis using a combined electromyographic, mechanomyographic, and force approach. Copyright © 2017 Elsevier Ltd. All rights reserved.

International Roughness Index (IRI) measurement using Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zhang, Wenjin; Wang, Ming L.

2018-03-01

International Roughness Index (IRI) is an important metric to measure condition of roadways. This index is usually used to justify the maintenance priority and scheduling for roadways. Various inspection methods and algorithms are used to assess this index through the use of road profiles. This study proposes to calculate IRI values using Hilbert-Huang Transform (HHT) algorithm. In particular, road profile data is provided using surface radar attached to a vehicle driving at highway speed. Hilbert-Huang transform (HHT) is used in this study because of its superior properties for nonstationary and nonlinear data. Empirical mode decomposition (EMD) processes the raw data into a set of intrinsic mode functions (IMFs), representing various dominating frequencies. These various frequencies represent noises from the body of the vehicle, sensor location, and the excitation induced by nature frequency of the vehicle, etc. IRI calculation can be achieved by eliminating noises that are not associated with the road profile including vehicle inertia effect. The resulting IRI values are compared favorably to the field IRI values, where the filtered IMFs captures the most characteristics of road profile while eliminating noises from the vehicle and the vehicle inertia effect. Therefore, HHT is an effect method for road profile analysis and for IRI measurement. Furthermore, the application of HHT method has the potential to eliminate the use of accelerometers attached to the vehicle as part of the displacement measurement used to offset the inertia effect.

Single-channel mixed signal blind source separation algorithm based on multiple ICA processing

NASA Astrophysics Data System (ADS)

Cheng, Xiefeng; Li, Ji

2017-01-01

Take separating the fetal heart sound signal from the mixed signal that get from the electronic stethoscope as the research background, the paper puts forward a single-channel mixed signal blind source separation algorithm based on multiple ICA processing. Firstly, according to the empirical mode decomposition (EMD), the single-channel mixed signal get multiple orthogonal signal components which are processed by ICA. The multiple independent signal components are called independent sub component of the mixed signal. Then by combining with the multiple independent sub component into single-channel mixed signal, the single-channel signal is expanded to multipath signals, which turns the under-determined blind source separation problem into a well-posed blind source separation problem. Further, the estimate signal of source signal is get by doing the ICA processing. Finally, if the separation effect is not very ideal, combined with the last time's separation effect to the single-channel mixed signal, and keep doing the ICA processing for more times until the desired estimated signal of source signal is get. The simulation results show that the algorithm has good separation effect for the single-channel mixed physiological signals.

Hilbert-Huang Transform: A Spectral Analysis Tool Applied to Sunspot Number and Total Solar Irradiance Variations, as well as Near-Surface Atmospheric Variables

NASA Astrophysics Data System (ADS)

Barnhart, B. L.; Eichinger, W. E.; Prueger, J. H.

2010-12-01

Hilbert-Huang transform (HHT) is a relatively new data analysis tool which is used to analyze nonstationary and nonlinear time series data. It consists of an algorithm, called empirical mode decomposition (EMD), which extracts the cyclic components embedded within time series data, as well as Hilbert spectral analysis (HSA) which displays the time and frequency dependent energy contributions from each component in the form of a spectrogram. The method can be considered a generalized form of Fourier analysis which can describe the intrinsic cycles of data with basis functions whose amplitudes and phases may vary with time. The HHT will be introduced and compared to current spectral analysis tools such as Fourier analysis, short-time Fourier analysis, wavelet analysis and Wigner-Ville distributions. A number of applications are also presented which demonstrate the strengths and limitations of the tool, including analyzing sunspot number variability and total solar irradiance proxies as well as global averaged temperature and carbon dioxide concentration. Also, near-surface atmospheric quantities such as temperature and wind velocity are analyzed to demonstrate the nonstationarity of the atmosphere.

Epileptic Seizures Prediction Using Machine Learning Methods

PubMed Central

Usman, Syed Muhammad

2017-01-01

Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures' sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects. PMID:29410700

Vital sign sensing method based on EMD in terahertz band

NASA Astrophysics Data System (ADS)

Xu, Zhengwu; Liu, Tong

2014-12-01

Non-contact respiration and heartbeat rates detection could be applied to find survivors trapped in the disaster or the remote monitoring of the respiration and heartbeat of a patient. This study presents an improved algorithm that extracts the respiration and heartbeat rates of humans by utilizing the terahertz radar, which further lessens the effects of noise, suppresses the cross-term, and enhances the detection accuracy. A human target echo model for the terahertz radar is first presented. Combining the over-sampling method, low-pass filter, and Empirical Mode Decomposition improves the signal-to-noise ratio. The smoothed pseudo Wigner-Ville distribution time-frequency technique and the centroid of the spectrogram are used to estimate the instantaneous velocity of the target's cardiopulmonary motion. The down-sampling method is adopted to prevent serious distortion. Finally, a second time-frequency analysis is applied to the centroid curve to extract the respiration and heartbeat rates of the individual. Simulation results show that compared with the previously presented vital sign sensing method, the improved algorithm enhances the signal-to-noise ratio to 1 dB with a detection accuracy of 80%. The improved algorithm is an effective approach for the detection of respiration and heartbeat signal in a complicated environment.

Flood mapping with multitemporal MODIS data

NASA Astrophysics Data System (ADS)

Son, Nguyen-Thanh; Chen, Chi-Farn; Chen, Cheng-Ru

2014-05-01

Flood is one of the most devastating and frequent disasters resulting in loss of human life and serve damage to infrastructure and agricultural production. Flood is phenomenal in the Mekong River Delta (MRD), Vietnam. It annually lasts from July to November. Information on spatiotemporal flood dynamics is thus important for planners to devise successful strategies for flood monitoring and mitigation of its negative effects. The main objective of this study is to develop an approach for weekly mapping flood dynamics with the Moderate Resolution Imaging Spectroradiometer data in MRD using the water fraction model (WFM). The data processed for 2009 comprises three main steps: (1) data pre-processing to construct smooth time series of the difference in the values (DVLE) between land surface water index (LSWI) and enhanced vegetation index (EVI) using the empirical mode decomposition (EMD), (2) flood derivation using WFM, and (3) accuracy assessment. The mapping results were compared with the ground reference data, which were constructed from Envisat Advanced Synthetic Aperture Radar (ASAR) data. As several error sources, including mixed-pixel problems and low-resolution bias between the mapping results and ground reference data, could lower the level of classification accuracy, the comparisons indicated satisfactory results with the overall accuracy of 80.5% and Kappa coefficient of 0.61, respectively. These results were reaffirmed by a close correlation between the MODIS-derived flood area and that of the ground reference map at the provincial level, with the correlation coefficients (R2) of 0.93. Considering the importance of remote sensing for monitoring floods and mitigating the damage caused by floods to crops and infrastructure, this study eventually leads to the realization of the value of using time-series MODIS DVLE data for weekly flood monitoring in MRD with the aid of EMD and WFM. Such an approach that could provide quantitative information on spatiotemporal flood dynamics for monitoring purposes was completely transferable to other regions in the world.

Aerosol vertical distribution and optical properties over the arid and semi-arid areas of Northwest China

NASA Astrophysics Data System (ADS)

Zhang, L.; Tian, P.; Cao, X.; Liang, J.

2017-12-01

Atmospheric aerosols affect the energy budget of the Earth-atmosphere system by direct interaction with solar radiation through scattering and absorption, also indirectly affect weather and climate by altering cloud formation, albedo, and lightning activity. To better understand the information on aerosols over the arid and semi-arid areas of Northwest China, we carried out a series of observation experiments in Wuwei, Zhangye, Dunhuang, and a permanent site SACOL (the Semi-Arid Climate and Environment Observatory of Lanzhou University) (35.95°N, 104.14°E) in Lanzhou, and optical properties using satellite and ground-based remote-sensing measurements. A modified dual-wavelength Mie-scattering lidar (L2S-SM II) inversion algorithm was proposed to simulate the optical property of dust aerosol more accurately. We introduced the physical significance of intrinsic mode functions (IMFs) and the noise component removed from the empirical mode decomposition (EMD) method into the denoising process of the micro-pulse lidar (CE370-2,Cimel) backscattering signal, and developed an EMD-based automatic data-denoising algorithm, which was proven to be better than the wavelet method. Also, we improved the cloud discrimination. On the basis of these studies, aerosol vertical distribution and optical properties were investigated. The main results were as follows:(1) Dust could be lifted up to a 8 km height over Northwest China; (2) From 2005 to 2008, and aerosol existed in the layer below 4 km at SACOL, and the daily average AOD was 87.8% below 0.4; (3) The average depolarization ratio, Ångström exponent α440/870nm and effective radius of black carbon aerosols were 0.24, 0.86±0.30 and 0.54±0.17 μm, respectively, from November 2010 to February 2011; (4) Compared to other regions of China, the Taklamakan Desert and Tibetan Plateau regions exhibit higher depolarization and color ratios because of the natural dust origin. Our studies provided the key information on the long-term seasonal and spatial variations in the aerosol vertical distribution and optical properties, regional aerosol types, long-range transport and atmospheric stability, which could be utilized to more precisely assess the direct and indirect aerosol effects on weather and climate.

Bond graph modeling and experimental verification of a novel scheme for fault diagnosis of rolling element bearings in special operating conditions

NASA Astrophysics Data System (ADS)

Mishra, C.; Samantaray, A. K.; Chakraborty, G.

2016-09-01

Vibration analysis for diagnosis of faults in rolling element bearings is complicated when the rotor speed is variable or slow. In the former case, the time interval between the fault-induced impact responses in the vibration signal are non-uniform and the signal strength is variable. In the latter case, the fault-induced impact response strength is weak and generally gets buried in the noise, i.e. noise dominates the signal. This article proposes a diagnosis scheme based on a combination of a few signal processing techniques. The proposed scheme initially represents the vibration signal in terms of uniformly resampled angular position of the rotor shaft by using the interpolated instantaneous angular position measurements. Thereafter, intrinsic mode functions (IMFs) are generated through empirical mode decomposition (EMD) of resampled vibration signal which is followed by thresholding of IMFs and signal reconstruction to de-noise the signal and envelope order tracking to diagnose the faults. Data for validating the proposed diagnosis scheme are initially generated from a multi-body simulation model of rolling element bearing which is developed using bond graph approach. This bond graph model includes the ball and cage dynamics, localized fault geometry, contact mechanics, rotor unbalance, and friction and slip effects. The diagnosis scheme is finally validated with experiments performed with the help of a machine fault simulator (MFS) system. Some fault scenarios which could not be experimentally recreated are then generated through simulations and analyzed through the developed diagnosis scheme.

Transpyloric Feeding Tube Placement Using Electromagnetic Placement Device in Children.

PubMed

Goggans, Margaret; Pickard, Sharon; West, Alina Nico; Shah, Samir; Kimura, Dai

2017-04-01

Transpyloric feeding tubes (TPT) are often recommended in critically ill children. Blind tube placement, however, can be difficult, be time-consuming, and incur multiple radiation exposures. An electromagnetic device (EMD) is available for confirmation of successful placement of TPTs. We conducted a retrospective cohort study to evaluate the efficacy of an EMD for TPT placement in children and determine its impact on placement success, radiation exposure, confirmation time, and cost for tube placement compared with traditional blind TPT placement. Retrospective data were collected in patients receiving a TPT before (pre-EMD group) and after implementation of an EMD (EMD group). Need for radiographic exposure decreased significantly in the EMD group (n = 40) compared with the pre-EMD group (n = 38) (0.6 vs 1.6 x-rays, P < .001). TPTs were placed and confirmed without abdominal x-ray in 21 of 40 patients in the EMD group. There were no serious adverse events such as misplacement into the lung or pneumothorax or perforation injury of the stomach. Successful tube confirmation took a significantly shorter time in the EMD group than in the pre-EMD group (1.45 vs 4.59 hours, P < .0001). There was an estimated cost savings of $245.10 per placement associated with decreased x-ray and fluoroscopy. The use of an EMD in children significantly decreased radiation exposure and confirmation time while maintaining TPT placement success. The use of an EMD can potentially offer large cost savings. Elimination of abdominal x-ray with EMD during TPT placement was achieved without any serious complications in approximately half of the children.

Enamel matrix derivative Emdogain as an adjuvant for a laterally-positioned flap in the treatment of gingival recession: an electron microscopic appraisal.

PubMed

Lafzi, A; Farahani, R M; Tubbs, R S; Roushangar, L; Shoja, M M

2007-05-01

Enamel matrix derivative (EMD), such as Emdogain, has been suggested for the improvement of wound healing in periodontal surgical therapy. The present qualitative study seeks to illustrate the ultrastructural changes associated with a human gingival wound at 10 days after the application of EMD as an adjunct to a laterally-positioned flap in a patient with gingival recession. An otherwise healthy patient, who had been suffering from bilateral gingival recession defects on teeth #23 and #26, was studied. One defect was treated with a laterally-positioned flap, while the other was treated with a combination of EMD and a laterally-positioned flap. Ten days after the operation gingival biopsy specimens were obtained from the dentogingival region and examined using a transmission electron microscope. A considerable difference was found in both the cellular and extracellular phases of EMD and non-EMD sites. The fibroblasts of EMD site were more rounded with plump cytoplasms and euchromatic nuclei. A well-developed rough endoplasmic reticulum and numerous mitochondria could be detected. In contrast, the fibroblasts of non-EMD site were of flattened spindle-like morphology. While the signs of apoptosis could rarely be detected at EMD site, apoptotic bodies and ultra-structural evidence of apoptosis (crescent-like heterochromatic nuclei and dilated nuclear envelopes) were consistent features at non-EMD site. The extracellular matrix at EMD site mainly consisted of well-organised collagen fibres, while non-EMD site contained sparse and incompletely-formed collagen fibres. Coccoid bacteria were noted within the extracellular matrix and neutrophils at non-EMD site. It seems that EMD may enhance certain features of gingival wound healing, which may be attributable to its anti-apoptotic, anti-bacterial or anti-inflammatory properties.

Different tissue distribution, elimination, and kinetics of thyroxine and its conformational analog, the synthetic flavonoid EMD 49209 in the rat.

PubMed

Schröder-van der Elst, J P; van der Heide, D; Rokos, H; Köhrle, J; Morreale de Escobar, G

1997-01-01

The synthetic flavonoids EMD 23188 and EMD 49209, developed as T4 analogs, displace T4 from transthyretin, and in vitro they inhibit 5'-deiodinase activity. In vivo EMD 21388 causes tissue-specific changes in thyroid hormone metabolism. In tissues that are dependent on T3 locally produced from T4, total T3 was diminished. It was not known whether it was the presence of EMD interfering with 5'-deiodinase type II in tissues or the decreased T4 (substrate) availability that caused the lowered T3. To study whether the flavonoids enter tissues and, if this were the case, whether they enter tissues similarly, [125I]EMD 49209 together with [131I]T4 were injected into female rats and rats pretreated with EMD 21388. Tissues were extracted and submitted to HPLC. [125I]EMD 49209 disappeared quickly from plasma and enters peripheral tissues; peak values were reached after 0.25-0.5 h. Then [125I]EMD 49209 appeared in the intestines (after 6 h 40% of the dose). Tissue uptake of [131I]T4 was very rapid. EMD 21388 pretreatment caused an increase in the excretion of [125I]EMD 49209 into the intestines (40% after 0.25 h). The uptake of [131I]T4 increased, but not high enough to ensure normal tissue T4 concentrations. In the 5'-deiodinase type II-expressing tissues, no [125I]EMD 49209 could be detected. We conclude that the decrease in T3 locally produced from T4 is caused by the shortage of T4 as substrate and not to a direct effect of EMD on the activity of 5'-deiodinases I and II.

Regeneration of the periodontium using enamel matrix derivative in combination with an injectable bone cement.

PubMed

Oortgiesen, Daniël A W; Meijer, Gert J; Bronckers, Antonius L J J; Walboomers, X Frank; Jansen, John A

2013-03-01

Enamel matrix derivative (EMD) has proven to enhance periodontal regeneration; however, its effect is mainly restricted to the soft periodontal tissues. Therefore, to stimulate not only the soft tissues, but also the hard tissues, in this study EMD is combined with an injectable calcium phosphate cement (CaP; bone graft material). The aim was to evaluate histologically the healing of a macroporous CaP in combination with EMD. Intrabony, three-wall periodontal defects (2 × 2 × 1.7 mm) were created mesial of the first upper molar in 15 rats (30 defects). Defects were randomly treated according to one of the three following strategies: EMD, calcium phosphate cement and EMD, or left empty. The animals were killed after 12 weeks, and retrieved samples were processed for histology and histomorphometry. Empty defects showed a reparative type of healing without periodontal ligament or bone regeneration. As measured with on a histological grading scale for periodontal regeneration, the experimental groups (EMD and CaP/EMD) scored equally, both threefold higher compared with empty defects. However, most bone formation was measured in the CaP/EMD group; addition of CAP to EMD significantly enhanced bone formation with 50 % compared with EMD alone. Within the limits of this animal study, the adjunctive use of EMD in combination with an injectable cement, although it did not affect epithelial downgrowth, appeared to be a promising treatment modality for regeneration of bone and ligament tissues in the periodontium. The adjunctive use of EMD in combination with an injectable cement appears to be a promising treatment modality for regeneration of the bone and ligament tissues in the periodontium.

Cellular viability and genetic expression of human gingival fibroblasts to zirconia with enamel matrix derivative (Emdogain®)

PubMed Central

Kwon, Yong-Dae; Choi, Hyun-jung; Lee, Heesu; Lee, Jung-Woo; Weber, Hans-Peter

2014-01-01

PURPOSE The objective of this study was to investigate the biologic effects of enamel matrix derivative (EMD) with different concentrations on cell viability and the genetic expression of human gingival fibroblasts (HGF) to zirconia surfaces. MATERIALS AND METHODS Immortalized human gingival fibroblasts (HGF) were cultured (1) without EMD, (2) with EMD 25 µg/mL, and (3) with EMD 100 µg/mL on zirconia discs. MTT assay was performed to evaluate the cell proliferation activity and SEM was carried out to examine the cellular morphology and attachment. The mRNA expression of collagen type I, osteopontin, fibronectin, and TGF-β1 was evaluated with the real-time polymerase chain reaction (RT-PCR). RESULTS From MTT assay, HGF showed more proliferation in EMD 25 µg/mL group than control and EMD 100 µg/mL group (P<.05). HGFs showed more flattened cellular morphology on the experimental groups than on the control group after 4h culture and more cellular attachments were observed on EMD 25 µg/mL group and EMD 100 µg/mL group after 24h culture. After 48h of culture, cellular attachment was similar in all groups. The mRNA expression of type I collagen increased in a concentration dependent manner. The genetic expression of osteopontin, fibronectin, and TGF-β1 was increased at EMD 100 µg/mL. However, the mRNA expression of proteins associated with cellular attachment was decreased at EMD 25 µg/mL. CONCLUSION Through this short term culture of HGF on zirconium discs, we conclude that EMD affects the proliferation, attachment, and cell morphology of HGF cells. Also, EMD stimulates production of extracellular matrix collagen, osteopontin, and TGF-β1 in high concentration levels. CLINICAL RELEVANCE With the use of EMD, protective barrier between attached gingiva and transmucosal zirconia abutment may be enhanced leading to final esthetic results with implants. PMID:25352963

Time-frequency techniques in biomedical signal analysis. a tutorial review of similarities and differences.

PubMed

Wacker, M; Witte, H

2013-01-01

This review outlines the methodological fundamentals of the most frequently used non-parametric time-frequency analysis techniques in biomedicine and their main properties, as well as providing decision aids concerning their applications. The short-term Fourier transform (STFT), the Gabor transform (GT), the S-transform (ST), the continuous Morlet wavelet transform (CMWT), and the Hilbert transform (HT) are introduced as linear transforms by using a unified concept of the time-frequency representation which is based on a standardized analytic signal. The Wigner-Ville distribution (WVD) serves as an example of the 'quadratic transforms' class. The combination of WVD and GT with the matching pursuit (MP) decomposition and that of the HT with the empirical mode decomposition (EMD) are explained; these belong to the class of signal-adaptive approaches. Similarities between linear transforms are demonstrated and differences with regard to the time-frequency resolution and interference (cross) terms are presented in detail. By means of simulated signals the effects of different time-frequency resolutions of the GT, CMWT, and WVD as well as the resolution-related properties of the interference (cross) terms are shown. The method-inherent drawbacks and their consequences for the application of the time-frequency techniques are demonstrated by instantaneous amplitude, frequency and phase measures and related time-frequency representations (spectrogram, scalogram, time-frequency distribution, phase-locking maps) of measured magnetoencephalographic (MEG) signals. The appropriate selection of a method and its parameter settings will ensure readability of the time-frequency representations and reliability of results. When the time-frequency characteristics of a signal strongly correspond with the time-frequency resolution of the analysis then a method may be considered 'optimal'. The MP-based signal-adaptive approaches are preferred as these provide an appropriate time-frequency resolution for all frequencies while simultaneously reducing interference (cross) terms.

Twofold processing for denoising ultrasound medical images.

PubMed

Kishore, P V V; Kumar, K V V; Kumar, D Anil; Prasad, M V D; Goutham, E N D; Rahul, R; Krishna, C B S Vamsi; Sandeep, Y

2015-01-01

Ultrasound medical (US) imaging non-invasively pictures inside of a human body for disease diagnostics. Speckle noise attacks ultrasound images degrading their visual quality. A twofold processing algorithm is proposed in this work to reduce this multiplicative speckle noise. First fold used block based thresholding, both hard (BHT) and soft (BST), on pixels in wavelet domain with 8, 16, 32 and 64 non-overlapping block sizes. This first fold process is a better denoising method for reducing speckle and also inducing object of interest blurring. The second fold process initiates to restore object boundaries and texture with adaptive wavelet fusion. The degraded object restoration in block thresholded US image is carried through wavelet coefficient fusion of object in original US mage and block thresholded US image. Fusion rules and wavelet decomposition levels are made adaptive for each block using gradient histograms with normalized differential mean (NDF) to introduce highest level of contrast between the denoised pixels and the object pixels in the resultant image. Thus the proposed twofold methods are named as adaptive NDF block fusion with hard and soft thresholding (ANBF-HT and ANBF-ST). The results indicate visual quality improvement to an interesting level with the proposed twofold processing, where the first fold removes noise and second fold restores object properties. Peak signal to noise ratio (PSNR), normalized cross correlation coefficient (NCC), edge strength (ES), image quality Index (IQI) and structural similarity index (SSIM), measure the quantitative quality of the twofold processing technique. Validation of the proposed method is done by comparing with anisotropic diffusion (AD), total variational filtering (TVF) and empirical mode decomposition (EMD) for enhancement of US images. The US images are provided by AMMA hospital radiology labs at Vijayawada, India.

Using electronic monitoring devices to measure inhaler adherence: a practical guide for clinicians.

PubMed

Chan, Amy Hai Yan; Harrison, Jeff; Black, Peter N; Mitchell, Edwin A; Foster, Juliet M

2015-01-01

Use of electronic monitoring devices (EMDs) for inhalers is growing rapidly because of their ability to provide objective and detailed adherence data to support clinical decision making. There is increasing potential for the use of EMDs in clinical settings, especially as cost-effectiveness is realized and device costs reduce. However, it is important for clinicians to know about the attributes of different EMDs so that they can select the right device for their patients and understand the factors that affect the reliability and accuracy of the data EMDs record. This article gives information on where to obtain EMDs, describes device specifications, and highlights useful features for the clinician and the patient, including user feedback data. We discuss the benefits and potential drawbacks of data collected by EMDs and provide device users with a set of tools to optimize the use of EMDs in clinical settings, such as advice on how to carry out brief EMD checks to ensure data quality and device reliability. New EMDs on the market require pretesting before use by patients. We provide information on how to carry out EMD pretesting in the clinic and patients' homes, which can be carried out by health professionals or in collaboration with researchers or manufacturers. Strategies for interpreting and managing common device malfunctions are also discussed. Copyright © 2015 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks.

PubMed

Sadrawi, Muammar; Fan, Shou-Zen; Abbod, Maysam F; Jen, Kuo-Kuang; Shieh, Jiann-Shing

2015-01-01

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.

Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks

PubMed Central

Sadrawi, Muammar; Fan, Shou-Zen; Abbod, Maysam F.; Jen, Kuo-Kuang; Shieh, Jiann-Shing

2015-01-01

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly. PMID:26568957

Detecting weak position fluctuations from encoder signal using singular spectrum analysis.

PubMed

Xu, Xiaoqiang; Zhao, Ming; Lin, Jing

2017-11-01

Mechanical fault or defect will cause some weak fluctuations to the position signal. Detection of such fluctuations via encoders can help determine the health condition and performance of the machine, and offer a promising alternative to the vibration-based monitoring scheme. However, besides the interested fluctuations, encoder signal also contains a large trend and some measurement noise. In applications, the trend is normally several orders larger than the concerned fluctuations in magnitude, which makes it difficult to detect the weak fluctuations without signal distortion. In addition, the fluctuations can be complicated and amplitude modulated under non-stationary working condition. To overcome this issue, singular spectrum analysis (SSA) is proposed for detecting weak position fluctuations from encoder signal in this paper. It enables complicated encode signal to be reduced into several interpretable components including a trend, a set of periodic fluctuations and noise. A numerical simulation is given to demonstrate the performance of the method, it shows that SSA outperforms empirical mode decomposition (EMD) in terms of capability and accuracy. Moreover, linear encoder signals from a CNC machine tool are analyzed to determine the magnitudes and sources of fluctuations during feed motion. The proposed method is proven to be feasible and reliable for machinery condition monitoring. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Temperature imaging with ultrasonic transmission tomography for treatment control

NASA Astrophysics Data System (ADS)

Chu, Zheqi; Pinter, Stephen. Z.; Yuan, Jie; Scarpelli, Matthew L.; Kripfgans, Oliver D.; Fowlkes, J. Brian; Duric, Neb; Carson, Paul L.

2017-03-01

Hyperthermia is a promising method to enhance chemo- or radiation therapy of breast cancer and the time-temperature profile in the target and surrounding areas is the primary monitoring method. Unlike with thermal ablation of lesions, in hyperthermia there are not good alternative treatment monitoring quantities. However, there is less problem with non-monotonic thermal coefficients of speed of sound used with ultrasonic imaging of temperature. This paper tests a long discussed but little investigated method of imaging temperature using speed of sound and proposes methods of reducing edge enhancement artifacts in the temperature image. Normally, when directly using the speed of sound to reconstruct the temperature image around the tumor, there will be an abnormal bipolar edge enhancement along the boundary between two materials with different speeds of sound at a given temperature. This due to partial volume effects and can be diminished by regularized, weighted deconvolution. An initial, manual deconvolution is shown, as well as an EMD (Empirical Mode Decomposition) method. Here we use the continuity and other constraints to choose the coefficient, reprocess the temperature field image and take the mean variations of the temperature in the adjacent pixels as the judgment criteria. Both methods effectively reduce the edge enhancement and produce a more precise image of temperature.

Effects of enamel matrix derivative and basic fibroblast growth factor with μ-tricalcium phosphate on periodontal regeneration in one-wall intrabony defects: an experimental study in dogs.

PubMed

Shirakata, Yoshinori; Takeuchi, Naoshi; Yoshimoto, Takehiko; Taniyama, Katsuyoshi; Noguchi, Kazuyuki

2013-01-01

This study evaluated the effects of enamel matrix derivative (EMD) and basic fibroblast growth factor (bFGF) with μ-tricalcium phosphate (μ-TCP) on periodontal healing in intrabony defects in dogs. One-wall intrabony defects created in dogs were treated with μ-TCP alone (μ-TCP), EMD with μ-TCP (EMD/μ-TCP), bFGF with μ-TCP (bFGF/μ-TCP), and a combination of each (EMD/bFGF/μ-TCP). The amount of new bone formation was not significant for any group. The EMD/bFGF/μ-TCP group induced significantly greater new cementum formation than the μ-TCP and bFGF/μ-TCP groups and, although not significantly, formed more new cementum than the EMD/μ-TCP group. These findings indicate that EMD/bFGF/μ-TCP treatment is effective for cementum regeneration.

Distribution of Electromechanical Delay in the Heart: Insights from a Three-Dimensional Electromechanical Model

PubMed Central

Gurev, V.; Constantino, J.; Rice, J.J.; Trayanova, N.A.

2010-01-01

In the intact heart, the distribution of electromechanical delay (EMD), the time interval between local depolarization and myocyte shortening onset, depends on the loading conditions. The distribution of EMD throughout the heart remains, however, unknown because current experimental techniques are unable to evaluate three-dimensional cardiac electromechanical behavior. The goal of this study was to determine the three-dimensional EMD distributions in the intact ventricles for sinus rhythm (SR) and epicardial pacing (EP) by using a new, to our knowledge, electromechanical model of the rabbit ventricles that incorporates a biophysical representation of myofilament dynamics. Furthermore, we aimed to ascertain the mechanisms that underlie the specific three-dimensional EMD distributions. The results revealed that under both conditions, the three-dimensional EMD distribution is nonuniform. During SR, EMD is longer at the epicardium than at the endocardium, and is greater near the base than at the apex. After EP, the three-dimensional EMD distribution is markedly different; it also changes with the pacing rate. For both SR and EP, late-depolarized regions were characterized with significant myofiber prestretch caused by the contraction of the early-depolarized regions. This prestretch delays myofiber-shortening onset, and results in a longer EMD, giving rise to heterogeneous three-dimensional EMD distributions. PMID:20682251

Atrial Electromechanical Properties in Inflammatory Bowel Disease.

PubMed

Efe, Tolga Han; Cimen, Tolga; Ertem, Ahmet Goktug; Coskun, Yusuf; Bilgin, Murat; Sahan, Haluk Furkan; Pamukcu, Hilal Erken; Yayla, Cagri; Sunman, Hamza; Yuksel, Ilhami; Yeter, Ekrem

2016-09-01

There is much evidence linking inflammation to the initiation and continuation of atrial fibrillation (AF). Inflammatory bowel diseases (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), are chronic systemic inflammatory disorders. Atrial electromechanical delay (EMD) has been known as an early marker of AF. The objectives of this study were to evaluate the atrial electromechanical properties in patients with IBD. Fifty-two patients with IBD and 26 healthy controls were recruited in the study. Twenty-five of patients with IBD were on active period, and the remaining 27 were on remission period. Atrial electromechanical properties were measured by using transthoracic echocardiography and tissue Doppler imaging and simultaneous surface ECG recording. Interatrial EMD, left intraatrial EMD, and right intraatrial EMD were calculated. Patients on activation with IBD had significantly prolonged left and right intraatrial EMDs and interatrial EMD compared to patients on remission (P = 0.048, P = 0.036, P < 0.001, respectively) and healthy controls (P < 0.001, for all comparisons). Left and right intraatrial EMDs and interatrial EMD were also found to be higher when patients on remission with IBD compared with healthy controls. No statistical difference was observed between UC and CD in terms of inter- and intraatrial EMDs. Atrial electromechanical conduction is prolonged in IBD, and exposure to chronic inflammation may lead to structural and electrophysiological changes in the atrial tissue that causes slow conduction. Measurement of atrial EMD parameters might be used to predict the risk for the development of AF in patients with IBD. © 2016, Wiley Periodicals, Inc.

Anti-inflammatory effects of EMD in the presence of biomechanical loading and interleukin-1β in vitro.

PubMed

Nokhbehsaim, Marjan; Deschner, Birgit; Winter, Jochen; Bourauel, Christoph; Jäger, Andreas; Jepsen, Søren; Deschner, James

2012-02-01

Enamel matrix derivative (EMD) used to promote periodontal regeneration has been shown to exert anti-inflammatory effects. This in vitro study was performed to investigate if the anti-inflammatory actions of EMD are modulated by the local cellular environment, such as inflammation or occlusal, i.e., biomechanical, loading. Human periodontal ligament cells were seeded on BioFlex plates and incubated with EMD under normal, inflammatory, and biomechanical loading conditions for 1 and 6 days. In order to mimic inflammatory and biomechanical loading conditions in vitro, cells were stimulated with interleukin (IL)-1β and exposed to dynamic tensile strain, respectively. The gene expression of IL-1β, IL-1 receptor antagonist (IL-1RN), IL-6, IL-8, IL-10, and cyclooxygenase (COX)-2 was analyzed by real-time RT-PCR and the IL-6 protein synthesis by enzyme-linked immunoassay. For statistical analysis, Student's t test, ANOVA, and post-hoc comparison tests were applied (p < 0.05). EMD downregulated significantly the expression of IL-1β and COX-2 at 1 day and of IL-6, IL-8, and COX-2 at 6 days in normal condition. In an inflammatory environment, the anti-inflammatory actions of EMD were significantly enhanced at 6 days. In the presence of low biomechanical loading, EMD caused a downregulation of IL-1β and IL-8, whereas high biomechanical loading significantly abrogated the anti-inflammatory effects of EMD at both days. Neither IL-1RN nor IL-10 was upregulated by EMD. These data suggest that high occlusal forces may abrogate anti-inflammatory effects of EMD and should, therefore, be avoided immediately after the application of EMD to achieve best healing results.

Effectiveness of Modal Decomposition for Tapping Atomic Force Microscopy Microcantilevers in Liquid Environment.

PubMed

Kim, Il Kwang; Lee, Soo Il

2016-05-01

The modal decomposition of tapping mode atomic force microscopy microcantilevers in liquid environments was studied experimentally. Microcantilevers with different lengths and stiffnesses and two sample surfaces with different elastic moduli were used in the experiment. The response modes of the microcantilevers were extracted as proper orthogonal modes through proper orthogonal decomposition. Smooth orthogonal decomposition was used to estimate the resonance frequency directly. The effects of the tapping setpoint and the elastic modulus of the sample under test were examined in terms of their multi-mode responses with proper orthogonal modes, proper orthogonal values, smooth orthogonal modes and smooth orthogonal values. Regardless of the stiffness of the microcantilever under test, the first mode was dominant in tapping mode atomic force microscopy under normal operating conditions. However, at lower tapping setpoints, the flexible microcantilever showed modal distortion and noise near the tip when tapping on a hard sample. The stiff microcantilever had a higher mode effect on a soft sample at lower tapping setpoints. Modal decomposition for tapping mode atomic force microscopy can thus be used to estimate the characteristics of samples in liquid environments.

Mode Analyses of Gyrokinetic Simulations of Plasma Microturbulence

NASA Astrophysics Data System (ADS)

Hatch, David R.

This thesis presents analysis of the excitation and role of damped modes in gyrokinetic simulations of plasma microturbulence. In order to address this question, mode decompositions are used to analyze gyrokinetic simulation data. A mode decomposition can be constructed by projecting a nonlinearly evolved gyrokinetic distribution function onto a set of linear eigenmodes, or alternatively by constructing a proper orthogonal decomposition of the distribution function. POD decompositions are used to examine the role of damped modes in saturating ion temperature gradient driven turbulence. In order to identify the contribution of different modes to the energy sources and sinks, numerical diagnostics for a gyrokinetic energy quantity were developed for the GENE code. The use of these energy diagnostics in conjunction with POD mode decompositions demonstrates that ITG turbulence saturates largely through dissipation by damped modes at the same perpendicular spatial scales as those of the driving instabilities. This defines a picture of turbulent saturation that is very different from both traditional hydrodynamic scenarios and also many common theories for the saturation of plasma turbulence. POD mode decompositions are also used to examine the role of subdominant modes in causing magnetic stochasticity in electromagnetic gyrokinetic simulations. It is shown that the magnetic stochasticity, which appears to be ubiquitous in electromagnetic microturbulence, is caused largely by subdominant modes with tearing parity. The application of higher-order singular value decomposition (HOSVD) to the full distribution function from gyrokinetic simulations is presented. This is an effort to demonstrate the ability to characterize and extract insight from a very large, complex, and high-dimensional data-set - the 5-D (plus time) gyrokinetic distribution function.

Atrial Electromechanical Properties in Coeliac Disease.

PubMed

Efe, Tolga Han; Ertem, Ahmet Goktug; Coskun, Yusuf; Bilgin, Murat; Algul, Engin; Beton, Osman; Asarcikli, Lale Dinc; Erat, Mehmet; Ayturk, Mehmet; Yuksel, Ilhami; Yeter, Ekrem

2016-02-01

Coeliac disease (CD) is an autoimmune and inflammatory disorder of the small intestine. There is reasonable evidence linking inflammation to the initiation and continuation of atrial fibrillation (AF) in inflammatory conditions. Atrial electro-mechanic delay (EMD) was suggested as an early marker of AF in previous studies. The objectives of this study were to evaluate atrial electromechanical properties measured by tissue Doppler imaging and simultaneous electrocardiography (ECG) tracing in patients with CD. Thirty-nine patients with coeliac disease (CD), and 26 healthy volunteers, matched for age and sex, were enrolled in the study. Atrial electromechanical properties were measured by using transthoracic echocardiography and surface ECG. Interatrial electro-mechanic delay (EMD), left intraatrial EMD, right intratrial EMD were calculated. There was no difference between CD patients and healthy volunteers in terms of basal characteristics. Patients with CD had significantly prolonged left and right intraatrial EMDs, and interatrial EMD compared to healthy controls (p= 0.03, p= 0.02, p<0.0001, respectively). Interatrial EMD was positively correlated with age, disease duration, anti-gliadin IgG, anti-endomysium and disease status. In multiple linear regression, interatrial EMD was independently associated with disease duration, anti-endomysium and disease status after adjusting for age and sex. In the present study, atrial EMDs were found significantly higher in patients with CD compared with healthy individuals. Measurement of atrial EMD parameters might be used to predict the risk of development of AF in patients with CD. Copyright © 2015 Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) and the Cardiac Society of Australia and New Zealand (CSANZ). Published by Elsevier B.V. All rights reserved.

Correlation among physical and electrochemical behaviour of nanostructured electrolytic manganese dioxide from leach liquor and synthetic for aqueous asymmetric capacitor.

PubMed

Minakshi Sundaram, Manickam; Biswal, Avijit; Mitchell, David; Jones, Rob; Fernandez, Carlos

2016-02-14

An attempt has been made to correlate the differences in structural parameters, surface areas, morphology etc. with the electrochemical capacitive behaviour of the EMDs. The nanostructured electrolytic manganese dioxides (EMD) have been synthesized through electrodepositing MnO2 from two different leach liquors and a synthetic analogue thereof. The structural and chemical state was determined using X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) respectively. Multiplet structure determination led to estimates of the manganese valence states present in the EMD. The EMDs have been tested in an asymmetric capacitor which we have developed. This used activated carbon as the negative electrode and the various EMDs as the positive electrode. Aqueous 2 M NaOH solution was used as the electrolyte. The capacitor achieved 1.6 V corresponding to a capacitance of ∼50 F g(-1) of the EMDs from leach liquors. The EMD derived from the synthetic solution showed an inferior capacitance of 25 F g(-1). Extended cycling (2000 cycles), showed 100% capacity retention was achieved for one EMD produced from the leach liquor derived from low-grade manganese ore/residue. This outstanding capacitor performance was correlated with the presence of a nanofibrous morphology. These findings open up the possibility of extracting a high performance EMD product from a low cost, low-grade source of manganese.

Joint angle affects volitional and magnetically-evoked neuromuscular performance differentially.

PubMed

Minshull, C; Rees, D; Gleeson, N P

2011-08-01

This study examined the volitional and magnetically-evoked neuromuscular performance of the quadriceps femoris at functional knee joint angles adjacent to full extension. Indices of volitional and magnetically-evoked neuromuscular performance (N=15 healthy males, 23.5 ± 2.9 years, 71.5 ± 5.4 kg, 176.5 ± 5.5 cm) were obtained at 25°, 35° and 45° of knee flexion. Results showed that volitional and magnetically-evoked peak force (PF(V) and P(T)F(E), respectively) and electromechanical delay (EMD(V) and EMD(E), respectively) were enhanced by increased knee flexion. However, greater relative improvements in volitional compared to evoked indices of neuromuscular performance were observed with increasing flexion from 25° to 45° (e.g. EMD(V), EMD(E): 36% vs. 11% improvement, respectively; F([2,14])=6.8, p<0.05). There were no significant correlations between EMD(V) and EMD(E) or PF(V) and P(T)F(E), at analogous joint positions. These findings suggest that the extent of the relative differential between volitional and evoked neuromuscular performance capabilities is joint angle-specific and not correlated with performance capabilities at adjacent angles, but tends to be smaller with increased flexion. As such, effective prediction of volitional from evoked performance capabilities at both analogous and adjacent knee joint positions would lack robustness. Copyright © 2011 Elsevier Ltd. All rights reserved.

Adsorption of enamel matrix proteins to a bovine-derived bone grafting material and its regulation of cell adhesion, proliferation, and differentiation.

PubMed

Miron, Richard J; Bosshardt, Dieter D; Hedbom, Erik; Zhang, Yufeng; Haenni, Beat; Buser, Daniel; Sculean, Anton

2012-07-01

The use of various combinations of enamel matrix derivative (EMD) and grafting materials has been shown to promote periodontal wound healing/regeneration. However, the downstream cellular behavior of periodontal ligament (PDL) cells and osteoblasts has not yet been studied. Furthermore, it is unknown to what extent the bleeding during regenerative surgery may influence the adsorption of exogenous proteins to the surface of bone grafting materials and the subsequent cellular behavior. In the present study, the aim is to test EMD adsorption to the surface of natural bone mineral (NBM) particles in the presence of blood and determine the effect of EMD coating to NBM particles on downstream cellular pathways, such as adhesion, proliferation, and differentiation of primary human osteoblasts and PDL cells. NBM particles were precoated in various settings with EMD or human blood and analyzed for protein adsorption patterns via fluorescent imaging and high-resolution immunocytochemistry with an anti-EMD antibody. Cell attachment and cell proliferation were quantified using fluorescent double-stranded DNA-binding dye. Cell differentiation was analyzed using real-time polymerase chain reaction for genes encoding runt-related transcription factor 2, alkaline phosphatase (ALP), osteocalcin (OC), and collagen1α1 (COL1A1), and mineralization was assessed using red dye staining. Analysis of cell attachment and cell proliferation revealed significantly higher osteoblast and PDL cell attachment on EMD-coated surfaces when compared with control and blood-coated surfaces. EMD also stimulated release of growth factors and cytokines, including bone morphogenetic protein 2 and transforming growth factor β1. Moreover, there were significantly higher mRNA levels of osteoblast differentiation markers, including COL1A1, ALP, and OC, in osteoblasts and PDL cells cultured on EMD-coated NBM particles. The present results suggest that 1) EMD enhances osteoblast and PDL cell attachment, proliferation, and differentiation on NBM particles, and 2) blood contamination of the grafting material before mixing with EMD may inhibit EMD adsorption.

Educational psychology in medical learning: a randomised controlled trial of two aide memoires for the recall of causes of electromechanical dissociation.

PubMed

Dyson, E; Voisey, S; Hughes, S; Higgins, B; McQuillan, P J

2004-07-01

Although mnemonics are commonly used in medical education there are few data on their effectiveness. A RCT was undertaken to test the hypothesis that a new aide memoire, "EMD-aide", would be superior to the conventional "4Hs+4Ts" mnemonic in facilitating recall of causes of electromechanical dissociation (EMD) among house officers. "EMD-aide", organises causes of EMD by frequency of occurrence and ease of reversibility: four groups organised by shape, colour, position, numbering, clockwise sequence, and use of arrows. Eight hospitals were randomised in a controlled trial and 149 house officers were then recruited by telephone. Baseline ability to recall causes of EMD was recorded at one minute and overall. House officers were then sent a copy of either "4Hs+4Ts" or "EMD-aide" according to randomisation group. Recall ability was retested at one month. 68 of 80 and 51 of 69 house officers completed the study in the "4Hs+4Ts" and "EMD-aide" groups respectively (NS) with similar baseline recall. After intervention median number of recalled causes was greater in the "EMD-aide" group, eight compared with seven at one minute (p = 0.034) and eight compared with seven overall, p = 0.067. Recall of all eight causes was more common in "EMD-aide" group, 54% compared with 35%, p = 0.054, and these house officers spent longer examining their aide memoire, p<0.001. "EMD-aide" may be superior to "4Hs+4Ts" in facilitating the recall of the causes of electromechanical dissociation. Educational psychology of medical learning and the use of aide memoires in general are worthy of further study.

Educational psychology in medical learning: a randomised controlled trial of two aide memoires for the recall of causes of electromechanical dissociation

PubMed Central

Dyson, E; Voisey, S; Hughes, S; Higgins, B; McQuillan, P

2004-01-01

Objectives: Although mnemonics are commonly used in medical education there are few data on their effectiveness. A RCT was undertaken to test the hypothesis that a new aide memoire, "EMD-aide", would be superior to the conventional "4Hs+4Ts" mnemonic in facilitating recall of causes of electromechanical dissociation (EMD) among house officers. Method: "EMD-aide", organises causes of EMD by frequency of occurrence and ease of reversibility: four groups organised by shape, colour, position, numbering, clockwise sequence, and use of arrows. Eight hospitals were randomised in a controlled trial and 149 house officers were then recruited by telephone. Baseline ability to recall causes of EMD was recorded at one minute and overall. House officers were then sent a copy of either "4Hs+4Ts" or "EMD-aide" according to randomisation group. Recall ability was retested at one month. Results: 68 of 80 and 51 of 69 house officers completed the study in the "4Hs+4Ts" and "EMD-aide" groups respectively (NS) with similar baseline recall. After intervention median number of recalled causes was greater in the "EMD-aide" group, eight compared with seven at one minute (p = 0.034) and eight compared with seven overall, p = 0.067. Recall of all eight causes was more common in "EMD-aide" group, 54% compared with 35%, p = 0.054, and these house officers spent longer examining their aide memoire, p<0.001. Conclusions: "EMD-aide" may be superior to "4Hs+4Ts" in facilitating the recall of the causes of electromechanical dissociation. Educational psychology of medical learning and the use of aide memoires in general are worthy of further study. PMID:15208230

The role of gastroesophageal reflux in relation to symptom onset in patients with proton pump inhibitor-refractory nonerosive reflux disease accompanied by an underlying esophageal motor disorder.

