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.

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.

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

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,

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

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.

Data analysis using a combination of independent component analysis and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Lin, Shih-Lin; Tung, Pi-Cheng; Huang, Norden E.

2009-06-01

A combination of independent component analysis and empirical mode decomposition (ICA-EMD) is proposed in this paper to analyze low signal-to-noise ratio data. The advantages of ICA-EMD combination are these: ICA needs few sensory clues to separate the original source from unwanted noise and EMD can effectively separate the data into its constituting parts. The case studies reported here involve original sources contaminated by white Gaussian noise. The simulation results show that the ICA-EMD combination is an effective data analysis tool.

System and methods for determining masking signals for applying empirical mode decomposition (EMD) and for demodulating intrinsic mode functions obtained from application of EMD

DOEpatents

Senroy, Nilanjan [New Delhi, IN; Suryanarayanan, Siddharth [Littleton, CO

2011-03-15

A computer-implemented method of signal processing is provided. The method includes generating one or more masking signals based upon a computed Fourier transform of a received signal. The method further includes determining one or more intrinsic mode functions (IMFs) of the received signal by performing a masking-signal-based empirical mode decomposition (EMD) using the at least one masking signal.

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.

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.

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

A hybrid filtering method based on a novel empirical mode decomposition for friction signals

NASA Astrophysics Data System (ADS)

Li, Chengwei; Zhan, Liwei

2015-12-01

During a measurement, the measured signal usually contains noise. To remove the noise and preserve the important feature of the signal, we introduce a hybrid filtering method that uses a new intrinsic mode function (NIMF) and a modified Hausdorff distance. The NIMF is defined as the difference between the noisy signal and each intrinsic mode function (IMF), which is obtained by empirical mode decomposition (EMD), ensemble EMD, complementary ensemble EMD, or complete ensemble EMD with adaptive noise (CEEMDAN). The relevant mode selecting is based on the similarity between the first NIMF and the rest of the NIMFs. With this filtering method, the EMD and improved versions are used to filter the simulation and friction signals. The friction signal between an airplane tire and the runaway is recorded during a simulated airplane touchdown and features spikes of various amplitudes and noise. The filtering effectiveness of the four hybrid filtering methods are compared and discussed. The results show that the filtering method based on CEEMDAN outperforms other signal filtering methods.

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.

Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-08-01

The main purpose of this work is to explore the usefulness of fractal descriptors estimated in multi-resolution domains to characterize biomedical digital image texture. In this regard, three multi-resolution techniques are considered: the well-known discrete wavelet transform (DWT) and the empirical mode decomposition (EMD), and; the newly introduced; variational mode decomposition mode (VMD). The original image is decomposed by the DWT, EMD, and VMD into different scales. Then, Fourier spectrum based fractal descriptors is estimated at specific scales and directions to characterize the image. The support vector machine (SVM) was used to perform supervised classification. The empirical study was applied to the problem of distinguishing between normal and abnormal brain magnetic resonance images (MRI) affected with Alzheimer disease (AD). Our results demonstrate that fractal descriptors estimated in VMD domain outperform those estimated in DWT and EMD domains; and also those directly estimated from the original image.

Fringe-projection profilometry based on two-dimensional empirical mode decomposition.

PubMed

Zheng, Suzhen; Cao, Yiping

2013-11-01

In 3D shape measurement, because deformed fringes often contain low-frequency information degraded with random noise and background intensity information, a new fringe-projection profilometry is proposed based on 2D empirical mode decomposition (2D-EMD). The fringe pattern is first decomposed into numbers of intrinsic mode functions by 2D-EMD. Because the method has partial noise reduction, the background components can be removed to obtain the fundamental components needed to perform Hilbert transformation to retrieve the phase information. The 2D-EMD can effectively extract the modulation phase of a single direction fringe and an inclined fringe pattern because it is a full 2D analysis method and considers the relationship between adjacent lines of a fringe patterns. In addition, as the method does not add noise repeatedly, as does ensemble EMD, the data processing time is shortened. Computer simulations and experiments prove the feasibility of this method.

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.

Time-frequency analysis : mathematical analysis of the empirical mode decomposition.

DOT National Transportation Integrated Search

2009-01-01

Invented over 10 years ago, empirical mode : decomposition (EMD) provides a nonlinear : time-frequency analysis with the ability to successfully : analyze nonstationary signals. Mathematical : Analysis of the Empirical Mode Decomposition : is a...

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.

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.