PubMed

Izawa, Shinya; Funaki, Yasushi; Iida, Akihito; Tokudome, Kentaro; Tamura, Yasuhiro; Ogasawara, Naotaka; Sasaki, Makoto; Kasugai, Kunio

2014-01-01

The symptom improvement rate is low with proton pump inhibitors (PPIs) in nonerosive reflux disease (NERD). The underlying pathogenic mechanism is complex. Esophageal motility disorders (EMDs) are thought to be a factor, but their prevalence, type, symptoms and the role played by gastroesophageal reflux (GER) in symptom onset have not been fully investigated. To investigate the role of GER in symptom onset in PPI-refractory NERD patients with EMDs. This study comprised 76 patients with PPI-refractory NERD. Manometry was performed during PPI treatment and patients were divided into an EMD group and normal motility (non-EMD) group. Then, multichannel intraluminal impedance-pH monitoring was performed and medical interviews were conducted. Nineteen patients (25%) had an EMD. Data were compared between 17 patients, excluding 2 with achalasia and 57 non-EMD patients. No significant differences were observed between groups in 24-hour intraesophageal pH <4 holding time (HT), mean number of GER episodes or mean number of proximal reflux episodes. The reflux-related symptom index (≥50%) showed a relationship between reflux and symptoms in 70.5% of EMD patients and 75% of non-EMD patients. In the EMD group, the score for FSSG (Frequency Scale for the Symptoms of GERD) question (Q)10 was significantly correlated with the number of GER episodes (r = 0.58, p = 0.02) and the number of proximal reflux episodes (r = 0.63, p = 0.02). In addition, the score for Q9 tended to be correlated with the number of GER episodes (r = 0.44, p = 0.06). Our results suggest that some PPI-refractory NERD patients have EMDs, and that GER plays a role in symptom onset.

Statistical properties and time-frequency analysis of temperature, salinity and turbidity measured by the MAREL Carnot station in the coastal waters of Boulogne-sur-Mer (France)

NASA Astrophysics Data System (ADS)

Kbaier Ben Ismail, Dhouha; Lazure, Pascal; Puillat, Ingrid

2016-10-01

In marine sciences, many fields display high variability over a large range of spatial and temporal scales, from seconds to thousands of years. The longer recorded time series, with an increasing sampling frequency, in this field are often nonlinear, nonstationary, multiscale and noisy. Their analysis faces new challenges and thus requires the implementation of adequate and specific methods. The objective of this paper is to highlight time series analysis methods already applied in econometrics, signal processing, health, etc. to the environmental marine domain, assess advantages and inconvenients and compare classical techniques with more recent ones. Temperature, turbidity and salinity are important quantities for ecosystem studies. The authors here consider the fluctuations of sea level, salinity, turbidity and temperature recorded from the MAREL Carnot system of Boulogne-sur-Mer (France), which is a moored buoy equipped with physico-chemical measuring devices, working in continuous and autonomous conditions. In order to perform adequate statistical and spectral analyses, it is necessary to know the nature of the considered time series. For this purpose, the stationarity of the series and the occurrence of unit-root are addressed with the Augmented-Dickey Fuller tests. As an example, the harmonic analysis is not relevant for temperature, turbidity and salinity due to the nonstationary condition, except for the nearly stationary sea level datasets. In order to consider the dominant frequencies associated to the dynamics, the large number of data provided by the sensors should enable the estimation of Fourier spectral analysis. Different power spectra show a complex variability and reveal an influence of environmental factors such as tides. However, the previous classical spectral analysis, namely the Blackman-Tukey method, requires not only linear and stationary data but also evenly-spaced data. Interpolating the time series introduces numerous artifacts to the data. The Lomb-Scargle algorithm is adapted to unevenly-spaced data and is used as an alternative. The limits of the method are also set out. It was found that beyond 50% of missing measures, few significant frequencies are detected, several seasonalities are no more visible, and even a whole range of high frequency disappears progressively. Furthermore, two time-frequency decomposition methods, namely wavelets and Hilbert-Huang Transformation (HHT), are applied for the analysis of the entire dataset. Using the Continuous Wavelet Transform (CWT), some properties of the time series are determined. Then, the inertial wave and several low-frequency tidal waves are identified by the application of the Empirical Mode Decomposition (EMD). Finally, EMD based Time Dependent Intrinsic Correlation (TDIC) analysis is applied to consider the correlation between two nonstationary time series.

The Influence of Arginine on the Response of Enamel Matrix Derivative (EMD) Proteins to Thermal Stress: Towards Improving the Stability of EMD-Based Products

PubMed Central

Bolisetty, Sreenath; Marascio, Matteo; Gemperli Graf, Anja; Garamszegi, Laszlo; Mezzenga, Raffaele; Fischer, Peter; Månson, Jan-Anders

2015-01-01

In a current procedure for periodontal tissue regeneration, enamel matrix derivative (EMD), which is the active component, is mixed with a propylene glycol alginate (PGA) gel carrier and applied directly to the periodontal defect. Exposure of EMD to physiological conditions then causes it to precipitate. However, environmental changes during manufacture and storage may result in modifications to the conformation of the EMD proteins, and eventually premature phase separation of the gel and a loss in therapeutic effectiveness. The present work relates to efforts to improve the stability of EMD-based formulations such as Emdogain™ through the incorporation of arginine, a well-known protein stabilizer, but one that to our knowledge has not so far been considered for this purpose. Representative EMD-buffer solutions with and without arginine were analyzed by 3D-dynamic light scattering, UV-Vis spectroscopy, transmission electron microscopy and Fourier transform infrared spectroscopy at different acidic pH and temperatures, T, in order to simulate the effect of pH variations and thermal stress during manufacture and storage. The results provided evidence that arginine may indeed stabilize EMD against irreversible aggregation with respect to variations in pH and T under these conditions. Moreover, stopped-flow transmittance measurements indicated arginine addition not to suppress precipitation of EMD from either the buffers or the PGA gel carrier when the pH was raised to 7, a fundamental requirement for dental applications. PMID:26670810

The Influence of Arginine on the Response of Enamel Matrix Derivative (EMD) Proteins to Thermal Stress: Towards Improving the Stability of EMD-Based Products.

PubMed

Apicella, Alessandra; Heunemann, Peggy; Bolisetty, Sreenath; Marascio, Matteo; Gemperli Graf, Anja; Garamszegi, Laszlo; Mezzenga, Raffaele; Fischer, Peter; Plummer, Christopher J; Månson, Jan-Anders

2015-01-01

In a current procedure for periodontal tissue regeneration, enamel matrix derivative (EMD), which is the active component, is mixed with a propylene glycol alginate (PGA) gel carrier and applied directly to the periodontal defect. Exposure of EMD to physiological conditions then causes it to precipitate. However, environmental changes during manufacture and storage may result in modifications to the conformation of the EMD proteins, and eventually premature phase separation of the gel and a loss in therapeutic effectiveness. The present work relates to efforts to improve the stability of EMD-based formulations such as Emdogain™ through the incorporation of arginine, a well-known protein stabilizer, but one that to our knowledge has not so far been considered for this purpose. Representative EMD-buffer solutions with and without arginine were analyzed by 3D-dynamic light scattering, UV-Vis spectroscopy, transmission electron microscopy and Fourier transform infrared spectroscopy at different acidic pH and temperatures, T, in order to simulate the effect of pH variations and thermal stress during manufacture and storage. The results provided evidence that arginine may indeed stabilize EMD against irreversible aggregation with respect to variations in pH and T under these conditions. Moreover, stopped-flow transmittance measurements indicated arginine addition not to suppress precipitation of EMD from either the buffers or the PGA gel carrier when the pH was raised to 7, a fundamental requirement for dental applications.

Gene-expression profiles of epithelial cells treated with EMD in vitro: analysis using complementary DNA arrays.

PubMed

Kapferer, I; Schmidt, S; Gstir, R; Durstberger, G; Huber, L A; Vietor, I

2011-02-01

During surgical periodontal treatment, EMD is topically applied in order to facilitate regeneration of the periodontal ligament, acellular cementum and alveolar bone. Suppresion of epithelial down-growth is essential for successful periodontal regeneration; however, the underlying mechanisms of how EMD influences epithelial wound healing are poorly understood. In the present study, the effects of EMD on gene-expression profiling in an epithelial cell line (HSC-2) model were investigated. Gene-expression modifications, determined using a comparative genome-wide expression-profiling strategy, were independently validated by quantitative real-time RT-PCR. Additionally, cell cycle, cell growth and in vitro wound-healing assays were conducted. A set of 43 EMD-regulated genes was defined, which may be responsible for the reduced epithelial down-growth upon EMD application. Gene ontology analysis revealed genes that could be attributed to pathways of locomotion, developmental processes and associated processes such as regulation of cell size and cell growth. Additionally, eight regulated genes have previously been reported to take part in the process of epithelial-to-mesenchymal transition. Several independent experimental assays revealed significant inhibition of cell migration, growth and cell cycle by EMD. The set of EMD-regulated genes identified in this study offers the opportunity to clarify mechanisms underlying the effects of EMD on epithelial cells. Reduced epithelial repopulation of the dental root upon periodontal surgery may be the consequence of reduced migration and cell growth, as well as epithelial-to-mesenchymal transition. © 2010 John Wiley & Sons A/S.

Enamel Matrix Derivative Promotes Healing of a Surgical Wound in the Rat Oral Mucosa.

PubMed

Maymon-Gil, Tal; Weinberg, Evgeny; Nemcovsky, Carlos; Weinreb, Miron

2016-05-01

Enamel matrix proteins (EMPs) play a role in enamel formation and the development of the periodontium. Sporadic clinical observations of periodontal regeneration treatments with enamel matrix derivative (EMD), a commercial formulation of EMPs, suggest that it also promotes post-surgical healing of soft tissues. In vitro studies showed that EMD stimulates various cellular effects, which could potentially enhance wound healing. This study examines the in vivo effects of EMD on healing of an oral mucosa surgical wound in rats. A bilateral oral mucosa wound was created via a crestal incision in the anterior edentulous maxilla of Sprague-Dawley rats. Full-thickness flaps were raised, and, after suturing, EMD was injected underneath the soft tissues on one side, whereas the EMD vehicle was injected in the contralateral side. Animals were sacrificed after 5 or 9 days, and the wound area was subjected to histologic and immunohistochemical analysis of the epithelial gap, number of macrophages, blood vessels, proliferating cells, and collagen content in the connective tissue (CT). Gene expression analysis was also conducted 2 days post-surgery. EMD had no effect on the epithelial gap of the wound. On both days 5 and 9, EMD treatment increased significantly the number of blood vessels and the collagen content. EMD also enhanced (by 20% to 40%) the expression of transforming growth factors β1 and β2, vascular endothelial growth factor, interleukin-1β, matrix metalloproteinase-1, versican, and fibronectin. EMD improves oral mucosa incisional wound healing by promoting formation of blood vessels and collagen fibers in CT.

Xenogenous Collagen Matrix and/or Enamel Matrix Derivative for Treatment of Localized Gingival Recessions: A Randomized Clinical Trial. Part I: Clinical Outcomes.

PubMed

Sangiorgio, João Paulo Menck; Neves, Felipe Lucas da Silva; Rocha Dos Santos, Manuela; França-Grohmann, Isabela Lima; Casarin, Renato Corrêa Viana; Casati, Márcio Zaffalon; Santamaria, Mauro Pedrine; Sallum, Enilson Antonio

2017-12-01

Considering xenogeneic collagen matrix (CM) and enamel matrix derivative (EMD) characteristics, it is suggested that their combination could promote superior clinical outcomes in root coverage procedures. Thus, the aim of this parallel, double-masked, dual-center, randomized clinical trial is to evaluate clinical outcomes after treatment of localized gingival recession (GR) by a coronally advanced flap (CAF) combined with CM and/or EMD. Sixty-eight patients presenting one Miller Class I or II GRs were randomly assigned to receive either CAF (n = 17); CAF + CM (n = 17); CAF + EMD (n = 17), or CAF + CM + EMD (n = 17). Recession height, probing depth, clinical attachment level, and keratinized tissue width and thickness were measured at baseline and 90 days and 6 months after surgery. The obtained root coverage was 68.04% ± 24.11% for CAF; 87.20% ± 15.01% for CAF + CM; 88.77% ± 20.66% for CAF + EMD; and 91.59% ± 11.08% for CAF + CM + EMD after 6 months. Groups that received biomaterials showed greater values (P <0.05). Complete root coverage (CRC) for CAF + EMD was 70.59%, significantly superior to CAF alone (23.53%); CAF + CM (52.94%), and CAF + CM + EMD (51.47%) (P <0.05). Keratinized tissue thickness gain was significant only in CM-treated groups (P <0.05). The three approaches are superior to CAF alone for root coverage. EMD provides highest levels of CRC; however, the addition of CM increases gingival thickness. The combination approach does not seem justified.

Enamel Matrix Derivative Promote Primary Human Pulp Cell Differentiation and Mineralization

PubMed Central

Riksen, Elisabeth Aurstad; Landin, Maria A.; Reppe, Sjur; Nakamura, Yukio; Lyngstadaas, Ståle Petter; Reseland, Janne E.

2014-01-01

Enamel matrix derivative (EMD) has been found to induce reactive dentin formation; however the molecular mechanisms involved are unclear. The effect of EMD (5–50 μg/mL) on primary human pulp cells were compared to untreated cells and cells incubated with 10−8 M dexamethasone (DEX) for 1, 2, 3, 7, and 14 days in culture. Expression analysis using Affymetrix microchips demonstrated that 10 μg/mL EMD regulated several hundred genes and stimulated the gene expression of proteins involved in mesenchymal proliferation and differentiation. Both EMD and DEX enhanced the expression of amelogenin (amel), and the dentinogenic markers dentin sialophosphoprotein (DSSP) and dentin matrix acidic phosphoprotein 1 (DMP1), as well as the osteogenic markers osteocalcin (OC, BGLAP) and collagen type 1 (COL1A1). Whereas, only EMD had effect on alkaline phosphatase (ALP) mRNA expression, the stimulatory effect were verified by enhanced secretion of OC and COL1A from EMD treated cells, and increased ALP activity in cell culture medium after EMD treatment. Increased levels of interleukin-6 (IL-6), interleukin-8 (IL-8), and monocyte chemoattractant proteins (MCP-1) in the cell culture medium were also found. Consequently, the suggested effect of EMD is to promote differentiation of pulp cells and increases the potential for pulpal mineralization to favor reactive dentine formation. PMID:24857913

Adaptive sparsest narrow-band decomposition method and its applications to rolling element bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Cheng, Junsheng; Peng, Yanfeng; Yang, Yu; Wu, Zhantao

2017-02-01

Enlightened by ASTFA method, adaptive sparsest narrow-band decomposition (ASNBD) method is proposed in this paper. In ASNBD method, an optimized filter must be established at first. The parameters of the filter are determined by solving a nonlinear optimization problem. A regulated differential operator is used as the objective function so that each component is constrained to be a local narrow-band signal. Afterwards, the signal is filtered by the optimized filter to generate an intrinsic narrow-band component (INBC). ASNBD is proposed aiming at solving the problems existed in ASTFA. Gauss-Newton type method, which is applied to solve the optimization problem in ASTFA, is irreplaceable and very sensitive to initial values. However, more appropriate optimization method such as genetic algorithm (GA) can be utilized to solve the optimization problem in ASNBD. Meanwhile, compared with ASTFA, the decomposition results generated by ASNBD have better physical meaning by constraining the components to be local narrow-band signals. Comparisons are made between ASNBD, ASTFA and EMD by analyzing simulation and experimental signals. The results indicate that ASNBD method is superior to the other two methods in generating more accurate components from noise signal, restraining the boundary effect, possessing better orthogonality and diagnosing rolling element bearing fault.

Treatment of the alcoholic organic brain syndrome with EMD 21657. A derivative of a pyritinolmetabolite: double-blind clinical, quantitative EEG and psychometric studies.

PubMed

Saletu, B; Grünberger, J; Saletu, M; Mader, R; Volavka, J

1978-01-01

The efficacy of EMD 21657--a derivative of a pyritinolmetabolite--with regard to the improvement of the organic brain syndrome (OBS) of chronic alcoholics was investigated in a double-blind study utilizing clinical, psychometric and quantitative EEG evaluation. Nineteen patients received 3 x 300 mg EMD and 21 patients 3 x 1 dragee placebo for 6 weeks. The groups did not differ in regard to age, sex, weight, height, alcohol anamnesis or IQ. The hospitalized patients were examined before as well as at the end of the second, fourth and sixth week of drug treatment. While the overall evaluation by the psychiatrist and patients at the end of the period of treatment did not show marked intergroup differences, the clinical global impression scale and the OBS rating scale demonstrated that both groups showed a significant reduction in their OBS and that improvement with EMD 21657 therapy was significantly superior to the one with placebo. Psychometric analysis also exhibited a significant superiority of EMD in regard to the general, associative, numeric and total verbal memory, concentration and attention variability. Psychovisual memory and the quantative aspects of attention showed opposite findings. Flickerlight fusion frequency, reaction time and after-image did not change significantly. The psychomotor activity improved significantly more with EMD than placebo; this was especially pronounced in the left hand. Affect and mood improved also more with EMD than placebo. Side effects were observed more frequently under active treatment and were characterized by temporary headaches. Power spectral density analysis of the EEG revealed in both groups a decrease of delta, fast alpha and beta activities and an increase in theta and slow alpha activity, but changes during EMD treatment more frequently reached the level of statistical significance than with placebo. The most consistant finding was the theta augmentation under EMD treatment. It was concluded that EMD 21657 is a CNS-effective drug with pronounced nootropic and slight thymotropic properties.

Probing the electrochemical properties of biopolymer modified EMD nanoflakes through electrodeposition for high performance alkaline batteries.

PubMed

Biswal, Avijit; Minakshi, Manickam; Tripathy, Bankim Chandra

2016-04-07

In the present work, a novel biopolymer approach has been made to electrodeposit manganese dioxide from manganese sulphate in a sulphuric acid bath containing chitosan in the absence and presence of glutaraldehyde as a cross-linking agent. Galvanostatically synthesised electrolytic manganese dioxide (EMD) nanoflakes were used as electrode materials and their electrochemical properties with the influence of biopolymer chitosan were systematically characterized. The structural determination, surface morphology and porosity of nanostructured EMD were evaluated using X-ray diffraction, Fourier transform infrared spectroscopy, field emission scanning electron microscopy and nitrogen adsorption-desorption techniques. The results obtained were compared with that of blank EMD (polymer free). The results indicated that the EMD having chitosan cross-linked with glutaraldehyde possesses a reduced particle size and more porous structure than the blank and EMDs synthesized in the presence of chitosan but without glutaraldehyde. The results revealed that chitosan was unable to play any significant role on its own but chitosan in the presence of glutaraldehyde forms a cross-linking structure, which in turn influences the nucleation and growth of the EMDs during electrodeposition. EMDs obtained in the presence of chitosan (1 g dm(-3)) and glutaraldehyde (1% glutaraldehyde) exhibited a reversible and better discharge capacity upon cycling than the blank which showed its typical capacity fading behaviour with cycling. In addition, EMD synthesized in the presence of 1 g dm(-3) chitosan and 2% glutaraldehyde exhibited a superior electrochemical performance than the blank and lower amounts (1%; 1.5%) of glutaraldehyde, showing a stable discharge capacity of 60 mA h g(-1) recorded up to 40 cycles in alkaline KOH electrolyte for a Zn-MnO2 system. Our results demonstrate the potential of using polymer modified EMDs as a new generation of alkaline battery materials. The XPS data show that a surface functional moiety arising from the cross-linked chitosan enhances the electrochemical properties of the Zn-MnO2 system.

Properties of a Bacteriocin Produced by Bacillus subtilis EMD4 Isolated from Ganjang (Soy Sauce).

PubMed

Liu, Xiaoming; Lee, Jae Yong; Jeong, Seon-Ju; Cho, Kye Man; Kim, Gyoung Min; Shin, Jung-Hye; Kim, Jong-Sang; Kim, Jeong Hwan

2015-09-01

A Bacillus species, EMD4, with strong antibacterial activity was isolated from ganjang (soy sauce) and identified as B. subtilis. B. subtilis EMD4 strongly inhibited the growth of B. cereus ATCC14579 and B. thuringiensis ATCC33679. The antibacterial activity was stable at pH 3-9 but inactive at pH 10 and above. The activity was fully retained after 15 min at 80°C but reduced by 50% after 15 min at 90°C. The activity was completely destroyed by proteinase K and protease treatment, indicating its proteinaceous nature. The bacteriocin (BacEMD4) was partially purified from culture supernatant by ammonium sulfate precipitation, and QSepharose and Sephadex G-50 column chromatographies. The specific activity was increased from 769.2 AU/mg protein to 8,347.8 AU/mg protein and the final yield was 12.6%. The size of BacEMD4 was determined to be 3.5 kDa by Tricine SDS-PAGE. The N-terminal amino acid sequence was similar with that of Subtilosin A. Nucleotide sequencing of the cloned gene confirmed that BacEMD4 was Subtilosin A. BacEMD4 showed bactericidal activity against B. cereus ATCC14579.

In vitro proliferation of human osteogenic cells in presence of different commercial bone substitute materials combined with enamel matrix derivatives

PubMed Central

2009-01-01

Background Cellular reactions to alloplastic bone substitute materials (BSM) are a subject of interest in basic research. In regenerative dentistry, these bone grafting materials are routinely combined with enamel matrix derivatives (EMD) in order to additionally enhance tissue regeneration. Materials and methods The aim of this study was to evaluate the proliferative activity of human osteogenic cells after incubation over a period of seven days with commercial BSM of various origin and chemical composition. Special focus was placed on the potential additional benefit of EMD on cellular proliferation. Results Except for PerioGlas®, osteogenic cell proliferation was significantly promoted by the investigated BSM. The application of EMD alone also resulted in significantly increased cellular proliferation. However, a combination of BSM and EMD resulted in only a moderate additional enhancement of osteogenic cell proliferation. Conclusion The application of most BSM, as well as the exclusive application of EMD demonstrated a positive impact on the proliferation of human osteogenic cells in vitro. In order to increase the benefit from substrate combination (BSM + EMD), further studies on the interactions between BSM and EMD are needed. PMID:19909545

A fast estimation of shock wave pressure based on trend identification

NASA Astrophysics Data System (ADS)

Yao, Zhenjian; Wang, Zhongyu; Wang, Chenchen; Lv, Jing

2018-04-01

In this paper, a fast method based on trend identification is proposed to accurately estimate the shock wave pressure in a dynamic measurement. Firstly, the collected output signal of the pressure sensor is reconstructed by discrete cosine transform (DCT) to reduce the computational complexity for the subsequent steps. Secondly, the empirical mode decomposition (EMD) is applied to decompose the reconstructed signal into several components with different frequency-bands, and the last few low-frequency components are chosen to recover the trend of the reconstructed signal. In the meantime, the optimal component number is determined based on the correlation coefficient and the normalized Euclidean distance between the trend and the reconstructed signal. Thirdly, with the areas under the gradient curve of the trend signal, the stable interval that produces the minimum can be easily identified. As a result, the stable value of the output signal is achieved in this interval. Finally, the shock wave pressure can be estimated according to the stable value of the output signal and the sensitivity of the sensor in the dynamic measurement. A series of shock wave pressure measurements are carried out with a shock tube system to validate the performance of this method. The experimental results show that the proposed method works well in shock wave pressure estimation. Furthermore, comparative experiments also demonstrate the superiority of the proposed method over the existing approaches in both estimation accuracy and computational efficiency.

Changing On Diurnal Cycle Of Rainfall In Northern Coastal Of West Java

NASA Astrophysics Data System (ADS)

Yulihastin, E.; Hadi, T. W.; Ningsih, N. S.

2017-12-01

The floods event in the north of Java was largely due to persistent of rainfall that occurred in the morning which indicated of deviation of diurnal pattern of rainfall. The shift of the phase of diurnal rainfall cycle using TRMM satellite hourly data of 3B41RT on the rainy period of 2000-2016 exhibits over land from Late Afternoon-Early Midnight (LA-EM) to morning. The peak of the cycle changes from diurnal to semidiurnal with a peak occurring in LA-EM and morning. Location of rainfall which usually occurs in the oceans shifted into near coastal area. The classification of diurnal rainfall cycles based on composite analysis shows four types: Normal (N) Type (45.6%) with one peak rainfall occurring in the afternoon until night, Diurnal (D) Type (26%) with one peak and phase opposite to normal type, Semidiurnal (SD) Type (6.5 %) with two peaks and the main peak occurring in the afternoon until night, Third Diurnal (TD) Type (21.7%) with three peaks and the main peak occurs in the morning. The classification was confirmed using the objective method of Empirical Mode Decomposition (EMD) and obtained three IMFs representing three diurnal cycle modes of Type TD (67.8%) with the main rain peak taking place in the afternoon, Type D with rain peak occurring in the early hours (18.9%), and SD type (9.9%) with the first peak occurred in the afternoon. For D Type, the results also prove that the diurnal cycle with significant deviations in amplitude occurred in February 2002, 2004, 2008, 2014, wich is the maximum rainfall occurs in the EM. It also seems that in those years, rainfall intensity is concentrated on the northern coast of West Java while in the Java Sea rainfall was minimum.

Alcohol intake may impair bone density and new cementum formation after enamel matrix derivative treatment: histometric study in rats.

PubMed

Corrêa, M G; Gomes Campos, M L; Marques, M R; Ambrosano, G M B; Casati, M Z; Nociti, F H; Sallum, E A

2016-02-01

Alcohol intake may interfere with bone metabolism; however, there is a lack of information about the outcomes of regenerative approaches in the presence of alcohol intake. Enamel matrix derivative (EMD) has been used in periodontal regenerative procedures resulting in improvement of clinical parameters. Thus, the aim of this histomorphometric study is to evaluate the healing of periodontal defects after treatment with EMD under the influence of alcohol intake. Twenty Wistar rats were randomly assigned to two groups: G1 = alcohol intake (n = 10) and G2 = non-exposed to alcohol intake (n = 10). Thirty days after initiation of alcohol intake, fenestration defects were created at the buccal aspect of the first mandibular molar of all animals from both groups. After the surgeries, the defects of each animal were randomly assigned to two subgroups: non-treated control and treated with EMD. The animals were killed 21 d later. G1 showed less defect fill for non-treated controls. Bone density (BD) and new cementum formation were lower for G1 when compared to G2, for EMD-treated and non-treated sites. EMD treatment resulted in greater BD and new cementum formation in both groups and defect fill was not significantly different between groups in the EMD-treated sites. The number of tartrate-resistant acid phosphatase-positive osteoclasts was significantly higher in G1 when compared to G2 and in EMD-treated sites of both groups. Alcohol intake may produce a significant detrimental effect on BD and new cementum formation, even in sites treated with EMD. A limited positive effect may be expected after EMD treatment under this condition. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Communication and protocol compliance and their relation to the quality of cardiopulmonary resuscitation (CPR): A mixed-methods study of simulated telephone-assisted CPR.

PubMed

Nord-Ljungquist, Helena; Brännström, Margareta; Bohm, Katarina

2015-07-01

In the event of a cardiac arrest, emergency medical dispatchers (EMDs) play a critical role by providing telephone-assisted cardiopulmonary resuscitation (T-CPR) to laypersons. The aim of our investigation was to describe compliance with the T-CPR protocol, the performance of the laypersons in a simulated T-CPR situation, and the communication between laypersons and EMDs during these actions. We conducted a retrospective observational study by analysing 20 recorded video and audio files. In a simulation, EMDs provided laypersons with instructions following T-CPR protocols. These were then analysed using a mixed method with convergent parallel design. If the EMDs complied with the T-CPR protocol, the laypersons performed the correct procedures in 71% of the actions. The single most challenging instruction of the T-CPR protocol, for both EMDs and laypersons, was airway control. Mean values for compression depth and frequency did not reach established guideline goals for CPR. Proper application of T-CPR protocols by EMDs resulted in better performance by laypersons in CPR. The most problematic task for EMDs as well for laypersons was airway management. The study results did not establish that the quality of communication between EMDs and laypersons performing CPR in a cardiac arrest situation led to statistically different outcomes, as measured by the quality and effectiveness of the CPR delivered. Copyright © 2014 Elsevier Ltd. All rights reserved.

Artifact removal from EEG data with empirical mode decomposition

NASA Astrophysics Data System (ADS)

Grubov, Vadim V.; Runnova, Anastasiya E.; Efremova, Tatyana Yu.; Hramov, Alexander E.

2017-03-01

In the paper we propose the novel method for dealing with the physiological artifacts caused by intensive activity of facial and neck muscles and other movements in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We introduce the mathematical algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from movement artifacts and show high efficiency of the method.

Estimation and confidence intervals for empirical mixing distributions

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.

1995-01-01

Questions regarding collections of parameter estimates can frequently be expressed in terms of an empirical mixing distribution (EMD). This report discusses empirical Bayes estimation of an EMD, with emphasis on the construction of interval estimates. Estimation of the EMD is accomplished by substitution of estimates of prior parameters in the posterior mean of the EMD. This procedure is examined in a parametric model (the normal-normal mixture) and in a semi-parametric model. In both cases, the empirical Bayes bootstrap of Laird and Louis (1987, Journal of the American Statistical Association 82, 739-757) is used to assess the variability of the estimated EMD arising from the estimation of prior parameters. The proposed methods are applied to a meta-analysis of population trend estimates for groups of birds.

Influence of biphasic calcium phosphate surfaces coated with Enamel Matrix Derivative on vertical bone growth in an extra-oral rabbit model.

PubMed

Wen, Bo; Li, Zhen; Nie, Rongrong; Liu, Chao; Zhang, Peng; Miron, Richard J; Dard, Michel M

2016-10-01

The aim of this study was to investigate the ability of Enamel Matrix Derivative (EMD) on vertical bone regeneration around dental implants placed in an extra-oral rabbit model. A total of 30 Straumann BL implants were partially embedded in transverse orientation into the posterior mandibles of 15 rabbits. Macro-structuring BiPhasicCaPST (BCPT1), micro-structuring BiPhasicCaPST (BCPT2), and deproteinized bovine bone mineral (DBBM) were placed around the implant and covered with a scaffold stabilizing "umbrella." EMD was incorporated within the scaffold for test sites, but not control sites. Histological analysis was performed on retrieved specimens after 10 weeks of healing to assess new bone formation. All treatment groups displayed new supracrestal bone formation as determined by histomorphometric measurements, with mean values of new bone height ranging between 0.62 and 1.13 mm. Histological analysis revealed a higher mean bone formation (%) around the test sites where EMD (34.7, 95%CI: 27.1-39.4) was released from the scaffold, whereas the control group without EMD release (26.4, 95%CI: 16.3-31.9) (P = 0.069). The mean fBIC (%) in the BCPT2 group increased by the addition of EMD relative to no EMD (67.2, 95%CI: 48.6-84.1) and (54.7, 95%CI: 32.3-68.9), respectively). The BCPT2/EMD and DBBM/EMD interventions showed the greatest mean bone density (BA/TA), respectively, (12.8, 95%CI: 8.9-36.5) and (11.2, 6.3-16.4) in ROI 1. Values in ROI 2 were, similarly, (24.9, 95%CI: 17.2-31.7) and (27.7, 19.2-35.3). BA/TA in ROI 2 differences between the BCPT2 groups with and without EMD was statistically significant (P = 0.026), as well as the DBBM groups with and without EMD (P = 0.038). A layer of new bone was formed in both test and controls. The release of EMD from BCPT2 and DBBM adjacent to a bone-level implant with an SLActive surface and scaffold retention umbrella consistently regenerated the greater fBIC and bone density along the length of the implant. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Peroneal electromechanical delay and fatigue in patients with chronic ankle instability.

PubMed

Flevas, Dimitrios A; Bernard, Manfred; Ristanis, Stavros; Moraiti, Constantina; Georgoulis, Anastasios D; Pappas, Evangelos

2017-06-01

The purpose of this study was to investigate the effect of chronic ankle instability (CAI) on electromechanical delay times (EMD) before and after fatigue. Understanding the mechanisms that contribute to CAI is essential for the development of effective rehabilitation programmes. It was hypothesized that patients with CAI will demonstrate prolonged EMD times compared to healthy subjects and that fatigue will cause greater increases in EMD times in the CAI group. Twenty-one male volunteers participated in the study providing data on 16 ankles with CAI and 26 with no history of ankle injury. EMD was measured on an isokinetic dynamometer. Measurements were taken with the ankle in neutral (0°) and at 30° of inversion. All subjects followed an isokinetic fatigue protocol until eversion torque fell below 50 % of initial torque for three consecutive repetitions. A 2 × 2 × 2 ANOVA was used to calculate the effect of ankle status (CAI vs. healthy), fatigue, angle (0° vs. 30°) and their interactions on EMD. Fatigue caused a significant increase on EMD [non-fatigued: 122(29)ms vs. fatigue 155(54)ms; p < 0.001]. EMD times were shorter at 30° of inversion compared to neutral [neutral: 145(39)ms vs. 30° of inversion: 132(40)ms, p = 0.015]. An interaction effect for ankle status and angle was found (p = 0.026) with CAI ankles demonstrating longer EMD [CAI: 156(45)ms vs. healthy: 133(40)ms] in neutral but not at 30° of inversion [CAI: 133(46)ms vs. 132(33)ms]. Patients with CAI had longer EMD times in neutral, but not when the ankle was placed in inversion. This suggests that rehabilitation programmes may be more effective when retraining occurs with the ankle in neutral position. It is likely that low EMD times prevent ankle acceleration at the beginning of the mechanism of injury, but they are less important when the ankle has already inverted at 30°. Both CAI and healthy subjects demonstrated longer EMD after fatigue, emphasizing the importance of proper conditioning in the prevention of delayed peroneal response and subsequent ankle injury. Improving resistance to fatigue of the peroneals may prove to be an effective prevention tool of ankle sprain recurrence in patients with CAI. III.

Detrending with Empirical Mode Decomposition (DEMD): Theory, Evaluation, and Application

NASA Astrophysics Data System (ADS)

Bolch, Michael Adam

Land-surface heterogeneity (LSH) at different scales has significant influence on atmospheric boundary layer (ABL) buoyant and shear turbulence generation and transfers of water, carbon and heat. The extent of proliferation of this influence into larger-scale circulations and atmospheric structures is a topic continually investigated in experimental and numerical studies, in many cases with the hopes of improving land-atmosphere parameterizations for modeling purposes. The blending height is a potential metric for the vertical propagation of LSH effects into the ABL, and has been the subject of study for several decades. Proper assessment of the efficacy of blending height theory invites the combination of observations throughout ABLs above different LSH scales with model simulations of the observed ABL and LSH conditions. The central goal of this project is to develop an apt and thoroughly scrutinized method for procuring ABL observations that are accurately detrended and justifiably relevant for such a study, referred to here as Detrending with Empirical Mode Decomposition (DEMD). The Duke University helicopter observation platform (HOP) provides ABL data [wind (u, v, and w), temperature ( T), moisture (q), and carbon dioxide (CO 2)] at a wide range of altitudes, especially in the lower ABL, where LSH effects are most prominent, and where other aircraft-based platforms cannot fly. Also, lower airspeeds translate to higher resolution of the scalars and fluxes needed to evaluate blending height theory. To confirm noninterference of the main rotor downwash with the HOP sensors, and also to identify optimal airspeeds, analytical, numerical, and observational studies are presented. Analytical analysis clears the main rotor downwash from the HOP nose at airspeeds above 10 m s-1. Numerical models find an acceptable range from 20-40 m s-1, due to a growing compressed air preceding the HOP nose. The first observational study finds no impact of different HOP airspeeds on measurements from ˜18 m s -1 to ˜55 m s-1 over a stable marine boundary layer (MBL). Another set of observations studies HOP and tower data, using the Duke University Mobile Micrometeorological Station (MMS) over an MBL, and concludes that HOP sensible heat (SH), latent heat (LE), and carbon dioxide (F CO2) fluxes align well with MMS findings. The HOP sensors provide ABL data at 40 Hz, as well as a real-time display of theta for in-flight ABL height estimation. Sensor calibration and alignment procedures indicate usable ABL measurements. HOP data are especially susceptible to the spurious influence of platform motion on ABL data, largely due to the low-altitude and low-airspeed capabilities of the HOP. For example, HOP altitude motion in the presence of a lapse rate can cause spurious T fluctuations. Empirical mode decomposition (EMD) can separate HOP data into a set of adaptive and unique intrinsic mode functions (IMFs), often with physical meaning. DEMD aims to correct for spurious contributions to HOP data, while merging EMD with a correlation analysis to adjust data without eliminating relevant ABL dynamics. To evaluate DEMD efficacy, two-dimensional synthetic T fields with simulated turbulence over a prescribed lapse rate are sampled with altitude fluctuations similar to HOP flights, and with a wide range of T perturbation and sampling path parameter variations. DEMD recovers the prescribed lapse rate within 1% on average for the 552 test cases passing the filtering criteria. The method is further evaluated via application to vertical cross sections taken from the Ocean-Land-Atmosphere Model (OLAM) large-eddy simulation (LES) results, where DEMD shows improved accuracy of SH recovery. DEMD is applied to three low-altitude HOP flight legs flown on 19 June 2007 during the Cloud and Land Surface Interaction Campaign (CLASIC), both as an example of practical application and to compare DEMD to the initially proposed method (Holder et al. 2011, hereafter H11). H11 dictates the elimination of correlated IMFs, along with other subtle differences from DEMD, which also eliminates any ABL motions embedded in those IMFs. As suspected, the H11 method produces marked reductions of variances and turbulence kinetic energy (TKE) and substantial deviations in SH, LE, and FCO2 compared to DEMD. DEMD detrends without unnecessary elimination. DEMD is vital for ensuring accurate scalars and fluxes from HOP data, and a strategy for future research is presented that integrates properly detrended observations from the CLASIC HOP dataset with OLAM simulations to explore LSH effects on ABL processes and evaluate blending height theory.

Analysis and Prediction of Sea Ice Evolution using Koopman Mode Decomposition Techniques

DTIC Science & Technology

2018-04-30

Title: Analysis and Prediction of Sea Ice Evolution using Koopman Mode Decomposition Techniques Subject: Monthly Progress Report Period of...Resources: N/A TOTAL: $18,687 2 TECHNICAL STATUS REPORT Abstract The program goal is analysis of sea ice dynamical behavior using Koopman Mode Decompo...sition (KMD) techniques. The work in the program’s first month consisted of improvements to data processing code, inclusion of additional arctic sea ice

Esophageal Motor Disorders Are a Strong and Independant Associated Factor of Barrett's Esophagus.

PubMed

Bazin, Camille; Benezech, Alban; Alessandrini, Marine; Grimaud, Jean-Charles; Vitton, Veronique

2018-04-30

Esophageal motor disorder (EMD) has been shown to be associated with gastroesophageal reflux disease (GERD). However, the association of EMD with a Barrett's esophagus (BE) is controversial. Our objective was to evaluate whether the presence of EMD was an independent factor associated with BE. A retrospective case-control study was conducted in GERD patients who all had oeso-gastroduodenal endoscopy and high-resolution esophageal manometry. The clinical data collected was known or potential risk factors for BE: male gender, smoking and alcohol consumption, age, body mass index, presence of hiatal hernia, frequency, and age of GERD. EMD were classified according to the Chicago classification into: ineffective motor syndrome, fragmented peristalsis and absence of peristalsis, lower esophageal sphincter hypotonia. Two hundred and one patients (101 in the GERD + BE group and 100 in the GERD without BE) were included. In univariate analysis, male gender, alcohol consumption, presence of hiatal hernia, and EMD appeared to be associated with the presence of BE. In a multivariate analysis, 3 independent factors were identified: the presence of EMD (odds ratio [OR], 3.99; 95% confidence interval [CI], 1.71-9.28; P = 0.001), the presence of hiatal hernia (OR, 5.60; 95% CI, 2.45-12.76; P ＜ 0.001), Helicobacter pylori infection (OR, 0.08; 95% CI, 0.01-0.84; P = 0.035). The presence of EMD (particularly ineffective motor syndrome and lower esophageal sphincter hypotonia) is a strong independent associated factor of BE. Searching systematically for an EMD in patients suffering from GERD could be a new strategy to organize the endoscopic follow-up.

In vitro evaluation of demineralized freeze-dried bone allograft in combination with enamel matrix derivative.

PubMed

Miron, Richard J; Bosshardt, Dieter D; Laugisch, Oliver; Dard, Michel; Gemperli, Anja C; Buser, Daniel; Gruber, Reinhard; Sculean, Anton

2013-11-01

Preclinical and clinical studies suggest that a combination of enamel matrix derivative (EMD) with demineralized freeze-dried bone allograft (DFDBA) may improve periodontal wound healing and regeneration. To date, no single study has characterized the effects of this combination on in vitro cell behavior. The aim of this study is to test the ability of EMD to adsorb to the surface of DFDBA particles and determine the effect of EMD coating on downstream cellular pathways such as adhesion, proliferation, and differentiation of primary human osteoblasts and periodontal ligament (PDL) cells. DFDBA particles were precoated with EMD or human blood and analyzed for protein adsorption patterns via scanning electron microscopy. Cell attachment and proliferation were quantified using a commercial assay. Cell differentiation was analyzed using real-time polymerase chain reaction for genes encoding Runx2, alkaline phosphatase, osteocalcin, and collagen 1α1, and mineralization was assessed using alizarinred staining. Analysis of cell attachment revealed no significant differences among control, blood-coated, and EMD-coated DFDBA particles. EMD significantly increased cell proliferation at 3 and 5 days after seeding for both osteoblasts and PDL cells compared to control and blood-coated samples. Moreover, there were significantly higher messenger ribonucleic acid levels of osteogenic differentiation markers, including collagen 1α1, alkaline phosphatase, and osteocalcin, in osteoblasts and PDL cells cultured on EMD-coated DFDBA particles at 3, 7, and 14 days. The results suggest that the addition of EMD to DFDBA particles may influence periodontal regeneration by stimulating PDL cell and osteoblast proliferation and differentiation.

76 FR 67228 - EMD Chemicals, Inc. Including On-Site Independent Contractors and Leased Workers From Ajilen...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-31

... Personnel, J&J Staffing, Accountemps/Robert Half, EMD Temps, Chromhelp, and Greentree Food Management... subject firm. The company reports that independent contract workers from Greentree Food Management were... Greentree Food Management working on-site at EMD Chemicals, Inc., Gibbstown, New Jersey. The amended notice...

Cathodic Polarization Coats Titanium Based Implant Materials with Enamel Matrix Derivate (EMD)

PubMed Central

Frank, Matthias J.; Walter, Martin S.; Rubert, Marina; Thiede, Bernd; Monjo, Marta; Reseland, Janne E.; Haugen, Håvard J.; Lyngstadaas, Ståle Petter

2014-01-01

The idea of a bioactive surface coating that enhances bone healing and bone growth is a strong focus of on-going research for bone implant materials. Enamel matrix derivate (EMD) is well documented to support bone regeneration and activates growth of mesenchymal tissues. Thus, it is a prime candidate for coating of existing implant surfaces. The aim of this study was to show that cathodic polarization can be used for coating commercially available implant surfaces with an immobilized but functional and bio-available surface layer of EMD. After coating, XPS revealed EMD-related bindings on the surface while SIMS showed incorporation of EMD into the surface. The hydride layer of the original surface could be activated for coating in an integrated one-step process that did not require any pre-treatment of the surface. SEM images showed nano-spheres and nano-rods on coated surfaces that were EMD-related. Moreover, the surface roughness remained unchanged after coating, as it was shown by optical profilometry. The mass peaks observed in the matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy (MALDI-TOF MS) analysis confirmed the integrity of EMD after coating. Assessment of the bioavailability suggested that the modified surfaces were active for osteoblast like MC3M3-E1 cells in showing enhanced Coll-1 gene expression and ALP activity. PMID:28788564

Subgrid-scale physical parameterization in atmospheric modeling: How can we make it consistent?

NASA Astrophysics Data System (ADS)

Yano, Jun-Ichi

2016-07-01

Approaches to subgrid-scale physical parameterization in atmospheric modeling are reviewed by taking turbulent combustion flow research as a point of reference. Three major general approaches are considered for its consistent development: moment, distribution density function (DDF), and mode decomposition. The moment expansion is a standard method for describing the subgrid-scale turbulent flows both in geophysics and engineering. The DDF (commonly called PDF) approach is intuitively appealing as it deals with a distribution of variables in subgrid scale in a more direct manner. Mode decomposition was originally applied by Aubry et al (1988 J. Fluid Mech. 192 115-73) in the context of wall boundary-layer turbulence. It is specifically designed to represent coherencies in compact manner by a low-dimensional dynamical system. Their original proposal adopts the proper orthogonal decomposition (empirical orthogonal functions) as their mode-decomposition basis. However, the methodology can easily be generalized into any decomposition basis. Among those, wavelet is a particularly attractive alternative. The mass-flux formulation that is currently adopted in the majority of atmospheric models for parameterizing convection can also be considered a special case of mode decomposition, adopting segmentally constant modes for the expansion basis. This perspective further identifies a very basic but also general geometrical constraint imposed on the massflux formulation: the segmentally-constant approximation. Mode decomposition can, furthermore, be understood by analogy with a Galerkin method in numerically modeling. This analogy suggests that the subgrid parameterization may be re-interpreted as a type of mesh-refinement in numerical modeling. A link between the subgrid parameterization and downscaling problems is also pointed out.

Esophageal Motor Disorders Are a Strong and Independant Associated Factor of Barrett’s Esophagus

PubMed Central

Bazin, Camille; Benezech, Alban; Alessandrini, Marine; Grimaud, Jean-Charles; Vitton, Veronique

2018-01-01

Background/Aims Esophageal motor disorder (EMD) has been shown to be associated with gastroesophageal reflux disease (GERD). However, the association of EMD with a Barrett’s esophagus (BE) is controversial. Our objective was to evaluate whether the presence of EMD was an independent factor associated with BE. Methods A retrospective case-control study was conducted in GERD patients who all had oeso-gastroduodenal endoscopy and high-resolution esophageal manometry. The clinical data collected was known or potential risk factors for BE: male gender, smoking and alcohol consumption, age, body mass index, presence of hiatal hernia, frequency, and age of GERD. EMD were classified according to the Chicago classification into: ineffective motor syndrome, fragmented peristalsis and absence of peristalsis, lower esophageal sphincter hypotonia. Results Two hundred and one patients (101 in the GERD + BE group and 100 in the GERD without BE) were included. In univariate analysis, male gender, alcohol consumption, presence of hiatal hernia, and EMD appeared to be associated with the presence of BE. In a multivariate analysis, 3 independent factors were identified: the presence of EMD (odds ratio [OR], 3.99; 95% confidence interval [CI], 1.71–9.28; P = 0.001), the presence of hiatal hernia (OR, 5.60; 95% CI, 2.45–12.76; P < 0.001), Helicobacter pylori infection (OR, 0.08; 95% CI, 0.01–0.84; P = 0.035). Conclusions The presence of EMD (particularly ineffective motor syndrome and lower esophageal sphincter hypotonia) is a strong independent associated factor of BE. Searching systematically for an EMD in patients suffering from GERD could be a new strategy to organize the endoscopic follow-up. PMID:29605977

Premature Osteoblast Clustering by Enamel Matrix Proteins Induces Osteoblast Differentiation through Up-Regulation of Connexin 43 and N-Cadherin

PubMed Central

Miron, Richard J.; Hedbom, Erik; Ruggiero, Sabrina; Bosshardt, Dieter D.; Zhang, Yufeng; Mauth, Corinna; Gemperli, Anja C.; Iizuka, Tateyuki; Buser, Daniel; Sculean, Anton

2011-01-01

In recent years, enamel matrix derivative (EMD) has garnered much interest in the dental field for its apparent bioactivity that stimulates regeneration of periodontal tissues including periodontal ligament, cementum and alveolar bone. Despite its widespread use, the underlying cellular mechanisms remain unclear and an understanding of its biological interactions could identify new strategies for tissue engineering. Previous in vitro research has demonstrated that EMD promotes premature osteoblast clustering at early time points. The aim of the present study was to evaluate the influence of cell clustering on vital osteoblast cell-cell communication and adhesion molecules, connexin 43 (cx43) and N-cadherin (N-cad) as assessed by immunofluorescence imaging, real-time PCR and Western blot analysis. In addition, differentiation markers of osteoblasts were quantified using alkaline phosphatase, osteocalcin and von Kossa staining. EMD significantly increased the expression of connexin 43 and N-cadherin at early time points ranging from 2 to 5 days. Protein expression was localized to cell membranes when compared to control groups. Alkaline phosphatase activity was also significantly increased on EMD-coated samples at 3, 5 and 7 days post seeding. Interestingly, higher activity was localized to cell cluster regions. There was a 3 fold increase in osteocalcin and bone sialoprotein mRNA levels for osteoblasts cultured on EMD-coated culture dishes. Moreover, EMD significantly increased extracellular mineral deposition in cell clusters as assessed through von Kossa staining at 5, 7, 10 and 14 days post seeding. We conclude that EMD up-regulates the expression of vital osteoblast cell-cell communication and adhesion molecules, which enhances the differentiation and mineralization activity of osteoblasts. These findings provide further support for the clinical evidence that EMD increases the speed and quality of new bone formation in vivo. PMID:21858092

Mode decomposition and Lagrangian structures of the flow dynamics in orbitally shaken bioreactors

NASA Astrophysics Data System (ADS)

Weheliye, Weheliye Hashi; Cagney, Neil; Rodriguez, Gregorio; Micheletti, Martina; Ducci, Andrea

2018-03-01

In this study, two mode decomposition techniques were applied and compared to assess the flow dynamics in an orbital shaken bioreactor (OSB) of cylindrical geometry and flat bottom: proper orthogonal decomposition and dynamic mode decomposition. Particle Image Velocimetry (PIV) experiments were carried out for different operating conditions including fluid height, h, and shaker rotational speed, N. A detailed flow analysis is provided for conditions when the fluid and vessel motions are in-phase (Fr = 0.23) and out-of-phase (Fr = 0.47). PIV measurements in vertical and horizontal planes were combined to reconstruct low order models of the full 3D flow and to determine its Finite-Time Lyapunov Exponent (FTLE) within OSBs. The combined results from the mode decomposition and the FTLE fields provide a useful insight into the flow dynamics and Lagrangian coherent structures in OSBs and offer a valuable tool to optimise bioprocess design in terms of mixing and cell suspension.

Introduction

USGS Publications Warehouse

Warwick, Peter D.

2007-01-01

The inevitable increase in demand and continuing depletion of accessible oil and gas resources during the 21st century will cause greater dependence on energy minerals such as coal, uranium, and unconventional sources of oil and natural gas to satisfy our increasing energy needs. The Energy Minerals Division (EMD) of the American Association of Petroleum Geologists (AAPG) is a membership-based technical interest group with goals to: (1) advance the science of geology, especially as it relates to exploration, discovery, and production of mineral resources and subsurface gas and liquids (other than conventional oil and gas) for energy-related purposes; (2) foster the spirit of scientific research; (3) disseminate information related to the geology of energy minerals and the associated technology of energy mineral resources extraction; and (4) advance the professional wellbeing of its members. This article contains a brief summary of some of the 2006 annual committee reports presented to the EMD Leadership. These reports are available to the EMD Membership at http://emd.aapg.org/members_only. This collection of short reports is presented here by the EMD as a service to the general geologic community and to simulate interest in the focus technical areas of EMD.

In vitro studies on human periodontal ligament stem cell sheets enhanced by enamel matrix derivative.

PubMed

Wang, Zhongshan; Feng, Zhihong; Wu, Guofeng; Bai, Shizhu; Dong, Yan; Zhao, Yimin

2016-05-01

Numerous preclinical and clinical studies have focused on the periodontal regenerative functions of enamel matrix derivative (EMD), a heat-treated preparation derived from enamel matrix proteins (EMPs) of developing porcine teeth. In this study, periodontal ligament (PDL) stem cells (PDLSCs) were isolated, and the effects of EMD on the extracorporeal induction process and the characteristics of PDLSC sheets were investigated for their potential as a more effective stem-cell therapy. EMD-enhanced cell sheets could be induced by complete medium supplemented with 50 μg/mL vitamin C and 100 μg/mL EMD. The EMD-enhanced cell sheets appeared thicker and more compact than the normal PDLSC sheets, demonstrated more layers of cells (3-7 layers), secreted richer extracellular matrix (ECM), showed varying degrees of increases in mRNA expression of periodontal tissue-specific genes (COL I, POSTN), calcification-related genes (RUNX2, OPN, OCN) and a cementum tissue-specific gene (CAP), and possessed a better mineralization ability in terms of osteogenic differentiation in vitro. These EMD-enhanced cell sheets may represent a potential option for stem-cell therapy for PDL regeneration. Copyright © 2016 Elsevier B.V. All rights reserved.

Relationship between esophageal clinical symptoms and manometry findings in patients with esophageal motility disorders: a cross-sectional study.

PubMed

FakhreYaseri, Hashem; FakhreYaseri, Ali Mohammad; Baradaran Moghaddam, Ali; Soltani Arabshhi, Seyed Kamran

2015-01-01

Manometry is the gold-standard diagnostic test for motility disorders in the esophagus. The development of high-resolution manometry catheters and software displays of manometry recordings in color-coded pressure plots have changed the diagnostic assessment of esophageal disease. The diagnostic value of particular esophageal clinical symptoms among patients suspected of esophageal motor disorders (EMDs) is still unknown. The aim of this study was to explore the sensitivity, specificity, and predictive accuracy of presenting esophageal symptoms between abnormal and normal esophageal manometry findings. We conducted a cross-sectional study of 623 patients aged 11-80 years. Data were collected from clinical examinations as well as patient questionnaires. The sensitivity, specificity, and accuracy were calculated after high-resolution manometry plots were reviewed according to the most recent Chicago Criteria. The clinical symptoms were not sensitive enough to discriminate between EMDs. Nevertheless, dysphagia, noncardiac chest pain, hoarseness, vomiting, and weight loss had high specificity and high accuracy to distinguish EMDs from normal findings. Regurgitation and heartburn did not have good accuracy for the diagnosis of EMDs. Clinical symptoms are not reliable enough to discriminate between EMDs. Clinical symptoms can, however, discriminate between normal findings and EMDs, especially achalasia.

Eye movement dysfunction in first-degree relatives of patients with schizophrenia: a meta-analytic evaluation of candidate endophenotypes.

PubMed

Calkins, Monica E; Iacono, William G; Ones, Deniz S

2008-12-01

Several forms of eye movement dysfunction (EMD) are regarded as promising candidate endophenotypes of schizophrenia. Discrepancies in individual study results have led to inconsistent conclusions regarding particular aspects of EMD in relatives of schizophrenia patients. To quantitatively evaluate and compare the candidacy of smooth pursuit, saccade and fixation deficits in first-degree biological relatives, we conducted a set of meta-analytic investigations. Among 18 measures of EMD, memory-guided saccade accuracy and error rate, global smooth pursuit dysfunction, intrusive saccades during fixation, antisaccade error rate and smooth pursuit closed-loop gain emerged as best differentiating relatives from controls (standardized mean differences ranged from .46 to .66), with no significant differences among these measures. Anticipatory saccades, but no other smooth pursuit component measures were also increased in relatives. Visually-guided reflexive saccades were largely normal. Moderator analyses examining design characteristics revealed few variables affecting the magnitude of the meta-analytically observed effects. Moderate effect sizes of relatives v. controls in selective aspects of EMD supports their endophenotype potential. Future work should focus on facilitating endophenotype utility through attention to heterogeneity of EMD performance, relationships among forms of EMD, and application in molecular genetics studies.

Preliminary flight test results of the F100 EMD engine in an F-15 airplane

NASA Technical Reports Server (NTRS)

Myers, L. P.; Burcham, F. W., Jr.

1984-01-01

A flight evaluation of the F100 Engine Model Derivative (EMD) is conducted. The F100 EMD is an advanced version of the F100 engine that powers the F15 and F16 airplanes. The F100 EMD features a bigger fan, higher temperature turbine, a Digital Electronic Engine Control system (DEEC), and a newly designed 16 segment afterburner, all of which results in a 15 to 20 percent increase in sea level thrust. The flight evaluations consist of investigation of performance (thrust, fuel flow, and airflow) and operability (transient response and airstart) in the F-15 airplane. The performance of the F100 EMD is excellent. Aircraft acceleration time to Mach 2.0 is reduced by 23 percent with two F100 EMD engines. Several anomalies are discovered in the operability evaluations. A software change to the DEEC improved the throttle, and subsequent Cooper Harper ratings of 3 to 4 are obtained. In the extreme upper left hand corner of the flight enveloped, compressor stalls occurr when the throttle is retarded to idle power. These stalls are not predicted by altitude facility tests or stability for the compressor.

Comparison of two interpolation methods for empirical mode decomposition based evaluation of radiographic femur bone images.

PubMed

Udhayakumar, Ganesan; Sujatha, Chinnaswamy Manoharan; Ramakrishnan, Swaminathan

2013-01-01

Analysis of bone strength in radiographic images is an important component of estimation of bone quality in diseases such as osteoporosis. Conventional radiographic femur bone images are used to analyze its architecture using bi-dimensional empirical mode decomposition method. Surface interpolation of local maxima and minima points of an image is a crucial part of bi-dimensional empirical mode decomposition method and the choice of appropriate interpolation depends on specific structure of the problem. In this work, two interpolation methods of bi-dimensional empirical mode decomposition are analyzed to characterize the trabecular femur bone architecture of radiographic images. The trabecular bone regions of normal and osteoporotic femur bone images (N = 40) recorded under standard condition are used for this study. The compressive and tensile strength regions of the images are delineated using pre-processing procedures. The delineated images are decomposed into their corresponding intrinsic mode functions using interpolation methods such as Radial basis function multiquadratic and hierarchical b-spline techniques. Results show that bi-dimensional empirical mode decomposition analyses using both interpolations are able to represent architectural variations of femur bone radiographic images. As the strength of the bone depends on architectural variation in addition to bone mass, this study seems to be clinically useful.

Don't Look Now - Tiltrotors Are Coming!

NASA Technical Reports Server (NTRS)

Dugan, Daniel C.; Hindson, William S. (Technical Monitor)

1997-01-01

This paper traces the history of tiltrotors, beginning with the XV-3 and XV-15, to the Marine's V-22 Osprey. The design of the first civil tiltrotor, the Bell-Boeing 609, is now complete and the revolutionary aircraft will debut in 2001 after completion of a rigorous test program. The XV-3 proved the safety and ease of transition from the helicopter mode to the airplane mode; however, it had aeroelastic stability and performance problems. The XV-15 was the tiltrotor of the late 70s and the 80s. In 1981, it was demonstrated to the international aviation community at the Paris Air Show. Its success led to the development of the V-22 Osprey and it returned to Paris in 1995 in the livery of a civil tiltrotor. There, it flew joint demonstrations with the Osprey. One is still flying today as a Civil Tiltrotor (CTR) demonstrator. The V-22 first flew in Mar 1989. After a stormy procurement cycle, the Full Scale Development (FSD) aircraft were superseded by the improved Engineering and Manufacturing Development (EMD) aircraft. The #7 Osprey made its first flight eight years later. Many highlights of the FSD and EMD flight test programs will be covered and illustrated with video clips. The Bell-Boeing 609 design was unveiled at the Smithsonian in November, 1996 and the first orders have been taken for this 250 knot, corporate size tiltrotor. Flight testing of this innovative aircraft will commence in 1999.

Self-organizing map network-based precipitation regionalization for the Tibetan Plateau and regional precipitation variability

NASA Astrophysics Data System (ADS)

Wang, Nini; Yin, Jianchuan

2017-12-01

A precipitation-based regionalization for the Tibetan Plateau (TP) was investigated for regional precipitation trend analysis and frequency analysis using data from 1113 grid points covering the period 1900-2014. The results utilizing self-organizing map (SOM) network suggest that four clusters of precipitation coherent zones can be identified, including the southwestern edge, the southern edge, the southeastern region, and the north central region. Regionalization results of the SOM network satisfactorily represent the influences of the atmospheric circulation systems such as the East Asian summer monsoon, the south Asian summer monsoon, and the mid-latitude westerlies. Regionalization results also well display the direct impacts of physical geographical features of the TP such as orography, topography, and land-sea distribution. Regional-scale annual precipitation trend as well as regional differences of annual and seasonal total precipitation were investigated by precipitation index such as precipitation concentration index (PCI) and Standardized Anomaly Index (SAI). Results demonstrate significant negative long-term linear trends in southeastern TP and the north central part of the TP, indicating arid and semi-arid regions in the TP are getting drier. The empirical mode decomposition (EMD) method shows an evolution of the main cycle with 4 and 12 months for all the representative grids of four sub-regions. The cross-wavelet analysis suggests that predominant and effective period of Indian Ocean Dipole (IOD) on monthly precipitation is around ˜12 months, except for the representative grid of the northwestern region.

The Effects of El Niño on Precipitation in Southern California Climate Divisions: Year 2016 Precipitation Forecast.

NASA Astrophysics Data System (ADS)

Perez Cruz, L.; Idris, N.; El-Askary, H. M.

2015-12-01

Recently, it has been reported by the National Oceanic and Atmospheric Administration (NOAA) that there is very high chance not only for El Niño to continue through Northern Hemisphere winter 2015-16, but also a remarkable chance for El Niño to last into early spring 2016. This research aims at: 1) investigating the impact of El Niño on precipitation in the Southern California Climate Divisions: Climate Division 6 South Coast Drainage, and Division 7 South Coast Desert Basin. 2) Analyzing the precipitation of Southern California region using the Empirical Mode Decomposition Method (EMD). 3) Looking at the SOI components and compare it with the precipitation components of Southern California Climate Divisions. 4) Comparing precipitation data with Niño indices: Niño 1+2, Niño 3, Nino 3.4, and Niño 4. As results, we found a significant cross correlation of 0.7 between SOI component 10 and precipitation component 10 in Climate Division 6. Furthermore, among all the Niño indices, Niño 3 region displayed the best correlation. When we compared precipitation division 7 component 9 with Niño 3 component 10, a 0.95 cross correlation value was obtained. The lowest cross correlation value of (0.33) was obtained from Climate Division 6, precipitation component 7 with Niño 4 component 7.

Synthesis, characterization and application of doped electrolytic manganese dioxides

NASA Astrophysics Data System (ADS)

Jantscher, Wolfgang; Binder, Leo; Fiedler, Dirk A.; Andreaus, Reinhard; Kordesch, Karl

Electrolytic manganese dioxides (EMDs) were prepared on the 100 g scale by anodic deposition from acidic aqueous solutions of manganese sulfate. In situ doping with titanium ions was achieved by addition of tetra- n-butoxytitanium to the electrolytic bath. Samples were also doped ex situ by washing the products with aqueous barium hydroxide solution. The EMDs were characterized by electron microscopy studies and BET surface area determinations. Cyclic abrasive stripping voltammetry was successfully applied to evaluate the rechargeability of the newly synthesized undoped and doped EMDs in 9 M KOH. Relative discharge capacities at different depths of discharge (DOD) with respect to the first one-electron reduction of γ-MnO 2 are compared for different EMDs. At about 30% DOD, resulting relative discharge capacities show essentially the same trend as those measured in AA cells from about 10 to 20 discharge/charge cycles onwards. Accordingly, titanium-doped EMD was shown to exhibit superior charge retention and rechargeability when compared to the titanium-free samples.

A valuable approach to the use of electronic medical data in primary care research: Panning for gold.

PubMed

Barnett, Stephen; Henderson, Joan; Hodgkins, Adam; Harrison, Christopher; Ghosh, Abhijeet; Dijkmans-Hadley, Bridget; Britt, Helena; Bonney, Andrew

2017-05-01

Electronic medical data (EMD) from electronic health records of general practice computer systems have enormous research potential, yet many variables are unreliable. The aim of this study was to compare selected data variables from general practice EMD with a reliable, representative national dataset (Bettering the Evaluation and Care of Health (BEACH)) in order to validate their use for primary care research. EMD variables were compared with encounter data from the nationally representative BEACH program using χ 2 tests and robust 95% confidence intervals to test their validity (measure what they reportedly measure). The variables focused on for this study were patient age, sex, smoking status and medications prescribed at the visit. The EMD sample from six general practices in the Illawarra region of New South Wales, Australia, yielded data on 196,515 patient encounters. Details of 90,553 encounters were recorded in the 2013 BEACH dataset from 924 general practitioners. No significant differences in patient age ( p = 0.36) or sex ( p = 0.39) were found. EMD had a lower rate of current smokers and higher average scripts per visit, but similar prescribing distribution patterns. Validating EMD variables offers avenues for improving primary care delivery and measuring outcomes of care to inform clinical practice and health policy.

Adaptive Filtration of Physiological Artifacts in EEG Signals in Humans Using Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Grubov, V. V.; Runnova, A. E.; Hramov, A. E.

2018-05-01

A new method for adaptive filtration of experimental EEG signals in humans and for removal of different physiological artifacts has been proposed. The algorithm of the method includes empirical mode decomposition of EEG, determination of the number of empirical modes that are considered, analysis of the empirical modes and search for modes that contains artifacts, removal of these modes, and reconstruction of the EEG signal. The method was tested on experimental human EEG signals and demonstrated high efficiency in the removal of different types of physiological EEG artifacts.

Introduction

USGS Publications Warehouse

Warwick, Peter D.

2011-01-01

The Energy Minerals Division (EMD) of the American Association of Petroleum Geologists (AAPG) is a membership-based, technical interest group having the primary goal of advancing the science of geology, especially as it relates to exploration, discovery, and production of unconventional energy resources. Current research on unconventional energy resources is rapidly changing and exploration and development efforts for these resources are constantly expanding. Nine summaries derived from 2011 committee reports presented at the EMD Annual Meeting in Houston, Texas in April, 2011, are contained in this review. The complete set of committee reports is available to AAPG members at http://emd.aapg.org/members_only/ annual2011/index.cfm. This report updates the 2006 and 2009 EMD unconventional energy review published in this journal (American Association of Petroleum Geologists, Energy Minerals Division 2007, 2009).

Lamb Waves Decomposition and Mode Identification Using Matching Pursuit Method

DTIC Science & Technology

2009-01-01

Wigner - Ville distribution ( WVD ). However, WVD suffers from severe interferences, called cross-terms. Cross- terms are the area of a time-frequency...transform (STFT), wavelet transform, Wigner - Ville distribution , matching pursuit decomposition, etc. 1 Report Documentation Page Form ApprovedOMB No...MP decomposition using chirplet dictionary was applied to a simulated S0 mode Lamb wave shown previously in Figure 2a. Wigner - Ville distribution of

NASA Lewis F100 engine testing

NASA Technical Reports Server (NTRS)

Werner, R. A.; Willoh, R. G., Jr.; Abdelwahab, M.

1984-01-01

Two builds of an F100 engine model derivative (EMD) engine were evaluated for improvements in engine components and digital electronic engine control (DEEC) logic. Two DEEC flight logics were verified throughout the flight envelope in support of flight clearance for the F100 engine model derivative program (EMPD). A nozzle instability and a faster augmentor transient capability was investigated in support of the F-15 DEEC flight program. Off schedule coupled system mode fan flutter, DEEC nose-boom pressure correlation, DEEC station six pressure comparison, and a new fan inlet variable vane (CIVV) schedule are identified.

Application of empirical mode decomposition in removing fidgeting interference in doppler radar life signs monitoring devices.

PubMed

Mostafanezhad, Isar; Boric-Lubecke, Olga; Lubecke, Victor; Mandic, Danilo P

2009-01-01

Empirical Mode Decomposition has been shown effective in the analysis of non-stationary and non-linear signals. As an application in wireless life signs monitoring in this paper we use this method in conditioning the signals obtained from the Doppler device. Random physical movements, fidgeting, of the human subject during a measurement can fall on the same frequency of the heart or respiration rate and interfere with the measurement. It will be shown how Empirical Mode Decomposition can break the radar signal down into its components and help separate and remove the fidgeting interference.

Effects of enamel matrix derivative on the proliferation and osteogenic differentiation of human gingival mesenchymal stem cells

PubMed Central

2014-01-01

Introduction Gingiva-derived mesenchymal stem cells (GMSCs) have recently been harvested and applied for rebuilding lost periodontal tissue. Enamel matrix derivative (EMD) has been used for periodontal regeneration and the formation of new cementum with inserting collagen fibers; however, alveolar bone formation is minimal. Recently, EMD has been shown to enhance the proliferation and mineralization of human bone marrow mesenchymal stem cells. Because the gingival flap is the major component to cover the surgical wound, the effects of EMD on the proliferation and mineralization of GMSCs were evaluated in the present study. Methods After single cell suspension, the GMSCs were isolated from the connective tissues of human gingiva. The colony forming unit assay of the isolated GMSCs was measured. The expression of stem cell markers was examined by flow cytometry. The cellular telomerase activity was identified by polymerase chain reaction (PCR). The osteogenic, adipogenic and neural differentiations of the GMSCs were further examined. The cell proliferation was determined by MTS assay, while the expression of mRNA and protein for mineralization (including core binding factor alpha, cbfα-1; alkaline phosphatase, ALP; and osteocalcin, OC; ameloblastin, AMBN) were analyzed by real time-PCR, enzyme activity and confocal laser scanning microscopy. Results The cell colonies could be easily identified and the colony forming rates and the telomerase activities increased after passaging. The GMSCs expressed high levels of surface markers for CD73, CD90, and CD105, but showed low expression of STRO-1. Osteogenic, adipogenic and neural differentiations were successfully induced. The proliferation of GMSCs was increased after EMD treatment. ALP mRNA was significantly augmented by treating with EMD for 3 hours, whereas AMBN mRNA was significantly increased at 6 hours after EMD treatment. The gene expression of OC was enhanced at the dose of 100 μg/ml EMD at day 3. Increased protein expression for cbfα-1 at day 3, for ALP at day 5 and 7, and for OC at week 4 after the EMD treatments were observed. Conclusions Human GMSCs could be successfully isolated and identified. EMD treatments not only induced the proliferation of GMSCs but also enhanced their osteogenic differentiation after induction. PMID:24739572

Evaluation of atrial electromechanical delay and diastolic functions in patients with hyperthyroidism.

PubMed

Sokmen, Abdullah; Acar, Gurkan; Sokmen, Gulizar; Akcay, Ahmet; Akkoyun, Murat; Koroglu, Sedat; Nacar, Alper Bugra; Ozkaya, Mesut

2013-11-01

Hyperthyroidism is a well-known cause of atrial fibrillation (AF) which is associated with increased morbidity and mortality. Atrial electromechanical delay (EMD) is a significant predictor of AF. The aim of this study was to assess the atrial EMD and diastolic functions in subclinical and overt hyperthyroidism by using tissue Doppler imaging (TDI). The study population consisted of 3 groups: group I (30 healthy subjects), group II (38 patients with subclinical hyperthyroidism), and group III (25 patients with overt hyperthyroidism). Atrial electromechanical coupling was measured with TDI. Standard echocardiographic measurements and parameters of diastolic function were obtained by conventional echocardiography and TDI. Intra- and inter-atrial EMD were significantly prolonged in subclinical and overt hyperthyroidism compared with control group (P = 0.03 and P < 0.001 for intra-atrial EMD; P < 0.001 for inter-atrial EMD). In groups II and III, mitral A velocity (P = 0.005 and P = 0.001) and mitral E-wave deceleration time (P < 0.001 and P = 0.02) were significantly increased, and mitral E/A ratio (P = 0.005 and P = 0.001) was significantly decreased compared with the control group. The lateral mitral Em /Am ratio in group II and group III was significantly lower than controls (P = 0.001). Mitral Em /Am ratio (β = -0.32, P = 0.002) and thyroid stimulating hormone (TSH) level (β = -0.27, P = 0.009) were negatively and independently correlated with inter-atrial EMD. This study showed that intra- and inter-atrial electromechanical intervals were prolonged and diastolic function was impaired in both overt and subclinical hyperthyroidism. TSH level and mitral Em /Am ratio were found as independent predictors of atrial EMD. © 2013, Wiley Periodicals, Inc.

Esophageal motor disorders are frequent during pre and post lung transplantation. Can they influence lung rejection?

PubMed

Ciriza de Los Ríos, Constanza; Canga Rodríguez-Valcárcel, Fernando; de Pablo Gafas, Alicia; Castel de Lucas, Isabel; Lora Pablos, David; Castellano Tortajada, Gregorio

2018-06-01

lung transplantation (LTx) is a viable option for most patients with end-stage lung diseases. Esophageal motor disorders (EMD) are frequent in candidates for LTx, but there is very little data about changes in esophageal motility post-LTx. the aim of our study was to assess esophageal motor disorders by high resolution manometry (HRM) both pre-LTx and six months post-LTx in patients with and without organ rejection. HRM (Manoscan®) was performed in 57 patients both pre-LTx and six months post-LTx. HRM plots were analyzed according to the Chicago classification 3.0. EMD were found in 33.3% and in 49.1% of patients pre-LTx and post-LTx, respectively, and abnormal peristalsis was more frequently found post-LTx (p = 0.018). Hypercontractile esophagus was frequently found post-LTx (1.8% and 19.3% pre-LTx and post-LTx, respectively). Esophagogastric junction (EGJ) morphology changed significantly pre-LTx and post-LTx; type I (normal) was more frequent post-LTx (63-2% and 82.5% respectively, p = 0.007). EMD were more frequent post-LTx in both the non-rejection and rejection group, although particularly in the rejection group (43.2% and 69.2% respectively, p = 0.09). EMD such as distal spasm, hypercontractile esophagus and EGJ outflow obstruction were also observed more frequently post-LTx in the rejection group. significant changes in esophageal motility were observed pre-LTx and particularly post-LTx; hypercontractile esophagus was a frequent EMD found post-LTx. EMD were more frequent in the group of patients that experienced organ rejection compared to the non-rejection group. EMD leading to an impaired esophageal clearance should be considered as an additional factor that contributes to LTx failure.

Microbiological and clinical effects of enamel matrix derivative and sustained-release micro-spherical minocycline application as an adjunct to non-surgical therapy in peri-implant mucosal inflammation.

PubMed

Faramarzi, Masumeh; Goharfar, Zahra; Pourabbas, Reza; Kashefimehr, Atabak; Shirmohmmadi, Adileh

2015-08-01

The purpose of this study was to compare the microbial and clinical effects of mechanical debridement (MD) alone or in combination with the application of enamel matrix derivative (EMD) and sustained-release micro-spherical minocycline (MSM) for treatment of peri-implant mucosal infl ammation (PIMI). Subjects with at least one implant with PIMI were included and divided into control and two different test groups. In all three groups, MD was performed. In the MSM group, following MD, MSM was placed subgingivally around the implants. In the EMD group, after MD, EMD was placed in the sulcus around the implants. Sampling of peri-implant crevicular fl uid for microbial analysis with real-time polymerase chain reaction and recording of probing depth (PD) and bleeding on probing (BOP) were performed prior to as well as two weeks and three months after treatment. Median values and interquartile range were estimated for each variable during the various assessment intervals of the study. In all groups, at two weeks and three months, the counts of Porphyromonas gingivalis decreased significantly compared to baseline. Levels of P. gingivalis were significantly reduced in MSM (P<0.001) and EMD (P=0.026) groups compared to the control group. Also, clinical parameters improved significantly at two weeks and three months. Reduction of PD was significant in MSM (P<0.001) and EMD (P<0.001) groups. The decrease in BOP in the MSM, EMD, and control groups was 60%, 50%, and 20%, respectively. The use of MSM and EMD can be an adjunctive treatment for management of PIMI and improves clinical parameters and reduces P. gingivalis burden three months after treatment.

Xenogenous Collagen Matrix and/or Enamel Matrix Derivative for Treatment of Localized Gingival Recessions: A Randomized Clinical Trial. Part II: Patient-Reported Outcomes.

PubMed

Rocha Dos Santos, Manuela; Sangiorgio, João Paulo Menck; Neves, Felipe Lucas da Silva; França-Grohmann, Isabela Lima; Nociti, Francisco Humberto; Silverio Ruiz, Karina Gonzales; Santamaria, Mauro Pedrine; Sallum, Enilson Antonio

2017-12-01

Gingival recession (GR) might be associated with patient discomfort due to cervical dentin hypersensitivity (CDH) and esthetic dissatisfaction. The aim is to evaluate the effect of root coverage procedure with a xenogenous collagen matrix (CM) and/or enamel matrix derivative (EMD) in combination with a coronally advanced flap (CAF) on CDH, esthetics, and oral health-related quality of life (OHRQoL) of patients with GR. Sixty-eight participants with single Miller Class I/II GRs were treated with CAF (n = 17), CAF + CM (n = 17), CAF + EMD (n = 17), and CAF + CM + EMD (n = 17). CDH was assessed by evaporative stimuli using a visual analog scale (VAS) and a Schiff scale. Esthetics outcome was assessed with VAS and the Questionnaire of Oral Esthetic Satisfaction. Oral Health Impact Profile-14 (OHIP-14) questionnaire was used to assess OHRQoL. All parameters were evaluated at baseline and after 6 months. Intragroup analysis showed statistically significant reduction in CDH and esthetic dissatisfaction with no intergroup significant differences (P >0.05). The impact of oral health on QoL after 6 months was significant for CAF + CM, CAF + EMD, and CAF + CM + EMD (P <0.05). Total OHIP-14 score and psychologic discomfort, psychologic disability, social disability, and handicap dimensions showed negative correlation with esthetics. OHIP-14 physical pain dimension had positive correlation with CDH (P <0.05). OHIP-14 showed no correlation with percentage of root coverage, keratinized tissue width, or keratinized tissue thickness (P >0.05). Root coverage procedures improve patient OHRQoL by impacting on a wide range of dimensions, perceived after reduction of CDH and esthetic dissatisfaction of patients with GRs treated with CAF + CM, CAF + EMD, and CAF + CM + EMD.

Cellular Effects of Enamel Matrix Derivative Are Associated With Specific Protein Components

DTIC Science & Technology

2005-05-01

porcine teeth. Although EMD has been shown to enhance both soft tissue healing and regeneration of the periodontium, the mechanism of this action is still...to regenerate periodontal tissues that have been lost due to disease. The effectiveness of EMD has been proven both clinically and histologically in...however, a contrarian study implied that there is no difference in soft - tissue wound healing following periodontal surgery with the use of EMD (Hagenaars

Collagen Membranes Adsorb the Transforming Growth Factor-β Receptor I Kinase-Dependent Activity of Enamel Matrix Derivative.

PubMed

Stähli, Alexandra; Miron, Richard J; Bosshardt, Dieter D; Sculean, Anton; Gruber, Reinhard

2016-05-01

Enamel matrix derivative (EMD) and collagen membranes (CMs) are simultaneously applied in regenerative periodontal surgery. The aim of this study is to evaluate the ability of two CMs and a collagen matrix to adsorb the activity intrinsic to EMD that provokes transforming growth factor (TGF)-β signaling in oral fibroblasts. Three commercially available collagen products were exposed to EMD or recombinant TGF-β1, followed by vigorous washing. Oral fibroblasts were either seeded directly onto collagen products or were incubated with the respective supernatant. Expression of TGF-β target genes interleukin (IL)-11 and proteoglycan 4 (PRG4) was evaluated by real time polymerase chain reaction. Proteomic analysis was used to study the fraction of EMD proteins binding to collagen. EMD or TGF-β1 provoked a significant increase of IL-11 and PRG4 expression of oral fibroblasts when seeded onto collagen products and when incubated with the respective supernatant. Gene expression was blocked by the TGF-β receptor I kinase inhibitor SB431542. Amelogenin bound most abundantly to gelatin-coated culture dishes. However, incubation of palatal fibroblasts with recombinant amelogenin did not alter expression of IL-11 and PRG4. These in vitro findings suggest that collagen products adsorb a TGF-β receptor I kinase-dependent activity of EMD and make it available for potential target cells.

Proline-Rich Peptide Mimics Effects of Enamel Matrix Derivative on Rat Oral Mucosa Incisional Wound Healing.

PubMed

Villa, Oscar; Wohlfahrt, Johan C; Mdla, Ibrahimu; Petzold, Christiane; Reseland, Janne E; Snead, Malcolm L; Lyngstadaas, Staale P

2015-12-01

Proline-rich peptides have been shown to promote periodontal regeneration. However, their effect on soft tissue wound healing has not yet been investigated. The aim of this study is to evaluate the effect of enamel matrix derivative (EMD), tyrosine-rich amelogenin peptide (TRAP), and a synthetic proline-rich peptide (P2) on acute wound healing after a full-thickness flap procedure in an incisional rat model. This experimental study has a split-mouth, randomized, placebo-controlled design. Test and control wounds were created on the palatal mucosa of 54 Sprague-Dawley rats. Wounds were histologically processed, and reepithelialization, leukocyte infiltration, and angiogenesis were assessed at days 1, 3, and 7 post-surgery. EMD and P2 significantly promoted early wound closure at day 1 (P <0.001 and P = 0.004, respectively). EMD maintained a significant acceleration of reepithelialization at day 3 (P = 0.004). Wounds treated by EMD and P2 showed increased angiogenesis during the first 3 days of healing (P = 0.03 and 0.001, respectively). Leukocyte infiltration was decreased in EMD-treated wounds at day 1 (P = 0.03), and P2 and TRAP induced a similar effect at days 3 (P = 0.002 and P <0.0001, respectively) and 7 (P = 0.005 and P <0.001). EMD and P2 promoted reepithelialization and neovascularization in full-thickness surgical wounds on rat oral mucosa.

Electromuscular incapacitating devices discharge and risk of severe bradycardia.

PubMed

Havranek, Stepan; Neuzil, Petr; Linhart, Ales

2015-06-01

Electromuscular incapacitating devices (EMDs) are high-voltage, low-current stimulators causing involuntary muscle contractions and sensory response. Existing evidence about cardiac effects of EMD remains inconclusive. The aim of our study was to analyze electrocardiographic, echocardiographic, and microvolt T-wave alternans (MTWA) changes induced by EMD discharge.We examined 26 volunteers (22 men; median age 30 years) who underwent single standard 5-second duration exposure to TASER X26 under continuous echocardiographic and electrocardiographic monitoring. Microvolt T-wave alternans testing was performed at baseline (MTWA-1), as well as immediately and 60 minutes after EMD exposure (MTWA-2 and MTWA-3, respectively).Mean heart rate (HR) increased significantly from 88 ± 17 beats per minute before to 129 ± 17 beats per minute after exposure (P < 0.001). However, in 2 individuals, an abrupt decrease in HR was observed. In one of them, interval between two consecutive beats increased up to 1.7 seconds during the discharge. New onset of supraventricular premature beats was observed after discharge in 1 patient. Results of MTWA-1, MTWA-2, and MTWA-3 tests were positive in one of the subjects, each time in a different case.Standard EMD exposure can be associated with a nonuniform reaction of HR and followed by heart rhythm disturbances. New MTWA positivity can reflect either the effect of EMD exposure or a potential false positivity of MTWA assessments.

Bio-inspired motion detection in an FPGA-based smart camera module.

PubMed

Köhler, T; Röchter, F; Lindemann, J P; Möller, R

2009-03-01

Flying insects, despite their relatively coarse vision and tiny nervous system, are capable of carrying out elegant and fast aerial manoeuvres. Studies of the fly visual system have shown that this is accomplished by the integration of signals from a large number of elementary motion detectors (EMDs) in just a few global flow detector cells. We developed an FPGA-based smart camera module with more than 10,000 single EMDs, which is closely modelled after insect motion-detection circuits with respect to overall architecture, resolution and inter-receptor spacing. Input to the EMD array is provided by a CMOS camera with a high frame rate. Designed as an adaptable solution for different engineering applications and as a testbed for biological models, the EMD detector type and parameters such as the EMD time constants, the motion-detection directions and the angle between correlated receptors are reconfigurable online. This allows a flexible and simultaneous detection of complex motion fields such as translation, rotation and looming, such that various tasks, e.g., obstacle avoidance, height/distance control or speed regulation can be performed by the same compact device.

Lumley decomposition of turbulent boundary layer at high Reynolds numbers

NASA Astrophysics Data System (ADS)

Tutkun, Murat; George, William K.

2017-02-01

The decomposition proposed by Lumley in 1966 is applied to a high Reynolds number turbulent boundary layer. The experimental database was created by a hot-wire rake of 143 probes in the Laboratoire de Mécanique de Lille wind tunnel. The Reynolds numbers based on momentum thickness (Reθ) are 9800 and 19 100. Three-dimensional decomposition is performed, namely, proper orthogonal decomposition (POD) in the inhomogeneous and bounded wall-normal direction, Fourier decomposition in the homogeneous spanwise direction, and Fourier decomposition in time. The first POD modes in both cases carry nearly 50% of turbulence kinetic energy when the energy is integrated over Fourier dimensions. The eigenspectra always peak near zero frequency and most of the large scale, energy carrying features are found at the low end of the spectra. The spanwise Fourier mode which has the largest amount of energy is the first spanwise mode and its symmetrical pair. Pre-multiplied eigenspectra have only one distinct peak and it matches the secondary peak observed in the log-layer of pre-multiplied velocity spectra. Energy carrying modes obtained from the POD scale with outer scaling parameters. Full or partial reconstruction of turbulent velocity signal based only on energetic modes or non-energetic modes revealed the behaviour of urms in distinct regions across the boundary layer. When urms is based on energetic reconstruction, there exists (a) an exponential decay from near wall to log-layer, (b) a constant layer through the log-layer, and (c) another exponential decay in the outer region. The non-energetic reconstruction reveals that urms has (a) an exponential decay from the near-wall to the end of log-layer and (b) a constant layer in the outer region. Scaling of urms using the outer parameters is best when both energetic and non-energetic profiles are combined.

Fast modal decomposition for optical fibers using digital holography.

PubMed

Lyu, Meng; Lin, Zhiquan; Li, Guowei; Situ, Guohai

2017-07-26

Eigenmode decomposition of the light field at the output end of optical fibers can provide fundamental insights into the nature of electromagnetic-wave propagation through the fibers. Here we present a fast and complete modal decomposition technique for step-index optical fibers. The proposed technique employs digital holography to measure the light field at the output end of the multimode optical fiber, and utilizes the modal orthonormal property of the basis modes to calculate the modal coefficients of each mode. Optical experiments were carried out to demonstrate the proposed decomposition technique, showing that this approach is fast, accurate and cost-effective.

Multi-focus image fusion based on window empirical mode decomposition

NASA Astrophysics Data System (ADS)

Qin, Xinqiang; Zheng, Jiaoyue; Hu, Gang; Wang, Jiao

2017-09-01

In order to improve multi-focus image fusion quality, a novel fusion algorithm based on window empirical mode decomposition (WEMD) is proposed. This WEMD is an improved form of bidimensional empirical mode decomposition (BEMD), due to its decomposition process using the adding window principle, effectively resolving the signal concealment problem. We used WEMD for multi-focus image fusion, and formulated different fusion rules for bidimensional intrinsic mode function (BIMF) components and the residue component. For fusion of the BIMF components, the concept of the Sum-modified-Laplacian was used and a scheme based on the visual feature contrast adopted; when choosing the residue coefficients, a pixel value based on the local visibility was selected. We carried out four groups of multi-focus image fusion experiments and compared objective evaluation criteria with other three fusion methods. The experimental results show that the proposed fusion approach is effective and performs better at fusing multi-focus images than some traditional methods.

The Army’s Armored Multi-Purpose Vehicle (AMPV): Background and Issues for Congress

DTIC Science & Technology

2017-01-11

award a five-year Engineering and Manufacturing Development (EMD) contract in May 2014 worth $458 million to a single contractor for 29 prototypes...had selected BAE Systems Land and Armaments L.P. as the winner of the EMD contract . The initial award is for 52 months, valued at about $382 million...289 vehicles for a total contract value of $1.2 billion. This EMD contract does not include EAB AMPV variants. The AMPV reportedly successfully

Diagnosis of combined faults in Rotary Machinery by Non-Naive Bayesian approach

NASA Astrophysics Data System (ADS)

Asr, Mahsa Yazdanian; Ettefagh, Mir Mohammad; Hassannejad, Reza; Razavi, Seyed Naser

2017-02-01

When combined faults happen in different parts of the rotating machines, their features are profoundly dependent. Experts are completely familiar with individuals faults characteristics and enough data are available from single faults but the problem arises, when the faults combined and the separation of characteristics becomes complex. Therefore, the experts cannot declare exact information about the symptoms of combined fault and its quality. In this paper to overcome this drawback, a novel method is proposed. The core idea of the method is about declaring combined fault without using combined fault features as training data set and just individual fault features are applied in training step. For this purpose, after data acquisition and resampling the obtained vibration signals, Empirical Mode Decomposition (EMD) is utilized to decompose multi component signals to Intrinsic Mode Functions (IMFs). With the use of correlation coefficient, proper IMFs for feature extraction are selected. In feature extraction step, Shannon energy entropy of IMFs was extracted as well as statistical features. It is obvious that most of extracted features are strongly dependent. To consider this matter, Non-Naive Bayesian Classifier (NNBC) is appointed, which release the fundamental assumption of Naive Bayesian, i.e., the independence among features. To demonstrate the superiority of NNBC, other counterpart methods, include Normal Naive Bayesian classifier, Kernel Naive Bayesian classifier and Back Propagation Neural Networks were applied and the classification results are compared. An experimental vibration signals, collected from automobile gearbox, were used to verify the effectiveness of the proposed method. During the classification process, only the features, related individually to healthy state, bearing failure and gear failures, were assigned for training the classifier. But, combined fault features (combined gear and bearing failures) were examined as test data. The achieved probabilities for the test data show that the combined fault can be identified with high success rate.

Decomposition of the complex system into nonlinear spatio-temporal modes: algorithm and application to climate data mining

NASA Astrophysics Data System (ADS)

Feigin, Alexander; Gavrilov, Andrey; Loskutov, Evgeny; Mukhin, Dmitry

2015-04-01

Proper decomposition of the complex system into well separated "modes" is a way to reveal and understand the mechanisms governing the system behaviour as well as discover essential feedbacks and nonlinearities. The decomposition is also natural procedure that provides to construct adequate and concurrently simplest models of both corresponding sub-systems, and of the system in whole. In recent works two new methods of decomposition of the Earth's climate system into well separated modes were discussed. The first method [1-3] is based on the MSSA (Multichannel Singular Spectral Analysis) [4] for linear expanding vector (space-distributed) time series and makes allowance delayed correlations of the processes recorded in spatially separated points. The second one [5-7] allows to construct nonlinear dynamic modes, but neglects delay of correlations. It was demonstrated [1-3] that first method provides effective separation of different time scales, but prevent from correct reduction of data dimension: slope of variance spectrum of spatio-temporal empirical orthogonal functions that are "structural material" for linear spatio-temporal modes, is too flat. The second method overcomes this problem: variance spectrum of nonlinear modes falls essentially sharply [5-7]. However neglecting time-lag correlations brings error of mode selection that is uncontrolled and increases with growth of mode time scale. In the report we combine these two methods in such a way that the developed algorithm allows constructing nonlinear spatio-temporal modes. The algorithm is applied for decomposition of (i) multi hundreds years globally distributed data generated by the INM RAS Coupled Climate Model [8], and (ii) 156 years time series of SST anomalies distributed over the globe [9]. We compare efficiency of different methods of decomposition and discuss the abilities of nonlinear spatio-temporal modes for construction of adequate and concurrently simplest ("optimal") models of climate systems. 1. Feigin A.M., Mukhin D., Gavrilov A., Volodin E.M., and Loskutov E.M. (2013) "Separation of spatial-temporal patterns ("climatic modes") by combined analysis of really measured and generated numerically vector time series", AGU 2013 Fall Meeting, Abstract NG33A-1574. 2. Alexander Feigin, Dmitry Mukhin, Andrey Gavrilov, Evgeny Volodin, and Evgeny Loskutov (2014) "Approach to analysis of multiscale space-distributed time series: separation of spatio-temporal modes with essentially different time scales", Geophysical Research Abstracts, Vol. 16, EGU2014-6877. 3. Dmitry Mukhin, Dmitri Kondrashov, Evgeny Loskutov, Andrey Gavrilov, Alexander Feigin, and Michael Ghil (2014) "Predicting critical transitions in ENSO models, Part II: Spatially dependent models", Journal of Climate (accepted, doi: 10.1175/JCLI-D-14-00240.1). 4. Ghil, M., R. M. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, et al. (2002) "Advanced spectral methods for climatic time series", Rev. Geophys. 40(1), 3.1-3.41. 5. Dmitry Mukhin, Andrey Gavrilov, Evgeny M Loskutov and Alexander M Feigin (2014) "Nonlinear Decomposition of Climate Data: a New Method for Reconstruction of Dynamical Modes", AGU 2014 Fall Meeting, Abstract NG43A-3752. 6. Andrey Gavrilov, Dmitry Mukhin, Evgeny Loskutov, and Alexander Feigin (2015) "Empirical decomposition of climate data into nonlinear dynamic modes", Geophysical Research Abstracts, Vol. 17, EGU2015-627. 7. Dmitry Mukhin, Andrey Gavrilov, Evgeny Loskutov, Alexander Feigin, and Juergen Kurths (2015) "Reconstruction of principal dynamical modes from climatic variability: nonlinear approach", Geophysical Research Abstracts, Vol. 17, EGU2015-5729. 8. http://83.149.207.89/GCM_DATA_PLOTTING/GCM_INM_DATA_XY_en.htm. 9. http://iridl.ldeo.columbia.edu/SOURCES/.KAPLAN/.EXTENDED/.v2/.ssta/.