Pseudo-fault signal assisted EMD for fault detection and isolation in rotating machines

NASA Astrophysics Data System (ADS)

Singh, Dheeraj Sharan; Zhao, Qing

2016-12-01

This paper presents a novel data driven technique for the detection and isolation of faults, which generate impacts in a rotating equipment. The technique is built upon the principles of empirical mode decomposition (EMD), envelope analysis and pseudo-fault signal for fault separation. Firstly, the most dominant intrinsic mode function (IMF) is identified using EMD of a raw signal, which contains all the necessary information about the faults. The envelope of this IMF is often modulated with multiple vibration sources and noise. A second level decomposition is performed by applying pseudo-fault signal (PFS) assisted EMD on the envelope. A pseudo-fault signal is constructed based on the known fault characteristic frequency of the particular machine. The objective of using external (pseudo-fault) signal is to isolate different fault frequencies, present in the envelope . The pseudo-fault signal serves dual purposes: (i) it solves the mode mixing problem inherent in EMD, (ii) it isolates and quantifies a particular fault frequency component. The proposed technique is suitable for real-time implementation, which has also been validated on simulated fault and experimental data corresponding to a bearing and a gear-box set-up, respectively.

Wavelet-bounded empirical mode decomposition for measured time series analysis

NASA Astrophysics Data System (ADS)

Moore, Keegan J.; Kurt, Mehmet; Eriten, Melih; McFarland, D. Michael; Bergman, Lawrence A.; Vakakis, Alexander F.

2018-01-01

Empirical mode decomposition (EMD) is a powerful technique for separating the transient responses of nonlinear and nonstationary systems into finite sets of nearly orthogonal components, called intrinsic mode functions (IMFs), which represent the dynamics on different characteristic time scales. However, a deficiency of EMD is the mixing of two or more components in a single IMF, which can drastically affect the physical meaning of the empirical decomposition results. In this paper, we present a new approached based on EMD, designated as wavelet-bounded empirical mode decomposition (WBEMD), which is a closed-loop, optimization-based solution to the problem of mode mixing. The optimization routine relies on maximizing the isolation of an IMF around a characteristic frequency. This isolation is measured by fitting a bounding function around the IMF in the frequency domain and computing the area under this function. It follows that a large (small) area corresponds to a poorly (well) separated IMF. An optimization routine is developed based on this result with the objective of minimizing the bounding-function area and with the masking signal parameters serving as free parameters, such that a well-separated IMF is extracted. As examples of application of WBEMD we apply the proposed method, first to a stationary, two-component signal, and then to the numerically simulated response of a cantilever beam with an essentially nonlinear end attachment. We find that WBEMD vastly improves upon EMD and that the extracted sets of IMFs provide insight into the underlying physics of the response of each system.

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.

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.

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.

Improving EMG based classification of basic hand movements using EMD.

PubMed

Sapsanis, Christos; Georgoulas, George; Tzes, Anthony; Lymberopoulos, Dimitrios

2013-01-01

This paper presents a pattern recognition approach for the identification of basic hand movements using surface electromyographic (EMG) data. The EMG signal is decomposed using Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs) and subsequently a feature extraction stage takes place. Various combinations of feature subsets are tested using a simple linear classifier for the detection task. Our results suggest that the use of EMD can increase the discrimination ability of the conventional feature sets extracted from the raw EMG signal.

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.

Noise reduction in Lidar signal using correlation-based EMD combined with soft thresholding and roughness penalty

NASA Astrophysics Data System (ADS)

Chang, Jianhua; Zhu, Lingyan; Li, Hongxu; Xu, Fan; Liu, Binggang; Yang, Zhenbo

2018-01-01

Empirical mode decomposition (EMD) is widely used to analyze the non-linear and non-stationary signals for noise reduction. In this study, a novel EMD-based denoising method, referred to as EMD with soft thresholding and roughness penalty (EMD-STRP), is proposed for the Lidar signal denoising. With the proposed method, the relevant and irrelevant intrinsic mode functions are first distinguished via a correlation coefficient. Then, the soft thresholding technique is applied to the irrelevant modes, and the roughness penalty technique is applied to the relevant modes to extract as much information as possible. The effectiveness of the proposed method was evaluated using three typical signals contaminated by white Gaussian noise. The denoising performance was then compared to the denoising capabilities of other techniques, such as correlation-based EMD partial reconstruction, correlation-based EMD hard thresholding, and wavelet transform. The use of EMD-STRP on the measured Lidar signal resulted in the noise being efficiently suppressed, with an improved signal to noise ratio of 22.25 dB and an extended detection range of 11 km.

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.

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.

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

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

[EMD Time-Frequency Analysis of Raman Spectrum and NIR].

PubMed

Zhao, Xiao-yu; Fang, Yi-ming; Tan, Feng; Tong, Liang; Zhai, Zhe

2016-02-01

This paper analyzes the Raman spectrum and Near Infrared Spectrum (NIR) with time-frequency method. The empirical mode decomposition spectrum becomes intrinsic mode functions, which the proportion calculation reveals the Raman spectral energy is uniform distributed in each component, while the NIR's low order intrinsic mode functions only undertakes fewer primary spectroscopic effective information. Both the real spectrum and numerical experiments show that the empirical mode decomposition (EMD) regard Raman spectrum as the amplitude-modulated signal, which possessed with high frequency adsorption property; and EMD regards NIR as the frequency-modulated signal, which could be preferably realized high frequency narrow-band demodulation during first-order intrinsic mode functions. The first-order intrinsic mode functions Hilbert transform reveals that during the period of empirical mode decomposes Raman spectrum, modal aliasing happened. Through further analysis of corn leaf's NIR in time-frequency domain, after EMD, the first and second orders components of low energy are cut off, and reconstruct spectral signal by using the remaining intrinsic mode functions, the root-mean-square error is 1.001 1, and the correlation coefficient is 0.981 3, both of these two indexes indicated higher accuracy in re-construction; the decomposition trend term indicates the absorbency is ascending along with the decreasing to wave length in the near-infrared light wave band; and the Hilbert transform of characteristic modal component displays, 657 cm⁻¹ is the specific frequency by the corn leaf stress spectrum, which could be regarded as characteristic frequency for identification.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

[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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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

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.