A combined approach of enamel matrix derivative gel and autogenous bone grafts in treatment of intrabony periodontal defects. A case report.

PubMed

Leung, George; Jin, Lijian

2003-04-01

Enamel matrix derivative (EMD) has recently been introduced as a new modality in regenerative periodontal therapy. This case report demonstrates a combined approach in topical application of EMD gel (Emdogain) and autogenous bone grafts for treatment of intrabony defects and furcation involvement defects in a patient with chronic periodontitis. The seven-month post-surgery clinical and radiographic results were presented. The combined application of EMD gel with autogenous bone grafts in intrabony osseous defects resulted in clinically significant gain of attachment on diseased root surfaces and bone fill on radiographs. Further controlled clinical studies are required to confirm the long-term effectiveness of the combination of EMD gel and autogenous bone grafts in treatment of various osseous defects in subjects with chronic periodontitis.

Characterization of selected elementary motion detector cells to image primitives.

PubMed

Benson, Leslie A; Barrett, Steven F; Wright, Cameron H G

2008-01-01

Developing a visual sensing system, complete with motion processing hardware and software would have many applications to current technology. It could be mounted on many autonomous vehicles to provide information about the navigational environment, as well as obstacle avoidance features. Incorporating the motion processing capabilities into the sensor requires a new approach to the algorithm implementation. This research, and that of many others, have turned to nature for inspiration. Elementary motion detector (EMD) cells are involved in a biological preprocessing network to provide information to the motion processing lobes of the house degrees y Musca domestica. This paper describes the response of the photoreceptor inputs to the EMDs. The inputs to the EMD components are tested as they are stimulated with varying image primitives. This is the first of many steps in characterizing the EMD response to image primitives.

Enamel Matrix Derivative has No Effect on the Chondrogenic Differentiation of Mesenchymal Stem Cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Groeneveldt, Lisanne C.; Knuth, Callie; Witte-Bouma, Janneke

2014-09-02

Background: Treatment of large bone defects due to trauma, tumor resection, or congenital abnormalities is challenging. Bone tissue engineering using mesenchymal stem cells (MSCs) represents a promising treatment option. However, the quantity and quality of engineered bone tissue are not sufficient to fill large bone defects. The aim of this study was to determine if the addition of enamel matrix derivative (EMD) improves in vitro chondrogenic priming of MSCs to ultimately improve in vivo MSC mediated endochondral bone formation. Methods: MSCs were chondrogenically differentiated in 2.0 × 10{sup 5} cell pellets in medium supplemented with TGFβ3 in the absence ormore » presence of 1, 10, or 100 μg/mL EMD. Samples were analyzed for gene expression of RUNX2, Col II, Col X, and Sox9. Protein and glycoaminoglycan (GAG) production were also investigated via DMB assays, histology, and immunohistochemistry. Osteogenic and adipogenic differentiation capacity were also assessed. Results: The addition of EMD did not negatively affect chondrogenic differentiation of adult human MSCs. EMD did not appear to alter GAG production or expression of chondrogenic genes. Osteogenic and adipogenic differentiation were also unaffected though a trend toward decreased adipogenic gene expression was observed. Conclusion: EMD does not affect chondrogenic differentiation of adult human MSCs. As such the use of EMD in combination with chondrogenically primed MSCs for periodontal bone tissue repair is unlikely to have negative effects on MSC differentiation.« less

ACL injury risk in elite female youth soccer: Changes in neuromuscular control of the knee following soccer-specific fatigue.

PubMed

De Ste Croix, M B A; Priestley, A M; Lloyd, R S; Oliver, J L

2015-10-01

Fatigue is known to influence dynamic knee joint stability from a neuromuscular perspective, and electromechanical delay (EMD) plays an important role as the feedback activation mechanism that stabilizes the joint. The aim of this study was to investigate the influence of soccer-specific fatigue on EMD in U13-, U15-, and U17-year-old female soccer players. Thirty-six youth soccer players performed eccentric actions of the hamstrings in a prone position at 60, 120, and 180°/s before and after a soccer-specific fatigue trial. Surface electromyography was used to determine EMD from the semitendinosus, biceps femoris and gastrocnemius. A time × age × muscle × velocity repeated measures analysis of variance was used to explore the influence of fatigue on EMD. A significant main effect for time (P = 0.001) indicated that EMD was significantly longer post- compared with pre-fatigue (58.4% increase). A significant time × group interaction effect (P = 0.046) indicated EMD was significantly longer in the U13 age group compared with the U15 (P = 0.011) and U17 (P = 0.021) groups and greater post-fatigue. Soccer-specific fatigue compromised neuromuscular feedback mechanisms and the age-related effects may represent a more compliant muscle-tendon system in younger compared with older girls, increasing risk of injury. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Optimal Averages for Nonlinear Signal Decompositions - Another Alternative for Empirical Mode Decomposition

DTIC Science & Technology

2014-10-01

nonlinear and non-stationary signals. It aims at decomposing a signal, via an iterative sifting procedure, into several intrinsic mode functions ...stationary signals. It aims at decomposing a signal, via an iterative sifting procedure into several intrinsic mode functions (IMFs), and each of the... function , optimization. 1 Introduction It is well known that nonlinear and non-stationary signal analysis is important and difficult. His- torically

Approaches for Subgrid Parameterization: Does Scaling Help?

NASA Astrophysics Data System (ADS)

Yano, Jun-Ichi

2016-04-01

Arguably the scaling behavior is a well-established fact in many geophysical systems. There are already many theoretical studies elucidating this issue. However, the scaling law is slow to be introduced in "operational" geophysical modelling, notably for weather forecast as well as climate projection models. The main purpose of this presentation is to ask why, and try to answer this question. As a reference point, the presentation reviews the three major approaches for traditional subgrid parameterization: moment, PDF (probability density function), and mode decomposition. The moment expansion is a standard method for describing the subgrid-scale turbulent flows both in the atmosphere and the oceans. The PDF approach is intuitively appealing as it directly deals with a distribution of variables in subgrid scale in a more direct manner. The third category, originally proposed by Aubry et al (1988) in context of the wall boundary-layer turbulence, is specifically designed to represent coherencies in compact manner by a low--dimensional dynamical system. Their original proposal adopts the proper orthogonal decomposition (POD, or empirical orthogonal functions, EOF) as their mode-decomposition basis. However, the methodology can easily be generalized into any decomposition basis. The mass-flux formulation that is currently adopted in majority of atmospheric models for parameterizing convection can also be considered a special case of the mode decomposition, adopting the segmentally-constant modes for the expansion basis. The mode decomposition can, furthermore, be re-interpreted as a type of Galarkin approach for numerically modelling the subgrid-scale processes. Simple extrapolation of this re-interpretation further suggests us that the subgrid parameterization problem may be re-interpreted as a type of mesh-refinement problem in numerical modelling. We furthermore see a link between the subgrid parameterization and downscaling problems along this line. The mode decomposition approach would also be the best framework for linking between the traditional parameterizations and the scaling perspectives. However, by seeing the link more clearly, we also see strength and weakness of introducing the scaling perspectives into parameterizations. Any diagnosis under a mode decomposition would immediately reveal a power-law nature of the spectrum. However, exploiting this knowledge in operational parameterization would be a different story. It is symbolic to realize that POD studies have been focusing on representing the largest-scale coherency within a grid box under a high truncation. This problem is already hard enough. Looking at differently, the scaling law is a very concise manner for characterizing many subgrid-scale variabilities in systems. We may even argue that the scaling law can provide almost complete subgrid-scale information in order to construct a parameterization, but with a major missing link: its amplitude must be specified by an additional condition. The condition called "closure" in the parameterization problem, and known to be a tough problem. We should also realize that the studies of the scaling behavior tend to be statistical in the sense that it hardly provides complete information for constructing a parameterization: can we specify the coefficients of all the decomposition modes by a scaling law perfectly when the first few leading modes are specified? Arguably, the renormalization group (RNG) is a very powerful tool for reducing a system with a scaling behavior into a low dimension, say, under an appropriate mode decomposition procedure. However, RNG is analytical tool: it is extremely hard to apply it to real complex geophysical systems. It appears that it is still a long way to go for us before we can begin to exploit the scaling law in order to construct operational subgrid parameterizations in effective manner.

On the intrinsic timescales of temporal variability in measurements of the surface solar radiation

NASA Astrophysics Data System (ADS)

Bengulescu, Marc; Blanc, Philippe; Wald, Lucien

2018-01-01

This study is concerned with the intrinsic temporal scales of the variability in the surface solar irradiance (SSI). The data consist of decennial time series of daily means of the SSI obtained from high-quality measurements of the broadband solar radiation impinging on a horizontal plane at ground level, issued from different Baseline Surface Radiation Network (BSRN) ground stations around the world. First, embedded oscillations sorted in terms of increasing timescales of the data are extracted by empirical mode decomposition (EMD). Next, Hilbert spectral analysis is applied to obtain an amplitude-modulation-frequency-modulation (AM-FM) representation of the data. The time-varying nature of the characteristic timescales of variability, along with the variations in the signal intensity, are thus revealed. A novel, adaptive null hypothesis based on the general statistical characteristics of noise is employed in order to discriminate between the different features of the data, those that have a deterministic origin and those being realizations of various stochastic processes. The data have a significant spectral peak corresponding to the yearly variability cycle and feature quasi-stochastic high-frequency variability components, irrespective of the geographical location or of the local climate. Moreover, the amplitude of this latter feature is shown to be modulated by variations in the yearly cycle, which is indicative of nonlinear multiplicative cross-scale couplings. The study has possible implications on the modeling and the forecast of the surface solar radiation, by clearly discriminating the deterministic from the quasi-stochastic character of the data, at different local timescales.

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform.

PubMed

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-12-02

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

PubMed Central

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-01-01

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works. PMID:27918414

A study of Bangladesh's sub-surface water storages using satellite products and data assimilation scheme.

PubMed

Khaki, M; Forootan, E; Kuhn, M; Awange, J; Papa, F; Shum, C K

2018-06-01

Climate change can significantly influence terrestrial water changes around the world particularly in places that have been proven to be more vulnerable such as Bangladesh. In the past few decades, climate impacts, together with those of excessive human water use have changed the country's water availability structure. In this study, we use multi-mission remotely sensed measurements along with a hydrological model to separately analyze groundwater and soil moisture variations for the period 2003-2013, and their interactions with rainfall in Bangladesh. To improve the model's estimates of water storages, terrestrial water storage (TWS) data obtained from the Gravity Recovery And Climate Experiment (GRACE) satellite mission are assimilated into the World-Wide Water Resources Assessment (W3RA) model using the ensemble-based sequential technique of the Square Root Analysis (SQRA) filter. We investigate the capability of the data assimilation approach to use a non-regional hydrological model for a regional case study. Based on these estimates, we investigate relationships between the model derived sub-surface water storage changes and remotely sensed precipitations, as well as altimetry-derived river level variations in Bangladesh by applying the empirical mode decomposition (EMD) method. A larger correlation is found between river level heights and rainfalls (78% on average) in comparison to groundwater storage variations and rainfalls (57% on average). The results indicate a significant decline in groundwater storage (∼32% reduction) for Bangladesh between 2003 and 2013, which is equivalent to an average rate of 8.73 ± 2.45mm/year. Copyright © 2018 Elsevier B.V. All rights reserved.

Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles.

PubMed

Williams, N J; Nasuto, S J; Saddy, J D

2015-07-30

The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. We propose a complete pipeline for the cluster analysis of ERP data. To increase the signal-to-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA) to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). After validating the pipeline on simulated data, we tested it on data from two experiments - a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership. Our analysis operates on denoised single-trials, the number of clusters are determined in a principled manner and the results are presented through an intuitive visualisation. Given the cluster structure in some experimental conditions, we suggest application of cluster analysis as a preliminary step before ensemble averaging. Copyright © 2015 Elsevier B.V. All rights reserved.

Towards a unified understanding of event-related changes in the EEG: the firefly model of synchronization through cross-frequency phase modulation.

PubMed

Burgess, Adrian P

2012-01-01

Although event-related potentials (ERPs) are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG) or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not. Using empirical mode decomposition (EMD), which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i) the shape and timing of ERPs at different scalp sites, ii) the event-related desynchronization in alpha and synchronization in theta, and iii) changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing.

Towards a Unified Understanding of Event-Related Changes in the EEG: The Firefly Model of Synchronization through Cross-Frequency Phase Modulation

PubMed Central

Burgess, Adrian P.

2012-01-01

Although event-related potentials (ERPs) are widely used to study sensory, perceptual and cognitive processes, it remains unknown whether they are phase-locked signals superimposed upon the ongoing electroencephalogram (EEG) or result from phase-alignment of the EEG. Previous attempts to discriminate between these hypotheses have been unsuccessful but here a new test is presented based on the prediction that ERPs generated by phase-alignment will be associated with event-related changes in frequency whereas evoked-ERPs will not. Using empirical mode decomposition (EMD), which allows measurement of narrow-band changes in the EEG without predefining frequency bands, evidence was found for transient frequency slowing in recognition memory ERPs but not in simulated data derived from the evoked model. Furthermore, the timing of phase-alignment was frequency dependent with the earliest alignment occurring at high frequencies. Based on these findings, the Firefly model was developed, which proposes that both evoked and induced power changes derive from frequency-dependent phase-alignment of the ongoing EEG. Simulated data derived from the Firefly model provided a close match with empirical data and the model was able to account for i) the shape and timing of ERPs at different scalp sites, ii) the event-related desynchronization in alpha and synchronization in theta, and iii) changes in the power density spectrum from the pre-stimulus baseline to the post-stimulus period. The Firefly Model, therefore, provides not only a unifying account of event-related changes in the EEG but also a possible mechanism for cross-frequency information processing. PMID:23049827

Comparison of wavelet based denoising schemes for gear condition monitoring: An Artificial Neural Network based Approach

NASA Astrophysics Data System (ADS)

Ahmed, Rounaq; Srinivasa Pai, P.; Sriram, N. S.; Bhat, Vasudeva

2018-02-01

Vibration Analysis has been extensively used in recent past for gear fault diagnosis. The vibration signals extracted is usually contaminated with noise and may lead to wrong interpretation of results. The denoising of extracted vibration signals helps the fault diagnosis by giving meaningful results. Wavelet Transform (WT) increases signal to noise ratio (SNR), reduces root mean square error (RMSE) and is effective to denoise the gear vibration signals. The extracted signals have to be denoised by selecting a proper denoising scheme in order to prevent the loss of signal information along with noise. An approach has been made in this work to show the effectiveness of Principal Component Analysis (PCA) to denoise gear vibration signal. In this regard three selected wavelet based denoising schemes namely PCA, Empirical Mode Decomposition (EMD), Neighcoeff Coefficient (NC), has been compared with Adaptive Threshold (AT) an extensively used wavelet based denoising scheme for gear vibration signal. The vibration signals acquired from a customized gear test rig were denoised by above mentioned four denoising schemes. The fault identification capability as well as SNR, Kurtosis and RMSE for the four denoising schemes have been compared. Features extracted from the denoised signals have been used to train and test artificial neural network (ANN) models. The performances of the four denoising schemes have been evaluated based on the performance of the ANN models. The best denoising scheme has been identified, based on the classification accuracy results. PCA is effective in all the regards as a best denoising scheme.

Evaluating skeletal muscle electromechanical delay with intramuscular pressure.

PubMed

Go, Shanette A; Litchy, William J; Evertz, Loribeth Q; Kaufman, Kenton R

2018-06-08

Intramuscular pressure (IMP) is the fluid pressure generated within skeletal muscle and directly reflects individual muscle tension. The purpose of this study was to assess the development of force, IMP, and electromyography (EMG) in the tibialis anterior (TA) muscle during ramped isometric contractions and evaluate electromechanical delay (EMD). Force, EMG, and IMP were simultaneously measured during ramped isometric contractions in eight young, healthy human subjects. The EMD between the onset of force and EMG activity (Δt-EMG force) and the onset of IMP and EMG activity (Δt EMG-IMP) were calculated. A statistically significant difference (p < 0.05) was found between the mean force-EMG EMD (36 ± 31 ms) and the mean IMP-EMG EMD (3 ± 21 ms). IMP reflects changes in muscle tension due to the contractile muscle elements. Copyright © 2018 Elsevier Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kolda, Tamara Gibson

We propose two new multilinear operators for expressing the matrix compositions that are needed in the Tucker and PARAFAC (CANDECOMP) decompositions. The first operator, which we call the Tucker operator, is shorthand for performing an n-mode matrix multiplication for every mode of a given tensor and can be employed to concisely express the Tucker decomposition. The second operator, which we call the Kruskal operator, is shorthand for the sum of the outer-products of the columns of N matrices and allows a divorce from a matricized representation and a very concise expression of the PARAFAC decomposition. We explore the properties ofmore » the Tucker and Kruskal operators independently of the related decompositions. Additionally, we provide a review of the matrix and tensor operations that are frequently used in the context of tensor decompositions.« less

Three dimensional empirical mode decomposition analysis apparatus, method and article manufacture

NASA Technical Reports Server (NTRS)

Gloersen, Per (Inventor)

2004-01-01

An apparatus and method of analysis for three-dimensional (3D) physical phenomena. The physical phenomena may include any varying 3D phenomena such as time varying polar ice flows. A repesentation of the 3D phenomena is passed through a Hilbert transform to convert the data into complex form. A spatial variable is separated from the complex representation by producing a time based covariance matrix. The temporal parts of the principal components are produced by applying Singular Value Decomposition (SVD). Based on the rapidity with which the eigenvalues decay, the first 3-10 complex principal components (CPC) are selected for Empirical Mode Decomposition into intrinsic modes. The intrinsic modes produced are filtered in order to reconstruct the spatial part of the CPC. Finally, a filtered time series may be reconstructed from the first 3-10 filtered complex principal components.

Intramolecular energy transfer and mode-specific effects in unimolecular reactions of 1,2-difluoroethane

NASA Astrophysics Data System (ADS)

Raff, Lionel M.

1989-06-01

The unimolecular decomposition reactions of 1,2-difluoroethane upon mode-specific excitation to a total internal energy of 7.5 eV are investigated using classical trajectory methods and a previously formulated empirical potential-energy surface. The decomposition channels for 1,2-difluoroethane are, in order of importance, four-center HF elimination, C-C bond rupture, and hydrogen-atom dissociation. This order is found to be independent of the particular vibrational mode excited. Neither fluorine-atom nor F2 elimination reactions are ever observed even though these dissociation channels are energetically open. For four-center HF elimination, the average fraction of the total energy partitioned into internal HF motion varies between 0.115-0.181 depending upon the particular vibrational mode initially excited. The internal energy of the fluoroethylene product lies in the range 0.716-0.776. Comparison of the present results with those previously obtained for a random distribution of the initial 1,2-difluoroethane internal energy [J. Phys. Chem. 92, 5111 (1988)], shows that numerous mode-specific effects are present in these reactions in spite of the fact that intramolecular energy transfer rates for this system are 5.88-25.5 times faster than any of the unimolecular reaction rates. Mode-specific excitation always leads to a total decomposition rate significantly larger than that obtained for a random distribution of the internal energy. Excitation of different 1,2-difluoroethane vibrational modes is found to produce as much as a 51% change in the total decomposition rate. Mode-specific effects are also seen in the product energy partitioning. The rate coefficients for decomposition into the various channels are very sensitive to the particular mode excited. A comparison of the calculated mode-specific effects with the previously determined mode-to-mode energy transfer rate coefficients [J. Chem. Phys. 89, 5680 (1988)] shows that, to some extent, the presence of mode-specific chemistry is correlated with the magnitude of the energy transfer rate. However, the particular pathways for energy flow seem to be more important than the magnitude of the rate coefficients. It is suggested that the propensity for the energy to remain isolated in small subset of modes, such as the CH2F deformation modes or the rocking modes, is primarily responsible for the observation of mode-specific chemistry. The results clearly demonstrate that an intramolecular energy transfer rate that is fast relative to the unimolecular reaction rate is not a sufficient condition to ensure the absence of mode-specific chemical effects.

Quantization of Electromagnetic Fields in Cavities

NASA Technical Reports Server (NTRS)

Kakazu, Kiyotaka; Oshiro, Kazunori

1996-01-01

A quantization procedure for the electromagnetic field in a rectangular cavity with perfect conductor walls is presented, where a decomposition formula of the field plays an essential role. All vector mode functions are obtained by using the decomposition. After expanding the field in terms of the vector mode functions, we get the quantized electromagnetic Hamiltonian.

Randomized Dynamic Mode Decomposition

NASA Astrophysics Data System (ADS)

Erichson, N. Benjamin; Brunton, Steven L.; Kutz, J. Nathan

2017-11-01

The dynamic mode decomposition (DMD) is an equation-free, data-driven matrix decomposition that is capable of providing accurate reconstructions of spatio-temporal coherent structures arising in dynamical systems. We present randomized algorithms to compute the near-optimal low-rank dynamic mode decomposition for massive datasets. Randomized algorithms are simple, accurate and able to ease the computational challenges arising with `big data'. Moreover, randomized algorithms are amenable to modern parallel and distributed computing. The idea is to derive a smaller matrix from the high-dimensional input data matrix using randomness as a computational strategy. Then, the dynamic modes and eigenvalues are accurately learned from this smaller representation of the data, whereby the approximation quality can be controlled via oversampling and power iterations. Here, we present randomized DMD algorithms that are categorized by how many passes the algorithm takes through the data. Specifically, the single-pass randomized DMD does not require data to be stored for subsequent passes. Thus, it is possible to approximately decompose massive fluid flows (stored out of core memory, or not stored at all) using single-pass algorithms, which is infeasible with traditional DMD algorithms.

On the predictability of high water level along the US East Coast: can the Florida Current measurement be an indicator for flooding caused by remote forcing?

NASA Astrophysics Data System (ADS)

Ezer, Tal; Atkinson, Larry P.

2017-06-01

Recent studies show that in addition to wind and air pressure effects, a significant portion of the variability of coastal sea level (CSL) along the US East Coast can be attributed to non-local factors such as variations in the Gulf Stream and the North Atlantic circulation; these variations can cause unpredictable coastal flooding. The Florida Current transport (FCT) measurement across the Florida Straits monitors those variations, and thus, the study evaluated the potential of using the FCT as an indicator for anomalously high water level along the coast. Hourly water level data from 12 tide gauge stations over 12 years are used to construct records of maximum daily water levels (MDWL) that are compared with the daily FCT data. An empirical mode decomposition (EMD) approach is used to divide the data into high-frequency modes (periods T < ˜30 days), middle-frequency modes (˜30 days < T < ˜90 days), and low-frequency modes (˜90 days < T < ˜1 year). Two predictive measures are tested: FCT and FCT change (FCC). FCT is anti-correlated with MDWL in high-frequency modes but positively correlated with MDWL in low-frequency modes. FCC on the other hand is always anti-correlated with MDWL for all frequency bands, and the high water signal lags behind FCC for almost all stations, thus providing a potential predictive skill (i.e., whenever a weakening trend is detected in the FCT, anomalously high water is expected along the coast over the next few days). The MDWL-FCT correlation in the high-frequency modes is maximum in the lower Mid-Atlantic Bight, suggesting influence from the meandering Gulf Stream after it separates from the coast. However, the correlation in low-frequency modes is maximum in the South Atlantic Bight, suggesting impact from variations in the wind pattern over subtropical regions. The middle-frequency and low-frequency modes of the FCT seem to provide the best predictor for medium to large flooding events; it is estimated that ˜10-25% of the sea level variability in those modes can be attributed to variations in the FCT. An example from Hurricane Joaquin (September-October, 2015) demonstrates how an offshore storm that never made landfall can cause a weakening of the FCT and unexpected high water level and flooding along the US East Coast. A regression-prediction model based on the MDWL-FCT correlation shows some skill in estimating high water levels during past storms; the water level prediction is more accurate for slow-moving and offshore storms than it is for fast-moving storms. The study can help to improve water level prediction since current storm surge models rely on local wind but may ignore remote forcing.

Deuterium and lithium-6 MAS NMR studies of manganese oxide electrode materials

NASA Astrophysics Data System (ADS)

Paik, Younkee

Electrolytic manganese dioxide (EMD) is used world wide as the cathode materials in both lithium and alkaline primary (non-rechargeable) batteries. We have developed deuterium and lithium MAS NMR techniques to study EMD and related manganese oxides and hydroxides, where diffraction techniques are of limited value due to a highly defective nature of the structures. Deuterons in EMD, manganite, groutite, and deuterium-intercalated pyrolusite and ramsdellite were detected by NMR, for the first time, and their locations and motions in the structures were analyzed by applying variable temperature NMR techniques. Discharge mechanisms of EMD in alkaline (aqueous) electrolytes were studied, in conjunction with step potential electrochemical spectroscopic (SPECS) method, and five distinctive discharge processes were proposed. EMD is usually heat-treated at about 300--400°C to remove water to be used in lithium batteries. Details of the effects of heat-treatment, such as structural and compositional changes as a function of heat-treatment temperature, were studied by a combination of MAS NMR, XRD, and thermogravimetric analysis. Lithium local environments in heat-treated EMD (HEMD) that were discharged in lithium cells, were described in terms of related environments found in model compounds pyrolusite and ramsdellite where specific Li + sites were detected by MAS NMR and the hyperfine shift scale method of Grey et al. Acid-leaching of Li2MnO3 represents an approach for synthesizing new or modified manganese oxide electrode materials for lithium rechargeable batteries. Progressive removal of lithium from specific crystallographic sites, followed by a gradual change of the crystal structure, was monitored by a combination of NMR and XRD techniques.

Evaluation of EMD 128 130 occupancy of the 5-HT1A and the D2 receptor: a human PET study with [11C]WAY-100635 and [11C]raclopride.

PubMed

Rabiner, Eugenii A; Gunn, Roger N; Wilkins, Martin R; Sedman, Ewen; Grasby, Paul M

2002-09-01

The use of so-called, atypical antipsychotic medication is becoming more widespread in the treatment of psychotic disorders. EMD 128 130 is a novel compound acting as an agonist at the 5-HT1A receptor, and as an antagonist at the dopamine-2 (D2) receptor. This dual action may confer additional benefits over selective D2 antagonists in the treatment of psychotic disorders. In this study, we investigated the occupancy of EMD 128 130 in vivo at the human D2 and 5-HT1A receptors with positron emission tomography using the radiotracers [11C]raclopride and [11C]WAY-100635. Seven healthy volunteers were examined before and after 5 days of treatment with EMD 128 130, administered in an incremental dose building up to 50 mg, b.d. A significant occupancy was demonstrated at the human D2 receptor (40% following a dose of 50 mg, b.d.) while there was no consistent effect observed at the 5-HT1A receptor, despite a similar affinity of EMD 128 130 for cloned human D2 and 5-HT1A receptors, and the presence of typical, central 5-HT1A agonist side-effects. The differential effects of EMD 128 130 at the D2 and the 5-HT1A receptor (antagonist at D2 receptor, agonist at the 5-HTIA receptor) may explain the differences in occupancy observed.

Enamel matrix derivative, inflammation and soft tissue wound healing.

PubMed

Miron, R J; Dard, M; Weinreb, M

2015-10-01

Over 15 years have now passed since enamel matrix derivative (EMD) emerged as an agent capable of periodontal regeneration. Following thorough investigation, evidenced-based clinical application is now established for a multitude of clinical settings to promote regeneration of periodontal hard tissues. Despite the large number of studies and review articles written on this topic, no single review has compiled the influence of EMD on tissue inflammation, an area of research that merits substantial attention in periodontology. The aim of the present review was to gather all studies that deal with the effects of EMD on tissue inflammation with particular interest in the cellular mechanisms involved in inflammation and soft tissue wound healing/resolution. The effects of EMD on monocytes, macrophages, lymphocytes, neutrophils, fibroblasts and endothelial cells were investigated for changes in cell behavior as well as release of inflammatory markers, including interleukins, prostaglandins, tumor necrosis factor-α, matrix metalloproteinases and members of the OPG-RANKL pathway. In summary, studies listed in this review have reported that EMD is able to significantly decrease interleukin-1b and RANKL expression, increase prostaglandin E2 and OPG expression, increase proliferation and migration of T lymphocytes, induce monocyte differentiation, increase bacterial and tissue debris clearance, as well as increase fibroplasias and angiogenesis by inducing endothelial cell proliferation, migration and capillary-like sprout formation. The outcomes from the present review article indicate that EMD is able to affect substantially the inflammatory and healing responses and lay the groundwork for future investigation in the field. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Outcome of enamel matrix derivative treatment in the presence of chronic stress: histometric study in rats.

PubMed

Corrêa, Mônica G; Gomes Campos, Mirella L; Marques, Marcelo Rocha; Bovi Ambrosano, Glaucia Maria; Casati, Marcio Z; Nociti, Francisco H; Sallum, Enilson A

2014-07-01

Psychologic stress and clinical hypercortisolism have been related to direct effects on bone metabolism. However, there is a lack of information regarding the outcomes of regenerative approaches under the influence of chronic stress (CS). Enamel matrix derivative (EMD) has been used in periodontal regenerative procedures, resulting in improvement of clinical parameters. Thus, the aim of this histomorphometric study is to evaluate the healing of periodontal defects after treatment with EMD under the influence of CS in the rat model. Twenty Wistar rats were randomly assigned to two groups; G1: CS (restraint stress for 12 hours/day) (n = 10), and G2: not exposed to CS (n = 10). Fifteen days after initiation of CS, fenestration defects were created at the buccal aspect of the first mandibular molar of all animals from both groups. After the surgeries, the defects of each animal were randomly assigned to two subgroups: non-treated control and treated with EMD. The animals were euthanized 21 days later. G1 showed less bone density (BD) compared to G2. EMD provided an increased defect fill (DF) in G1 and higher BD and new cementum formation (NCF) in both groups. The number of tartrate-resistant acid phosphatase-positive osteoclasts was significantly higher in G1 when compared to G2 and in EMD-treated sites of both groups. CS may produce a significant detrimental effect on BD. EMD may provide greater DF compared to non-treated control in the presence of CS and increased BD and NCF in the presence or absence of CS.

Universal explosive detection system for homeland security applications

NASA Astrophysics Data System (ADS)

Lee, Vincent Y.; Bromberg, Edward E. A.

2010-04-01

L-3 Communications CyTerra Corporation has developed a high throughput universal explosive detection system (PassPort) to automatically screen the passengers in airports without requiring them to remove their shoes. The technical approach is based on the patented energetic material detection (EMD) technology. By analyzing the results of sample heating with an infrared camera, one can distinguish the deflagration or decomposition of an energetic material from other clutters such as flammables and general background substances. This becomes the basis of a universal explosive detection system that does not require a library and is capable of detecting trace levels of explosives with a low false alarm rate. The PassPort is a simple turnstile type device and integrates a non-intrusive aerodynamic sampling scheme that has been shown capable of detecting trace levels of explosives on shoes. A detailed description of the detection theory and the automated sampling techniques, as well as the field test results, will be presented.

Nonlinear mode decomposition: A noise-robust, adaptive decomposition method

NASA Astrophysics Data System (ADS)

Iatsenko, Dmytro; McClintock, Peter V. E.; Stefanovska, Aneta

2015-09-01

The signals emanating from complex systems are usually composed of a mixture of different oscillations which, for a reliable analysis, should be separated from each other and from the inevitable background of noise. Here we introduce an adaptive decomposition tool—nonlinear mode decomposition (NMD)—which decomposes a given signal into a set of physically meaningful oscillations for any wave form, simultaneously removing the noise. NMD is based on the powerful combination of time-frequency analysis techniques—which, together with the adaptive choice of their parameters, make it extremely noise robust—and surrogate data tests used to identify interdependent oscillations and to distinguish deterministic from random activity. We illustrate the application of NMD to both simulated and real signals and demonstrate its qualitative and quantitative superiority over other approaches, such as (ensemble) empirical mode decomposition, Karhunen-Loève expansion, and independent component analysis. We point out that NMD is likely to be applicable and useful in many different areas of research, such as geophysics, finance, and the life sciences. The necessary matlab codes for running NMD are freely available for download.

Structural system identification based on variational mode decomposition

NASA Astrophysics Data System (ADS)

Bagheri, Abdollah; Ozbulut, Osman E.; Harris, Devin K.

2018-03-01

In this paper, a new structural identification method is proposed to identify the modal properties of engineering structures based on dynamic response decomposition using the variational mode decomposition (VMD). The VMD approach is a decomposition algorithm that has been developed as a means to overcome some of the drawbacks and limitations of the empirical mode decomposition method. The VMD-based modal identification algorithm decomposes the acceleration signal into a series of distinct modal responses and their respective center frequencies, such that when combined their cumulative modal responses reproduce the original acceleration response. The decaying amplitude of the extracted modal responses is then used to identify the modal damping ratios using a linear fitting function on modal response data. Finally, after extracting modal responses from available sensors, the mode shape vector for each of the decomposed modes in the system is identified from all obtained modal response data. To demonstrate the efficiency of the algorithm, a series of numerical, laboratory, and field case studies were evaluated. The laboratory case study utilized the vibration response of a three-story shear frame, whereas the field study leveraged the ambient vibration response of a pedestrian bridge to characterize the modal properties of the structure. The modal properties of the shear frame were computed using analytical approach for a comparison with the experimental modal frequencies. Results from these case studies demonstrated that the proposed method is efficient and accurate in identifying modal data of the structures.

On the motion of hairy black holes in Einstein-Maxwell-dilaton theories

NASA Astrophysics Data System (ADS)

Julié, Félix-Louis

2018-01-01

Starting from the static, spherically symmetric black hole solutions in massless Einstein-Maxwell-dilaton (EMD) theories, we build a "skeleton" action, that is, we phenomenologically replace black holes by an appropriate effective point particle action, which is well suited to the formal treatment of the many-body problem in EMD theories. We find that, depending crucially on the value of their scalar cosmological environment, black holes can undergo steep "scalarization" transitions, inducing large deviations to the general relativistic two-body dynamics, as shown, for example, when computing the first post-Keplerian Lagrangian of EMD theories.

Pharmacological evaluation of the anxiolytic-like effects of EMD 386088, a partial 5-HT6 receptor agonist, in the rat elevated plus-maze and Vogel conflict tests.

PubMed

Jastrzębska-Więsek, Magdalena; Siwek, Agata; Partyka, Anna; Kubacka, Monika; Mogilski, Szczepan; Wasik, Anna; Kołaczkowski, Marcin; Wesołowska, Anna

2014-10-01

The 5-HT6 is one of the most recent additions to the 5-HT receptor family. Its pharmacological profile and anatomical distribution is suggestive of a putative role in mood disorders. Most of preclinical evidence suggests an anxiolytic-like action of 5-HT6 receptor antagonists. Evaluation the anxiolytic-like effects of EMD 386088, a partial 5-HT6receptor agonist, and its putative mechanism of action in rats. EMD 386088, administered intraperitoneally at a dose of 2.5 mg/kg evoked specific anxiolytic-like activity in the automated version of the conflict drinking Vogel and the elevated plus-maze tests visible by increasing all parameters indicating a potential anti-anxiety effect. Its activity was blocked by the selective 5-HT6 receptor antagonist SB 271046, but not by the selective GABAA/benzodiazepine receptor antagonist flumazenil. EMD 386088 did not intensify an anxiolytic-like effect produced by diazepam in the elevated plus-maze test. These findings suggest that EMD 386088, a 5-HT6 receptor agonist, produces anxiolytic-like activity after systemic administration which may result from direct stimulation of 5-HT6 receptors. Copyright © 2014 Elsevier Ltd. All rights reserved.

Enamel matrix derivative (Emdogain) for periodontal tissue regeneration in intrabony defects. A Cochrane systematic review.

PubMed

Esposito, Marco; Grusovin, Maria Gabriella; Papanikolaou, Nikolaos; Coulthard, Paul; Worthington, Helen V

2009-01-01

Periodontitis is a chronic infective disease of the gums caused by bacteria present in dental plaque. This condition induces the breakdown of the tooth supporting apparatus until teeth are lost. Surgery may be indicated to arrest disease progression and regenerate lost tissues. Several surgical techniques have been developed to regenerate periodontal tissues including guided tissue regeneration (GTR), bone grafting (BG) and the use of enamel matrix derivative (EMD). EMD is an extract of enamel matrix and contains amelogenins of various molecular weights. Amelogenins are involved in the formation of enamel and periodontal attachment formation during tooth development. To test whether EMD is effective, and to compare EMD versus GTR, and various BG procedures for the treatment of intrabony defects. The Cochrane Oral Health Group Trials Register, CENTRAL, MEDLINE and EMBASE were searched. Several dental journals were hand searched. No language restrictions were applied. Authors of randomised controlled trials (RCTs) identified, personal contacts and the manufacturer were contacted to identify unpublished trials. The last electronic search was conducted on 4 February 2009. RCTs on patients affected by periodontitis having intrabony defects of at least 3 mm treated with EMD compared with open flap debridement, GTR and various BG procedures with at least 1 year of follow-up. The outcome measures considered were: tooth loss, changes in probing attachment levels (PAL), pocket depths (PPD), gingival recessions (REC), bone levels from the bottom of the defects on intraoral radiographs, aesthetics and adverse events. The following time points were to be evaluated: 1, 5 and 10 years. Screening of eligible studies, assessment of the methodological quality of the trials and data extraction were conducted in duplicate and independently by at least two authors. Results were expressed as random-effects models using mean differences for continuous outcomes and risk ratios (RR) for dichotomous outcomes with 95% confidence intervals (CI). It was decided not to investigate heterogeneity, but a sensitivity analysis for the risk of bias of the trials was performed. A total of 13 trials were included out of 35 potentially eligible trials. No included trial presented data after 5 years of follow-up, therefore all data refer to the 1-year time point. A meta-analysis including nine trials showed that EMD treated sites displayed statistically significant PAL improvements (mean difference 1.1 mm, 95% CI 0.61 to 1.55) and PPD reduction (0.9 mm, 95% CI 0.44 to 1.31) when compared to placebo or control treated sites, though a high degree of heterogeneity was found. Significantly more sites had < 2 mm PAL gain in the control group, with RR 0.53 (95% CI 0.34 to 0.82). Approximately nine patients needed to be treated (NNT) to have one patient gaining 2 mm or more PAL over the control group, based on a prevalence in the control group of 25%. No differences in tooth loss or aesthetic appearance as judged by the patients were observed. When evaluating only trials at a low risk of bias in a sensitivity analysis (four trials), the effect size for PAL was 0.62 mm (95% CI 0.28 to 0.96), which was less than 1.1 mm for the overall result. Comparing EMD with GTR (five trials), GTR showed significantly more post-operative complications (three trials, RR 0.12, 95% CI 0.02 to 0.85) and more REC (0.4 mm 95% CI 0.15 to 0.66). The only trial comparing EMD with a bioactive ceramic filler found statistically significantly more REC (-1.60 mm, 95% CI -2.74 to - 0.46) at the EMD treated sites. One year after its application, EMD significantly improved PAL levels (1.1 mm) and reduced PPD (0.9 mm) when compared to a placebo or control, however, the high degree of heterogeneity observed among trials suggests that the results have to be interpreted with great caution. In addition, a sensitivity analysis indicated that the overall treatment effect might be overestimated. The actual clinical advantages of using EMD are unknown. With the exception of significantly more postoperative complications in the GTR group, there was no evidence of clinically important differences between GTR and EMD. Bone substitutes may be associated with less REC than EMD.

A reduced-order model for compressible flows with buffeting condition using higher order dynamic mode decomposition with a mode selection criterion

NASA Astrophysics Data System (ADS)

Kou, Jiaqing; Le Clainche, Soledad; Zhang, Weiwei

2018-01-01

This study proposes an improvement in the performance of reduced-order models (ROMs) based on dynamic mode decomposition to model the flow dynamics of the attractor from a transient solution. By combining higher order dynamic mode decomposition (HODMD) with an efficient mode selection criterion, the HODMD with criterion (HODMDc) ROM is able to identify dominant flow patterns with high accuracy. This helps us to develop a more parsimonious ROM structure, allowing better predictions of the attractor dynamics. The method is tested in the solution of a NACA0012 airfoil buffeting in a transonic flow, and its good performance in both the reconstruction of the original solution and the prediction of the permanent dynamics is shown. In addition, the robustness of the method has been successfully tested using different types of parameters, indicating that the proposed ROM approach is a tool promising for using in both numerical simulations and experimental data.

Dynamic mode decomposition for plasma diagnostics and validation.

PubMed

Taylor, Roy; Kutz, J Nathan; Morgan, Kyle; Nelson, Brian A

2018-05-01

We demonstrate the application of the Dynamic Mode Decomposition (DMD) for the diagnostic analysis of the nonlinear dynamics of a magnetized plasma in resistive magnetohydrodynamics. The DMD method is an ideal spatio-temporal matrix decomposition that correlates spatial features of computational or experimental data while simultaneously associating the spatial activity with periodic temporal behavior. DMD can produce low-rank, reduced order surrogate models that can be used to reconstruct the state of the system with high fidelity. This allows for a reduction in the computational cost and, at the same time, accurate approximations of the problem, even if the data are sparsely sampled. We demonstrate the use of the method on both numerical and experimental data, showing that it is a successful mathematical architecture for characterizing the helicity injected torus with steady inductive (HIT-SI) magnetohydrodynamics. Importantly, the DMD produces interpretable, dominant mode structures, including a stationary mode consistent with our understanding of a HIT-SI spheromak accompanied by a pair of injector-driven modes. In combination, the 3-mode DMD model produces excellent dynamic reconstructions across the domain of analyzed data.

Dynamic mode decomposition for plasma diagnostics and validation

NASA Astrophysics Data System (ADS)

Taylor, Roy; Kutz, J. Nathan; Morgan, Kyle; Nelson, Brian A.

2018-05-01

We demonstrate the application of the Dynamic Mode Decomposition (DMD) for the diagnostic analysis of the nonlinear dynamics of a magnetized plasma in resistive magnetohydrodynamics. The DMD method is an ideal spatio-temporal matrix decomposition that correlates spatial features of computational or experimental data while simultaneously associating the spatial activity with periodic temporal behavior. DMD can produce low-rank, reduced order surrogate models that can be used to reconstruct the state of the system with high fidelity. This allows for a reduction in the computational cost and, at the same time, accurate approximations of the problem, even if the data are sparsely sampled. We demonstrate the use of the method on both numerical and experimental data, showing that it is a successful mathematical architecture for characterizing the helicity injected torus with steady inductive (HIT-SI) magnetohydrodynamics. Importantly, the DMD produces interpretable, dominant mode structures, including a stationary mode consistent with our understanding of a HIT-SI spheromak accompanied by a pair of injector-driven modes. In combination, the 3-mode DMD model produces excellent dynamic reconstructions across the domain of analyzed data.

Adaptive DSPI phase denoising using mutual information and 2D variational mode decomposition

NASA Astrophysics Data System (ADS)

Xiao, Qiyang; Li, Jian; Wu, Sijin; Li, Weixian; Yang, Lianxiang; Dong, Mingli; Zeng, Zhoumo

2018-04-01

In digital speckle pattern interferometry (DSPI), noise interference leads to a low peak signal-to-noise ratio (PSNR) and measurement errors in the phase map. This paper proposes an adaptive DSPI phase denoising method based on two-dimensional variational mode decomposition (2D-VMD) and mutual information. Firstly, the DSPI phase map is subjected to 2D-VMD in order to obtain a series of band-limited intrinsic mode functions (BLIMFs). Then, on the basis of characteristics of the BLIMFs and in combination with mutual information, a self-adaptive denoising method is proposed to obtain noise-free components containing the primary phase information. The noise-free components are reconstructed to obtain the denoising DSPI phase map. Simulation and experimental results show that the proposed method can effectively reduce noise interference, giving a PSNR that is higher than that of two-dimensional empirical mode decomposition methods.

Microvessel Density Evaluation of the Effect of Enamel Matrix Derivative on Soft Tissue After Implant Placement: A Preliminary Study.

PubMed

Guimarães, George Furtado; de Araújo, Vera Cavalcanti; Nery, James Carlos; Peruzzo, Daiane Cristina; Soares, Andresa Borges

2015-01-01

Enamel matrix derivative (EMD) is commonly used in periodontal therapy and has been used successfully for periodontal regeneration. In addition, this material has a possible angiogenic effect that has been associated with enhanced wound healing. The aim of this study was to evaluate the effect of EMD on microvessel density (angiogenesis) on the soft tissues surrounding newly placed implants after 14 days. Five patients were selected, each requiring at least one implant on each side of the maxilla, in a split-mouth experimental design. The implants were placed in a two-stage procedure. Each side was then randomized as test or control. On the test side, 0.1 mL of EMD was topically applied to the soft tissues surrounding the implants, while the control side did not receive any treatment. Second-stage surgery was performed after 14 days. A 6-mm punch biopsy was performed for each implant, with the samples subsequently prepared for histology and immunohistochemistry. Quantitative vascularization analysis was performed, which involved counting three areas or "hotspots" containing vessels strongly positive for CD34 and CD105, a pan-endothelial and new vessel marker, respectively. There was no significant difference between test and control groups when evaluating the formation of new blood vessels. The total number of blood vessels, however, was significantly higher in the group treated with EMD (test group). Within the limits of the present study, it can be concluded that topical application of EMD on the soft tissues surrounding newly placed implants resulted in an increased number of blood vessels at 14 days, suggesting that EMD may play a beneficial role in this aspect of wound healing.

Peripheral Processing Facilitates Optic Flow-Based Depth Perception

PubMed Central

Li, Jinglin; Lindemann, Jens P.; Egelhaaf, Martin

2016-01-01

Flying insects, such as flies or bees, rely on consistent information regarding the depth structure of the environment when performing their flight maneuvers in cluttered natural environments. These behaviors include avoiding collisions, approaching targets or spatial navigation. Insects are thought to obtain depth information visually from the retinal image displacements (“optic flow”) during translational ego-motion. Optic flow in the insect visual system is processed by a mechanism that can be modeled by correlation-type elementary motion detectors (EMDs). However, it is still an open question how spatial information can be extracted reliably from the responses of the highly contrast- and pattern-dependent EMD responses, especially if the vast range of light intensities encountered in natural environments is taken into account. This question will be addressed here by systematically modeling the peripheral visual system of flies, including various adaptive mechanisms. Different model variants of the peripheral visual system were stimulated with image sequences that mimic the panoramic visual input during translational ego-motion in various natural environments, and the resulting peripheral signals were fed into an array of EMDs. We characterized the influence of each peripheral computational unit on the representation of spatial information in the EMD responses. Our model simulations reveal that information about the overall light level needs to be eliminated from the EMD input as is accomplished under light-adapted conditions in the insect peripheral visual system. The response characteristics of large monopolar cells (LMCs) resemble that of a band-pass filter, which reduces the contrast dependency of EMDs strongly, effectively enhancing the representation of the nearness of objects and, especially, of their contours. We furthermore show that local brightness adaptation of photoreceptors allows for spatial vision under a wide range of dynamic light conditions. PMID:27818631

Dealing with noise and physiological artifacts in human EEG recordings: empirical mode methods

NASA Astrophysics Data System (ADS)

Runnova, Anastasiya E.; Grubov, Vadim V.; Khramova, Marina V.; Hramov, Alexander E.

2017-04-01

In the paper we propose the new method for removing noise and physiological artifacts in human EEG recordings based on empirical mode decomposition (Hilbert-Huang transform). As physiological artifacts we consider specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the proposed method with steps including empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing these empirical modes and reconstructing of initial EEG signal. We show the efficiency of the method on the example of filtration of human EEG signal from eye-moving artifacts.

Adaptive Fourier decomposition based ECG denoising.

PubMed

Wang, Ze; Wan, Feng; Wong, Chi Man; Zhang, Liming

2016-10-01

A novel ECG denoising method is proposed based on the adaptive Fourier decomposition (AFD). The AFD decomposes a signal according to its energy distribution, thereby making this algorithm suitable for separating pure ECG signal and noise with overlapping frequency ranges but different energy distributions. A stop criterion for the iterative decomposition process in the AFD is calculated on the basis of the estimated signal-to-noise ratio (SNR) of the noisy signal. The proposed AFD-based method is validated by the synthetic ECG signal using an ECG model and also real ECG signals from the MIT-BIH Arrhythmia Database both with additive Gaussian white noise. Simulation results of the proposed method show better performance on the denoising and the QRS detection in comparing with major ECG denoising schemes based on the wavelet transform, the Stockwell transform, the empirical mode decomposition, and the ensemble empirical mode decomposition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effectiveness of enamel matrix derivative on the clinical and microbiological outcomes following surgical regenerative treatment of peri-implantitis. A randomized controlled trial.

PubMed

Isehed, Catrine; Holmlund, Anders; Renvert, Stefan; Svenson, Björn; Johansson, Ingegerd; Lundberg, Pernilla

2016-10-01

This randomized clinical trial aimed at comparing radiological, clinical and microbial effects of surgical treatment of peri-implantitis alone or in combination with enamel matrix derivative (EMD). Twenty-six subjects were treated with open flap debridement and decontamination of the implant surfaces with gauze and saline preceding adjunctive EMD or no EMD. Bone level (BL) change was primary outcome and secondary outcomes were changes in pocket depth (PD), plaque, pus, bleeding and the microbiota of the peri-implant biofilm analyzed by the Human Oral Microbe Identification Microarray over a time period of 12 months. In multivariate modelling, increased marginal BL at implant site was significantly associated with EMD, the number of osseous walls in the peri-implant bone defect and a Gram+/aerobic microbial flora, whereas reduced BL was associated with a Gram-/anaerobic microbial flora and presence of bleeding and pus, with a cross-validated predictive capacity (Q(2) ) of 36.4%. Similar, but statistically non-significant, trends were seen for BL, PD, plaque, pus and bleeding in univariate analysis. Adjunctive EMD to surgical treatment of peri-implantitis was associated with prevalence of Gram+/aerobic bacteria during the follow-up period and increased marginal BL 12 months after treatment. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Are anesthesia start and end times randomly distributed? The influence of electronic records.

PubMed

Deal, Litisha G; Nyland, Michael E; Gravenstein, Nikolaus; Tighe, Patrick

2014-06-01

To perform a frequency analysis of start minute digits (SMD) and end minute digits (EMD) taken from the electronic, computer-assisted, and manual anesthesia billing-record systems. Retrospective cross-sectional review. University medical center. This cross-sectional review was conducted on billing records from a single healthcare institution over a 15-month period. A total of 30,738 cases were analyzed. For each record, the start time and end time were recorded. Distributions of SMD and EMD were tested against the null hypothesis of a frequency distribution equivalently spread between zero and nine. SMD and EMD aggregate distributions each differed from equivalency (P < 0.0001). When stratified by type of anesthetic record, no differences were found between the recorded and expected equivalent distribution patterns for electronic anesthesia records for start minute (P < 0.98) or end minute (P < 0.55). Manual and computer-assisted records maintained nonequivalent distribution patterns for SMD and EMD (P < 0.0001 for each comparison). Comparison of cumulative distributions between SMD and EMD distributions suggested a significant difference between the two patterns (P < 0.0001). An electronic anesthesia record system, with automated time capture of events verified by the user, produces a more unified distribution of billing times than do more traditional methods of entering billing times. Copyright © 2014 Elsevier Inc. All rights reserved.

Explosive decomposition of hydrazine by rapid compression of a gas volume

NASA Technical Reports Server (NTRS)

Bunker, R. L.; Baker, D. L.; Lee, J. H. S.

1991-01-01

In the present investigation of the initiation mechanism and the explosion mode of hydrazine decomposition, a 20 cm-long column of liquid hydrazine was accelerated into a column of gaseous nitrogen, from which it was separated by a thin Teflon diaphragm, in a close-ended cylindrical chamber. Video data obtained reveal the formation of a froth generated by the acceleration of hydrazine into nitrogen at the liquid hydrazine-gaseous nitrogen interface. The explosive hydrazine decomposition had as its initiation mechanism the formation of a froth at a critical temperature; the explosion mode of hydrazine is a confined thermal runaway reaction.

Using dynamic mode decomposition for real-time background/foreground separation in video

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kutz, Jose Nathan; Grosek, Jacob; Brunton, Steven

The technique of dynamic mode decomposition (DMD) is disclosed herein for the purpose of robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time. Foreground/background separation is achieved at the computational cost of just one singular value decomposition (SVD) and one linear equation solve, thus producing results orders of magnitude faster than robust principal component analysis (RPCA). Additional techniques, including techniques for analyzing the video for multi-resolution time-scale components, and techniques for reusing computations to allow processing of streaming video in real time, are also described herein.

TEMPORAL SIGNATURES OF AIR QUALITY OBSERVATIONS AND MODEL OUTPUTS: DO TIME SERIES DECOMPOSITION METHODS CAPTURE RELEVANT TIME SCALES?

EPA Science Inventory

Time series decomposition methods were applied to meteorological and air quality data and their numerical model estimates. Decomposition techniques express a time series as the sum of a small number of independent modes which hypothetically represent identifiable forcings, thereb...

Analysis of Coherent Phonon Signals by Sparsity-promoting Dynamic Mode Decomposition

NASA Astrophysics Data System (ADS)

Murata, Shin; Aihara, Shingo; Tokuda, Satoru; Iwamitsu, Kazunori; Mizoguchi, Kohji; Akai, Ichiro; Okada, Masato

2018-05-01

We propose a method to decompose normal modes in a coherent phonon (CP) signal by sparsity-promoting dynamic mode decomposition. While the CP signals can be modeled as the sum of finite number of damped oscillators, the conventional method such as Fourier transform adopts continuous bases in a frequency domain. Thus, the uncertainty of frequency appears and it is difficult to estimate the initial phase. Moreover, measurement artifacts are imposed on the CP signal and deforms the Fourier spectrum. In contrast, the proposed method can separate the signal from the artifact precisely and can successfully estimate physical properties of the normal modes.

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization

PubMed Central

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods. PMID:28222194

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.

PubMed

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

Enamel matrix derivative (Emdogain(R)) for periodontal tissue regeneration in intrabony defects.

PubMed

Esposito, Marco; Grusovin, Maria Gabriella; Papanikolaou, Nikolaos; Coulthard, Paul; Worthington, Helen V

2009-10-07

Periodontitis is a chronic infective disease of the gums caused by bacteria present in dental plaque. This condition induces the breakdown of the tooth supporting apparatus until teeth are lost. Surgery may be indicated to arrest disease progression and regenerate lost tissues. Several surgical techniques have been developed to regenerate periodontal tissues including guided tissue regeneration (GTR), bone grafting (BG) and the use of enamel matrix derivative (EMD). EMD is an extract of enamel matrix and contains amelogenins of various molecular weights. Amelogenins are involved in the formation of enamel and periodontal attachment formation during tooth development. To test whether EMD is effective, and to compare EMD versus GTR, and various BG procedures for the treatment of intrabony defects. We searched the Cochrane Oral Health Group Trials Register, CENTRAL, MEDLINE and EMBASE. Several journals were handsearched. No language restrictions were applied. Authors of randomised controlled trials (RCTs) identified, personal contacts and the manufacturer were contacted to identify unpublished trials. Most recent search: February 2009. RCTs on patients affected by periodontitis having intrabony defects of at least 3 mm treated with EMD compared with open flap debridement, GTR and various BG procedures with at least 1 year follow up. The outcome measures considered were: tooth loss, changes in probing attachment levels (PAL), pocket depths (PPD), gingival recessions (REC), bone levels from the bottom of the defects on intraoral radiographs, aesthetics and adverse events. The following time-points were to be evaluated: 1, 5 and 10 years. Screening of eligible studies, assessment of the methodological quality of the trials and data extraction were conducted in duplicate and independently by two authors. Results were expressed as random-effects models using mean differences for continuous outcomes and risk ratios (RR) for dichotomous outcomes with 95% confidence intervals (CI). It was decided not to investigate heterogeneity, but a sensitivity analysis for the risk of bias of the trials was performed. Thirteen trials were included out of 35 potentially eligible trials. No included trial presented data after 5 years of follow up, therefore all data refer to the 1-year time point. A meta-analysis including nine trials showed that EMD treated sites displayed statistically significant PAL improvements (mean difference 1.1 mm, 95% CI 0.61 to 1.55) and PPD reduction (0.9 mm, 95% CI 0.44 to 1.31) when compared to placebo or control treated sites, though a high degree of heterogeneity was found. Significantly more sites had < 2 mm PAL gain in the control group, with RR 0.53 (95% CI 0.34 to 0.82). Approximately nine patients needed to be treated (NNT) to have one patient gaining 2 mm or more PAL over the control group, based on a prevalence in the control group of 25%. No differences in tooth loss or aesthetic appearance as judged by the patients were observed. When evaluating only trials at a low risk of bias in a sensitivity analysis (four trials), the effect size for PAL was 0.62 mm (95% CI 0.28 to 0.96), which was less than 1.1 mm for the overall result. Comparing EMD with GTR (five trials), GTR showed statistically significant more postoperative complications (three trials, RR 0.12, 95% CI 0.02 to 0.85) and more REC (0.4 mm 95% CI 0.15 to 0.66). The only trial comparing EMD with a bioactive ceramic filler found statistically significant more REC (-1.60 mm, 95% CI -2.74 to -0.46) at the EMG treated sites. One year after its application, EMD significantly improved PAL levels (1.1 mm) and PPD reduction (0.9 mm) when compared to a placebo or control, however, the high degree of heterogeneity observed among trials suggests that results have to be interpreted with great caution. In addition, a sensitivity analysis indicated that the overall treatment effect might be overestimated. The actual clinical advantages of using EMD are unknown. With the exception of significantly more postoperative complications in the GTR group, there was no evidence of clinically important differences between GTR and EMD. Bone substitutes may be associated with less REC than EMD.

VELOCITY FIELD OF COMPRESSIBLE MAGNETOHYDRODYNAMIC TURBULENCE: WAVELET DECOMPOSITION AND MODE SCALINGS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kowal, Grzegorz; Lazarian, A., E-mail: kowal@astro.wisc.ed, E-mail: lazarian@astro.wisc.ed

We study compressible magnetohydrodynamic turbulence, which holds the key to many astrophysical processes, including star formation and cosmic-ray propagation. To account for the variations of the magnetic field in the strongly turbulent fluid, we use wavelet decomposition of the turbulent velocity field into Alfven, slow, and fast modes, which presents an extension of the Cho and Lazarian decomposition approach based on Fourier transforms. The wavelets allow us to follow the variations of the local direction of the magnetic field and therefore improve the quality of the decomposition compared to the Fourier transforms, which are done in the mean field referencemore » frame. For each resulting component, we calculate the spectra and two-point statistics such as longitudinal and transverse structure functions as well as higher order intermittency statistics. In addition, we perform a Helmholtz- Hodge decomposition of the velocity field into incompressible and compressible parts and analyze these components. We find that the turbulence intermittency is different for different components, and we show that the intermittency statistics depend on whether the phenomenon was studied in the global reference frame related to the mean magnetic field or in the frame defined by the local magnetic field. The dependencies of the measures we obtained are different for different components of the velocity; for instance, we show that while the Alfven mode intermittency changes marginally with the Mach number, the intermittency of the fast mode is substantially affected by the change.« less

Palm vein recognition based on directional empirical mode decomposition

NASA Astrophysics Data System (ADS)

Lee, Jen-Chun; Chang, Chien-Ping; Chen, Wei-Kuei

2014-04-01

Directional empirical mode decomposition (DEMD) has recently been proposed to make empirical mode decomposition suitable for the processing of texture analysis. Using DEMD, samples are decomposed into a series of images, referred to as two-dimensional intrinsic mode functions (2-D IMFs), from finer to large scale. A DEMD-based 2 linear discriminant analysis (LDA) for palm vein recognition is proposed. The proposed method progresses through three steps: (i) a set of 2-D IMF features of various scale and orientation are extracted using DEMD, (ii) the 2LDA method is then applied to reduce the dimensionality of the feature space in both the row and column directions, and (iii) the nearest neighbor classifier is used for classification. We also propose two strategies for using the set of 2-D IMF features: ensemble DEMD vein representation (EDVR) and multichannel DEMD vein representation (MDVR). In experiments using palm vein databases, the proposed MDVR-based 2LDA method achieved recognition accuracy of 99.73%, thereby demonstrating its feasibility for palm vein recognition.

Spatial variations of sea level along the coast of Thailand: Impacts of extreme land subsidence, earthquakes and the seasonal monsoon

NASA Astrophysics Data System (ADS)

Saramul, Suriyan; Ezer, Tal

2014-11-01

The study addresses two important issues associated with sea level along the coasts of Thailand: first, the fast sea level rise and its spatial variation, and second, the monsoonal-driven seasonal variations in sea level. Tide gauge data that are more extensive than in past studies were obtained from several different local and global sources, and relative sea level rise (RSLR) rates were obtained from two different methods, linear regressions and non-linear Empirical Mode Decomposition/Hilbert-Huang Transform (EMD/HHT) analysis. The results show extremely large spatial variations in RSLR, with rates varying from ~ 1 mm y-1 to ~ 20 mm y-1; the maximum RSLR is found in the upper Gulf of Thailand (GOT) near Bangkok, where local land subsidence due to groundwater extraction dominates the trend. Furthermore, there are indications that RSLR rates increased significantly in all locations after the 2004 Sumatra-Andaman Earthquake and the Indian Ocean tsunami that followed, so that recent RSLR rates seem to have less spatial differences than in the past, but with high rates of ~ 20-30 mm y-1 almost everywhere. The seasonal sea level cycle was found to be very different between stations in the GOT, which have minimum sea level in June-July, and stations in the Andaman Sea, which have minimum sea level in February. The seasonal sea-level variations in the GOT are driven mostly by large-scale wind-driven set-up/set-down processes associated with the seasonal monsoon and have amplitudes about ten times larger than either typical steric changes at those latitudes or astronomical annual tides.

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

NASA Astrophysics Data System (ADS)

Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

2016-06-01

To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

Aircraft Survivability. Susceptibility Reduction. Fall 2010

DTIC Science & Technology

2010-01-01

limits flexibility when issues are encountered during development. Once a program enters Engineering, Manufacturing, and Development (EMD), the...using a flexible , efficient computational environment based on a credible set of components. Unfortunately, current survivability codes contain many...approach limits flexibility when issues are encountered during development. Once a program enters Engineering Manufacturing and Development (EMD), the

EMDS users guide (version 2.0): knowledge-based decision support for ecological assessment.

Treesearch

Keith M. Reynolds

1999-01-01

The USDA Forest Service Pacific Northwest Research Station in Corvallis, Oregon, has developed the ecosystem management decision support (EMDS) system. The system integrates the logical formalism of knowledge-based reasoning into a geographic information system (GIS) environment to provide decision support for ecological landscape assessment and evaluation. The...

Two Dimensional Finite Element Based Magnetotelluric Inversion using Singular Value Decomposition Method on Transverse Electric Mode

NASA Astrophysics Data System (ADS)

Tjong, Tiffany; Yihaa’ Roodhiyah, Lisa; Nurhasan; Sutarno, Doddy

2018-04-01

In this work, an inversion scheme was performed using a vector finite element (VFE) based 2-D magnetotelluric (MT) forward modelling. We use an inversion scheme with Singular value decomposition (SVD) method toimprove the accuracy of MT inversion.The inversion scheme was applied to transverse electric (TE) mode of MT. SVD method was used in this inversion to decompose the Jacobian matrices. Singular values which obtained from the decomposition process were analyzed. This enabled us to determine the importance of data and therefore to define a threshold for truncation process. The truncation of singular value in inversion processcould improve the resulted model.

Mechanistic insight into prolonged electromechanical delay in dyssynchronous heart failure: a computational study

PubMed Central

Constantino, Jason; Hu, Yuxuan; Lardo, Albert C.

2013-01-01

In addition to the left bundle branch block type of electrical activation, there are further remodeling aspects associated with dyssynchronous heart failure (HF) that affect the electromechanical behavior of the heart. Among the most important are altered ventricular structure (both geometry and fiber/sheet orientation), abnormal Ca2+ handling, slowed conduction, and reduced wall stiffness. In dyssynchronous HF, the electromechanical delay (EMD), the time interval between local myocyte depolarization and myofiber shortening onset, is prolonged. However, the contributions of the four major HF remodeling aspects in extending EMD in the dyssynchronous failing heart remain unknown. The goal of this study was to determine the individual and combined contributions of HF-induced remodeling aspects to EMD prolongation. We used MRI-based models of dyssynchronous nonfailing and HF canine electromechanics and constructed additional models in which varying combinations of the four remodeling aspects were represented. A left bundle branch block electrical activation sequence was simulated in all models. The simulation results revealed that deranged Ca2+ handling is the primary culprit in extending EMD in dyssynchronous HF, with the other aspects of remodeling contributing insignificantly. Mechanistically, we found that abnormal Ca2+ handling in dyssynchronous HF slows myofiber shortening velocity at the early-activated septum and depresses both myofiber shortening and stretch rate at the late-activated lateral wall. These changes in myofiber dynamics delay the onset of myofiber shortening, thus giving rise to prolonged EMD in dyssynchronous HF. PMID:23934857

Molecular modeling study of binding to the catalytic site of PDE4 enzymes by a novel class of inhibitors

NASA Astrophysics Data System (ADS)

Lawrenz, Morgan E.; Salter, E. A.; Wierzbicki, Andrzej; Thompson, W. J.

Cyclic nucleotide phosphodiesterases (PDEs) comprise a superfamily of enzymes that hydrolyze the second messengers adenosine and guanosine 3',5'-cyclic monophosphate (cAMP and cGMP) to their noncyclic nucleotides (5'-AMP and 5'-GMP). Selective inhibitors of all 11 gene families of PDEs are being sought based on the different biochemical properties of the different isoforms, including their substrate specificities. The PDE4 gene family consists of cAMP-specific isoforms; selective PDE4 inhibitors such as rolipram have been developed, and related agents are used clinically as anti-inflammatory agents for asthma and COPD. The known crystal structures of PDE4 bound with rolipram and IBMX have allowed us to define plausible binding orientations for a novel class of benzylpyridazinone-based PDE4 inhibitors represented by EMD 94360 and EMD 95832 that are structurally distinct from rolipram. Molecular mechanics modeling with autodocking is used to explore energetically favorable binding orientations within the PDE4 catalytic site. We present two putative orientations for EMD 94360/95832 inhibitor binding. Our estimated interaction energies for rolipram, IBMX, EMD 94360, and EMD 95832 are consistent with the experimental data for their IC50 values. Key binding residues and interactions in these orientations are identified and compared with known binding motifs proposed for rolipram. The experimentally observed improved strength of inhibition exhibited by this novel class of PDE4 inhibitors is explained by the molecular modeling reported here.

Electronic medical devices: a primer for pathologists.

PubMed

Weitzman, James B

2003-07-01

Electronic medical devices (EMDs) with downloadable memories, such as implantable cardiac pacemakers, defibrillators, drug pumps, insulin pumps, and glucose monitors, are now an integral part of routine medical practice in the United States, and functional organ replacements, such as the artificial heart, pancreas, and retina, will most likely become commonplace in the near future. Often, EMDs end up in the hands of the pathologist as a surgical specimen or at autopsy. No established guidelines for systematic examination and reporting or comprehensive reviews of EMDs currently exist for the pathologist. To provide pathologists with a general overview of EMDs, including a brief history; epidemiology; essential technical aspects, indications, contraindications, and complications of selected devices; potential applications in pathology; relevant government regulations; and suggested examination and reporting guidelines. Articles indexed on PubMed of the National Library of Medicine, various medical and history of medicine textbooks, US Food and Drug Administration publications and product information, and specifications provided by device manufacturers. Studies were selected on the basis of relevance to the study objectives. Descriptive data were selected by the author. Suggested examination and reporting guidelines for EMDs received as surgical specimens and retrieved at autopsy. Electronic medical devices received as surgical specimens and retrieved at autopsy are increasing in number and level of sophistication. They should be systematically examined and reported, should have electronic memories downloaded when indicated, will help pathologists answer more questions with greater certainty, and should become an integral part of the formal knowledge base, research focus, training, and practice of pathology.

What are the pros and cons of electronically monitoring inhaler use in asthma? A multistakeholder perspective.

PubMed

Howard, Sam; Lang, Alexandra; Sharples, Sarah; Shaw, Dominick

2016-01-01

Electronic monitoring devices (EMDs) are the optimal method for collecting objective data on inhaler use in asthma. Recent research has investigated the attitudes of patients with asthma towards these devices. However, no research to date has formally considered the opinions of stakeholders and decision-makers in asthma care. These individuals have important clinical requirements that need to be taken into account if EMDs are to be successfully provisioned, making collecting their opinions on the key barriers facing these devices a valuable process. Three rounds of surveys in a Delphi format were used to assess the most important pros and cons of EMDs for asthma care in a sample of 31 stakeholders which included healthcare professionals and members of clinical commissioning groups. The respondents identified 29 pros and 32 cons. Pros that were rated as most important included new visual evidence to aid clinical discussions with a patient and an increase in patient involvement and motivation. The cons that were rated as most important included a need for more clinical evidence of the effectiveness of EMDs, as well as better clarity over who has responsibilities in managing, interpreting and discussing data with a patient. The research provides a guide for EMD developers by highlighting where these devices may provide the most benefit as well as prioritising the key issues that need addressing if they are to be used effectively in everyday asthma care.

What are the pros and cons of electronically monitoring inhaler use in asthma? A multistakeholder perspective

PubMed Central

Howard, Sam; Lang, Alexandra; Sharples, Sarah; Shaw, Dominick

2016-01-01

Introduction Electronic monitoring devices (EMDs) are the optimal method for collecting objective data on inhaler use in asthma. Recent research has investigated the attitudes of patients with asthma towards these devices. However, no research to date has formally considered the opinions of stakeholders and decision-makers in asthma care. These individuals have important clinical requirements that need to be taken into account if EMDs are to be successfully provisioned, making collecting their opinions on the key barriers facing these devices a valuable process. Methods Three rounds of surveys in a Delphi format were used to assess the most important pros and cons of EMDs for asthma care in a sample of 31 stakeholders which included healthcare professionals and members of clinical commissioning groups. Results The respondents identified 29 pros and 32 cons. Pros that were rated as most important included new visual evidence to aid clinical discussions with a patient and an increase in patient involvement and motivation. The cons that were rated as most important included a need for more clinical evidence of the effectiveness of EMDs, as well as better clarity over who has responsibilities in managing, interpreting and discussing data with a patient. Conclusions The research provides a guide for EMD developers by highlighting where these devices may provide the most benefit as well as prioritising the key issues that need addressing if they are to be used effectively in everyday asthma care. PMID:27933181

Regenerative effect of basic fibroblast growth factor on periodontal healing in two-wall intrabony defects in dogs.

PubMed

Shirakata, Yoshinori; Taniyama, Katsuyoshi; Yoshimoto, Takehiko; Miyamoto, Motoharu; Takeuchi, Naoshi; Matsuyama, Takashi; Noguchi, Kazuyuki

2010-04-01

The aim of the present study was to evaluate the effect of a basic fibroblast growth factor (bFGF) candidate treatment on periodontal healing in two-wall intrabony defects in dogs. Two-wall intrabony defects (5 x 5 x 5 mm) were created surgically on the distal and mesial sides of bilateral mandibular second and fourth premolars in four Beagle dogs. bFGF, enamel matrix derivative (EMD) and platelet-derived growth factor with beta-tricalcium phosphate (PDGF/beta-TCP) treatments, and sham-surgery (OFD) were rotated among the four defects in each animal, EMD and PDGF/beta-TCP serving as benchmark controls. The animals were euthanized for radiographic and histologic evaluation at 8 weeks. Bone formation was significantly greater in the bFGF group (4.11 +/- 0.77 mm) than in the EMD (3.32 +/- 0.71 mm; p<0.05) and OFD (3.09 +/- 0.52 mm; p<0.01) groups. The EMD (4.59 +/- 1.19 mm) and PDGF/beta-TCP (4.66 +/- 0.7 mm) groups exhibited significantly greater cementum regeneration with periodontal ligament-like tissue than the OFD group (2.96 +/- 0.69 mm; p<0.01). No significant differences were observed between the bFGF and the PDGF/beta-TCP groups in any of the histometric parameters. The candidate bFGF treatment supported periodontal regeneration comparable with that of established benchmarks: EMD and PDGF/beta-TCP.

Depth information in natural environments derived from optic flow by insect motion detection system: a model analysis

PubMed Central

Schwegmann, Alexander; Lindemann, Jens P.; Egelhaaf, Martin

2014-01-01

Knowing the depth structure of the environment is crucial for moving animals in many behavioral contexts, such as collision avoidance, targeting objects, or spatial navigation. An important source of depth information is motion parallax. This powerful cue is generated on the eyes during translatory self-motion with the retinal images of nearby objects moving faster than those of distant ones. To investigate how the visual motion pathway represents motion-based depth information we analyzed its responses to image sequences recorded in natural cluttered environments with a wide range of depth structures. The analysis was done on the basis of an experimentally validated model of the visual motion pathway of insects, with its core elements being correlation-type elementary motion detectors (EMDs). It is the key result of our analysis that the absolute EMD responses, i.e., the motion energy profile, represent the contrast-weighted nearness of environmental structures during translatory self-motion at a roughly constant velocity. In other words, the output of the EMD array highlights contours of nearby objects. This conclusion is largely independent of the scale over which EMDs are spatially pooled and was corroborated by scrutinizing the motion energy profile after eliminating the depth structure from the natural image sequences. Hence, the well-established dependence of correlation-type EMDs on both velocity and textural properties of motion stimuli appears to be advantageous for representing behaviorally relevant information about the environment in a computationally parsimonious way. PMID:25136314

Diagnostic utility, safety, and cost-effectiveness of emergency department-initiated early scheduled technetium-99m single photon emission computed tomography imaging followed by expedited outpatient cardiac clinic visits in acute chest pain syndromes.

PubMed

Wong, Raymond C; Sinha, Arvind Kumar; Mahadevan, Malcolm; Yeo, Tiong Cheng

2010-09-01

Conventional emergency department (EMD) approach to triaging acute chest pain syndromes may lead to unnecessary admissions, resulting to in-hospital bed occupancy and increased healthcare costs. We explore the diagnostic utility of early (less than a week) outpatient scheduled single photon emission computed tomography (SPECT) in intermediate-risk chest pain subjects who presented to EMD with non-diagnostic electrocardiogram and negative serum troponin level. Additionally, we intend to study the safety and cost-effectiveness of such a strategy. We conduct a prospective, non-randomized study of 108 subjects who fit the inclusion criteria. After SPECT studies, all subjects were evaluated in the cardiac clinic within 2 weeks of EMD visits. Final diagnosis of coronary artery disease and subsequent disposition to standard medical therapy or follow-on angiography were decided by incorporating pre-test clinical data and SPECT results. Adverse events defined as myocardial infarction and cardiac death was tracked between EMD visit and eventual therapy (either medical therapy or coronary revascularization). Finally, cost-effectiveness was determined based on estimated cost and days of hospitalization saved between standard strategies of ward admission for further evaluation versus the present early outpatient SPECT-based workflow. Among 108 subjects (mean age 58 years, 59% male) included for analysis, 82 (76%) had normal perfusion status. There was no statistical difference in baseline characteristics and prior ischemic heart disease history between groups. In the 26 abnormal perfusion subjects, seven had follow-on coronary angiography in which three were found to have significant stenotic coronary lesions, but only one had intervention performed. There was an unscheduled coronary angiography in the normal perfusion group that yielded normal coronary anatomy. There was no adverse clinical event in both groups. Compared with standard strategy, early outpatient SPECT initiated by EMD physicians followed by cardiac clinic evaluation resulted in 2.9 days of hospitalization or $781.23 saved per patient per EMD visit. EMD-initiated early SPECT studies followed by cardiac clinic evaluation in intermediate-risk acute chest pain syndromes with non-diagnostic ECG and negative serum troponin levels carries excellent diagnostic and therapeutic utility, in addition to being safe and cost-effective.

Linear stability analysis of detonations via numerical computation and dynamic mode decomposition

NASA Astrophysics Data System (ADS)

Kabanov, Dmitry I.; Kasimov, Aslan R.

2018-03-01

We introduce a new method to investigate linear stability of gaseous detonations that is based on an accurate shock-fitting numerical integration of the linearized reactive Euler equations with a subsequent analysis of the computed solution via the dynamic mode decomposition. The method is applied to the detonation models based on both the standard one-step Arrhenius kinetics and two-step exothermic-endothermic reaction kinetics. Stability spectra for all cases are computed and analyzed. The new approach is shown to be a viable alternative to the traditional normal-mode analysis used in detonation theory.

Fast Algorithms for Earth Mover Distance Based on Optimal Transport and L1 Regularization II

DTIC Science & Technology

2016-09-01

of optimal transport, the EMD problem can be reformulated as a familiar L1 minimization. We use a regularization which gives us a unique solution for...plays a central role in many applications, including image processing, computer vision and statistics etc. [13, 17, 20, 24]. The EMD is a metric defined

Eye Movement Dysfunction in First-Degree Relatives of Patients with Schizophrenia: A Meta-Analytic Evaluation of Candidate Endophenotypes

ERIC Educational Resources Information Center

Calkins, Monica E.; Iacono, William G.; Ones, Deniz S.

2008-01-01

Several forms of eye movement dysfunction (EMD) are regarded as promising candidate endophenotypes of schizophrenia. Discrepancies in individual study results have led to inconsistent conclusions regarding particular aspects of EMD in relatives of schizophrenia patients. To quantitatively evaluate and compare the candidacy of smooth pursuit,…

Some critical issues in the characterization of nanoscale thermal conductivity by molecular dynamics analysis

NASA Astrophysics Data System (ADS)

Ehsan Khaled, Mohammad; Zhang, Liangchi; Liu, Weidong

2018-07-01

The nanoscale thermal conductivity of a material can be significantly different from its value at the macroscale. Although a number of studies using the equilibrium molecular dynamics (EMD) with Green–Kubo (GK) formula have been conducted for nano-conductivity predictions, there are many problems in the analysis that have made the EMD results unreliable or misleading. This paper aims to clarify such critical issues through a thorough investigation on the effect and determination of the vital physical variables in the EMD-GK analysis, using the prediction of the nanoscale thermal conductivity of Si as an example. The study concluded that to have a reliable prediction, quantum correction, time step, simulation time, correlation time and system size are all crucial.

Filtration of human EEG recordings from physiological artifacts with empirical mode method

NASA Astrophysics Data System (ADS)

Grubov, Vadim V.; Runnova, Anastasiya E.; Khramova, Marina V.

2017-03-01

In the paper we propose the new method for dealing with noise and physiological artifacts in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We consider noises and physiological artifacts on EEG as specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from eye-moving artifacts and show high efficiency of the method.

Computer implemented empirical mode decomposition method, apparatus, and article of manufacture for two-dimensional signals

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2001-01-01

A computer implemented method of processing two-dimensional physical signals includes five basic components and the associated presentation techniques of the results. The first component decomposes the two-dimensional signal into one-dimensional profiles. The second component is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF's) from each profile based on local extrema and/or curvature extrema. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the profiles. In the third component, the IMF's of each profile are then subjected to a Hilbert Transform. The fourth component collates the Hilbert transformed IMF's of the profiles to form a two-dimensional Hilbert Spectrum. A fifth component manipulates the IMF's by, for example, filtering the two-dimensional signal by reconstructing the two-dimensional signal from selected IMF(s).

A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

PubMed

Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier

2017-02-15

The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

Decomposition-Based Failure Mode Identification Method for Risk-Free Design of Large Systems

NASA Technical Reports Server (NTRS)

Tumer, Irem Y.; Stone, Robert B.; Roberts, Rory A.; Clancy, Daniel (Technical Monitor)

2002-01-01

When designing products, it is crucial to assure failure and risk-free operation in the intended operating environment. Failures are typically studied and eliminated as much as possible during the early stages of design. The few failures that go undetected result in unacceptable damage and losses in high-risk applications where public safety is of concern. Published NASA and NTSB accident reports point to a variety of components identified as sources of failures in the reported cases. In previous work, data from these reports were processed and placed in matrix form for all the system components and failure modes encountered, and then manipulated using matrix methods to determine similarities between the different components and failure modes. In this paper, these matrices are represented in the form of a linear combination of failures modes, mathematically formed using Principal Components Analysis (PCA) decomposition. The PCA decomposition results in a low-dimensionality representation of all failure modes and components of interest, represented in a transformed coordinate system. Such a representation opens the way for efficient pattern analysis and prediction of failure modes with highest potential risks on the final product, rather than making decisions based on the large space of component and failure mode data. The mathematics of the proposed method are explained first using a simple example problem. The method is then applied to component failure data gathered from helicopter, accident reports to demonstrate its potential.

Assessment of club patrons' alcohol and drug use: the use of biological markers.

PubMed

Miller, Brenda A; Byrnes, Hilary F; Branner, Amy C; Voas, Robert; Johnson, Mark B

2013-11-01

Young adulthood (ages 18-25 years) represents a time when high-risk behaviors, including alcohol and drug use, peak. Electronic music dance events (EMDEs) featured at clubs provide an ecologic niche for these high-risk behaviors. This paper examines the prevalence of alcohol and drug use among EMDE patrons. Examination of personal characteristics associated with exit levels of alcohol and drug use identifies important indicators of risk taking for prevention strategies. Data were collected anonymously during 2010-2012 from 2028 patrons as they entered and exited clubs in the San Francisco Bay area featuring EMDEs. Nearly half were aged ≤25 years. Biological measures of drug and alcohol and self-reported personal characteristics were attained. Analyses were completed in 2012. At entrance, more than one fifth of patrons were positive for drug use and one fourth arrived either impaired (blood alcohol concentration [BAC]: 0.05%-0.079%) or intoxicated (BAC: >0.08%) by alcohol. At exit, one fourth tested positive for drugs, and nearly half were impaired or intoxicated by alcohol. Individual characteristics that were important for levels of risk included prior alcohol use behaviors, sexual identity, ethnic/racial identity, and transportation to the event. Gender did not differentiate for alcohol use but fewer women used drugs. Findings confirm the importance of targeting EMDEs for prevention efforts. EMDEs attract young working adults who are engaged in heavy alcohol and/or drug use. Targeting these social settings for delivering public health prevention strategies regarding alcohol and drug use and related harm is indicated by the findings. © 2013 American Journal of Preventive Medicine.

Ecosystem Management Decision Support (EMDS) Applied to Watershed Assessment on California's North Coast

Treesearch

Rich Walker; Chris Keithley; Russ Henly; Scott Downie; Steve Cannata

2007-01-01

In 2001, the state of California initiated the North Coast Watershed Assessment Program (2003a) to assemble information on the status of coastal watersheds that have historically supported anadromous fish. The five-agency consortium explored the use of Ecosystem Management Decision Support (EMDS) (Reynolds and others 1996) as a means to help assess overall watershed...

Decision support for evaluating the U.S. national criteria and indicators for forest ecosystem sustainability

Treesearch

Keith M. Reynolds

2006-01-01

This paper describes and illustrates the use of the Ecosystem Management Decision Support (EMDS) system for evaluating the U.S. national criteria and indicators for forest ecosystem sustainability at the scale of Resource Planning Act (RPA) regions. The evaluation component of EMDS uses a logic engine to evaluate landscape condition, and the RPA-scale application...

National fuel-treatment budgeting in US federal agencies: capturing opportunities for transparent decision-making

Treesearch

Keith M. Reynolds; Paul F. Hessburg; Robert E. Keane; James P. Menakis

2009-01-01

The Ecosystem Management Decision Support (EMDS) system has been used by the US Department of Agriculture, Forest Service and Bureaus of the Department of the Interior since 2006 to evaluate wildfire potential across all administrative units in the continental US, and to establish priorities for allocating fuel-treatment budgets. This article discusses an EMDS fuels-...

The English Monolingual Dictionary: Its Use among Second Year Students of University Technology of Malaysia, International Campus, Kuala Lumpur

ERIC Educational Resources Information Center

Manan, Amerrudin Abd.; Al-Zubaidi, Khairi Obaid

2011-01-01

This research was conducted to seek information on English Monolingual Dictionary (EMD) use among 2nd year students of Universiti Teknologi Malaysia, International Campus, Kuala Lumpur (UTMKL). Specifically, the researchers wish to discover, firstly, the students' habit and attitude in EMD use; secondly, to discover their knowledge with regard to…

Automatic network coupling analysis for dynamical systems based on detailed kinetic models.

PubMed

Lebiedz, Dirk; Kammerer, Julia; Brandt-Pollmann, Ulrich

2005-10-01

We introduce a numerical complexity reduction method for the automatic identification and analysis of dynamic network decompositions in (bio)chemical kinetics based on error-controlled computation of a minimal model dimension represented by the number of (locally) active dynamical modes. Our algorithm exploits a generalized sensitivity analysis along state trajectories and subsequent singular value decomposition of sensitivity matrices for the identification of these dominant dynamical modes. It allows for a dynamic coupling analysis of (bio)chemical species in kinetic models that can be exploited for the piecewise computation of a minimal model on small time intervals and offers valuable functional insight into highly nonlinear reaction mechanisms and network dynamics. We present results for the identification of network decompositions in a simple oscillatory chemical reaction, time scale separation based model reduction in a Michaelis-Menten enzyme system and network decomposition of a detailed model for the oscillatory peroxidase-oxidase enzyme system.

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

PubMed Central

Lv, Yong; Song, Gangbing

2018-01-01

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal. PMID:29659510

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.

PubMed

Yuan, Rui; Lv, Yong; Song, Gangbing

2018-04-16

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.

A Generalized Framework for Reduced-Order Modeling of a Wind Turbine Wake

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamilton, Nicholas; Viggiano, Bianca; Calaf, Marc

A reduced-order model for a wind turbine wake is sought from large eddy simulation data. Fluctuating velocity fields are combined in the correlation tensor to form the kernel of the proper orthogonal decomposition (POD). Proper orthogonal decomposition modes resulting from the decomposition represent the spatially coherent turbulence structures in the wind turbine wake; eigenvalues delineate the relative amount of turbulent kinetic energy associated with each mode. Back-projecting the POD modes onto the velocity snapshots produces dynamic coefficients that express the amplitude of each mode in time. A reduced-order model of the wind turbine wake (wakeROM) is defined through a seriesmore » of polynomial parameters that quantify mode interaction and the evolution of each POD mode coefficients. The resulting system of ordinary differential equations models the wind turbine wake composed only of the large-scale turbulent dynamics identified by the POD. Tikhonov regularization is used to recalibrate the dynamical system by adding additional constraints to the minimization seeking polynomial parameters, reducing error in the modeled mode coefficients. The wakeROM is periodically reinitialized with new initial conditions found by relating the incoming turbulent velocity to the POD mode coefficients through a series of open-loop transfer functions. The wakeROM reproduces mode coefficients to within 25.2%, quantified through the normalized root-mean-square error. A high-level view of the modeling approach is provided as a platform to discuss promising research directions, alternate processes that could benefit stability and efficiency, and desired extensions of the wakeROM.« less

Computer implemented empirical mode decomposition method, apparatus and article of manufacture

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

1999-01-01

A computer implemented physical signal analysis method is invented. This method includes two essential steps and the associated presentation techniques of the results. All the steps exist only in a computer: there are no analytic expressions resulting from the method. The first step is a computer implemented Empirical Mode Decomposition to extract a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform. The final result is the Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum.

Coherent mode decomposition using mixed Wigner functions of Hermite-Gaussian beams.

PubMed

Tanaka, Takashi

2017-04-15

A new method of coherent mode decomposition (CMD) is proposed that is based on a Wigner-function representation of Hermite-Gaussian beams. In contrast to the well-known method using the cross spectral density (CSD), it directly determines the mode functions and their weights without solving the eigenvalue problem. This facilitates the CMD of partially coherent light whose Wigner functions (and thus CSDs) are not separable, in which case the conventional CMD requires solving an eigenvalue problem with a large matrix and thus is numerically formidable. An example is shown regarding the CMD of synchrotron radiation, one of the most important applications of the proposed method.

Eliminating the zero spectrum in Fourier transform profilometry using empirical mode decomposition.

PubMed

Li, Sikun; Su, Xianyu; Chen, Wenjing; Xiang, Liqun

2009-05-01

Empirical mode decomposition is introduced into Fourier transform profilometry to extract the zero spectrum included in the deformed fringe pattern without the need for capturing two fringe patterns with pi phase difference. The fringe pattern is subsequently demodulated using a standard Fourier transform profilometry algorithm. With this method, the deformed fringe pattern is adaptively decomposed into a finite number of intrinsic mode functions that vary from high frequency to low frequency by means of an algorithm referred to as a sifting process. Then the zero spectrum is separated from the high-frequency components effectively. Experiments validate the feasibility of this method.

EMDS 3.0: A modeling framework for coping with complexity in environmental assessment and planning.

Treesearch

K.M. Reynolds

2006-01-01

EMDS 3.0 is implemented as an ArcMap® extension and integrates the logic engine of NetWeaver® to perform landscape evaluations, and the decision modeling engine of Criterium DecisionPlus® for evaluating management priorities. Key features of the system's evaluation component include abilities to (1) reason about large, abstract, multifaceted ecosystem management...

Mobile videoconferencing for enhanced emergency medical communication - a shot in the dark or a walk in the park? ‒‒ A simulation study.