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.

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.

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.

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.

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

Conception of discrete systems decomposition algorithm using p-invariants and hypergraphs

NASA Astrophysics Data System (ADS)

Stefanowicz, Ł.

2016-09-01

In the article author presents an idea of decomposition algorithm of discrete systems described by Petri Nets using pinvariants. Decomposition process is significant from the point of view of discrete systems design, because it allows separation of the smaller sequential parts. Proposed algorithm uses modified Martinez-Silva method as well as author's selection algorithm. The developed method is a good complement of classical decomposition algorithms using graphs and hypergraphs.

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.

Fast Algorithms for Earth Mover’s Distance Based on Optimal Transport and L1 Type Regularization I

DTIC Science & Technology

2016-09-01

which EMD can be reformulated as a familiar homogeneous degree 1 regularized minimization. The new minimization problem is very similar to problems which...which is also named the Monge problem or the Wasserstein metric, plays a central role in many applications, including image processing, computer vision

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.

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.

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".

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

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.

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.

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.

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

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.

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.

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.

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

Morphological decomposition of 2-D binary shapes into convex polygons: a heuristic algorithm.

PubMed

Xu, J

2001-01-01

In many morphological shape decomposition algorithms, either a shape can only be decomposed into shape components of extremely simple forms or a time consuming search process is employed to determine a decomposition. In this paper, we present a morphological shape decomposition algorithm that decomposes a two-dimensional (2-D) binary shape into a collection of convex polygonal components. A single convex polygonal approximation for a given image is first identified. This first component is determined incrementally by selecting a sequence of basic shape primitives. These shape primitives are chosen based on shape information extracted from the given shape at different scale levels. Additional shape components are identified recursively from the difference image between the given image and the first component. Simple operations are used to repair certain concavities caused by the set difference operation. The resulting hierarchical structure provides descriptions for the given shape at different detail levels. The experiments show that the decomposition results produced by the algorithm seem to be in good agreement with the natural structures of the given shapes. The computational cost of the algorithm is significantly lower than that of an earlier search-based convex decomposition algorithm. Compared to nonconvex decomposition algorithms, our algorithm allows accurate approximations for the given shapes at low coding costs.

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.

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.

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.

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.

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.

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.

An NN-Based SRD Decomposition Algorithm and Its Application in Nonlinear Compensation

PubMed Central

Yan, Honghang; Deng, Fang; Sun, Jian; Chen, Jie

2014-01-01

In this study, a neural network-based square root of descending (SRD) order decomposition algorithm for compensating for nonlinear data generated by sensors is presented. The study aims at exploring the optimized decomposition of data 1.00,0.00,0.00 and minimizing the computational complexity and memory space of the training process. A linear decomposition algorithm, which automatically finds the optimal decomposition of N subparts and reduces the training time to 1N and memory cost to 1N, has been implemented on nonlinear data obtained from an encoder. Particular focus is given to the theoretical access of estimating the numbers of hidden nodes and the precision of varying the decomposition method. Numerical experiments are designed to evaluate the effect of this algorithm. Moreover, a designed device for angular sensor calibration is presented. We conduct an experiment that samples the data of an encoder and compensates for the nonlinearity of the encoder to testify this novel algorithm. PMID:25232912

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.

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.

A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array

PubMed Central

Gao, Yu-Fei; Gui, Guan; Xie, Wei; Zou, Yan-Bin; Yang, Yue; Wan, Qun

2017-01-01

This paper investigates a two-dimensional angle of arrival (2D AOA) estimation algorithm for the electromagnetic vector sensor (EMVS) array based on Type-2 block component decomposition (BCD) tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters with higher resolution. However, existing tensor decomposition methods encounter many restrictions in applications of the EMVS array, such as the strict requirement for uniqueness conditions of decomposition, the inability to handle partially-polarized signals, etc. To solve these problems, this paper investigates tensor modeling for partially-polarized signals of an L-shaped EMVS array. The 2D AOA estimation algorithm based on rank-(L1,L2,·) BCD is developed, and the uniqueness condition of decomposition is analyzed. By means of the estimated steering matrix, the proposed algorithm can automatically achieve angle pair-matching. Numerical experiments demonstrate that the present algorithm has the advantages of both accuracy and robustness of parameter estimation. Even under the conditions of lower SNR, small angular separation and limited snapshots, the proposed algorithm still possesses better performance than subspace methods and the canonical polyadic decomposition (CPD) method. PMID:28448431

A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array.

PubMed

Gao, Yu-Fei; Gui, Guan; Xie, Wei; Zou, Yan-Bin; Yang, Yue; Wan, Qun

2017-04-27

This paper investigates a two-dimensional angle of arrival (2D AOA) estimation algorithm for the electromagnetic vector sensor (EMVS) array based on Type-2 block component decomposition (BCD) tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters with higher resolution. However, existing tensor decomposition methods encounter many restrictions in applications of the EMVS array, such as the strict requirement for uniqueness conditions of decomposition, the inability to handle partially-polarized signals, etc. To solve these problems, this paper investigates tensor modeling for partially-polarized signals of an L-shaped EMVS array. The 2D AOA estimation algorithm based on rank- ( L 1 , L 2 , · ) BCD is developed, and the uniqueness condition of decomposition is analyzed. By means of the estimated steering matrix, the proposed algorithm can automatically achieve angle pair-matching. Numerical experiments demonstrate that the present algorithm has the advantages of both accuracy and robustness of parameter estimation. Even under the conditions of lower SNR, small angular separation and limited snapshots, the proposed algorithm still possesses better performance than subspace methods and the canonical polyadic decomposition (CPD) method.

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.

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.

Integrated Network Decompositions and Dynamic Programming for Graph Optimization (INDDGO)

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

The INDDGO software package offers a set of tools for finding exact solutions to graph optimization problems via tree decompositions and dynamic programming algorithms. Currently the framework offers serial and parallel (distributed memory) algorithms for finding tree decompositions and solving the maximum weighted independent set problem. The parallel dynamic programming algorithm is implemented on top of the MADNESS task-based runtime.

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.

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

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

Application of composite dictionary multi-atom matching in gear fault diagnosis.

PubMed

Cui, Lingli; Kang, Chenhui; Wang, Huaqing; Chen, Peng

2011-01-01

The sparse decomposition based on matching pursuit is an adaptive sparse expression method for signals. This paper proposes an idea concerning a composite dictionary multi-atom matching decomposition and reconstruction algorithm, and the introduction of threshold de-noising in the reconstruction algorithm. Based on the structural characteristics of gear fault signals, a composite dictionary combining the impulse time-frequency dictionary and the Fourier dictionary was constituted, and a genetic algorithm was applied to search for the best matching atom. The analysis results of gear fault simulation signals indicated the effectiveness of the hard threshold, and the impulse or harmonic characteristic components could be separately extracted. Meanwhile, the robustness of the composite dictionary multi-atom matching algorithm at different noise levels was investigated. Aiming at the effects of data lengths on the calculation efficiency of the algorithm, an improved segmented decomposition and reconstruction algorithm was proposed, and the calculation efficiency of the decomposition algorithm was significantly enhanced. In addition it is shown that the multi-atom matching algorithm was superior to the single-atom matching algorithm in both calculation efficiency and algorithm robustness. Finally, the above algorithm was applied to gear fault engineering signals, and achieved good results.

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.

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.

Applications of singular value analysis and partial-step algorithm for nonlinear orbit determination

NASA Technical Reports Server (NTRS)

Ryne, Mark S.; Wang, Tseng-Chan

1991-01-01

An adaptive method in which cruise and nonlinear orbit determination problems can be solved using a single program is presented. It involves singular value decomposition augmented with an extended partial step algorithm. The extended partial step algorithm constrains the size of the correction to the spacecraft state and other solve-for parameters. The correction is controlled by an a priori covariance and a user-supplied bounds parameter. The extended partial step method is an extension of the update portion of the singular value decomposition algorithm. It thus preserves the numerical stability of the singular value decomposition method, while extending the region over which it converges. In linear cases, this method reduces to the singular value decomposition algorithm with the full rank solution. Two examples are presented to illustrate the method's utility.

Optimal cost design of water distribution networks using a decomposition approach

NASA Astrophysics Data System (ADS)

Lee, Ho Min; Yoo, Do Guen; Sadollah, Ali; Kim, Joong Hoon

2016-12-01

Water distribution network decomposition, which is an engineering approach, is adopted to increase the efficiency of obtaining the optimal cost design of a water distribution network using an optimization algorithm. This study applied the source tracing tool in EPANET, which is a hydraulic and water quality analysis model, to the decomposition of a network to improve the efficiency of the optimal design process. The proposed approach was tested by carrying out the optimal cost design of two water distribution networks, and the results were compared with other optimal cost designs derived from previously proposed optimization algorithms. The proposed decomposition approach using the source tracing technique enables the efficient decomposition of an actual large-scale network, and the results can be combined with the optimal cost design process using an optimization algorithm. This proves that the final design in this study is better than those obtained with other previously proposed optimization algorithms.