PubMed

Melbye, Sigurd; Hotvedt, Martin; Bolle, Stein Roald

2014-06-02

Videoconferencing on mobile phones may enhance communication, but knowledge on its quality in various situations is needed before it can be used in medical emergencies. Mobile phones automatically activate loudspeaker functionality during videoconferencing, making calls particularly vulnerable to background noise. The aim of this study was to investigate if videoconferencing can be used between lay bystanders and Emergency Medical Dispatch (EMD) operators for initial emergency calls during medical emergencies, under suboptimal sound and light conditions. Videoconferencing was tested between 90 volunteers and an emergency medical dispatcher in a standardized scenario of a medical emergency. Three different environments were used for the trials: indoors with moderate background noise, outdoors with daylight and much background noise, and outdoors during nighttime with little background noise. Thirty participants were recruited for each of the three locations. After informed consent, each participant was asked to use a video mobile phone to communicate with an EMD operator. During the video call the EMD operator gave instructions for tasks to be performed by the participant. The video quality from the caller to the EMD was evaluated by the EMD operator and rated on a five step scale ranging from "not able to see" to "good video quality". Sound quality between participants and EMD operators was assessed by a method developed for this trial. Kruskal - Wallis and Chi-square tests were used for statistical analysis. Video quality was significantly different between the groups (p <0.001), and the nighttime group had lower video quality. For most sessions in the nighttime group it was still possible to see actions done at the simulated emergency site. All participants were able to perform their tasks according to the instructions given by dispatchers, although with a need for more repetitions during sessions with much background noise. No calls were rated by dispatchers as incomprehensible due to low sound quality and only 3% of the calls were considered somewhat difficult or very difficult to understand. Videoconferencing on mobile phones can be used for the initial emergency call during medical emergencies also in suboptimal conditions.

Experimental Modal Analysis and Dynamic Component Synthesis. Volume 3. Modal Parameter Estimation

DTIC Science & Technology

1987-12-01

residues as well as poles is achieved. A singular value decomposition method has been used to develop a complex mode indicator function ( CMIF )[70...which can be used to help determine the number of poles before the analysis. The CMIF is formed by performing a singular value decomposition of all of...servo systems which can include both low and high damping modes. "• CMIF can be used to indicate close or repeated eigenvalues before the parameter

Koopman Mode Decomposition Methods in Dynamic Stall: Reduced Order Modeling and Control

DTIC Science & Technology

2015-11-10

the flow phenomena by separating them into individual modes. The technique of Proper Orthogonal Decomposition (POD), see [ Holmes : 1998] is a popular...sampled values h(k), k = 0,…,2M-1, of the exponential sum 1. Solve the following linear system where 2. Compute all zeros zj  D, j = 1,…,M...of the Prony polynomial i.e., calculate all eigenvalues of the associated companion matrix and form fj = log zj for j = 1,…,M, where log is the

Electronic properties of CdWO{sub 4}: Use of hybrid exchange and correlation functionals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meena, B. S., E-mail: bsmphysics@gmail.com; Mund, H. S.; Ahuja, B. L.

Energy bands, density of states (DOS), Mulliken population (MP) and electron momentum densities (EMDs) of CdWO{sub 4} are presented using hybrid exchange and correlation functionals namely B3LYP, B3PW and PBE0. To validate the present hybrid potentials, theoretical EMDs have been compared with the experimental Compton profile. It is found that LCAO-B3LYP based Compton profile gives a better agreement with experiment than other theoretical profiles. The energy bands and DOS show a wide band gap semiconducting nature of CdWO{sub 4}. The theoretical band gap obtained using B3LYP scheme reconciles well with the available experimental data. In addition, we have also presentedmore » the anisotropies in EMDs along [100], [110] and [001] directions and the bonding effects using the MP data.« less

High-speed imaging of submerged jet: visualization analysis using proper orthogonality decomposition

NASA Astrophysics Data System (ADS)

Liu, Yingzheng; He, Chuangxin

2016-11-01

In the present study, the submerged jet at low Reynolds numbers was visualized using laser induced fluoresce and high-speed imaging in a water tank. Well-controlled calibration was made to determine linear dependency region of the fluoresce intensity on its concentration. Subsequently, the jet fluid issuing from a circular pipe was visualized using a high-speed camera. The animation sequence of the visualized jet flow field was supplied for the snapshot proper orthogonality decomposition (POD) analysis. Spatio-temporally varying structures superimposed in the unsteady fluid flow were identified, e.g., the axisymmetric mode and the helical mode, which were reflected from the dominant POD modes. The coefficients of the POD modes give strong indication of temporal and spectral features of the corresponding unsteady events. The reconstruction using the time-mean visualization and the selected POD modes was conducted to reveal the convective motion of the buried vortical structures. National Natural Science Foundation of China.

Joint Land Attack Cruise Missile Defense Elevated Netted Sensor System Not Ready for Production Decision (REDACTED)

DTIC Science & Technology

2012-09-07

Average Procurement Unit Cost CMDS Cruise Missile Defense Systems CPD Capability Production Document EMD Engineering and Manufacturing...Defense for Acquisition, Technology and Logistics also determined that continuing test and evaluation of the two JLENS Engineering and Manufacturing...Program (Category ID) that was established in January 1996 and, during the audit, was in the Engineering and Manufacturing Development (EMD) phase of

Influence of LVAD function on mechanical unloading and electromechanical delay: a simulation study.

PubMed

Heikhmakhtiar, Aulia Khamas; Ryu, Ah Jin; Shim, Eun Bo; Song, Kwang-Soup; Trayanova, Natalia A; Lim, Ki Moo

2018-05-01

This study hypothesized that a left ventricular assist device (LVAD) shortens the electromechanical delay (EMD) by mechanical unloading. The goal of this study is to examine, by computational modeling, the influence of LVAD on EMD for four heart failure (HF) cases ranging from mild HF to severe HF. We constructed an integrated model of an LVAD-implanted cardiovascular system, then we altered the Ca 2+ transient magnitude, with scaling factors 1, 0.9, 0.8, and 0.7 representing HF1, HF2, HF3, and HF4, respectively, in order of increasing HF severity. The four HF conditions are classified into two groups. Group one is the four HF conditions without LVAD, and group two is the conditions treated with continuous LVAD pump. The single-cell mechanical responses showed that EMD was prolonged with the higher load. The findings indicated that in group one, the HF-induced Ca2 + transient remodeling prolonged the mechanical activation time (MAT) and decreased the contractile tension, which reduced the left ventricle (LV) pressure, and increased the end-diastolic strain. In group two, LVAD shortened MAT of the ventricles. Furthermore, LVAD reduced the contractile tension, and end-diastolic strain, but increased the aortic pressure. The computational study demonstrated that LVAD shortens EMD by mechanical unloading of the ventricle.

Contribution of the serotonin 5-HT1A receptor agonism of 8-OH-DPAT and EMD 128130 to the regulation of haloperidol-induced muscle rigidity in rats.

PubMed

Lorenc-Koci, E; Wardas, J; Bartoszyk, G D; Wolfarth, S

2003-12-01

The aim of the present study was to find out whether (+/-)-8-hydroxy-2(di-n-propylamino)tetralin (8-OH-DPAT), a prototypical 5-HT1A agonist, and (R)-(-)-2-[5-(4-fluorophenyl)-3-pyridylmethylaminomethyl]-chromane HCl (EMD 128130), a compound with serotonin 5-HT1A-agonist and dopamine D2-like antagonist properties, are able to attenuate the haloperidol-induced (1 mg/kg) muscle rigidity in rats. Muscle tone was examined using a combined mechano- and electromyographic (EMG) method that simultaneously measured the mechanical muscle resistance (MMG) of the rat's hind foot to passive movements in the ankle joint, and the EMG activity of two antagonist muscles. Both 8-OH-DPAT (0.125-0.5 mg/kg i.p.) and EMD 128130 (1-10 mg/kg i.p.) dose-dependently decreased the haloperidol-enhanced MMG to passive movements, as well as the tonic and the long-latency reflex EMG activities. Provided these results can be extrapolated to humans, the efficacy of EMD 128130 in relieving the haloperidol-induced muscle rigidity supports the concept that novel antipsychotics with 5-HT1A agonist and dopamine D2 antagonist activities should have a favourable extrapyramidal side-effect profile.

Evaluation of peri-implant crevicular fluid prostaglandin E2 levels in augmented extraction sockets by different biomaterials.

PubMed

Alkan, Eylem Ayhan; Tüter, Gülay; Parlar, Ateş; Yücel, Ayşegül; Kurtiş, Bülent

2016-10-01

This study compares peri-implant crevicular fluid (PICF) prostaglandin E 2 (PGE 2 ) levels, clinical parameters and implant stability quotient (ISQ) values around implants placed in augmented extraction sockets. The sockets (24 in total) were randomly augmented using either EMD or Bio-Oss Collagen. Implant placements were performed after three months of healing. ISQ readings were evaluated at three points: at the time of surgery, at the first month and at the third month. PICF was collected for PGE 2 evaluation after the first and the third months of implant surgery. After the first month, a higher level of PICF PGE 2 was observed in the EMD group than in the Bio-Oss Collagen group, and this increase was of statistical significance; however, at the third month there was no statistically significant difference in PICF PGE 2 levels between the two groups. For implants placed in EMD sites, ISQ values were statistically higher at the third month than at the first month, while no significant differences in ISQ value were detected between the first and third months in Bio-Oss Collagen sites. The results of this research suggest that both EMD and Bio-Oss Collagen are effective treatment modalities for stimulating the formation of new bone at extraction sites prior to implant surgery.

The processing of aluminum gasarites via thermal decomposition of interstitial hydrides

NASA Astrophysics Data System (ADS)

Licavoli, Joseph J.

Gasarite structures are a unique type of metallic foam containing tubular pores. The original methods for their production limited them to laboratory study despite appealing foam properties. Thermal decomposition processing of gasarites holds the potential to increase the application of gasarite foams in engineering design by removing several barriers to their industrial scale production. The following study characterized thermal decomposition gasarite processing both experimentally and theoretically. It was found that significant variation was inherent to this process therefore several modifications were necessary to produce gasarites using this method. Conventional means to increase porosity and enhance pore morphology were studied. Pore morphology was determined to be more easily replicated if pores were stabilized by alumina additions and powders were dispersed evenly. In order to better characterize processing, high temperature and high ramp rate thermal decomposition data were gathered. It was found that the high ramp rate thermal decomposition behavior of several hydrides was more rapid than hydride kinetics at low ramp rates. This data was then used to estimate the contribution of several pore formation mechanisms to the development of pore structure. It was found that gas-metal eutectic growth can only be a viable pore formation mode if non-equilibrium conditions persist. Bubble capture cannot be a dominant pore growth mode due to high bubble terminal velocities. Direct gas evolution appears to be the most likely pore formation mode due to high gas evolution rate from the decomposing particulate and microstructural pore growth trends. The overall process was evaluated for its economic viability. It was found that thermal decomposition has potential for industrialization, but further refinements are necessary in order for the process to be viable.

A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition

PubMed Central

Wang, Huaqing; Li, Ruitong; Tang, Gang; Yuan, Hongfang; Zhao, Qingliang; Cao, Xi

2014-01-01

A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system. PMID:25289644

Analysis of turbulent synthetic jet by dynamic mode decomposition

NASA Astrophysics Data System (ADS)

Hyhlík, Tomáš; Netřebská, Hana; Devera, Jakub; Kalinay, Radomír

The article deals with the analysis of CFD results of the turbulent synthetic jet. The numerical simulation of Large Eddy Simulation (LES) using commercial solver ANSYS CFX has been performed. The unsteady flow field is studied from the point of view of identification of the moving vortex ring, which has been identified both on the snapshots of flow field using swirling-strength criterion and using the Dynamic Mode Decomposition (DMD) of five periods. It is shown that travelling vortex ring vanishes due to interaction with vortex structures in the synthesised turbulent jet. DMD modes with multiple of the basic frequency of synthetic jet, which are connected with travelling vortex structure, have largest DMD amplitudes.

Signal enhancement based on complex curvelet transform and complementary ensemble empirical mode decomposition

NASA Astrophysics Data System (ADS)

Dong, Lieqian; Wang, Deying; Zhang, Yimeng; Zhou, Datong

2017-09-01

Signal enhancement is a necessary step in seismic data processing. In this paper we utilize the complementary ensemble empirical mode decomposition (CEEMD) and complex curvelet transform (CCT) methods to separate signal from random noise further to improve the signal to noise (S/N) ratio. Firstly, the original data with noise is decomposed into a series of intrinsic mode function (IMF) profiles with the aid of CEEMD. Then the IMFs with noise are transformed into CCT domain. By choosing different thresholds which are based on the noise level difference of each IMF profile, the noise in original data can be suppressed. Finally, we illustrate the effectiveness of the approach by simulated and field datasets.

Modal characteristics of a simplified brake rotor model using semi-analytical Rayleigh Ritz method

NASA Astrophysics Data System (ADS)

Zhang, F.; Cheng, L.; Yam, L. H.; Zhou, L. M.

2006-10-01

Emphasis of this paper is given to the modal characteristics of a brake rotor which is utilized in automotive disc brake system. The brake rotor is modeled as a combined structure comprising an annular plate connected to a segment of cylindrical shell by distributed artificial springs. Modal analysis shows the existence of three types of modes for the combined structure, depending on the involvement of each substructure. A decomposition technique is proposed, allowing each mode of the combined structure to be decomposed into a linear combination of the individual substructure modes. It is shown that the decomposition coefficients provide a direct and systematic means to carry out modal classification and quantification.

Analysis of Self-Excited Combustion Instabilities Using Decomposition Techniques

DTIC Science & Technology

2016-07-05

are evaluated for the study of self-excited longitudinal combustion instabilities in laboratory-scaled single-element gas turbine and rocket...Air Force Base, California 93524 DOI: 10.2514/1.J054557 Proper orthogonal decomposition and dynamic mode decomposition are evaluated for the study of...instabilities. In addition, we also evaluate the capabilities of the methods to deal with data sets of different spatial extents and temporal resolution

Approaches to optimization of SS/TDMA time slot assignment. [satellite switched time division multiple access

NASA Technical Reports Server (NTRS)

Wade, T. O.

1984-01-01

Reduction techniques for traffic matrices are explored in some detail. These matrices arise in satellite switched time-division multiple access (SS/TDMA) techniques whereby switching of uplink and downlink beams is required to facilitate interconnectivity of beam zones. A traffic matrix is given to represent that traffic to be transmitted from n uplink beams to n downlink beams within a TDMA frame typically of 1 ms duration. The frame is divided into segments of time and during each segment a portion of the traffic is represented by a switching mode. This time slot assignment is characterized by a mode matrix in which there is not more than a single non-zero entry on each line (row or column) of the matrix. Investigation is confined to decomposition of an n x n traffic matrix by mode matrices with a requirement that the decomposition be 100 percent efficient or, equivalently, that the line(s) in the original traffic matrix whose sum is maximal (called critical line(s)) remain maximal as mode matrices are subtracted throughout the decomposition process. A method of decomposition of an n x n traffic matrix by mode matrices results in a number of steps that is bounded by n(2) - 2n + 2. It is shown that this upper bound exists for an n x n matrix wherein all the lines are maximal (called a quasi doubly stochastic (QDS) matrix) or for an n x n matrix that is completely arbitrary. That is, the fact that no method can exist with a lower upper bound is shown for both QDS and arbitrary matrices, in an elementary and straightforward manner.

Damage detection of an in-service condensation pipeline joint

NASA Astrophysics Data System (ADS)

Briand, Julie; Rezaei, Davood; Taheri, Farid

2010-04-01

The early detection of damage in structural or mechanical systems is of vital importance. With early detection, the damage may be repaired before the integrity of the system is jeopardized, resulting in monetary losses, loss of life or limb, and environmental impacts. Among the various types of structural health monitoring techniques, vibration-based methods are of significant interest since the damage location does not need to be known beforehand, making it a more versatile approach. The non-destructive damage detection method used for the experiments herein is a novel vibration-based method which uses an index called the EMD Energy Damage Index, developed with the aim of providing improved qualitative results compared to those methods currently available. As part of an effort to establish the integrity and limitation of this novel damage detection method, field testing was completed on a mechanical pipe joint on a condensation line, located in the physical plant of Dalhousie University. Piezoceramic sensors, placed at various locations around the joint were used to monitor the free vibration of the pipe imposed through the use of an impulse hammer. Multiple damage progression scenarios were completed, each having a healthy state and multiple damage cases. Subsequently, the recorded signals from the healthy and damaged joint were processed through the EMD Energy Damage Index developed in-house in an effort to detect the inflicted damage. The proposed methodology successfully detected the inflicted damages. In this paper, the effects of impact location, sensor location, frequency bandwidth, intrinsic mode functions, and boundary conditions are discussed.

Empirical mode decomposition apparatus, method and article of manufacture for analyzing biological signals and performing curve fitting

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2004-01-01

A computer implemented physical signal analysis method includes four basic steps and the associated presentation techniques of the results. The first step is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform which produces a Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum. The third step filters the physical signal by combining a subset of the IMFs. In the fourth step, a curve may be fitted to the filtered signal which may not have been possible with the original, unfiltered signal.

Empirical mode decomposition apparatus, method and article of manufacture for analyzing biological signals and performing curve fitting

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2002-01-01

A computer implemented physical signal analysis method includes four basic steps and the associated presentation techniques of the results. The first step is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform which produces a Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum. The third step filters the physical signal by combining a subset of the IMFs. In the fourth step, a curve may be fitted to the filtered signal which may not have been possible with the original, unfiltered signal.

Computer implemented empirical mode decomposition method apparatus, and article of manufacture utilizing curvature extrema

NASA Technical Reports Server (NTRS)

Shen, Zheng (Inventor); Huang, Norden Eh (Inventor)

2003-01-01

A computer implemented physical signal analysis method is includes two essential steps and the associated presentation techniques of the results. All the steps exist only in a computer: there are no analytic expressions resulting from the method. The first step is a computer implemented Empirical Mode Decomposition to extract a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals based on local extrema and curvature extrema. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform. The final result is the Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum.

Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM10 forecasting

NASA Astrophysics Data System (ADS)

Luo, Hongyuan; Wang, Deyun; Yue, Chenqiang; Liu, Yanling; Guo, Haixiang

2018-03-01

In this paper, a hybrid decomposition-ensemble learning paradigm combining error correction is proposed for improving the forecast accuracy of daily PM10 concentration. The proposed learning paradigm is consisted of the following two sub-models: (1) PM10 concentration forecasting model; (2) error correction model. In the proposed model, fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) are applied to disassemble original PM10 concentration series and error sequence, respectively. The extreme learning machine (ELM) model optimized by cuckoo search (CS) algorithm is utilized to forecast the components generated by FEEMD and VMD. In order to prove the effectiveness and accuracy of the proposed model, two real-world PM10 concentration series respectively collected from Beijing and Harbin located in China are adopted to conduct the empirical study. The results show that the proposed model performs remarkably better than all other considered models without error correction, which indicates the superior performance of the proposed model.

Robust-mode analysis of hydrodynamic flows

NASA Astrophysics Data System (ADS)

Roy, Sukesh; Gord, James R.; Hua, Jia-Chen; Gunaratne, Gemunu H.

2017-04-01

The emergence of techniques to extract high-frequency high-resolution data introduces a new avenue for modal decomposition to assess the underlying dynamics, especially of complex flows. However, this task requires the differentiation of robust, repeatable flow constituents from noise and other irregular features of a flow. Traditional approaches involving low-pass filtering and principle components analysis have shortcomings. The approach outlined here, referred to as robust-mode analysis, is based on Koopman decomposition. Three applications to (a) a counter-rotating cellular flame state, (b) variations in financial markets, and (c) turbulent injector flows are provided.

Defect inspection using a time-domain mode decomposition technique

NASA Astrophysics Data System (ADS)

Zhu, Jinlong; Goddard, Lynford L.

2018-03-01

In this paper, we propose a technique called time-varying frequency scanning (TVFS) to meet the challenges in killer defect inspection. The proposed technique enables the dynamic monitoring of defects by checking the hopping in the instantaneous frequency data and the classification of defect types by comparing the difference in frequencies. The TVFS technique utilizes the bidimensional empirical mode decomposition (BEMD) method to separate the defect information from the sea of system errors. This significantly improve the signal-to-noise ratio (SNR) and moreover, it potentially enables reference-free defect inspection.

Fundamental Studies of Beta Phase Decomposition Modes in Titanium Alloys

DTIC Science & Technology

1989-01-31

and H. I. Aaronson, "The Carbon-Carbon Interaction Energy in Alpha Fe- C Alloys", Acta Met., in press. Raju V. Ramanujan , H. I. Aaronson and P. H. Leo...ACCESSIO% %. C 20332 61102F 2306 Al 11 TITLE (Include Security Classification) FUNDAMENTAL STUDIES OF BETA PHASE DECOMPOSITION MODES IN TITANIUM ALLOYS 12...SECUR1Tv CLASSiI-CAtION M) UNCLASSIFIED/UNLIMITED C SAME AS RPT C ] YfC ’SERS UNCLASSIFIED 22a NAME OF RESPONSIBLE INOI’JIDUAL 22b TELEPwONE (Include Area

A novel energy conversion based method for velocity correction in molecular dynamics simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Hanhui; Collaborative Innovation Center of Advanced Aero-Engine, Hangzhou 310027; Liu, Ningning

2017-05-01

Molecular dynamics (MD) simulation has become an important tool for studying micro- or nano-scale dynamics and the statistical properties of fluids and solids. In MD simulations, there are mainly two approaches: equilibrium and non-equilibrium molecular dynamics (EMD and NEMD). In this paper, a new energy conversion based correction (ECBC) method for MD is developed. Unlike the traditional systematic correction based on macroscopic parameters, the ECBC method is developed strictly based on the physical interaction processes between the pair of molecules or atoms. The developed ECBC method can apply to EMD and NEMD directly. While using MD with this method, themore » difference between the EMD and NEMD is eliminated, and no macroscopic parameters such as external imposed potentials or coefficients are needed. With this method, many limits of using MD are lifted. The application scope of MD is greatly extended.« less

Analysis and Prediction of Sea Ice Evolution using Koopman Mode Decomposition Techniques

DTIC Science & Technology

Koopman Mode Analysis was newly applied to southern hemisphere sea ice concentration data. The resulting Koopman modes from analysis of both the...southern and northern hemisphere sea ice concentration data shows geographical regions where sea ice coverage has decreased over multiyear time scales.

Trading strategy based on dynamic mode decomposition: Tested in Chinese stock market

NASA Astrophysics Data System (ADS)

Cui, Ling-xiao; Long, Wen

2016-11-01

Dynamic mode decomposition (DMD) is an effective method to capture the intrinsic dynamical modes of complex system. In this work, we adopt DMD method to discover the evolutionary patterns in stock market and apply it to Chinese A-share stock market. We design two strategies based on DMD algorithm. The strategy which considers only timing problem can make reliable profits in a choppy market with no prominent trend while fails to beat the benchmark moving-average strategy in bull market. After considering the spatial information from spatial-temporal coherent structure of DMD modes, we improved the trading strategy remarkably. Then the DMD strategies profitability is quantitatively evaluated by performing SPA test to correct the data-snooping effect. The results further prove that DMD algorithm can model the market patterns well in sideways market.

Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection

NASA Astrophysics Data System (ADS)

Yuan, Jing; Ji, Feng; Gao, Yuan; Zhu, Jun; Wei, Chenjun; Zhou, Yu

2018-05-01

A new branch of fault detection is utilizing the noise such as enhancing, adding or estimating the noise so as to improve the signal-to-noise ratio (SNR) and extract the fault signatures. Hereinto, ensemble noise-reconstructed empirical mode decomposition (ENEMD) is a novel noise utilization method to ameliorate the mode mixing and denoised the intrinsic mode functions (IMFs). Despite the possibility of superior performance in detecting weak and multiple faults, the method still suffers from the major problems of the user-defined parameter and the powerless capability for a high SNR case. Hence, integrated ensemble noise-reconstructed empirical mode decomposition is proposed to overcome the drawbacks, improved by two noise estimation techniques for different SNRs as well as the noise estimation strategy. Independent from the artificial setup, the noise estimation by the minimax thresholding is improved for a low SNR case, which especially shows an outstanding interpretation for signature enhancement. For approximating the weak noise precisely, the noise estimation by the local reconfiguration using singular value decomposition (SVD) is proposed for a high SNR case, which is particularly powerful for reducing the mode mixing. Thereinto, the sliding window for projecting the phase space is optimally designed by the correlation minimization. Meanwhile, the reasonable singular order for the local reconfiguration to estimate the noise is determined by the inflection point of the increment trend of normalized singular entropy. Furthermore, the noise estimation strategy, i.e. the selection approaches of the two estimation techniques along with the critical case, is developed and discussed for different SNRs by means of the possible noise-only IMF family. The method is validated by the repeatable simulations to demonstrate the synthetical performance and especially confirm the capability of noise estimation. Finally, the method is applied to detect the local wear fault from a dual-axis stabilized platform and the gear crack from an operating electric locomotive to verify its effectiveness and feasibility.

Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach

NASA Technical Reports Server (NTRS)

Bregon, Anibal; Daigle, Matthew; Roychoudhury, Indranil

2016-01-01

Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.

Clinical and histologic evaluation of an enamel matrix derivative combined with a biphasic calcium phosphate for the treatment of human intrabony periodontal defects.

PubMed

Sculean, Anton; Windisch, Péter; Szendröi-Kiss, Dóra; Horváth, Attila; Rosta, Péter; Becker, Jürgen; Gera, István; Schwarz, Frank

2008-10-01

The goal of this study was to evaluate clinically and histologically the healing of advanced intrabony defects following regenerative periodontal surgery with an enamel matrix derivative (EMD) combined with a new biphasic calcium phosphate (BCP). Ten subjects, each of them displaying advanced combined 1- and 2-wall intrabony defects around teeth scheduled for extraction because of advanced chronic periodontitis and further prosthodontic considerations, were included in the study. The defects were consecutively treated with a combination of EMD + BCP. A notch was placed at the most apical extent of the calculus present on the root surface or at the most apical part of the defect (if no calculus was present) to serve as a reference for the histologic evaluation. At 9 months after regenerative surgery, nine of 10 teeth were extracted with some of their surrounding soft and hard tissues and processed for histologic evaluation. There were no adverse effects related to EMD or the graft material used in any of the treated subjects. One tooth was not extracted because of the excellent clinical outcome. The clinical measurements at the nine biopsied teeth demonstrated a mean probing depth reduction of 3.3 +/- 1.4 mm and a mean clinical attachment level gain of 3.0 +/- 1.6 mm. The histologic findings indicated formation of cementum with inserting collagen fibers to a varying extent. A long junctional epithelium was observed in three of the nine biopsies. Mean new connective tissue attachment (i.e., new cementum with inserting collagen fibers) varied from 0.0 to 2.1 mm. The amount of newly formed bone was limited and varied from 0.0 to 0.7 mm. At 9 months, graft particles were still present and were mostly encapsulated in connective tissue, whereas formation of bone around the graft particles was observed only occasionally. Direct contact between the graft particles and the root surface (cementum or dentin) was not observed in any of the analyzed specimens. The combination of EMD with a BCP bone substitute did not interfere with the regenerative potential reported for EMD and may result in formation of new cementum with an associated periodontal ligament. However, the combination of EMD + BCP resulted in no to minimal new bone formation.

Identifying Key Words in 9-1-1 Calls for Stroke: A Mixed Methods Approach.

PubMed

Richards, Christopher T; Wang, Baiyang; Markul, Eddie; Albarran, Frank; Rottman, Doreen; Aggarwal, Neelum T; Lindeman, Patricia; Stein-Spencer, Leslee; Weber, Joseph M; Pearlman, Kenneth S; Tataris, Katie L; Holl, Jane L; Klabjan, Diego; Prabhakaran, Shyam

2017-01-01

Identifying stroke during a 9-1-1 call is critical to timely prehospital care. However, emergency medical dispatchers (EMDs) recognize stroke in less than half of 9-1-1 calls, potentially due to the words used by callers to communicate stroke signs and symptoms. We hypothesized that callers do not typically use words and phrases considered to be classical descriptors of stroke, such as focal neurologic deficits, but that a mixed-methods approach can identify words and phrases commonly used by 9-1-1 callers to describe acute stroke victims. We performed a mixed-method, retrospective study of 9-1-1 call audio recordings for adult patients with confirmed stroke who were transported by ambulance in a large urban city. Content analysis, a qualitative methodology, and computational linguistics, a quantitative methodology, were used to identify key words and phrases used by 9-1-1 callers to describe acute stroke victims. Because a caller's level of emotional distress contributes to the communication during a 9-1-1 call, the Emotional Content and Cooperation Score was scored by a multidisciplinary team. A total of 110 9-1-1 calls, received between June and September 2013, were analyzed. EMDs recognized stroke in 48% of calls, and the emotional state of most callers (95%) was calm. In 77% of calls in which EMDs recognized stroke, callers specifically used the word "stroke"; however, the word "stroke" was used in only 38% of calls. Vague, non-specific words and phrases were used to describe stroke victims' symptoms in 55% of calls, and 45% of callers used distractor words and phrases suggestive of non-stroke emergencies. Focal neurologic symptoms were described in 39% of calls. Computational linguistics identified 9 key words that were more commonly used in calls where the EMD identified stroke. These words were concordant with terms identified through qualitative content analysis. Most 9-1-1 callers used vague, non-specific, or distractor words and phrases and infrequently provide classic stroke descriptions during 9-1-1 calls for stroke. Both qualitative and quantitative methodologies identified similar key words and phrases associated with accurate EMD stroke recognition. This study suggests that tools incorporating commonly used words and phrases could potentially improve EMD stroke recognition.

Dominant modal decomposition method

NASA Astrophysics Data System (ADS)

Dombovari, Zoltan

2017-03-01

The paper deals with the automatic decomposition of experimental frequency response functions (FRF's) of mechanical structures. The decomposition of FRF's is based on the Green function representation of free vibratory systems. After the determination of the impulse dynamic subspace, the system matrix is formulated and the poles are calculated directly. By means of the corresponding eigenvectors, the contribution of each element of the impulse dynamic subspace is determined and the sufficient decomposition of the corresponding FRF is carried out. With the presented dominant modal decomposition (DMD) method, the mode shapes, the modal participation vectors and the modal scaling factors are identified using the decomposed FRF's. Analytical example is presented along with experimental case studies taken from machine tool industry.

Whole-body vibration does not influence knee joint neuromuscular function or proprioception.

PubMed

Hannah, R; Minshull, C; Folland, J P

2013-02-01

This study examined the acute effects of whole-body vibration (WBV) on knee joint position sense and indices of neuromuscular function, specifically strength, electromechanical delay and the rate of force development. Electromyography and electrically evoked contractions were used to investigate neural and contractile responses to WBV. Fourteen healthy males completed two treatment conditions on separate occasions: (1) 5 × 1 min of unilateral isometric squat exercise on a synchronous vibrating platform [30 Hz, 4 mm peak-to-peak amplitude] (WBV) and (2) a control condition (CON) of the same exercise without WBV. Knee joint position sense (joint angle replication task) and quadriceps neuromuscular function were assessed pre-, immediately-post and 1 h post-exercise. During maximum voluntary knee extensions, the peak force (PF(V)), electromechanical delay (EMD(V)), rate of force development (RFD(V)) and EMG of the quadriceps were measured. Twitch contractions of the knee extensors were electrically evoked to assess EMD(E) and RFD(E). The results showed no influence of WBV on knee joint position, EMD(V), PF(V) and RFD(V) during the initial 50, 100 or 150 ms of contraction. Similarly, electrically evoked neuromuscular function and neural activation remained unchanged following the vibration exercise. A single session of unilateral WBV did not influence any indices of thigh muscle neuromuscular performance or knee joint proprioception. © 2011 John Wiley & Sons A/S.

Efficacy of Eye Movement Desensitization in the treatment of cognitive intrusions related to a past stressful event.

PubMed

Lytle, Richard A; Hazlett-Stevens, Holly; Borkovec, T D

2002-01-01

Much of the Eye Movement Desensitization and Reprocessing (EMDR) efficacy research has been widely criticized, limiting scientific understanding of its therapeutic components. The present investigation of Eye Movement Desensitization (EMD) effectiveness included undergraduate students reporting current intrusive cognitions conceming a traumatic event. Forty-five participants received a single treatment session of either: (a) EMD, as described by Shapiro [J. Behav. Ther. Exp. Psychiatry 20 (1989b) 211], (b) an identical procedure which employed eye fixation on a stationary target, or (c) non-directive counseling. Standardized self-report, subjective rating, Daily Diary, and intrusive thought sampling measures were collected before and after treatment. Results indicated that participants in the eye fixation group reported marginally (p < .052) fewer cognitive intrusions than the non-directive group 1 week following treatment. No significant differences between the EMD and non-directive conditions or between the EMD and eye fixation conditions on this measure were found. During the treatment session, both desensitization groups were superior to the non-directive group in reducing reported vividness of the mental image of the original event. However, the non-directive group improved to the level of the two other groups by the following week. Rapid saccadic eye movements were therefore unrelated to immediate treatment effects for this sub-clinical sample, and non-directive treatment largely yielded eventual outcomes equivalent to the two desensitization conditions.

Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2015-11-01

The purpose of this study is to investigate long-range dependence in trend and short variation of stock market price and return series before, during, and after 2008 financial crisis. Variational mode decomposition (VMD), a newly introduced technique for signal processing, is adopted to decompose stock market data into a finite set of modes so as to obtain long term trends and short term movements of stock market data. Then, the detrended fluctuation analysis (DFA) and range scale (R/S) analysis are used to estimate Hurst exponent in each variational mode obtained from VMD. For both price and return series, the empirical results from twelve international stock markets show evidence that long term trends are persistent, whilst short term variations are anti-persistent before, during, and after 2008 financial crisis.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN.

PubMed

Liu, Chang; Cheng, Gang; Chen, Xihui; Pang, Yusong

2018-05-11

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

PubMed Central

Cheng, Gang; Chen, Xihui

2018-01-01

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. PMID:29751671

Assessment of atrial electromechanical delay and P-wave dispersion in patients with type 2 diabetes mellitus.

PubMed

Demir, Kenan; Avci, Ahmet; Kaya, Zeynettin; Marakoglu, Kamile; Ceylan, Esra; Yilmaz, Ahmet; Ersecgin, Ahmet; Armutlukuyu, Mustafa; Altunkeser, Bulent Behlul

2016-04-01

Diabetes mellitus is an independent and strong risk factor for development of atrial fibrillation (AF). Electrophysiologic and electromechanical abnormalities are associated with a higher risk of AF. In this study we aimed to determine the correlation of atrial conduction abnormalities between the surface electrocardiographic and tissue Doppler echocardiographic measurements in type 2 diabetes mellitus (T2DM) patients. A total of 88 consecutive T2DM patients and 49 age-, gender-, and body mass index-matched healthy volunteers were included in the present study. Baseline characteristics were recorded and 24-hour ambulatory blood pressure monitoring, transthoracic echocardiography, and 12-lead surface electrocardiography were performed for all study participants. Atrial electromechanical delay (EMD) intervals were measured. Maximum P-wave duration and P-wave dispersion (Pd) were significantly higher in patients with T2DM (105.7±10.2ms vs. 102.2±7.5ms, p=0.02; 40.6±7.6ms vs. 33.6±5.9ms, p<0.001, respectively). Interatrial, intraatrial, and intraleft atrial EMD were significantly higher in the T2DM patients when compared with the controls (16.5±7.8ms vs.11.2±4.4ms, p<0.001; 9.0±7.3ms vs. 6.0±3.8ms, p=0.002, and 7.4±5.2ms vs. 5.1±3.2ms, p=0.002 respectively). Correlation analysis showed a positive correlation between interatrial EMD and Pd (r=0.429, p<0.001) and left atrial volume (r=0.428, p<0.001). In this study, there was significant EMD and Pd in patients with T2DM as compared with healthy volunteers. Additionally, interatrial EMD was correlated with Pd and left atrial volume index. Copyright © 2015 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.

The Antidepressant 5-HT2A Receptor Antagonists Pizotifen and Cyproheptadine Inhibit Serotonin-Enhanced Platelet Function

PubMed Central

Lin, Olivia A.; Karim, Zubair A.; Vemana, Hari Priya; Espinosa, Enma V. P.; Khasawneh, Fadi T.

2014-01-01

There is considerable interest in defining new agents or targets for antithrombotic purposes. The 5-HT2A receptor is a G-protein coupled receptor (GPCR) expressed on many cell types, and a known therapeutic target for many disease states. This serotonin receptor is also known to regulate platelet function. Thus, in our FDA-approved drug repurposing efforts, we investigated the antiplatelet activity of cyproheptadine and pizotifen, two antidepressant 5-HT2A Receptor antagonists. Our results revealed that cyproheptadine and pizotifen reversed serotonin-enhanced ADP-induced platelet aggregation in vitro and ex vivo. And the inhibitory effects of these two agents were found to be similar to that of EMD 281014, a 5-HT2A Receptor antagonist under development. In separate experiments, our studies revealed that these 5-HT2A receptor antagonists have the capacity to reduce serotonin-enhanced ADP-induced elevation in intracellular calcium levels and tyrosine phosphorylation. Using flow cytometry, we also observed that cyproheptadine, pizotifen, and EMD 281014 inhibited serotonin-enhanced ADP-induced phosphatidylserine (PS) exposure, P-selectin expression, and glycoprotein IIb-IIIa activation. Furthermore, using a carotid artery thrombosis model, these agents prolonged the time for thrombotic occlusion in mice in vivo. Finally, the tail-bleeding time was investigated to assess the effect of cyproheptadine and pizotifen on hemostasis. Our findings indicated prolonged bleeding time in both cyproheptadine- and pizotifen-treated mice. Notably, the increases in occlusion and bleeding times associated with these two agents were comparable to that of EMD 281014, and to clopidogrel, a commonly used antiplatelet drug, again, in a fashion comparable to clopidogrel and EMD 281014. Collectively, our data indicate that the antidepressant 5-HT2A antagonists, cyproheptadine and pizotifen do exert antiplatelet and thromboprotective effects, but similar to clopidogrel and EMD 281014, their use may interfere with normal hemostasis. PMID:24466319

Osteoblastic differentiation of human stem cells derived from bone marrow and periodontal ligament under the effect of enamel matrix derivative and transforming growth factor-beta.

PubMed

Houshmand, Behzad; Behnia, Hossein; Khoshzaban, Ahad; Morad, Golnaz; Behrouzi, Gholamreza; Dashti, Seyedeh Ghazaleh; Khojasteh, Arash

2013-01-01

To increase the understanding of the applicability of biomaterials and growth factors in enhancing stem cell-based bone regeneration modalities, this study evaluated the effects of enamel matrix derivative (EMD) and recombinant human transforming growth factor-beta (rhTGF-β) on osteoblastic differentiation of human bone marrow mesenchymal stem cells (hBMSCs) as well as human periodontal ligament stem cells (hPDLSCs). hBMSCs and hPDLSCs were obtained, and identification of stem cell surface markers was performed according to the criteria of the International Society for Cellular Therapy. Each group of stem cells was separately treated with a serial dilution of EMD (10, 50, and 100 μg/mL) or rhTGF-β (10 ng/mL). Osteoblastic differentiation was examined through in vitro matrix mineralization by alizarin red staining, and mRNA expression of osteopontin and osteonectin was determined by quantitative reverse-transcriptase polymerase chain reaction. hPDLSCs were further assessed for osteocalcin mRNA expression. Stem cells cultured in osteogenic medium were employed as a standard positive control group. In none of the experimental groups were bone-related mRNAs detected subsequent to treatment with EMD for 5, 10, and 15 days. Alizarin red staining on day 21 was negative in EMD-treated BMSC and PDLSC cultures. In rhTGF-β-supplemented BMSC culture, expression of osteonectin mRNA was demonstrated on day 15, which was statistically comparable to the positive control group. Nevertheless, extracellular matrix mineralization was inhibited in both groups of stem cells. Within the limitations of this study, it could be concluded that EMD with a concentration of 10, 50, or 100 μg/mL has no appreciable effect on osteoblastic differentiation of BMSCs and PDLSCs. Application of rhTGF-β increased osteonectin mRNA expression in BMSCs. This finding corroborates the hypothesis that TGF-β might be involved in early osteoblastic maturation.

Evaluating imaging quality between different ghost imaging systems based on the coherent-mode representation

NASA Astrophysics Data System (ADS)

Shen, Qian; Bai, Yanfeng; Shi, Xiaohui; Nan, Suqin; Qu, Lijie; Li, Hengxing; Fu, Xiquan

2017-07-01

The difference in imaging quality between different ghost imaging schemes is studied by using coherent-mode representation of partially coherent fields. It is shown that the difference mainly relies on the distribution changes of the decomposition coefficients of the object imaged when the light source is fixed. For a new-designed imaging scheme, we only need to give the distribution of the decomposition coefficients and compare them with that of the existing imaging system, thus one can predict imaging quality. By choosing several typical ghost imaging systems, we theoretically and experimentally verify our results.

Thermal decomposition reactions of cotton fabric treated with piperazine-phosphonates derivatives as a flame retardant

USDA-ARS?s Scientific Manuscript database

There has been a great scientific interest in exploring the great potential of the piperazine-phosphonates in flame retardant (FR) application on cotton fabric by investigating the thermal decomposition of cotton fabric treated with them. This research tries to understand the mode of action of the t...

Investigation of shock-induced chemical decomposition of sensitized nitromethane through time-resolved Raman spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pangilinan, G.I.; Constantinou, C.P.; Gruzdkov, Y.A.

1996-07-01

Molecular processes associated with shock induced chemical decomposition of a mixture of nitromethane with ethylenediamine (0.1 wt%) are examined using time-resolved, Raman scattering. When shocked by stepwise loading to 14.2 GPa pressure, changes in the nitromethane vibrational modes and the spectral background characterize the onset of reaction. The CN stretch mode softens and disappears even as the NO{sub 2} and CH{sub 3} stretch modes, though modified, retain their identities. The shape and intensity of the spectral background also shows changes characteristic of reaction. Changes in the background, which are observed even at lower peak pressures of 11.4 GPa, are assignedmore » to luminescence from reaction intermediates. The implications of these results to various molecular models of sensitization are discussed.« less

Earth Mover's Distance (EMD): A True Metric for Comparing Biomarker Expression Levels in Cell Populations.

PubMed

Orlova, Darya Y; Zimmerman, Noah; Meehan, Stephen; Meehan, Connor; Waters, Jeffrey; Ghosn, Eliver E B; Filatenkov, Alexander; Kolyagin, Gleb A; Gernez, Yael; Tsuda, Shanel; Moore, Wayne; Moss, Richard B; Herzenberg, Leonore A; Walther, Guenther

2016-01-01

Changes in the frequencies of cell subsets that (co)express characteristic biomarkers, or levels of the biomarkers on the subsets, are widely used as indices of drug response, disease prognosis, stem cell reconstitution, etc. However, although the currently available computational "gating" tools accurately reveal subset frequencies and marker expression levels, they fail to enable statistically reliable judgements as to whether these frequencies and expression levels differ significantly between/among subject groups. Here we introduce flow cytometry data analysis pipeline which includes the Earth Mover's Distance (EMD) metric as solution to this problem. Well known as an informative quantitative measure of differences between distributions, we present three exemplary studies showing that EMD 1) reveals clinically-relevant shifts in two markers on blood basophils responding to an offending allergen; 2) shows that ablative tumor radiation induces significant changes in the murine colon cancer tumor microenvironment; and, 3) ranks immunological differences in mouse peritoneal cavity cells harvested from three genetically distinct mouse strains.

ULF waves: the main periodicities and their relationships with solar wind structures and magnetospheric electron flux

NASA Astrophysics Data System (ADS)

Piersanti, M.; Alberti, T.; Lepreti, F.; Vecchio, A.; Villante, U.; Carbone, V.; Waters, C. L.

2015-12-01

We use high latitude ULF wave power in the range 2-7 mHz (Pc5 geomagnetic micropulsations), solar wind speed and dynamic pressure, and relativistic magnetospheric electron flux (E > 0.6 MeV), in the period January - September 2008, in order to detect typical periodicities and physical mechanisms involved into the solar wind-magnetosphere coupling during the declining phase of the 23th solar cycle. Using the Empirical Mode Decomposition (EMD) and applying a statistical test and cross-correlation analysis,we investigate the timescales and the physical mechanisms involved into the solar wind-magnetosphere coupling.Summarizing, we obtain the following results:1. We note the existence of two different timescales into the four datasets which are related to the short-term dynamics, with a characteristic timescale τ<3 days, and to the longer timescale dynamics, with a timescale between 7 and 80 days. The short-term variations could be related to the fluctuations around a characteristic mean value, while longer timescales dynamics can be associated with solar rotational periodicity and mechanisms regarding the occurrence of high-speed streams and corotating interaction regions but also with stream-stream interactions and synodic solar rotation.2. The cross-correlation analysis highlights the relevant role of the dynamical coupling between solar wind and magnetosphere via pressure balance and direct transfer of compressional waves into the magnetosphere. Moreover, it shows that the Kelvin-Helmholtz instability is not the primary source of geomagnetic ultra-low frequency wave activity. These results are in agreement with previous works [Engebretson et al, 1998].3. The cross-correlation coefficient between Pc5 wave power and relativistic electron flux longscale reconstructions shows that Pc5 wave activity leads enhancements in magnetospheric electron flux to relativistic energy with a characteristic time delay of about 54 hours, which is in agreement with the lag of about 2 days found by [Mann et al., 2004].

Longterm monitoring of pressure, tilt and temperature at Logatchev Hydrothermal Vent Field, Mid-Atlantic Ridge

NASA Astrophysics Data System (ADS)

Villinger, H. W.; Gennerich, H.-H.; Fabian, M.

2009-04-01

The geophysical parameters of pressure, tilt, acceleration and temperature at the Logatchev Hydrothermal Vent Field (LHF) which is located in 3050m water depth at about 15˚ N at the Mid-Atlantic Ridge, were monitored with high resolution for more than two and a half years, from May 2005 until December 2007. An autonomously operating Ocean Bottom Pressure Station (OBP; resolution of 80 Pa in the first year, improved to 8 Pa afterwards, sampling period of 2 minutes in the first year, increased to 2 seconds afterwards) and a programmable Ocean Bottom Tiltmeter (OBT; resolution 1 rad, sampling period 6 seconds) measured local ocean-floor point motions derived from tilt and absolute pressure. In addition, vertical acceleration was measured using a MEMS accelerometer (resolution 10-5 m/s2, sampling rate 1.33 Hz) within the housing of the OBT. Numerous autonomous temperature loggers (resolution 0.001˚ C, sampling period 15 minutes) were installed at prominent places like mussel fields or soil fissures within the LHF. Time series are analyzed using Fourier-Transformation techniques but also using the novel approach called Empirical Mode Decomposition (EMD). Pressure records show a modulated background noise level with increased amplitudes lasting for several days to weeks, and most likely show signals generated by local earthquakes. Bottom water temperature has transients with peak-to-peak-amplitudes of up to 0.1˚ C, which correlate for a number of events directly with earthquake signals. A comparison of pressure, tilt, acceleration and temperature data events shows that all four records are correlated. For a few of those events a direct causal link can be firmly established. The study is funded by the Deutsche Forschungsgemeinschaft (DFG) and part of Priority Program 1144 ("From Mantle to Ocean: Energy-, Material- and Life-cycles at Spreading Axes").