Dispatcher Recognition of Stroke Using the National Academy Medical Priority Dispatch System

PubMed Central

Buck, Brian H; Starkman, Sidney; Eckstein, Marc; Kidwell, Chelsea S; Haines, Jill; Huang, Rainy; Colby, Daniel; Saver, Jeffrey L

2009-01-01

Background Emergency Medical Dispatchers (EMDs) play an important role in optimizing stroke care if they are able to accurately identify calls regarding acute cerebrovascular disease. This study was undertaken to assess the diagnostic accuracy of the current national protocol guiding dispatcher questioning of 911 callers to identify stroke, QA Guide v 11.1 of the National Academy Medical Priority Dispatch System (MPDS). Methods We identified all Los Angeles Fire Department paramedic transports of patients to UCLA Medical Center during the 12 month period from January to December 2005 in a prospectively maintained database. Dispatcher-assigned MPDS codes for each of these patient transports were abstracted from the paramedic run sheets and compared to final hospital discharge diagnosis. Results Among 3474 transported patients, 96 (2.8%) had a final diagnosis of stroke or transient ischemic attack. Dispatchers assigned a code of potential stroke to 44.8% of patients with a final discharge diagnosis of stroke or TIA. Dispatcher identification of stroke showed a sensitivity of 0.41, specificity of 0.96, positive predictive value of 0.45, and negative predictive value of 0.95. Conclusions Dispatcher recognition of stroke calls using the widely employed MPDS algorithm is suboptimal, with failure to identify more than half of stroke patients as likely stroke. Revisions to the current national dispatcher structured interview and complaint identification algorithm for stroke may facilitate more accurate recognition of stroke by EMDs. PMID:19390065

Accuracy assessment of a surface electromyogram decomposition system in human first dorsal interosseus muscle

NASA Astrophysics Data System (ADS)

Hu, Xiaogang; Rymer, William Z.; Suresh, Nina L.

2014-04-01

Objective. The aim of this study is to assess the accuracy of a surface electromyogram (sEMG) motor unit (MU) decomposition algorithm during low levels of muscle contraction. Approach. A two-source method was used to verify the accuracy of the sEMG decomposition system, by utilizing simultaneous intramuscular and surface EMG recordings from the human first dorsal interosseous muscle recorded during isometric trapezoidal force contractions. Spike trains from each recording type were decomposed independently utilizing two different algorithms, EMGlab and dEMG decomposition algorithms. The degree of agreement of the decomposed spike timings was assessed for three different segments of the EMG signals, corresponding to specified regions in the force task. A regression analysis was performed to examine whether certain properties of the sEMG and force signal can predict the decomposition accuracy. Main results. The average accuracy of successful decomposition among the 119 MUs that were common to both intramuscular and surface records was approximately 95%, and the accuracy was comparable between the different segments of the sEMG signals (i.e., force ramp-up versus steady state force versus combined). The regression function between the accuracy and properties of sEMG and force signals revealed that the signal-to-noise ratio of the action potential and stability in the action potential records were significant predictors of the surface decomposition accuracy. Significance. The outcomes of our study confirm the accuracy of the sEMG decomposition algorithm during low muscle contraction levels and provide confidence in the overall validity of the surface dEMG decomposition algorithm.

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.

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.

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.

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.

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.

Near-lossless multichannel EEG compression based on matrix and tensor decompositions.

PubMed

Dauwels, Justin; Srinivasan, K; Reddy, M Ramasubba; Cichocki, Andrzej

2013-05-01

A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

On a concurrent element-by-element preconditioned conjugate gradient algorithm for multiple load cases

NASA Technical Reports Server (NTRS)

Watson, Brian; Kamat, M. P.

1990-01-01

Element-by-element preconditioned conjugate gradient (EBE-PCG) algorithms have been advocated for use in parallel/vector processing environments as being superior to the conventional LDL(exp T) decomposition algorithm for single load cases. Although there may be some advantages in using such algorithms for a single load case, when it comes to situations involving multiple load cases, the LDL(exp T) decomposition algorithm would appear to be decidedly more cost-effective. The authors have outlined an EBE-PCG algorithm suitable for multiple load cases and compared its effectiveness to the highly efficient LDL(exp T) decomposition scheme. The proposed algorithm offers almost no advantages over the LDL(exp T) algorithm for the linear problems investigated on the Alliant FX/8. However, there may be some merit in the algorithm in solving nonlinear problems with load incrementation, but that remains to be investigated.

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.

Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.

PubMed

Xiao, Xiaolin; Moreno-Moral, Aida; Rotival, Maxime; Bottolo, Leonardo; Petretto, Enrico

2014-01-01

Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co-expressed heat shock protein (Hsp) and cardiomyopathy genes (Bag3, Cryab, Kras, Emd, Plec), which was significantly replicated using separate failing heart and liver gene expression datasets in humans, thus revealing a conserved functional role for Hsp genes in cardiovascular disease.

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.

Renewable energy in electric utility capacity planning: a decomposition approach with application to a Mexican utility

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

Staschus, K.

1985-01-01

In this dissertation, efficient algorithms for electric-utility capacity expansion planning with renewable energy are developed. The algorithms include a deterministic phase that quickly finds a near-optimal expansion plan using derating and a linearized approximation to the time-dependent availability of nondispatchable energy sources. A probabilistic second phase needs comparatively few computer-time consuming probabilistic simulation iterations to modify this solution towards the optimal expansion plan. For the deterministic first phase, two algorithms, based on a Lagrangian Dual decomposition and a Generalized Benders Decomposition, are developed. The probabilistic second phase uses a Generalized Benders Decomposition approach. Extensive computational tests of the algorithms aremore » reported. Among the deterministic algorithms, the one based on Lagrangian Duality proves fastest. The two-phase approach is shown to save up to 80% in computing time as compared to a purely probabilistic algorithm. The algorithms are applied to determine the optimal expansion plan for the Tijuana-Mexicali subsystem of the Mexican electric utility system. A strong recommendation to push conservation programs in the desert city of Mexicali results from this implementation.« less

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.

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...

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.

Hybrid Nested Partitions and Math Programming Framework for Large-scale Combinatorial Optimization

DTIC Science & Technology

2010-03-31

optimization problems: 1) exact algorithms and 2) metaheuristic algorithms . This project will integrate concepts from these two technologies to develop...optimal solutions within an acceptable amount of computation time, and 2) metaheuristic algorithms such as genetic algorithms , tabu search, and the...integer programming decomposition approaches, such as Dantzig Wolfe decomposition and Lagrangian relaxation, and metaheuristics such as the Nested

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

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.

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

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.

Data-driven process decomposition and robust online distributed modelling for large-scale processes

NASA Astrophysics Data System (ADS)

Shu, Zhang; Lijuan, Li; Lijuan, Yao; Shipin, Yang; Tao, Zou

2018-02-01

With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.

Power System Decomposition for Practical Implementation of Bulk-Grid Voltage Control Methods

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

Vallem, Mallikarjuna R.; Vyakaranam, Bharat GNVSR; Holzer, Jesse T.

Power system algorithms such as AC optimal power flow and coordinated volt/var control of the bulk power system are computationally intensive and become difficult to solve in operational time frames. The computational time required to run these algorithms increases exponentially as the size of the power system increases. The solution time for multiple subsystems is less than that for solving the entire system simultaneously, and the local nature of the voltage problem lends itself to such decomposition. This paper describes an algorithm that can be used to perform power system decomposition from the point of view of the voltage controlmore » problem. Our approach takes advantage of the dominant localized effect of voltage control and is based on clustering buses according to the electrical distances between them. One of the contributions of the paper is to use multidimensional scaling to compute n-dimensional Euclidean coordinates for each bus based on electrical distance to perform algorithms like K-means clustering. A simple coordinated reactive power control of photovoltaic inverters for voltage regulation is used to demonstrate the effectiveness of the proposed decomposition algorithm and its components. The proposed decomposition method is demonstrated on the IEEE 118-bus system.« less

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.

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.

Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zheng, Yang; Chen, Xihao; Zhu, Rui

2017-07-01

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

About decomposition approach for solving the classification problem

NASA Astrophysics Data System (ADS)

Andrianova, A. A.

2016-11-01

This article describes the features of the application of an algorithm with using of decomposition methods for solving the binary classification problem of constructing a linear classifier based on Support Vector Machine method. Application of decomposition reduces the volume of calculations, in particular, due to the emerging possibilities to build parallel versions of the algorithm, which is a very important advantage for the solution of problems with big data. The analysis of the results of computational experiments conducted using the decomposition approach. The experiment use known data set for binary classification problem.

An Orthogonal Evolutionary Algorithm With Learning Automata for Multiobjective Optimization.

PubMed

Dai, Cai; Wang, Yuping; Ye, Miao; Xue, Xingsi; Liu, Hailin

2016-12-01

Research on multiobjective optimization problems becomes one of the hottest topics of intelligent computation. In order to improve the search efficiency of an evolutionary algorithm and maintain the diversity of solutions, in this paper, the learning automata (LA) is first used for quantization orthogonal crossover (QOX), and a new fitness function based on decomposition is proposed to achieve these two purposes. Based on these, an orthogonal evolutionary algorithm with LA for complex multiobjective optimization problems with continuous variables is proposed. The experimental results show that in continuous states, the proposed algorithm is able to achieve accurate Pareto-optimal sets and wide Pareto-optimal fronts efficiently. Moreover, the comparison with the several existing well-known algorithms: nondominated sorting genetic algorithm II, decomposition-based multiobjective evolutionary algorithm, decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes, multiobjective optimization by LA, and multiobjective immune algorithm with nondominated neighbor-based selection, on 15 multiobjective benchmark problems, shows that the proposed algorithm is able to find more accurate and evenly distributed Pareto-optimal fronts than the compared ones.

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.