Time Frequency Analysis of The Land Subsidence Monitored Data with Exploration Geophysics

NASA Astrophysics Data System (ADS)

Wang, Shang-Wei

2014-05-01

Taiwan geographic patterns and various industry water, caused Zhuoshui River Fan groundwater extraction of excess leads to land subsidence, affect the safety of high-speed railway traffic and public construction. It is necessary to do the deeply research on the reason and behavior of subsidence. All the related element will be confer including the water extracted groundwater that be used on each industry or the impact of climate change rainfall and the ground formation characteristics. Conducted a series of in situ measurements and monitoring data with Hilbert Huang Transform. Discussion of subsidence mechanism and estimate the future high-speed rail traffic may affect the extent of providing for future reference remediation. We investigate and experiment on the characteristic of land subsidence in Yun Lin area. The Hilbert-Huang Transform (HHT) and signal normalized are be used to discuss the physical meanings and interactions among the time series data of settlement, groundwater, pumping, rainfall and micro-tremor of ground. The broadband seismic signals of the Broadband Array in Taiwan for Seismology, (BATS) obtained near the Zhuoshui River (WLGB in Chia Yi, WGKB in Yun Lin and RLNB in Zhang Hua) were analyzed by using HHT and empirical mode decomposition (EMD) to discuss the micro-tremor characteristics of the settled ground. To compare among ten years series data of micro-tremor, groundwater and land subsidence monitoring wells, we can get more information about land subsidence. The electrical resistivity tomography (ERT) were performed to correlate the resistivity profile and borehole logging data at the test area. The relationships among resistivity, groundwater variation, and ground subsidence obtained from the test area have been discussed. Active and passive multichannel analysis of surface waves method (MASW) can calculate Poisson's ratio by using shear velocity and pressure velocity. The groundwater level can be presumed when Poisson's ratio arrive 0.5. We can know about undulate groundwater stages and variation of ground by more times measurements.

Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements.

PubMed

Xiong, Chunbao; Lu, Huali; Zhu, Jinsong

2017-02-23

Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation.

Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements

PubMed Central

Xiong, Chunbao; Lu, Huali; Zhu, Jinsong

2017-01-01

Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation. PMID:28241472

LLNL demonstration of liquid gun propellant destruction in a 0.1 gallon per minute scale reactor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cena, R.J.; Thorsness, C.B.; Coburn, T.T.

1994-06-01

The Lawrence Livermore National Laboratory (LLNL) has built and operated a pilot plant for processing oil shale using recirculating hot solids. This pilot plant, was adapted in 1993 to demonstrate the feasibility of decomposing a liquid gun propellant (LGP), LP XM46, a mixture of 76% HAN (NH{sub 3}OHNO{sub 3}) and 24% TEAN (HOCH{sub 2}CH{sub 2}){sub 3} NHNO{sub 3} diluted 1:3 in water. In the Livermore process, the LPG is thermally treated in a moving packed bed of ceramic spheres, where TEAN and HAN decompose, forming a suite of gases including: methane, carbon monoxide, oxygen, nitrogen oxides, ammonia and molecular nitrogen.more » The ceramic spheres are circulated and heated, providing the energy required for thermal decomposition. The authors performed an extended one day (8 hour) test of the solids recirculation system, with continuous injection of approximately 0.1 gal/min of LGP, diluted 1:3 in water, for a period of eight hours. The apparatus operated smoothly over the course of the eight hour run during which 144 kg of solution was processed, containing 36 kg of LGP. Continuous on-line gas analysis was invaluable in tracking the progress of the experiment and quantifying the decomposition products. The reactor was operated in two modes, a {open_quotes}Pyrolysis{close_quotes} mode, where decomposition products were removed from the moving bed reactor exit, passing through condensers to a flare, and in a {open_quotes}Combustion{close_quotes} mode, where the products were oxidized in air lift pipe prior to exiting the system. In the {open_quotes}Pyrolysis{close_quotes} mode, driver gases were recycled producing a small, concentrated stream of decomposition products. In the {open_quotes}Combustion mode{close_quotes}, the driver gases were not recycled, resulting in 40 times higher gas flow rates and correspondingly lower concentrations of nitrogen bearing gases.« less

Low-dimensional modelling of a transient cylinder wake using double proper orthogonal decomposition

NASA Astrophysics Data System (ADS)

Siegel, Stefan G.; Seidel, J.?Rgen; Fagley, Casey; Luchtenburg, D. M.; Cohen, Kelly; McLaughlin, Thomas

For the systematic development of feedback flow controllers, a numerical model that captures the dynamic behaviour of the flow field to be controlled is required. This poses a particular challenge for flow fields where the dynamic behaviour is nonlinear, and the governing equations cannot easily be solved in closed form. This has led to many versions of low-dimensional modelling techniques, which we extend in this work to represent better the impact of actuation on the flow. For the benchmark problem of a circular cylinder wake in the laminar regime, we introduce a novel extension to the proper orthogonal decomposition (POD) procedure that facilitates mode construction from transient data sets. We demonstrate the performance of this new decomposition by applying it to a data set from the development of the limit cycle oscillation of a circular cylinder wake simulation as well as an ensemble of transient forced simulation results. The modes obtained from this decomposition, which we refer to as the double POD (DPOD) method, correctly track the changes of the spatial modes both during the evolution of the limit cycle and when forcing is applied by transverse translation of the cylinder. The mode amplitudes, which are obtained by projecting the original data sets onto the truncated DPOD modes, can be used to construct a dynamic mathematical model of the wake that accurately predicts the wake flow dynamics within the lock-in region at low forcing amplitudes. This low-dimensional model, derived using nonlinear artificial neural network based system identification methods, is robust and accurate and can be used to simulate the dynamic behaviour of the wake flow. We demonstrate this ability not just for unforced and open-loop forced data, but also for a feedback-controlled simulation that leads to a 90% reduction in lift fluctuations. This indicates the possibility of constructing accurate dynamic low-dimensional models for feedback control by using unforced and transient forced data only.

Alternatives to connective tissue graft in the treatment of localized gingival recessions: A systematic review.

PubMed

Amine, K; El Amrani, Y; Chemlali, S; Kissa, J

2018-02-01

The aim of this Systematic Review (SR) was to assess the clinical efficacy of alternatives procedures; Acellular Dermal Matrix (ADM), Xenogeneic Collagen Matrix (XCM), Enamel Matrix Derivative (EMD) and Platelet Rich Fibrin (PRF), compared to conventional procedures in the treatment of localized gingival recessions. Electronic searches were performed to identify randomized clinical trials (RCTs) on treatment of single gingival recession with at least 6 months of follow-up. Applying guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analyses statement (PRISMA). The risk of bias was assessed using the Cochrane Collaboration's Risk of Bias tool. Eighteen randomized controlled trials (RCTs) with a total of 390 treated patients (606 recessions) were included. This systematic review showed that: Coronally Advanced Flap (CAF) in conjunction with ADM was significantly better than CAF alone, while the comparison between CAF+ADM and CTG was affected by large uncertainty. The CAF+EMD was significantly better than CAF alone, whereas the comparison between CAF+EMD and CTG was affected by large uncertainty. No significant difference was recorded when comparing CAF+XCM with CAF alone, and the comparison between CAF+XCM and CTG was affected by large uncertainty. The comparison between PRF and others technique was affected by large uncertainty. ADM, XCM and EMD assisted to CAF might be considered alternatives of CTG in the treatment of Miller class I and II gingival recession. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Sun, Moon and Earthquakes

NASA Astrophysics Data System (ADS)

Kolvankar, V. G.

2013-12-01

During a study conducted to find the effect of Earth tides on the occurrence of earthquakes, for small areas [typically 1000km X1000km] of high-seismicity regions, it was noticed that the Sun's position in terms of universal time [GMT] shows links to the sum of EMD [longitude of earthquake location - longitude of Moon's foot print on earth] and SEM [Sun-Earth-Moon angle]. This paper provides the details of this relationship after studying earthquake data for over forty high-seismicity regions of the world. It was found that over 98% of the earthquakes for these different regions, examined for the period 1973-2008, show a direct relationship between the Sun's position [GMT] and [EMD+SEM]. As the time changes from 00-24 hours, the factor [EMD+SEM] changes through 360 degree, and plotting these two variables for earthquakes from different small regions reveals a simple 45 degree straight-line relationship between them. This relationship was tested for all earthquakes and earthquake sequences for magnitude 2.0 and above. This study conclusively proves how Sun and the Moon govern all earthquakes. Fig. 12 [A+B]. The left-hand figure provides a 24-hour plot for forty consecutive days including the main event (00:58:23 on 26.12.2004, Lat.+3.30, Long+95.980, Mb 9.0, EQ count 376). The right-hand figure provides an earthquake plot for (EMD+SEM) vs GMT timings for the same data. All the 376 events including the main event faithfully follow the straight-line curve.

A survey on regenerative surgery performed by Swiss specialists in periodontology with special emphasis on the application of enamel matrix derivatives in infrabony defects.

PubMed

Schröen, Ola; Sahrmann, Philipp; Roos, Malgorzata; Attin, Thomas; Schmidlin, Patrick R

2011-01-01

This survey aimed to evaluate the common practice of regenerative periodontal surgery with special regard to the use of enamel matrix derivatives (EMD, Emdogain® ) by board-certified specialists in periodontology and non-certified, but active members of the Swiss Society of Periodontology (SSP). A cross-sectional postal survey of 533 dentists, representing all members of the SSP practising in Switzerland, was conducted. The questionnaire consisted of three sections, assessing: 1) general personal information regarding the practice setting and education, 2) general questions regarding periodontal surgery practices and 3) specific questions regarding the use of EMD. The information obtained was compared and differences between specialists and non-specialists were calculated. P-values smaller than 5% were considered significant. Sixty-nine percent of the specialists answered the questionnaire, compared to only 37.4% of the non-specialists (overall: 42.4%). In general, specialists performed surgeries more frequently, and presented a significantly higher percentage of EMD users than the non-specialists. The application guidelines were followed in general. Some differences were observed in application and selection criteria. The subjective perception of clinical success varied greatly among clinicians. Residual pockets were reported to be present in approximately one third of the defects after therapy. In conclusion, this survey revealed that EMD was used on a regular basis by dentists performing periodontal therapy. In addition, the answers by both groups generally corresponded well with the current available literature.

Time-frequency analysis of neuronal populations with instantaneous resolution based on noise-assisted multivariate empirical mode decomposition.

PubMed

Alegre-Cortés, J; Soto-Sánchez, C; Pizá, Á G; Albarracín, A L; Farfán, F D; Felice, C J; Fernández, E

2016-07-15

Linear analysis has classically provided powerful tools for understanding the behavior of neural populations, but the neuron responses to real-world stimulation are nonlinear under some conditions, and many neuronal components demonstrate strong nonlinear behavior. In spite of this, temporal and frequency dynamics of neural populations to sensory stimulation have been usually analyzed with linear approaches. In this paper, we propose the use of Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD), a data-driven template-free algorithm, plus the Hilbert transform as a suitable tool for analyzing population oscillatory dynamics in a multi-dimensional space with instantaneous frequency (IF) resolution. The proposed approach was able to extract oscillatory information of neurophysiological data of deep vibrissal nerve and visual cortex multiunit recordings that were not evidenced using linear approaches with fixed bases such as the Fourier analysis. Texture discrimination analysis performance was increased when Noise-Assisted Multivariate Empirical Mode plus Hilbert transform was implemented, compared to linear techniques. Cortical oscillatory population activity was analyzed with precise time-frequency resolution. Similarly, NA-MEMD provided increased time-frequency resolution of cortical oscillatory population activity. Noise-Assisted Multivariate Empirical Mode Decomposition plus Hilbert transform is an improved method to analyze neuronal population oscillatory dynamics overcoming linear and stationary assumptions of classical methods. Copyright © 2016 Elsevier B.V. All rights reserved.

A technique for plasma velocity-space cross-correlation

NASA Astrophysics Data System (ADS)

Mattingly, Sean; Skiff, Fred

2018-05-01

An advance in experimental plasma diagnostics is presented and used to make the first measurement of a plasma velocity-space cross-correlation matrix. The velocity space correlation function can detect collective fluctuations of plasmas through a localized measurement. An empirical decomposition, singular value decomposition, is applied to this Hermitian matrix in order to obtain the plasma fluctuation eigenmode structure on the ion distribution function. A basic theory is introduced and compared to the modes obtained by the experiment. A full characterization of these modes is left for future work, but an outline of this endeavor is provided. Finally, the requirements for this experimental technique in other plasma regimes are discussed.

Identification of flow structures in fully developed canonical and wavy channels by means of modal decomposition techniques

NASA Astrophysics Data System (ADS)

Ghebali, Sacha; Garicano-Mena, Jesús; Ferrer, Esteban; Valero, Eusebio

2018-04-01

A Dynamic Mode Decomposition (DMD) of Direct Numerical Simulations (DNS) of fully developed channel flows is undertaken in order to study the main differences in flow features between a plane-channel flow and a passively “controlled” flow wherein the mean friction was reduced relative to the baseline by modifying the geometry in order to generate a streamwise-periodic spanwise pressure gradient, as is the case for an oblique wavy wall. The present analysis reports POD and DMD modes for the plane channel, jointly with the application of a sparsity-promoting method, as well as a reconstruction of the Reynolds shear stress with the dynamic modes. Additionally, a dynamic link between the streamwise velocity fluctuations and the friction on the wall is sought by means of a composite approach both in the plane and wavy cases. One of the DMD modes associated with the wavy-wall friction exhibits a meandering motion which was hardly identifiable on the instantaneous friction fluctuations.

Heterogeneous decomposition of silane in a fixed bed reactor

NASA Technical Reports Server (NTRS)

Iya, S. K.; Flagella, R. N.; Dipaolo, F. S.

1982-01-01

Heterogeneous decomposition of silane in a fluidized bed offers an attractive route for the low-cost production of silicon for photovoltaic application. To obtain design data for a fluid bed silane pyrolysis reactor, deposition experiments were conducted in a small-scale fixed bed apparatus. Data on the decomposition mode, plating rate, and deposition morphology were obtained in the temperature range 600-900 C. Conditions favorable for heterogeneous decomposition with good deposition morphology were identified. The kinetic rate data showed the reaction to be first order with an activation energy of 38.8 kcal/mol, which agrees well with work done by others. The results are promising for the development of an economically attractive fluid bed process.

Deconvolution of reacting-flow dynamics using proper orthogonal and dynamic mode decompositions

NASA Astrophysics Data System (ADS)

Roy, Sukesh; Hua, Jia-Chen; Barnhill, Will; Gunaratne, Gemunu H.; Gord, James R.

2015-01-01

Analytical and computational studies of reacting flows are extremely challenging due in part to nonlinearities of the underlying system of equations and long-range coupling mediated by heat and pressure fluctuations. However, many dynamical features of the flow can be inferred through low-order models if the flow constituents (e.g., eddies or vortices) and their symmetries, as well as the interactions among constituents, are established. Modal decompositions of high-frequency, high-resolution imaging, such as measurements of species-concentration fields through planar laser-induced florescence and of velocity fields through particle-image velocimetry, are the first step in the process. A methodology is introduced for deducing the flow constituents and their dynamics following modal decomposition. Proper orthogonal (POD) and dynamic mode (DMD) decompositions of two classes of problems are performed and their strengths compared. The first problem involves a cellular state generated in a flat circular flame front through symmetry breaking. The state contains two rings of cells that rotate clockwise at different rates. Both POD and DMD can be used to deconvolve the state into the two rings. In POD the contribution of each mode to the flow is quantified using the energy. Each DMD mode can be associated with an energy as well as a unique complex growth rate. Dynamic modes with the same spatial symmetry but different growth rates are found to be combined into a single POD mode. Thus, a flow can be approximated by a smaller number of POD modes. On the other hand, DMD provides a more detailed resolution of the dynamics. Two classes of reacting flows behind symmetric bluff bodies are also analyzed. In the first, symmetric pairs of vortices are released periodically from the two ends of the bluff body. The second flow contains von Karman vortices also, with a vortex being shed from one end of the bluff body followed by a second shedding from the opposite end. The way in which DMD can be used to deconvolve the second flow into symmetric and von Karman vortices is demonstrated. The analyses performed illustrate two distinct advantages of DMD: (1) Unlike proper orthogonal modes, each dynamic mode is associated with a unique complex growth rate. By comparing DMD spectra from multiple nominally identical experiments, it is possible to identify "reproducible" modes in a flow. We also find that although most high-energy modes are reproducible, some are not common between experimental realizations; in the examples considered, energy fails to differentiate between reproducible and nonreproducible modes. Consequently, it may not be possible to differentiate reproducible and nonreproducible modes in POD. (2) Time-dependent coefficients of dynamic modes are complex. Even in noisy experimental data, the dynamics of the phase of these coefficients (but not their magnitude) are highly regular. The phase represents the angular position of a rotating ring of cells and quantifies the downstream displacement of vortices in reacting flows. Thus, it is suggested that the dynamical characterizations of complex flows are best made through the phase dynamics of reproducible DMD modes.

Low-frequency Raman scattering in a Xe hydrate.

PubMed

Adichtchev, S V; Belosludov, V R; Ildyakov, A V; Malinovsky, V K; Manakov, A Yu; Subbotin, O S; Surovtsev, N V

2013-09-12

The physics of gas hydrates are rich in interesting phenomena such as anomalies for thermal conductivity, self-preservation effects for decomposition, and others. Some of these phenomena are presumably attributed to the resonance interaction of the rattling motions of guest molecules or atoms with the lattice modes. This can be expected to induce some specific features in the low-frequency (THz) vibrational response. Here we present results for low-frequency Raman scattering in a Xe hydrate, supported by numerical calculations of vibrational density of states. A number of narrow lines, located in the range from 18 to 90 cm(-1), were found in the Raman spectrum. Numerical calculations confirm that these lines correspond to resonance modes of the Xe hydrate. Also, low-frequency Raman scattering was studied during gas hydrate decomposition, and two scenarios were observed. The first one is the direct decomposition of the Xe hydrate to water and gas. The second one is the hydrate decomposition to ice and gas with subsequent melting of ice. In the latter case, a transient low-frequency Raman band is observed, which is associated with low-frequency bands (e.g., boson peak) of disordered solids.

Complete ensemble local mean decomposition with adaptive noise and its application to fault diagnosis for rolling bearings

NASA Astrophysics Data System (ADS)

Wang, Lei; Liu, Zhiwen; Miao, Qiang; Zhang, Xin

2018-06-01

Mode mixing resulting from intermittent signals is an annoying problem associated with the local mean decomposition (LMD) method. Based on noise-assisted approach, ensemble local mean decomposition (ELMD) method alleviates the mode mixing issue of LMD to some degree. However, the product functions (PFs) produced by ELMD often contain considerable residual noise, and thus a relatively large number of ensemble trials are required to eliminate the residual noise. Furthermore, since different realizations of Gaussian white noise are added to the original signal, different trials may generate different number of PFs, making it difficult to take ensemble mean. In this paper, a novel method is proposed called complete ensemble local mean decomposition with adaptive noise (CELMDAN) to solve these two problems. The method adds a particular and adaptive noise at every decomposition stage for each trial. Moreover, a unique residue is obtained after separating each PF, and the obtained residue is used as input for the next stage. Two simulated signals are analyzed to illustrate the advantages of CELMDAN in comparison to ELMD and CEEMDAN. To further demonstrate the efficiency of CELMDAN, the method is applied to diagnose faults for rolling bearings in an experimental case and an engineering case. The diagnosis results indicate that CELMDAN can extract more fault characteristic information with less interference than ELMD.

Automated Identification of MHD Mode Bifurcation and Locking in Tokamaks

NASA Astrophysics Data System (ADS)

Riquezes, J. D.; Sabbagh, S. A.; Park, Y. S.; Bell, R. E.; Morton, L. A.

2017-10-01

Disruption avoidance is critical in reactor-scale tokamaks such as ITER to maintain steady plasma operation and avoid damage to device components. A key physical event chain that leads to disruptions is the appearance of rotating MHD modes, their slowing by resonant field drag mechanisms, and their locking. An algorithm has been developed that automatically detects bifurcation of the mode toroidal rotation frequency due to loss of torque balance under resonant braking, and mode locking for a set of shots using spectral decomposition. The present research examines data from NSTX, NSTX-U and KSTAR plasmas which differ significantly in aspect ratio (ranging from A = 1.3 - 3.5). The research aims to examine and compare the effectiveness of different algorithms for toroidal mode number discrimination, such as phase matching and singular value decomposition approaches, and to examine potential differences related to machine aspect ratio (e.g. mode eigenfunction shape variation). Simple theoretical models will be compared to the dynamics found. Main goals are to detect or potentially forecast the event chain early during a discharge. This would serve as a cue to engage active mode control or a controlled plasma shutdown. Supported by US DOE Contracts DE-SC0016614 and DE-AC02-09CH11466.

Clinical and histologic evaluation of non-surgical periodontal therapy with enamel matrix derivative: a report of four cases.

PubMed

Mellonig, James T; Valderrama, Pilar; Gregory, Holly J; Cochran, David L

2009-09-01

Enamel matrix derivative (EMD) is a composite of proteins that was demonstrated histologically to work as an adjunct to periodontal regenerative surgical therapy. The purpose of this study was to evaluate the clinical and histologic effects of EMD as an adjunct to scaling and root planing. Four patients with severe chronic periodontitis and scheduled to receive complete dentures were accrued. Probing depth and clinical attachment levels were obtained. Unlimited time was allowed for hand and ultrasonic instrumentation. A notch was placed in the root >or=1 to 2 mm from the apical extent of root planing. EMD was inserted into the pocket, and a periodontal dressing was placed. Patients were seen every 2 weeks for plaque control. At 6 months post-treatment, soft tissue measurements were repeated, and the teeth were removed en bloc and prepared for histomorphologic analysis. Probing depth reduction and clinical attachment level gain were obtained in three-fourths of the specimens. Three of the four specimens analyzed histologically demonstrated new cementum, bone, periodontal ligament, and connective tissue attachment coronal to the notch. In one specimen, the gingival margin had receded below the notch. The results were unexpected and may represent an aberration. However, the substantial reduction in deep probing depths and clinical attachment level gain in three of four specimens, in addition to the histologic findings of new cementum, new bone, a new periodontal ligament, and a new connective tissue attachment, suggest that EMD may be useful as an adjunct to scaling and root planing in single-rooted teeth.

Association of dopamine gene variants, emotion dysregulation and ADHD in autism spectrum disorder.

PubMed

Gadow, Kenneth D; Pinsonneault, Julia K; Perlman, Greg; Sadee, Wolfgang

2014-07-01

The aim of the present study was to evaluate the association of dopaminergic gene variants with emotion dysregulation (EMD) and attention-deficit/hyperactivity disorder (ADHD) symptoms in children with autism spectrum disorder (ASD). Three dopamine transporter gene (SLC6A3/DAT1) polymorphisms (intron8 5/6 VNTR, 3'-UTR 9/10 VNTR, rs27072 in the 3'-UTR) and one dopamine D2 receptor gene (DRD2) variant (rs2283265) were selected for genotyping based on à priori evidence of regulatory activity or, in the case of DAT1 9/10 VNTR, commonly reported associations with ADHD. A sample of 110 children with ASD was assessed with a rigorously validated DSM-IV-referenced rating scale. Global EMD severity (parents' ratings) was associated with DAT1 intron8 (ηp(2)=.063) and rs2283265 (ηp(2)=.044). Findings for DAT1 intron8 were also significant for two EMD subscales, generalized anxiety (ηp(2)=.065) and depression (ηp(2)=.059), and for DRD2 rs2283265, depression (ηp(2)=.053). DRD2 rs2283265 was associated with teachers' global ratings of ADHD (ηp(2)=.052). DAT1 intron8 was associated with parent-rated hyperactivity (ηp(2)=.045) and both DAT1 9/10 VNTR (ηp(2)=.105) and DRD2 rs2283265 (ηp(2)=.069) were associated with teacher-rated inattention. These findings suggest that dopaminergic gene polymorphisms may modulate EMD and ADHD symptoms in children with ASD but require replication with larger independent samples. Copyright © 2014 Elsevier Ltd. All rights reserved.

Diffusion and practice of ultrasound in emergency medicine departments in Italy

PubMed Central

Sofia, S.; Angelini, F.; Cianci, V.; Copetti, R.; Farina, R.; Scuderi, M.

2009-01-01

Introduction This paper reports data from a cognitive survey on the diffusion, practice and organization of ultrasound (US) in emergency medicine departments (EMDs) in Italy. The study was carried out by the Emergency Medicine Section of the Italian Society for Ultrasound in Medicine and Biology (SIUMB) in collaboration with the Italian Society for Emergency Medicine and Urgent Care (SIMEU). Methods We created a questionnaire with 10 items, relating to 4 thematic areas. The questionnaires were administered from September 2007 to February 2008, by email, telephone or regular mail. In August 2008 the data were subjected to nonparametric statistical analysis (Spearman's Rho and Pearson's chi-square – software SPSS). Results We analyzed 170 questionnaires from the EMDs of all Italian regions. A US scanner is present in 64.7% of the ERs, emergency US (E-US) is practiced only in 47.6% of the ERs, and only in 24% of these more than 60% of the ER team members have training in US. The diffusion of US in other operative units of the EMDs ranges from 8.2% to 26.5%. Discussion The presence of a US scanner in the ER is essential for the practice and training and is correlated with the level of the EMD. The use of US appears to be less common in less equipped hospitals, regardless of the size of the ER and the availability of radiological services. Wider diffusion of US and greater integration with other services for the installment of the required equipment is to be hoped for. PMID:23396902

Bringing isolated dark matter out of isolation: Late-time reheating and indirect detection

NASA Astrophysics Data System (ADS)

Erickcek, Adrienne L.; Sinha, Kuver; Watson, Scott

2016-09-01

In standard cosmology, the growth of structure becomes significant following matter-radiation equality. In nonthermal histories, where an effectively matter-dominated phase occurs due to scalar oscillations prior to big bang nucleosynthesis, a new scale at smaller wavelengths appears in the matter power spectrum. Density perturbations that enter the horizon during the early matter-dominated era (EMDE) grow linearly with the scale factor prior to the onset of radiation domination, which leads to enhanced inhomogeneity on small scales if dark matter (DM) thermally and kinetically decouples during the EMDE. The microhalos that form from these enhanced perturbations significantly boost the self-annihilation rate for dark matter. This has important implications for indirect detection experiments: the larger annihilation rate may result in observable signals from dark matter candidates that are usually deemed untestable. As a proof of principle, we consider binos in heavy supersymmetry with an intermediate extended Higgs sector and all other superpartners decoupled. We find that these isolated binos, which lie under the neutrino floor, can account for the dark matter relic density and decouple from the standard model early enough to preserve the enhanced small-scale inhomogeneity generated during the EMDE. If early forming microhalos survive as subhalos within larger microhalos, the resulting boost to the annihilation rate for bino dark matter near the pseudoscalar resonance exceeds the upper limit established by Fermi-LAT's observations of dwarf spheroidal galaxies. These DM candidates motivate the N -body simulations required to eliminate uncertainties in the microhalos' internal structure by exemplifying how an EMDE can enable Fermi-LAT to probe isolated dark matter.

Equivalence Testing of Complex Particle Size Distribution Profiles Based on Earth Mover's Distance.

PubMed

Hu, Meng; Jiang, Xiaohui; Absar, Mohammad; Choi, Stephanie; Kozak, Darby; Shen, Meiyu; Weng, Yu-Ting; Zhao, Liang; Lionberger, Robert

2018-04-12

Particle size distribution (PSD) is an important property of particulates in drug products. In the evaluation of generic drug products formulated as suspensions, emulsions, and liposomes, the PSD comparisons between a test product and the branded product can provide useful information regarding in vitro and in vivo performance. Historically, the FDA has recommended the population bioequivalence (PBE) statistical approach to compare the PSD descriptors D50 and SPAN from test and reference products to support product equivalence. In this study, the earth mover's distance (EMD) is proposed as a new metric for comparing PSD particularly when the PSD profile exhibits complex distribution (e.g., multiple peaks) that is not accurately described by the D50 and SPAN descriptor. EMD is a statistical metric that measures the discrepancy (distance) between size distribution profiles without a prior assumption of the distribution. PBE is then adopted to perform statistical test to establish equivalence based on the calculated EMD distances. Simulations show that proposed EMD-based approach is effective in comparing test and reference profiles for equivalence testing and is superior compared to commonly used distance measures, e.g., Euclidean and Kolmogorov-Smirnov distances. The proposed approach was demonstrated by evaluating equivalence of cyclosporine ophthalmic emulsion PSDs that were manufactured under different conditions. Our results show that proposed approach can effectively pass an equivalent product (e.g., reference product against itself) and reject an inequivalent product (e.g., reference product against negative control), thus suggesting its usefulness in supporting bioequivalence determination of a test product to the reference product which both possess multimodal PSDs.

The free-flight response of Drosophila to motion of the visual environment.

PubMed

Mronz, Markus; Lehmann, Fritz-Olaf

2008-07-01

In the present study we investigated the behavioural strategies with which freely flying fruit flies (Drosophila) control their flight trajectories during active optomotor stimulation in a free-flight arena. We measured forward, turning and climbing velocities of single flies using high-speed video analysis and estimated the output of a 'Hassenstein-Reichardt' elementary motion detector (EMD) array and the fly's gaze to evaluate flight behaviour in response to a rotating visual panorama. In a stationary visual environment, flight is characterized by flight saccades during which the animals turn on average 120 degrees within 130 ms. In a rotating environment, the fly's behaviour typically changes towards distinct, concentric circular flight paths where the radius of the paths increases with increasing arena velocity. The EMD simulation suggests that this behaviour is driven by a rotation-sensitive EMD detector system that minimizes retinal slip on each compound eye, whereas an expansion-sensitive EMD system with a laterally centred visual focus potentially helps to achieve centring response on the circular flight path. We developed a numerical model based on force balance between horizontal, vertical and lateral forces that allows predictions of flight path curvature at a given locomotor capacity of the fly. The model suggests that turning flight in Drosophila is constrained by the production of centripetal forces needed to avoid side-slip movements. At maximum horizontal velocity this force may account for up to 70% of the fly's body weight during yaw turning. Altogether, our analyses are widely consistent with previous studies on Drosophila free flight and those on the optomotor response under tethered flight conditions.

Mulliken's populations and electron momentum densities of transition metal tungstates using LCAO scheme

NASA Astrophysics Data System (ADS)

Meena, B. S.; Heda, N. L.; Ahuja, B. L.

2018-05-01

We have computed the Mulliken's populations (MP) and electron momentum densities (EMDs) for TMWO4 (TM=Co, Ni, Cu and Zn) using linear combination of atomic orbitals (LCAO) scheme. The latest hybridization of Hartree-Fock (HF) and density functional theory (DFT) under the framework of LCAO approximations (so called WC1LYP and B1WC) have been employed. The theoretical EMDs have been compared with the available experimental data which show that WC1LYP scheme gives slightly better agreement with the experimental data for all the reported tungstates. Such trend shows the applicability of Lee-Yang-Parr (LYP) correlation energies within hybrid approximations in predicting the electronic properties of these compounds. Further, the MP data show the charge transfer from Co/Ni/Cu/Zn and W to O atoms. In addition, we have plotted the total EMDs at the same normalized area which show almost similar type of localization of 3d electrons (in real space) of Cu and Zn, which is lower than that of Ni and Co atoms in their tungstates environment.

Minimum time and fuel flight profiles for an F-15 airplane with a Highly Integrated Digital Electronic Control (HIDEC) system

NASA Technical Reports Server (NTRS)

Haering, E. A., Jr.; Burcham, F. W., Jr.

1984-01-01

A simulation study was conducted to optimize minimum time and fuel consumption paths for an F-15 airplane powered by two F100 Engine Model Derivative (EMD) engines. The benefits of using variable stall margin (uptrim) to increase performance were also determined. This study supports the NASA Highly Integrated Digital Electronic Control (HIDEC) program. The basis for this comparison was minimum time and fuel used to reach Mach 2 at 13,716 m (45,000 ft) from the initial conditions of Mach 0.15 at 1524 m (5000 ft). Results were also compared to a pilot's estimated minimum time and fuel trajectory determined from the F-15 flight manual and previous experience. The minimum time trajectory took 15 percent less time than the pilot's estimate for the standard EMD engines, while the minimum fuel trajectory used 1 percent less fuel than the pilot's estimate for the minimum fuel trajectory. The F-15 airplane with EMD engines and uptrim, was 23 percent faster than the pilot's estimate. The minimum fuel used was 5 percent less than the estimate.

Ensemble empirical mode decomposition based fluorescence spectral noise reduction for low concentration PAHs

NASA Astrophysics Data System (ADS)

Wang, Shu-tao; Yang, Xue-ying; Kong, De-ming; Wang, Yu-tian

2017-11-01

A new noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed to improve the detection effect for fluorescence spectra. Polycyclic aromatic hydrocarbons (PAHs) pollutants, as a kind of important current environmental pollution source, are highly oncogenic. Using the fluorescence spectroscopy method, the PAHs pollutants can be detected. However, instrument will produce noise in the experiment. Weak fluorescent signals can be affected by noise, so we propose a way to denoise and improve the detection effect. Firstly, we use fluorescence spectrometer to detect PAHs to obtain fluorescence spectra. Subsequently, noises are reduced by EEMD algorithm. Finally, the experiment results show the proposed method is feasible.

Multi-faults decoupling on turbo-expander using differential-based ensemble empirical mode decomposition

NASA Astrophysics Data System (ADS)

Li, Hongguang; Li, Ming; Li, Cheng; Li, Fucai; Meng, Guang

2017-09-01

This paper dedicates on the multi-faults decoupling of turbo-expander rotor system using Differential-based Ensemble Empirical Mode Decomposition (DEEMD). DEEMD is an improved version of DEMD to resolve the imperfection of mode mixing. The nonlinear behaviors of the turbo-expander considering temperature gradient with crack, rub-impact and pedestal looseness faults are investigated respectively, so that the baseline for the multi-faults decoupling can be established. DEEMD is then utilized on the vibration signals of the rotor system with coupling faults acquired by numerical simulation, and the results indicate that DEEMD can successfully decouple the coupling faults, which is more efficient than EEMD. DEEMD is also applied on the vibration signal of the misalignment coupling with rub-impact fault obtained during the adjustment of the experimental system. The conclusion shows that DEEMD can decompose the practical multi-faults signal and the industrial prospect of DEEMD is verified as well.

Decomposition Technique for Remaining Useful Life Prediction

NASA Technical Reports Server (NTRS)

Saha, Bhaskar (Inventor); Goebel, Kai F. (Inventor); Saxena, Abhinav (Inventor); Celaya, Jose R. (Inventor)

2014-01-01

The prognostic tool disclosed here decomposes the problem of estimating the remaining useful life (RUL) of a component or sub-system into two separate regression problems: the feature-to-damage mapping and the operational conditions-to-damage-rate mapping. These maps are initially generated in off-line mode. One or more regression algorithms are used to generate each of these maps from measurements (and features derived from these), operational conditions, and ground truth information. This decomposition technique allows for the explicit quantification and management of different sources of uncertainty present in the process. Next, the maps are used in an on-line mode where run-time data (sensor measurements and operational conditions) are used in conjunction with the maps generated in off-line mode to estimate both current damage state as well as future damage accumulation. Remaining life is computed by subtracting the instance when the extrapolated damage reaches the failure threshold from the instance when the prediction is made.

Linear dynamical modes as new variables for data-driven ENSO forecast

NASA Astrophysics Data System (ADS)

Gavrilov, Andrey; Seleznev, Aleksei; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander; Kurths, Juergen

2018-05-01

A new data-driven model for analysis and prediction of spatially distributed time series is proposed. The model is based on a linear dynamical mode (LDM) decomposition of the observed data which is derived from a recently developed nonlinear dimensionality reduction approach. The key point of this approach is its ability to take into account simple dynamical properties of the observed system by means of revealing the system's dominant time scales. The LDMs are used as new variables for empirical construction of a nonlinear stochastic evolution operator. The method is applied to the sea surface temperature anomaly field in the tropical belt where the El Nino Southern Oscillation (ENSO) is the main mode of variability. The advantage of LDMs versus traditionally used empirical orthogonal function decomposition is demonstrated for this data. Specifically, it is shown that the new model has a competitive ENSO forecast skill in comparison with the other existing ENSO models.

Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization.

PubMed

Sun, Yanfeng; Gao, Junbin; Hong, Xia; Mishra, Bamdev; Yin, Baocai

2016-03-01

Tensor clustering is an important tool that exploits intrinsically rich structures in real-world multiarray or Tensor datasets. Often in dealing with those datasets, standard practice is to use subspace clustering that is based on vectorizing multiarray data. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model taking into account cluster membership information. We propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the multinomial manifold for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.

Analysis of Vibration and Noise of Construction Machinery Based on Ensemble Empirical Mode Decomposition and Spectral Correlation Analysis Method

NASA Astrophysics Data System (ADS)

Chen, Yuebiao; Zhou, Yiqi; Yu, Gang; Lu, Dan

In order to analyze the effect of engine vibration on cab noise of construction machinery in multi-frequency bands, a new method based on ensemble empirical mode decomposition (EEMD) and spectral correlation analysis is proposed. Firstly, the intrinsic mode functions (IMFs) of vibration and noise signals were obtained by EEMD method, and then the IMFs which have the same frequency bands were selected. Secondly, we calculated the spectral correlation coefficients between the selected IMFs, getting the main frequency bands in which engine vibration has significant impact on cab noise. Thirdly, the dominated frequencies were picked out and analyzed by spectral analysis method. The study result shows that the main frequency bands and dominated frequencies in which engine vibration have serious impact on cab noise can be identified effectively by the proposed method, which provides effective guidance to noise reduction of construction machinery.

Bio-inspired multi-mode optic flow sensors for micro air vehicles

NASA Astrophysics Data System (ADS)

Park, Seokjun; Choi, Jaehyuk; Cho, Jihyun; Yoon, Euisik

2013-06-01

Monitoring wide-field surrounding information is essential for vision-based autonomous navigation in micro-air-vehicles (MAV). Our image-cube (iCube) module, which consists of multiple sensors that are facing different angles in 3-D space, can be applied to the wide-field of view optic flows estimation (μ-Compound eyes) and to attitude control (μ- Ocelli) in the Micro Autonomous Systems and Technology (MAST) platforms. In this paper, we report an analog/digital (A/D) mixed-mode optic-flow sensor, which generates both optic flows and normal images in different modes for μ- Compound eyes and μ-Ocelli applications. The sensor employs a time-stamp based optic flow algorithm which is modified from the conventional EMD (Elementary Motion Detector) algorithm to give an optimum partitioning of hardware blocks in analog and digital domains as well as adequate allocation of pixel-level, column-parallel, and chip-level signal processing. Temporal filtering, which may require huge hardware resources if implemented in digital domain, is remained in a pixel-level analog processing unit. The rest of the blocks, including feature detection and timestamp latching, are implemented using digital circuits in a column-parallel processing unit. Finally, time-stamp information is decoded into velocity from look-up tables, multiplications, and simple subtraction circuits in a chip-level processing unit, thus significantly reducing core digital processing power consumption. In the normal image mode, the sensor generates 8-b digital images using single slope ADCs in the column unit. In the optic flow mode, the sensor estimates 8-b 1-D optic flows from the integrated mixed-mode algorithm core and 2-D optic flows with an external timestamp processing, respectively.

Use of Knowledge Base Systems (EMDS) in Strategic and Tactical Forest Planning

NASA Astrophysics Data System (ADS)

Jensen, M. E.; Reynolds, K.; Stockmann, K.

2008-12-01

The USDA Forest Service 2008 Planning Rule requires Forest plans to provide a strategic vision for maintaining the sustainability of ecological, economic, and social systems across USFS lands through the identification of desired conditions and objectives. In this paper we show how knowledge-based systems can be efficiently used to evaluate disparate natural resource information to assess desired conditions and related objectives in Forest planning. We use the Ecosystem Management Decision Support (EMDS) system (http://www.institute.redlands.edu/emds/), which facilitates development of both logic-based models for evaluating ecosystem sustainability (desired conditions) and decision models to identify priority areas for integrated landscape restoration (objectives). The study area for our analysis spans 1,057 subwatersheds within western Montana and northern Idaho. Results of our study suggest that knowledge-based systems such as EMDS are well suited to both strategic and tactical planning and that the following points merit consideration in future National Forest (and other land management) planning efforts: 1) Logic models provide a consistent, transparent, and reproducible method for evaluating broad propositions about ecosystem sustainability such as: are watershed integrity, ecosystem and species diversity, social opportunities, and economic integrity in good shape across a planning area? The ability to evaluate such propositions in a formal logic framework also allows users the opportunity to evaluate statistical changes in outcomes over time, which could be very useful for regional and national reporting purposes and for addressing litigation; 2) The use of logic and decision models in strategic and tactical Forest planning provides a repository for expert knowledge (corporate memory) that is critical to the evaluation and management of ecosystem sustainability over time. This is especially true for the USFS and other federal resource agencies, which are likely to experience rapid turnover in tenured resource specialist positions within the next five years due to retirements; 3) Use of logic model output in decision models is an efficient method for synthesizing the typically large amounts of information needed to support integrated landscape restoration. Moreover, use of logic and decision models to design customized scenarios for integrated landscape restoration, as we have demonstrated with EMDS, offers substantial improvements to traditional GIS-based procedures such as suitability analysis. To our knowledge, this study represents the first attempt to link evaluations of desired conditions for ecosystem sustainability in strategic planning to tactical planning regarding the location of subwatersheds that best meet the objectives of integrated landscape restoration. The basic knowledge-based approach implemented in EMDS, with its logic (NetWeaver) and decision (Criterion Decision Plus) engines, is well suited both to multi-scale strategic planning and to multi-resource tactical planning.

Empirical Mode Decomposition and k-Nearest Embedding Vectors for Timely Analyses of Antibiotic Resistance Trends

PubMed Central

Teodoro, Douglas; Lovis, Christian

2013-01-01

Background Antibiotic resistance is a major worldwide public health concern. In clinical settings, timely antibiotic resistance information is key for care providers as it allows appropriate targeted treatment or improved empirical treatment when the specific results of the patient are not yet available. Objective To improve antibiotic resistance trend analysis algorithms by building a novel, fully data-driven forecasting method from the combination of trend extraction and machine learning models for enhanced biosurveillance systems. Methods We investigate a robust model for extraction and forecasting of antibiotic resistance trends using a decade of microbiology data. Our method consists of breaking down the resistance time series into independent oscillatory components via the empirical mode decomposition technique. The resulting waveforms describing intrinsic resistance trends serve as the input for the forecasting algorithm. The algorithm applies the delay coordinate embedding theorem together with the k-nearest neighbor framework to project mappings from past events into the future dimension and estimate the resistance levels. Results The algorithms that decompose the resistance time series and filter out high frequency components showed statistically significant performance improvements in comparison with a benchmark random walk model. We present further qualitative use-cases of antibiotic resistance trend extraction, where empirical mode decomposition was applied to highlight the specificities of the resistance trends. Conclusion The decomposition of the raw signal was found not only to yield valuable insight into the resistance evolution, but also to produce novel models of resistance forecasters with boosted prediction performance, which could be utilized as a complementary method in the analysis of antibiotic resistance trends. PMID:23637796

Effects of Plyometric and Resistance Training on Muscle Strength, Explosiveness and Neuromuscular Function in Young Adolescent Soccer Players.

PubMed

McKinlay, Brandon John; Wallace, Phillip; Dotan, Raffy; Long, Devon; Tokuno, Craig; Gabriel, David; Falk, Bareket

2018-01-04

This study examined the effect of 8-weeks of free-weight-resistance (RT) and plyometric (PLYO) training on maximal strength, explosiveness and jump performance compared with no added training (CON), in young male soccer players. Forty-one 11[FIGURE DASH]13-year-old soccer players were divided into three groups (RT, PLYO, CON). All participants completed isometric and dynamic (240°/s) knee extensions pre- and post-training. Peak torque (pT), peak rate of torque development (pRTD), electromechanical-delay (EMD), rate of muscle activation (Q50), m. vastus-lateralis thickness (VLT), and jump performance were examined. pT, pRTD and jump performance significantly improved in both training groups. Training resulted in significant (p<0.05) increases in isometric pT (23.4 vs. 15.8%) and pRTD (15.0 vs. 17.6%), in RT and PLYO, respectively. During dynamic contractions, training resulted in significant increases in pT (12.4 and 10.8% in RT and PLYO, respectively), but not pRTD. Jump performance increased in both training groups (RT=10.0%, PLYO=16.2%), with only PLYO significantly different from CON. Training resulted in significant increases in VLT (RT=6.7%. PLYO=8.1%). There were no significant EMD changes. In conclusion, 8-week free-weight resistance and plyometric training resulted in significant improvements in muscle strength and jump performance. Training resulted in similar muscle hypertrophy in the two training modes, with no clear differences in muscle performance. Plyometric training was more effective in improving jump performance, while free-weight resistance training was more advantageous in improving peak torque, where the stretch reflex was not involved.

Phase-field modeling of diffusional phase behaviors of solid surfaces: A case study of phase-separating Li XFePO 4 electrode particles

DOE PAGES

Heo, Tae Wook; Chen, Long-Qing; Wood, Brandon C.