An Integrated Centroid Finding and Particle Overlap Decomposition Algorithm for Stereo Imaging Velocimetry

NASA Technical Reports Server (NTRS)

McDowell, Mark

2004-01-01

An integrated algorithm for decomposing overlapping particle images (multi-particle objects) along with determining each object s constituent particle centroid(s) has been developed using image analysis techniques. The centroid finding algorithm uses a modified eight-direction search method for finding the perimeter of any enclosed object. The centroid is calculated using the intensity-weighted center of mass of the object. The overlap decomposition algorithm further analyzes the object data and breaks it down into its constituent particle centroid(s). This is accomplished with an artificial neural network, feature based technique and provides an efficient way of decomposing overlapping particles. Combining the centroid finding and overlap decomposition routines into a single algorithm allows us to accurately predict the error associated with finding the centroid(s) of particles in our experiments. This algorithm has been tested using real, simulated, and synthetic data and the results are presented and discussed.

Fast polar decomposition of an arbitrary matrix

NASA Technical Reports Server (NTRS)

Higham, Nicholas J.; Schreiber, Robert S.

1988-01-01

The polar decomposition of an m x n matrix A of full rank, where m is greater than or equal to n, can be computed using a quadratically convergent algorithm. The algorithm is based on a Newton iteration involving a matrix inverse. With the use of a preliminary complete orthogonal decomposition the algorithm can be extended to arbitrary A. How to use the algorithm to compute the positive semi-definite square root of a Hermitian positive semi-definite matrix is described. A hybrid algorithm which adaptively switches from the matrix inversion based iteration to a matrix multiplication based iteration due to Kovarik, and to Bjorck and Bowie is formulated. The decision when to switch is made using a condition estimator. This matrix multiplication rich algorithm is shown to be more efficient on machines for which matrix multiplication can be executed 1.5 times faster than matrix inversion.

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.

Spectral Diffusion: An Algorithm for Robust Material Decomposition of Spectral CT Data

PubMed Central

Clark, Darin P.; Badea, Cristian T.

2014-01-01

Clinical successes with dual energy CT, aggressive development of energy discriminating x-ray detectors, and novel, target-specific, nanoparticle contrast agents promise to establish spectral CT as a powerful functional imaging modality. Common to all of these applications is the need for a material decomposition algorithm which is robust in the presence of noise. Here, we develop such an algorithm which uses spectrally joint, piece-wise constant kernel regression and the split Bregman method to iteratively solve for a material decomposition which is gradient sparse, quantitatively accurate, and minimally biased. We call this algorithm spectral diffusion because it integrates structural information from multiple spectral channels and their corresponding material decompositions within the framework of diffusion-like denoising algorithms (e.g. anisotropic diffusion, total variation, bilateral filtration). Using a 3D, digital bar phantom and a material sensitivity matrix calibrated for use with a polychromatic x-ray source, we quantify the limits of detectability (CNR = 5) afforded by spectral diffusion in the triple-energy material decomposition of iodine (3.1 mg/mL), gold (0.9 mg/mL), and gadolinium (2.9 mg/mL) concentrations. We then apply spectral diffusion to the in vivo separation of these three materials in the mouse kidneys, liver, and spleen. PMID:25296173

Spectral diffusion: an algorithm for robust material decomposition of spectral CT data.

PubMed

Clark, Darin P; Badea, Cristian T

2014-11-07

Clinical successes with dual energy CT, aggressive development of energy discriminating x-ray detectors, and novel, target-specific, nanoparticle contrast agents promise to establish spectral CT as a powerful functional imaging modality. Common to all of these applications is the need for a material decomposition algorithm which is robust in the presence of noise. Here, we develop such an algorithm which uses spectrally joint, piecewise constant kernel regression and the split Bregman method to iteratively solve for a material decomposition which is gradient sparse, quantitatively accurate, and minimally biased. We call this algorithm spectral diffusion because it integrates structural information from multiple spectral channels and their corresponding material decompositions within the framework of diffusion-like denoising algorithms (e.g. anisotropic diffusion, total variation, bilateral filtration). Using a 3D, digital bar phantom and a material sensitivity matrix calibrated for use with a polychromatic x-ray source, we quantify the limits of detectability (CNR = 5) afforded by spectral diffusion in the triple-energy material decomposition of iodine (3.1 mg mL(-1)), gold (0.9 mg mL(-1)), and gadolinium (2.9 mg mL(-1)) concentrations. We then apply spectral diffusion to the in vivo separation of these three materials in the mouse kidneys, liver, and spleen.

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.

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.

Incremental k-core decomposition: Algorithms and evaluation

DOE PAGES

Sariyuce, Ahmet Erdem; Gedik, Bugra; Jacques-SIlva, Gabriela; ...