2015-04-08

In this paper, we present a comprehensive phase-field model for simulating diffusion-mediated kinetic phase behaviors near the surface of a solid particle. The model incorporates elastic inhomogeneity and anisotropy, diffusion mobility anisotropy, interfacial energy anisotropy, and Cahn–Hilliard diffusion kinetics. The free energy density function is formulated based on the regular solution model taking into account the possible solute-surface interaction near the surface. The coherency strain energy is computed using the Fourier-spectral iterative-perturbation method due to the strong elastic inhomogeneity with a zero surface traction boundary condition. Employing a phase-separating Li XFePO 4 electrode particle for Li-ion batteries as a modelmore » system, we perform parametric three-dimensional computer simulations. The model permits the observation of surface phase behaviors that are different from the bulk counterpart. For instance, it reproduces the theoretically well-established surface modes of spinodal decomposition of an unstable solid solution: the surface mode of coherent spinodal decomposition and the surface-directed spinodal decomposition mode. We systematically investigate the influences of major factors on the kinetic surface phase behaviors during the diffusional process. Finally, our simulation study provides insights for tailoring the internal phase microstructure of a particle by controlling the surface phase morphology.« less

A New Method for Nonlinear and Nonstationary Time Series Analysis and Its Application to the Earthquake and Building Response Records

NASA Technical Reports Server (NTRS)

Huang, Norden E.

1999-01-01

A new method for analyzing nonlinear and nonstationary data has been developed. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy-frequency-time distribution, designated as the Hilbert Spectrum, Example of application of this method to earthquake and building response will be given. The results indicate those low frequency components, totally missed by the Fourier analysis, are clearly identified by the new method. Comparisons with Wavelet and window Fourier analysis show the new method offers much better temporal and frequency resolutions.

Acoustics flow analysis in circular duct using sound intensity and dynamic mode decomposition

NASA Astrophysics Data System (ADS)

Weyna, S.

2014-08-01

Sound intensity generation in hard-walled duct with acoustic flow (no mean-flow) is treated experimentally and shown graphically. In paper, numerous methods of visualization illustrating the vortex flow (2D, 3D) can graphically explain diffraction and scattering phenomena occurring inside the duct and around open end area. Sound intensity investigation in annular duct gives a physical picture of sound waves in any duct mode. In the paper, modal energy analysis are discussed with particular reference to acoustics acoustic orthogonal decomposition (AOD). The image of sound intensity fields before and above "cut-off" frequency region are found to compare acoustic modes which might resonate in duct. The experimental results show also the effects of axial and swirling flow. However acoustic field is extremely complicated, because pressures in non-propagating (cut-off) modes cooperate with the particle velocities in propagating modes, and vice versa. Measurement in cylindrical duct demonstrates also the cut-off phenomenon and the effect of reflection from open end. The aim of experimental study was to obtain information on low Mach number flows in ducts in order to improve physical understanding and validate theoretical CFD and CAA models that still may be improved.

Thermal conductance at the interface between crystals using equilibrium and nonequilibrium molecular dynamics

NASA Astrophysics Data System (ADS)

Merabia, Samy; Termentzidis, Konstantinos

2012-09-01

In this article, we compare the results of nonequilibrium (NEMD) and equilibrium (EMD) molecular dynamics methods to compute the thermal conductance at the interface between solids. We propose to probe the thermal conductance using equilibrium simulations measuring the decay of the thermally induced energy fluctuations of each solid. We also show that NEMD and EMD give generally speaking inconsistent results for the thermal conductance: Green-Kubo simulations probe the Landauer conductance between two solids which assumes phonons on both sides of the interface to be at equilibrium. On the other hand, we show that NEMD give access to the out-of-equilibrium interfacial conductance consistent with the interfacial flux describing phonon transport in each solid. The difference may be large and reaches typically a factor 5 for interfaces between usual semiconductors. We analyze finite size effects for the two determinations of the interfacial thermal conductance, and show that the equilibrium simulations suffer from severe size effects as compared to NEMD. We also compare the predictions of the two above-mentioned methods—EMD and NEMD—regarding the interfacial conductance of a series of mass mismatched Lennard-Jones solids. We show that the Kapitza conductance obtained with EMD can be well described using the classical diffuse mismatch model (DMM). On the other hand, NEMD simulation results are consistent with an out-of-equilibrium generalization of the acoustic mismatch model (AMM). These considerations are important in rationalizing previous results obtained using molecular dynamics, and help in pinpointing the physical scattering mechanisms taking place at atomically perfect interfaces between solids, which is a prerequisite to understand interfacial heat transfer across real interfaces.

A detailed evaluation of TomoDirect 3DCRT planning for whole-breast radiation therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fields, Emma C.; Rabinovitch, Rachel; Ryan, Nicole E.

2013-01-01

The goal of this work was to develop planning strategies for whole-breast radiotherapy (WBRT) using TomoDirect three-dimensional conformal radiation therapy (TD-3DCRT) and to compare TD-3DCRT with conventional 3DCRT and TD intensity-modulated radiation therapy (TD-IMRT) to evaluate differences in WBRT plan quality. Computed tomography (CT) images of 10 women were used to generate 150 WBRT plans, varying in target structures, field width (FW), pitch, and number of beams. Effects on target and external maximum doses (EMD), organ-at-risk (OAR) doses, and treatment time were assessed for each parameter to establish an optimal planning technique. Using this technique, TD-3DCRT plans were generated andmore » compared with TD-IMRT and standard 3DCRT plans. FW 5.0 cm with pitch = 0.250 cm significantly decreased EMD without increasing lung V20 Gy. Increasing number of beams from 2 to 6 and using an additional breast planning structure decreased EMD though increased lung V20 Gy. Changes in pitch had minimal effect on plan metrics. TD-3DCRT plans were subsequently generated using FW 5.0 cm, pitch = 0.250 cm, and 2 beams, with additional beams or planning structures added to decrease EMD when necessary. TD-3DCRT and TD-IMRT significantly decreased target maximum dose compared to standard 3DCRT. FW 5.0 cm with 2 to 6 beams or novel planning structures or both allow for TD-3DCRT WBRT plans with excellent target coverage and OAR doses. TD-3DCRT plans are comparable to plans generated using TD-IMRT and provide an alternative to conventional 3DCRT for WBRT.« less

Black-Hole Solutions to Einstein's Equations in the Presence of Matter and Modifications of Gravitation in Extra Dimensions

NASA Astrophysics Data System (ADS)

Goutéraux, B.

2010-11-01

In this thesis, we wish to examine the black-hole solutions of modified gravity theories inspired by String Theory or Cosmology. Namely, these modifications will take the guise of additional gauge and scalar fields for the so-called Einstein-Maxwell-Dilaton theories with an exponential Liouville potential; and of extra spatial dimensions for Einstein-Gauss-Bonnet theories. The black-hole solutions of EMD theories as well as their integrability are reviewed. One of the main results is that a master equation is obtained in the case of planar horizon topology, which allows to completely integrate the problem for s special relationship between the couplings. We also classify existing solutions. We move on to the study of Gauss-Bonnet black holes, focusing on the six-dimensional case. It is found that the Gauss-Bonnet coupling exposes the Weyl tensor of the horizon to the dynamics, severely restricting the Einstein spaces admissible and effectively lifting some of the degeneracy on the horizon topology. We then turn to the study of the thermodynamic properties of black holes, in General Relativity as well as in EMD theories. For the latter, phase transitions may be found in the canonical ensemble, which resemble the phase transitions for Reissner-Nordström black holes. Generically, we find that the thermodynamic properties (stability, order of phase transitions) depend crucially on the values of the EMD coupling constants. Finally, we interpret our planar EMD solutions holographically as Infra-Red geometries through the AdS/CFT correspondence, taking into account various validity constraints. We also compute AC and DC conductivities as applications to Condensed Matter Systems, and find some properties characteristic of strange metal behaviour.

Muscle group specific changes in the electromechanical delay following short-term resistance training.

PubMed

Stock, Matt S; Olinghouse, Kendra D; Mota, Jacob A; Drusch, Alexander S; Thompson, Brennan J

2016-09-01

The time delay between the onset of a muscle's electrical activity and force is believed to have important functional implications, and has been shown to decrease following resistance training in males. The purpose of this investigation was to examine changes in the voluntary electromechanical delay (EMD) for the leg extensors and flexors following a short-term resistance training intervention in females. Pretest/posttest control group experiment. Twenty-two previously untrained females (mean±SD age=21±2 years; mass=65.4±13.3kg) were randomly assigned to training (n=10) and control (n=12) groups. The training group performed barbell back squats and deadlifts twice per week for four weeks. EMD for the vastus lateralis (extensors) and biceps femoris (flexors) was examined during maximal voluntary contractions at pre- and posttesting. Data were examined using analyses of covariance (ANCOVAs) with the pretest and posttest scores serving as the covariate and dependent variable, respectively, and by evaluating the number of participants that exceeded the minimal difference statistic. For the leg extensors, the adjusted EMD posttest mean for the training group was significantly lower than that for the control group (74.3 vs. 91.8ms; p=0.015; ή(2)=0.275), and five training participants displayed decreases that exceeded the minimal difference. The ANCOVA for the leg flexors was not significant (adjusted means=98.0 vs. 90.0ms; p=0.487; ή(2)=.026). Four weeks of multi-joint resistance training resulted in decreased EMD for the leg extensors, but not the flexors. Copyright © 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Electromagnetic Dissociation and Spacecraft Electronics Damage

NASA Technical Reports Server (NTRS)

Norbury, John W.

2016-01-01

When protons or heavy ions from galactic cosmic rays (GCR) or solar particle events (SPE) interact with target nuclei in spacecraft, there can be two different types of interactions. The more familiar strong nuclear interaction often dominates and is responsible for nuclear fragmentation in either the GCR or SPE projectile nucleus or the spacecraft target nucleus. (Of course, the proton does not break up, except possibly to produce pions or other hadrons.) The less familiar, second type of interaction is due to the very strong electromagnetic fields that exist when two charged nuclei pass very close to each other. This process is called electromagnetic dissociation (EMD) and primarily results in the emission of neutrons, protons and light ions (isotopes of hydrogen and helium). The cross section for particle production is approximately defined as the number of particles produced in nucleus-nucleus collisions or other types of reactions. (There are various kinematic and other factors which multiply the particle number to arrive at the cross section.) Strong, nuclear interactions usually dominate the nuclear reactions of most interest that occur between GCR and target nuclei. However, for heavy nuclei (near Fe and beyond) at high energy the EMD cross section can be much larger than the strong nuclear interaction cross section. This paper poses a question: Are there projectile or target nuclei combinations in the interaction of GCR or SPE where the EMD reaction cross section plays a dominant role? If the answer is affirmative, then EMD mechanisms should be an integral part of codes that are used to predict damage to spacecraft electronics. The question can become more fine-tuned and one can ask about total reaction cross sections as compared to double differential cross sections. These issues will be addressed in the present paper.

Expanding the Extent of a UMLS Semantic Type via Group Neighborhood Auditing

PubMed Central

Chen, Yan; Gu, Huanying; Perl, Yehoshua; Halper, Michael; Xu, Junchuan

2009-01-01

Objective Each Unified Medical Language System (UMLS) concept is assigned one or more semantic types (ST). A dynamic methodology for aiding an auditor in finding concepts that are missing the assignment of a given ST, S is presented. Design The first part of the methodology exploits the previously introduced Refined Semantic Network and accompanying refined semantic types (RST) to help narrow the search space for offending concepts. The auditing is focused in a neighborhood surrounding the extent of an RST, T (of S) called an envelope, consisting of parents and children of concepts in the extent. The audit moves outward as long as missing assignments are discovered. In the second part, concepts not reached previously are processed and reassigned T as needed during the processing of S's other RSTs. The set of such concepts is expanded in a similar way to that in the first part. Measurements The number of errors discovered is reported. To measure the methodology's efficiency, “error hit rates” (i.e., errors found in concepts examined) are computed. Results The methodology was applied to three STs: Experimental Model of Disease (EMD), Environmental Effect of Humans, and Governmental or Regulatory Activity. The EMD experienced the most drastic change. For its RST “EMD ∩ Neoplastic Process” (RST “EMD”) with only 33 (31) original concepts, 915 (134) concepts were found by the first (second) part to be missing the EMD assignment. Changes to the other two STs were smaller. Conclusion The results show that the proposed auditing methodology can help to effectively and efficiently identify concepts lacking the assignment of a particular semantic type. PMID:19567802

Effect of Emdogain enamel matrix derivative and BMP-2 on the gene expression and mineralized nodule formation of alveolar bone proper-derived stem/progenitor cells.

PubMed

Fawzy El-Sayed, Karim M; Dörfer, Christof; Ungefroren, Hendrick; Kassem, Neemat; Wiltfang, Jörg; Paris, Sebastian

2014-07-01

The objective of this study was to evaluate the effect of Emdogain (Enamel Matrix Derivative, EMD) and Bone Morphogenetic Protein-2 (BMP-2), either solely or in combination, on the gene expression and mineralized nodule formation of alveolar bone proper-derived stem/progenitor cells. Stem/progenitor cells were isolated from human alveolar bone proper, magnetically sorted using STRO-1 antibodies, characterized flowcytometrically for their surface markers' expression, and examined for colony formation and multilineage differentiation potential. Subsequently, cells were treated over three weeks with 100 μg/ml Emdogain (EMD-Group), or 100 ng/ml BMP-2 (BMP-Group), or a combination of 100 ng/ml BMP-2 and 100 μg/ml Emdogain (BMP/EMD-Group). Unstimulated stem/progenitor cells (MACS(+)-Group) and osteoblasts (OB-Group) served as controls. Osteogenic gene expression was analyzed using RTq-PCR after 1, 2 and 3 weeks (N = 3/group). Mineralized nodule formation was evaluated by Alizarin-Red staining. BMP and EMD up-regulated the osteogenic gene expression. The BMP Group showed significantly higher expression of Collagen-I, III, and V, Alkaline phosphatase and Osteonectin compared to MACS(+)- and OB-Group (p < 0.05; Two-way ANOVA/Bonferroni) with no mineralized nodule formation. Under in-vitro conditions, Emdogain and BMP-2 up-regulate the osteogenic gene expression of stem/progenitor cells. The combination of BMP-2 and Emdogain showed no additive effect and would not be recommended for a combined clinical stimulation. Copyright © 2013 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

New methodology for capillary electrophoresis with ESI-MS detection: Electrophoretic focusing on inverse electromigration dispersion gradient. High-sensitivity analysis of sulfonamides in waters.

PubMed

Malá, Zdena; Gebauer, Petr; Boček, Petr

2016-09-07

This article describes for the first time the combination of electrophoretic focusing on inverse electromigration dispersion (EMD) gradient, a new separation principle described in 2010, with electrospray-ionization (ESI) mass spectrometric detection. The separation of analytes along the electromigrating EMD profile proceeds so that each analyte is focused and concentrated within the profile at a particular position given by its pKa and ionic mobility. The proposed methodology combines this principle with the transport of the focused zones to the capillary end by superimposed electromigration, electroosmotic flow and ESI suction, and their detection by the MS detector. The designed electrolyte system based on maleic acid and 2,6-lutidine is suitable to create an inverse EMD gradient of required properties and its components are volatile enough to be compatible with the ESI interface. The characteristic properties of the proposed electrolyte system and of the formed inverse gradient are discussed in detail using calculated diagrams and computer simulations. It is shown that the system is surprisingly robust and allows sensitive analyses of trace amounts of weak acids in the pKa range between approx. 6 and 9. As a first practical application of electrophoretic focusing on inverse EMD gradient, the analysis of several sulfonamides in waters is reported. It demonstrates the potential of the developed methodology for fast and high-sensitivity analyses of ionic trace analytes, with reached LODs around 3 × 10(-9) M (0.8 ng mL(-1)) of sulfonamides in spiked drinking water without any sample pretreatment. Copyright © 2016 Elsevier B.V. All rights reserved.

Do recent observations of very large electromagnetic dissociation cross sections signify a transition towards non-perturbative QED?

NASA Technical Reports Server (NTRS)

Norbury, John W.

1992-01-01

The very large electromagnetic dissociation (EMD) cross section recently observed by Hill, Wohn, Schwellenbach, and Smith do not agree with Weizsacker-Williams (WW) theory or any simple modification thereof. Calculations are presented for the reaction probabilities for this experiment and the entire single and double nucleon removal EMD data set. It is found that for those few reactions where theory and experiment disagree, the probabilities are exceptionally large. This indicates that WW theory is not valid for these reactions and that one must consider higher order corrections and perhaps even a non-perturbative approach to quantum electrodynamics (QED).

Alzheimer's disease-like impaired cognition in endothelial-specific megalin-null mice.

PubMed

Dietrich, Marcelo; Antequera, Desiree; Pascual, Consuelo; Castro, Nerea; Bolos, Marta; Carro, Eva

2014-01-01

Megalin has been suggested to be involved in Alzheimer's disease (AD), mediating blood-brain barrier (BBB) transport of multiple ligands, including amyloid-β peptide (Aβ), but also neuroprotective factors. Because no transgenic model is currently available to study this concept, we have obtained transgenic mice blocking megalin expression at the BBB. These endothelial megalin deficient (EMD) mice developed increased anxiety behavior and impaired learning ability and recognition memory, similar to symptoms described in AD. Degenerating neurons were also observed in the cerebral cortex of EMD mice. In view of our findings we suggest that, in mice, megalin deficiency at the BBB leads to neurodegeneration.

Modeling and measuring the visual detection of ecologically relevant motion by an Anolis lizard.

PubMed

Pallus, Adam C; Fleishman, Leo J; Castonguay, Philip M

2010-01-01

Motion in the visual periphery of lizards, and other animals, often causes a shift of visual attention toward the moving object. This behavioral response must be more responsive to relevant motion (predators, prey, conspecifics) than to irrelevant motion (windblown vegetation). Early stages of visual motion detection rely on simple local circuits known as elementary motion detectors (EMDs). We presented a computer model consisting of a grid of correlation-type EMDs, with videos of natural motion patterns, including prey, predators and windblown vegetation. We systematically varied the model parameters and quantified the relative response to the different classes of motion. We carried out behavioral experiments with the lizard Anolis sagrei and determined that their visual response could be modeled with a grid of correlation-type EMDs with a spacing parameter of 0.3 degrees visual angle, and a time constant of 0.1 s. The model with these parameters gave substantially stronger responses to relevant motion patterns than to windblown vegetation under equivalent conditions. However, the model is sensitive to local contrast and viewer-object distance. Therefore, additional neural processing is probably required for the visual system to reliably distinguish relevant from irrelevant motion under a full range of natural conditions.

Electric machine differential for vehicle traction control and stability control

NASA Astrophysics Data System (ADS)

Kuruppu, Sandun Shivantha

Evolving requirements in energy efficiency and tightening regulations for reliable electric drivetrains drive the advancement of the hybrid electric (HEV) and full electric vehicle (EV) technology. Different configurations of EV and HEV architectures are evaluated for their performance. The future technology is trending towards utilizing distinctive properties in electric machines to not only to improve efficiency but also to realize advanced road adhesion controls and vehicle stability controls. Electric machine differential (EMD) is such a concept under current investigation for applications in the near future. Reliability of a power train is critical. Therefore, sophisticated fault detection schemes are essential in guaranteeing reliable operation of a complex system such as an EMD. The research presented here emphasize on implementation of a 4kW electric machine differential, a novel single open phase fault diagnostic scheme, an implementation of a real time slip optimization algorithm and an electric machine differential based yaw stability improvement study. The proposed d-q current signature based SPO fault diagnostic algorithm detects the fault within one electrical cycle. The EMD based extremum seeking slip optimization algorithm reduces stopping distance by 30% compared to hydraulic braking based ABS.

DISPLACEMENT CASCADE SIMULATION IN TUNGSTEN UP TO 200 KEV OF DAMAGE ENERGY AT 300, 1025, AND 2050 K

DOE Office of Scientific and Technical Information (OSTI.GOV)

Setyawan, Wahyu; Nandipati, Giridhar; Roche, Kenneth J.

2015-09-22

We generated molecular dynamics database of primary defects that adequately covers the range of tungsten recoil energy imparted by 14-MeV neutrons. During this semi annual period, cascades at 150 and 200 keV at 300 and 1025 K were simulated. Overall, we included damage energy up to 200 keV at 300 and 1025 K, and up to 100 keV at 2050 K. We report the number of surviving Frenkel pairs (NF) and the size distribution of defect clusters. The slope of the NF curve versus cascade damage energy (EMD), on a log-log scale, changes at a transition energy (μ). For EMDmore » > μ, the cascade forms interconnected damage regions that facilitate the formation of large clusters of defects. At 300 K and EMD = 200 keV, the largest size of interstitial cluster and vacancy cluster is 266 and 335, respectively. Similarly, at 1025 K and EMD = 200 keV, the largest size of interstitial cluster and vacancy cluster is 296 and 338, respectively. At 2050 K, large interstitial clusters also routinely form, but practically no large vacancy clusters do« less

The structure and ordering of ɛ-MnO 2

NASA Astrophysics Data System (ADS)

Kim, Chang-Hoon; Akase, Zentaro; Zhang, Lichun; Heuer, Arthur H.; Newman, Aron E.; Hughes, Paula J.

2006-03-01

The presence of ɛ-MnO 2 as a major component of electrolytic manganese dioxide (EMD) has been demonstrated by a combined X-ray diffraction/transmission electron microscopy (TEM) study. ɛ-MnO 2 usually has a partially ordered defect NiAs structure containing 50% cation vacancies; these vacancies can be fully ordered by a low temperature (200 °C) heat treatment to form a pseudohexagonal but monoclinic superlattice. Numerous fine-scale anti-phase domain boundaries are present in ordered ɛ-MnO 2 and cause extensive peak broadening and a massive shift of a very intense, 0.37 nm superlattice peak. This suggests a radically different explanation of the ubiquitous, very broad ˜0.42 nm peak (˜21-22° 2 θ, Cu Kα radiation) in EMDs, which heretofore has been attributed to Ramsdellite containing numerous planar defects. This work confirms the multi-phase model of equiaxed EMDs proposed by Heuer et al. [ITE Lett. 1(6) (2000) B50; Proc. Seventh Int. Symp. Adv. Phys. Fields 92 (2001)], rather than the defective single-phase model of Chabre and Pannetier [Prog. Solid State Chem. 23 (1995) 1] and Bowden et al. [ITE Lett. 4(1) (2003) B1].

Electromagnetic Dissociation Cross Sections for High LET Fragments

NASA Technical Reports Server (NTRS)

Norbury, John

2016-01-01

Nuclear interaction cross sections are used in space radiation transport codes to calculate the probability of fragment emission in high energy nucleus-nucleus collisions. Strong interactions usually dominate in these collisions, but electromagnetic (EM) interactions can also sometimes be important. Strong interactions typically occur when the projectile nucleus hits a target nucleus, with a small impact parameter. For impact parameters larger than the sum of the nuclear radii, EM reactions dominate and the process is called electromagnetic dissociation (EMD) if one of the nuclei undergo fragmentation. Previous models of EMD have been used to calculate single proton (p) production, single neutron (n) production or light ion production, where a light ion is defined as an isotope of hydrogen (H) or helium (He), such as a deuteron (2H), a triton (3H), a helion (3He) or an alpha particle (4He). A new model is described which can also account for multiple nucleon production, such as 2p, 2n, 1p1n, 2p1n, 2p2n, etc. in addition to light ion production. Such processes are important to include for the following reasons. Consider, for example, the EMD reaction 56Fe + Al --> 52Cr + X + Al, for a 56Fe projectile impacting Al, which produces the high linear energy transfer (LET) fragment 52Cr. In this reaction, the most probable particles representing X are either 2p2n or 4He. Therefore, production of the high LET fragment 52Cr, must include the multiple nucleon production of 2p2n in addition to the light ion production of 4He. Previous models, such as the NUCFRG3 model, could only account for the 4He production process in this reaction and could not account for 2p2n. The new EMD model presented in this work accounts for both the light ion and multiple nucleon processes, and is therefore able to correctly account for the production of high LET products such as 52Cr. The model will be described and calculations will be presented that show the importance of light ion and multiple nucleon production. The work will also show that EMD reactions contribute most to those fragments with the highest LET.

Occupational burnout levels in emergency medicine--a nationwide study and analysis.

PubMed

Popa, Florian; Raed, Arafat; Purcarea, Victor Lorin; Lală, Adrian; Bobirnac, George

2010-01-01

The specificity of the emergency medical act strongly manifests itself on account of a wide series of psycho-traumatizing factors augmented both by the vulnerable situation of the patient and the paroxysmal state of the act. Also, it has been recognized that the physical solicitation and distress levels are the highest among all medical specialties, this being a valuable marker for establishing the quality of the medical act. We have surveyed a total of 4725 emergency medical workers with the MBI-HSS instrument, receiving 4693 valid surveys (99.32% response rate). Professional categories included Emergency Department doctors (M-EMD), ambulance doctors (M-AMB), ED doctors with field work in emergency and resuscitation (including mobile intensive care units and airborne intensive care units) (D-SMU), medical nurses in Emergency Departments (N-EMD), medical nurses in the ambulance service (N-AMB), ED medical nurses with field activity in emergency and resuscitation (N-SMU), ambulance drivers (DRV) and paramedic (EMT). The n values for every category of subjects and percentage of system coverage (table 3) shows that we have covered an estimated total of 29.94% of the Romanian emergency medical field workers. MBI-HSS results show a moderate to high level of occupational stress for the surveyed subjects. The average values for the three parameters, corresponding to the entire Romanian emergency medical field were 1.41 for EE, 0.99 for DP and 4.47 for PA (95% CI). Average results stratified by professional category show higher EE average values (v) for the M-SMU (v=2.01, 95%CI) and M-EMD (v=2.21, 95% CI) groups corresponding to higher DP values for the same groups (vM-EMD=1.41 and vM-SMU=1.22, 95% CI). PA values for these groups are below average, corresponding to an increased risk factor for high degrees of burnout. Calculated PA values are 4.30 for the M-EMD group and 4.20 for the M-SMU group. Of all surveyed groups, our study shows a high risk of burnout consisting of high emotional exhaustion (EE) and high depersonalization (DP) values for Emergency Department doctors, Emergency, and Resuscitation Service doctors (M-SMU). Possible explanations for this might be linked to high patient flow, Emergency Department crowding, long work hours and individual parameters such as coping mechanisms, social development and work environment.

Iterative variational mode decomposition based automated detection of glaucoma using fundus images.

PubMed

Maheshwari, Shishir; Pachori, Ram Bilas; Kanhangad, Vivek; Bhandary, Sulatha V; Acharya, U Rajendra

2017-09-01

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Speech rhythm analysis with decomposition of the amplitude envelope: characterizing rhythmic patterns within and across languages.

PubMed

Tilsen, Sam; Arvaniti, Amalia

2013-07-01

This study presents a method for analyzing speech rhythm using empirical mode decomposition of the speech amplitude envelope, which allows for extraction and quantification of syllabic- and supra-syllabic time-scale components of the envelope. The method of empirical mode decomposition of a vocalic energy amplitude envelope is illustrated in detail, and several types of rhythm metrics derived from this method are presented. Spontaneous speech extracted from the Buckeye Corpus is used to assess the effect of utterance length on metrics, and it is shown how metrics representing variability in the supra-syllabic time-scale components of the envelope can be used to identify stretches of speech with targeted rhythmic characteristics. Furthermore, the envelope-based metrics are used to characterize cross-linguistic differences in speech rhythm in the UC San Diego Speech Lab corpus of English, German, Greek, Italian, Korean, and Spanish speech elicited in read sentences, read passages, and spontaneous speech. The envelope-based metrics exhibit significant effects of language and elicitation method that argue for a nuanced view of cross-linguistic rhythm patterns.

A data-driven decomposition approach to model aerodynamic forces on flapping airfoils

NASA Astrophysics Data System (ADS)

Raiola, Marco; Discetti, Stefano; Ianiro, Andrea

2017-11-01

In this work, we exploit a data-driven decomposition of experimental data from a flapping airfoil experiment with the aim of isolating the main contributions to the aerodynamic force and obtaining a phenomenological model. Experiments are carried out on a NACA 0012 airfoil in forward flight with both heaving and pitching motion. Velocity measurements of the near field are carried out with Planar PIV while force measurements are performed with a load cell. The phase-averaged velocity fields are transformed into the wing-fixed reference frame, allowing for a description of the field in a domain with fixed boundaries. The decomposition of the flow field is performed by means of the POD applied on the velocity fluctuations and then extended to the phase-averaged force data by means of the Extended POD approach. This choice is justified by the simple consideration that aerodynamic forces determine the largest contributions to the energetic balance in the flow field. Only the first 6 modes have a relevant contribution to the force. A clear relationship can be drawn between the force and the flow field modes. Moreover, the force modes are closely related (yet slightly different) to the contributions of the classic potential models in literature, allowing for their correction. This work has been supported by the Spanish MINECO under Grant TRA2013-41103-P.

Applications of Hilbert Spectral Analysis for Speech and Sound Signals

NASA Technical Reports Server (NTRS)

Huang, Norden E.

2003-01-01

A new method for analyzing nonlinear and nonstationary data has been developed, and the natural applications are to speech and sound signals. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time, which give sharp identifications of imbedded structures. This method invention can be used to process all acoustic signals. Specifically, it can process the speech signals for Speech synthesis, Speaker identification and verification, Speech recognition, and Sound signal enhancement and filtering. Additionally, as the acoustical signals from machinery are essentially the way the machines are talking to us. Therefore, the acoustical signals, from the machines, either from sound through air or vibration on the machines, can tell us the operating conditions of the machines. Thus, we can use the acoustic signal to diagnosis the problems of machines.

Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis

NASA Astrophysics Data System (ADS)

Jiang, Fan; Zhu, Zhencai; Li, Wei; Zhou, Gongbo; Chen, Guoan

2014-07-01

Accurately identifying faults in rotor-bearing systems by analyzing vibration signals, which are nonlinear and nonstationary, is challenging. To address this issue, a new approach based on ensemble empirical mode decomposition (EEMD) and self-zero space projection analysis is proposed in this paper. This method seeks to identify faults appearing in a rotor-bearing system using simple algebraic calculations and projection analyses. First, EEMD is applied to decompose the collected vibration signals into a set of intrinsic mode functions (IMFs) for features. Second, these extracted features under various mechanical health conditions are used to design a self-zero space matrix according to space projection analysis. Finally, the so-called projection indicators are calculated to identify the rotor-bearing system's faults with simple decision logic. Experiments are implemented to test the reliability and effectiveness of the proposed approach. The results show that this approach can accurately identify faults in rotor-bearing systems.

The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

PubMed

Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo

2016-09-01

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

Low-Temperature Aging Characteristics of Type 316L Stainless Steel Welds: Dependence on Solidification Mode

NASA Astrophysics Data System (ADS)

Abe, Hiroshi; Watanabe, Yutaka

2008-06-01

Thermal aging embrittlement of light water reactor (LWR) components made of stainless steel cast has been recognized as a potential degradation issue, and careful attention has been paid to it. Although welds of austenitic stainless steels have γ-δ duplex microstructure, which is similar to that of the stainless steel cast, examination of the thermal aging characteristics of the stainless steel welds is very limited. In this investigation, two types of type 316L stainless steel weld metal with different solidification modes were prepared using two kinds of filler metals having tailored Ni equivalent and Cr equivalent. Differences between the two weld metals in the morphology of microstructure, in the composition of δ-ferrite, and in hardening behaviors with isothermal aging at 335 °C have been investigated. The hardness of the ferrite phase has increased with aging time, while the hardness of austenite phase has stayed the same. The mottled aspect has been observed in δ-ferrite of aged samples by transmission electron microscopy (TEM) observation. These characteristics suggest that spinodal decomposition has occurred in δ-ferrite by aging at 335 °C. The age-hardening rate of δ-ferrite was faster for the primary austenite solidification mode (AF mode) sample than the primary ferrite solidification mode (FA mode) sample in the initial stage of the aging up to 2000 hours. It has been suggested that the solidification mode can affect the kinetics of spinodal decomposition.

Multi-scale fluctuation analysis of precipitation in Beijing by Extreme-point Symmetric Mode Decomposition

NASA Astrophysics Data System (ADS)

Li, Jiqing; Duan, Zhipeng; Huang, Jing

2018-06-01

With the aggravation of the global climate change, the shortage of water resources in China is becoming more and more serious. Using reasonable methods to study changes in precipitation is very important for planning and management of water resources. Based on the time series of precipitation in Beijing from 1951 to 2015, the multi-scale features of precipitation are analyzed by the Extreme-point Symmetric Mode Decomposition (ESMD) method to forecast the precipitation shift. The results show that the precipitation series have periodic changes of 2.6, 4.3, 14 and 21.7 years, and the variance contribution rate of each modal component shows that the inter-annual variation dominates the precipitation in Beijing. It is predicted that precipitation in Beijing will continue to decrease in the near future.

A novel approach for baseline correction in 1H-MRS signals based on ensemble empirical mode decomposition.

PubMed

Parto Dezfouli, Mohammad Ali; Dezfouli, Mohsen Parto; Rad, Hamidreza Saligheh

2014-01-01

Proton magnetic resonance spectroscopy ((1)H-MRS) is a non-invasive diagnostic tool for measuring biochemical changes in the human body. Acquired (1)H-MRS signals may be corrupted due to a wideband baseline signal generated by macromolecules. Recently, several methods have been developed for the correction of such baseline signals, however most of them are not able to estimate baseline in complex overlapped signal. In this study, a novel automatic baseline correction method is proposed for (1)H-MRS spectra based on ensemble empirical mode decomposition (EEMD). This investigation was applied on both the simulated data and the in-vivo (1)H-MRS of human brain signals. Results justify the efficiency of the proposed method to remove the baseline from (1)H-MRS signals.

The first of a series of high efficiency, high bmep, turbocharged two-stroke cycle diesel engines; the general motors EMD 645FB engine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kotlin, J.J.; Dunteman, N.R.; Scott, D.I.

1983-01-01

The current Electro-Motive Division 645 Series turbocharged engines are the Model FB and EC. The FB engine combines the highest thermal efficiency with the highest specific output of any EMD engine to date. The FB Series incorporates 16:1 compression ratio with a fire ring piston and an improved turbocharger design. Engine components included in the FB engine provide very high output levels with exceptional reliability. This paper also describes the performance of the lower rated Model EC engine series which feature high thermal efficiency and utilize many engine components well proven in service and basic to the Model FB Series.

A Novel Approach to Resonant Absorption of the Fast Magnetohydrodynamic Eigenmodes of a Coronal Arcade

NASA Astrophysics Data System (ADS)

Hindman, Bradley W.; Jain, Rekha

2018-05-01

The arched field lines forming coronal arcades are often observed to undulate as magnetohydrodynamic waves propagate both across and along the magnetic field. These waves are most likely a combination of resonantly coupled fast magnetoacoustic waves and Alfvén waves. The coupling results in resonant absorption of the fast waves, converting fast wave energy into Alfvén waves. The fast eigenmodes of the arcade have proven difficult to compute or derive analytically, largely because of the mathematical complexity that the coupling introduces. When a traditional spectral decomposition is employed, the discrete spectrum associated with the fast eigenmodes is often subsumed into the continuous Alfvén spectrum. Thus fast eigenmodes become collective modes or quasi-modes. Here we present a spectral decomposition that treats the eigenmodes as having real frequencies but complex wavenumbers. Using this procedure we derive dispersion relations, spatial damping rates, and eigenfunctions for the resonant, fast eigenmodes of the arcade. We demonstrate that resonant absorption introduces a fast mode that would not exist otherwise. This new mode is heavily damped by resonant absorption, travelling only a few wavelengths before losing most of its energy.

Variational mode decomposition based approach for accurate classification of color fundus images with hemorrhages

NASA Astrophysics Data System (ADS)

Lahmiri, Salim; Shmuel, Amir

2017-11-01

Diabetic retinopathy is a disease that can cause a loss of vision. An early and accurate diagnosis helps to improve treatment of the disease and prognosis. One of the earliest characteristics of diabetic retinopathy is the appearance of retinal hemorrhages. The purpose of this study is to design a fully automated system for the detection of hemorrhages in a retinal image. In the first stage of our proposed system, a retinal image is processed with variational mode decomposition (VMD) to obtain the first variational mode, which captures the high frequency components of the original image. In the second stage, four texture descriptors are extracted from the first variational mode. Finally, a classifier trained with all computed texture descriptors is used to distinguish between images of healthy and unhealthy retinas with hemorrhages. Experimental results showed evidence of the effectiveness of the proposed system for detection of hemorrhages in the retina, since a perfect detection rate was achieved. Our proposed system for detecting diabetic retinopathy is simple and easy to implement. It requires only short processing time, and it yields higher accuracy in comparison with previously proposed methods for detecting diabetic retinopathy.

Reduced quantum dynamics with arbitrary bath spectral densities: hierarchical equations of motion based on several different bath decomposition schemes.

PubMed

Liu, Hao; Zhu, Lili; Bai, Shuming; Shi, Qiang

2014-04-07

We investigated applications of the hierarchical equation of motion (HEOM) method to perform high order perturbation calculations of reduced quantum dynamics for a harmonic bath with arbitrary spectral densities. Three different schemes are used to decompose the bath spectral density into analytical forms that are suitable to the HEOM treatment: (1) The multiple Lorentzian mode model that can be obtained by numerically fitting the model spectral density. (2) The combined Debye and oscillatory Debye modes model that can be constructed by fitting the corresponding classical bath correlation function. (3) A new method that uses undamped harmonic oscillator modes explicitly in the HEOM formalism. Methods to extract system-bath correlations were investigated for the above bath decomposition schemes. We also show that HEOM in the undamped harmonic oscillator modes can give detailed information on the partial Wigner transform of the total density operator. Theoretical analysis and numerical simulations of the spin-Boson dynamics and the absorption line shape of molecular dimers show that the HEOM formalism for high order perturbations can serve as an important tool in studying the quantum dissipative dynamics in the intermediate coupling regime.

Reduced quantum dynamics with arbitrary bath spectral densities: Hierarchical equations of motion based on several different bath decomposition schemes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Hao; Zhu, Lili; Bai, Shuming

2014-04-07

We investigated applications of the hierarchical equation of motion (HEOM) method to perform high order perturbation calculations of reduced quantum dynamics for a harmonic bath with arbitrary spectral densities. Three different schemes are used to decompose the bath spectral density into analytical forms that are suitable to the HEOM treatment: (1) The multiple Lorentzian mode model that can be obtained by numerically fitting the model spectral density. (2) The combined Debye and oscillatory Debye modes model that can be constructed by fitting the corresponding classical bath correlation function. (3) A new method that uses undamped harmonic oscillator modes explicitly inmore » the HEOM formalism. Methods to extract system-bath correlations were investigated for the above bath decomposition schemes. We also show that HEOM in the undamped harmonic oscillator modes can give detailed information on the partial Wigner transform of the total density operator. Theoretical analysis and numerical simulations of the spin-Boson dynamics and the absorption line shape of molecular dimers show that the HEOM formalism for high order perturbations can serve as an important tool in studying the quantum dissipative dynamics in the intermediate coupling regime.« less

Fault detection, isolation, and diagnosis of self-validating multifunctional sensors.

PubMed

Yang, Jing-Li; Chen, Yin-Sheng; Zhang, Li-Li; Sun, Zhen

2016-06-01

A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

Handling Qualities of Large Flexible Aircraft. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Poopaka, S.

1980-01-01

The effects on handling qualities of elastic modes interaction with the rigid body dynamics of a large flexible aircraft are studied by a mathematical computer simulation. An analytical method to predict the pilot ratings when there is a severe modes interactions is developed. This is done by extending the optimal control model of the human pilot response to include the mode decomposition mechanism into the model. The handling qualities are determined for a longitudinal tracking task using a large flexible aircraft with parametric variations in the undamped natural frequencies of the two lowest frequency, symmetric elastic modes made to induce varying amounts of mode interaction.

Raman intensity and vibrational modes of armchair CNTs

NASA Astrophysics Data System (ADS)

Hur, Jaewoong; Stuart, Steven J.

2017-07-01

Raman intensity changes and frequency patterns have been studied using the various armchair (n, n) to understand the variations of bond polarizability, in regard to changing diameters, lengths, and the number of atoms in the (n, n). The Raman intensity trends of the (n, n) are validated by those of Cn isomers. For frequency trends, similar frequency patterns and frequency inward shifts for the (n, n) are characterized. Also, VDOS trends of the (n, n) expressing Raman modes are interpreted. The decomposition of vibrational modes in the (n, n) into radial, longitudinal, and tangential mode is beneficially used to recognize the distinct characteristics of vibrational modes.

Human histologic evaluation of an intrabony defect treated with enamel matrix derivative, xenograft, and GTR.

PubMed

Sculean, Anton; Windisch, Peter; Chiantella, Giovanni Carlo

2004-08-01

The purpose of the present case report is to clinically and histologically evaluate the healing of one advanced intrabony defect following treatment with an enamel matrix protein derivative (EMD) combined with a bovine-derived xenograft (BDX) and guided tissue regeneration (GTR). One patient with generalized chronic periodontitis and one advanced intrabony defect was treated with EMD + BDX + GTR. Notches were placed in the root at the level of the calculus and alveolar crest to aid histologic identification of new periodontal tissues. Postoperative healing was uneventful. At the 7-month histologic examination, healing in the intrabony component of the defect was characterized by formation of new connective tissue attachment (new cellular cementum with inserting collagen fibers) and new bone in the intrabony component. The BDX particles were surrounded by bone-like tissue. No direct contact between the graft particles and root surface (cementum or dentin) was observed. Healing in the suprabony defect component occurred through epithelial downgrowth that stopped at the level of the coronal notch. The BDX particles were entirely encapsulated in dense connective tissue, without any signs of bone formation. The present case report shows formation of new attachment apparatus consisting of new bone, cementum, and periodontal ligament in the intrabony component of one human defect treated with EMD + BDX + GTR.

Nonadherence in the era of severe asthma biologics and thermoplasty

PubMed Central

Lee, Joy; Tay, Tunn Ren; Radhakrishna, Naghmeh; Hore-Lacy, Fiona; Mackay, Anna; Hoy, Ryan; Dabscheck, Eli; O'Hehir, Robyn; Hew, Mark

2018-01-01

Nonadherence to inhaled preventers impairs asthma control. Electronic monitoring devices (EMDs) can objectively measure adherence. Their use has not been reported in difficult asthma patients potentially suitable for novel therapies, i.e. biologics and bronchial thermoplasty. Consecutive patients with difficult asthma were assessed for eligibility for novel therapies. Medication adherence, defined as taking >75% of prescribed doses, was assessed by EMD and compared with standardised clinician assessment over an 8-week period. Among 69 difficult asthma patients, adherence could not be analysed in 13, due to device incompatibility or malfunction. Nonadherence was confirmed in 20 out of 45 (44.4%) patients. Clinical assessment of nonadherence was insensitive (physician 15%, nurse 28%). Serum eosinophils were higher in nonadherent patients. Including 11 patients with possible nonadherence (device refused or not returned) increased the nonadherence rate to 31 out of 56 (55%) patients. Severe asthma criteria were fulfilled by 59 out of 69 patients. 47 were eligible for novel therapies, with confirmed nonadherence in 16 out of 32 (50%) patients with EMD data; including seven patients with possible nonadherence increased the nonadherence rate to 23 out of 39 (59%). At least half the patients eligible for novel therapies were nonadherent to preventers. Nonadherence was often undetectable by clinical assessments. Preventer adherence must be confirmed objectively before employing novel severe asthma therapies. PMID:29519922

Drug and alcohol-impaired driving among electronic music dance event attendees

PubMed Central

Furr-Holden, Debra; Voas, Robert B.; Kelley-Baker, Tara; Miller, Brenda

2011-01-01

Background Drug-impaired driving has received increased attention resulting from development of rapid drug-screening procedures used by police and state laws establishing per se limits for drug levels in drivers. Venues that host electronic music dance events (EMDEs) provide a unique opportunity to assess drug-impaired driving among a high proportion of young adult drug users. EMDEs are late-night dance parties marked by a substantial number of young adult attendees and elevated drug involvement. No studies to date have examined drug-impaired driving in a natural environment with active drug and alcohol users. Methods Six EMDEs were sampled in San Diego, California, and Baltimore, Maryland. A random sample of approximately 40 attendees per event were administered surveys about alcohol and other drug (AOD) use and driving status, given breath tests for alcohol, and asked to provide oral fluid samples to test for illicit drug use upon entering and exiting the events. Results Driving status reduced the level of alcohol use (including abstaining) but the impact on drug-taking was not significant. However, 62% of individuals who reported their intention to drive away from the events were positive for drugs or alcohol upon leaving. This suggests that these events and settings are appropriate ones for developing interventions for reducing risks for young adults. PMID:16675160

Drug and alcohol-impaired driving among electronic music dance event attendees.

PubMed

Furr-Holden, Debra; Voas, Robert B; Kelley-Baker, Tara; Miller, Brenda

2006-10-15

Drug-impaired driving has received increased attention resulting from development of rapid drug-screening procedures used by police and state laws establishing per se limits for drug levels in drivers. Venues that host electronic music dance events (EMDEs) provide a unique opportunity to assess drug-impaired driving among a high proportion of young adult drug users. EMDEs are late-night dance parties marked by a substantial number of young adult attendees and elevated drug involvement. No studies to date have examined drug-impaired driving in a natural environment with active drug and alcohol users. Six EMDEs were sampled in San Diego, California, and Baltimore, Maryland. A random sample of approximately 40 attendees per event were administered surveys about alcohol and other drug (AOD) use and driving status, given breath tests for alcohol, and asked to provide oral fluid samples to test for illicit drug use upon entering and exiting the events. Driving status reduced the level of alcohol use (including abstaining) but the impact on drug-taking was not significant. However, 62% of individuals who reported their intention to drive away from the events were positive for drugs or alcohol upon leaving. This suggests that these events and settings are appropriate ones for developing interventions for reducing risks for young adults.