2016-02-01

A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-hard problems on real networks efficiently, like maximal clique finding. In many real-world applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for dynamic graph data. In this paper, we propose a suite of incremental k-core decomposition algorithms for dynamic graph data. These algorithms locate a small subgraph that ismore » guaranteed to contain the list of vertices whose maximum k-core values have changed and efficiently process this subgraph to update the k-core decomposition. We present incremental algorithms for both insertion and deletion operations, and propose auxiliary vertex state maintenance techniques that can further accelerate these operations. Our results show a significant reduction in runtime compared to non-incremental alternatives. We illustrate the efficiency of our algorithms on different types of real and synthetic graphs, at varying scales. Furthermore, for a graph of 16 million vertices, we observe relative throughputs reaching a million times, relative to the non-incremental algorithms.« less

Iterative image-domain decomposition for dual-energy CT

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

Niu, Tianye; Dong, Xue; Petrongolo, Michael

2014-04-15

Purpose: Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its capability of material decomposition. Direct decomposition via matrix inversion suffers from significant degradation of image signal-to-noise ratios, which reduces clinical values of DECT. Existing denoising algorithms achieve suboptimal performance since they suppress image noise either before or after the decomposition and do not fully explore the noise statistical properties of the decomposition process. In this work, the authors propose an iterative image-domain decomposition method for noise suppression in DECT, using the full variance-covariance matrix of the decomposed images. Methods: The proposed algorithm ismore » formulated in the form of least-square estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, the authors include the inverse of the estimated variance-covariance matrix of the decomposed images as the penalty weight in the least-square term. The regularization term enforces the image smoothness by calculating the square sum of neighboring pixel value differences. To retain the boundary sharpness of the decomposed images, the authors detect the edges in the CT images before decomposition. These edge pixels have small weights in the calculation of the regularization term. Distinct from the existing denoising algorithms applied on the images before or after decomposition, the method has an iterative process for noise suppression, with decomposition performed in each iteration. The authors implement the proposed algorithm using a standard conjugate gradient algorithm. The method performance is evaluated using an evaluation phantom (Catphan©600) and an anthropomorphic head phantom. The results are compared with those generated using direct matrix inversion with no noise suppression, a denoising method applied on the decomposed images, and an existing algorithm with similar formulation as the proposed method but with an edge-preserving regularization term. Results: On the Catphan phantom, the method maintains the same spatial resolution on the decomposed images as that of the CT images before decomposition (8 pairs/cm) while significantly reducing their noise standard deviation. Compared to that obtained by the direct matrix inversion, the noise standard deviation in the images decomposed by the proposed algorithm is reduced by over 98%. Without considering the noise correlation properties in the formulation, the denoising scheme degrades the spatial resolution to 6 pairs/cm for the same level of noise suppression. Compared to the edge-preserving algorithm, the method achieves better low-contrast detectability. A quantitative study is performed on the contrast-rod slice of Catphan phantom. The proposed method achieves lower electron density measurement error as compared to that by the direct matrix inversion, and significantly reduces the error variation by over 97%. On the head phantom, the method reduces the noise standard deviation of decomposed images by over 97% without blurring the sinus structures. Conclusions: The authors propose an iterative image-domain decomposition method for DECT. The method combines noise suppression and material decomposition into an iterative process and achieves both goals simultaneously. By exploring the full variance-covariance properties of the decomposed images and utilizing the edge predetection, the proposed algorithm shows superior performance on noise suppression with high image spatial resolution and low-contrast detectability.« less

The Speech multi features fusion perceptual hash algorithm based on tensor decomposition

NASA Astrophysics Data System (ADS)

Huang, Y. B.; Fan, M. H.; Zhang, Q. Y.

2018-03-01

With constant progress in modern speech communication technologies, the speech data is prone to be attacked by the noise or maliciously tampered. In order to make the speech perception hash algorithm has strong robustness and high efficiency, this paper put forward a speech perception hash algorithm based on the tensor decomposition and multi features is proposed. This algorithm analyses the speech perception feature acquires each speech component wavelet packet decomposition. LPCC, LSP and ISP feature of each speech component are extracted to constitute the speech feature tensor. Speech authentication is done by generating the hash values through feature matrix quantification which use mid-value. Experimental results showing that the proposed algorithm is robust for content to maintain operations compared with similar algorithms. It is able to resist the attack of the common background noise. Also, the algorithm is highly efficiency in terms of arithmetic, and is able to meet the real-time requirements of speech communication and complete the speech authentication quickly.

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).

Error reduction in EMG signal decomposition

PubMed Central

Kline, Joshua C.

2014-01-01

Decomposition of the electromyographic (EMG) signal into constituent action potentials and the identification of individual firing instances of each motor unit in the presence of ambient noise are inherently probabilistic processes, whether performed manually or with automated algorithms. Consequently, they are subject to errors. We set out to classify and reduce these errors by analyzing 1,061 motor-unit action-potential trains (MUAPTs), obtained by decomposing surface EMG (sEMG) signals recorded during human voluntary contractions. Decomposition errors were classified into two general categories: location errors representing variability in the temporal localization of each motor-unit firing instance and identification errors consisting of falsely detected or missed firing instances. To mitigate these errors, we developed an error-reduction algorithm that combines multiple decomposition estimates to determine a more probable estimate of motor-unit firing instances with fewer errors. The performance of the algorithm is governed by a trade-off between the yield of MUAPTs obtained above a given accuracy level and the time required to perform the decomposition. When applied to a set of sEMG signals synthesized from real MUAPTs, the identification error was reduced by an average of 1.78%, improving the accuracy to 97.0%, and the location error was reduced by an average of 1.66 ms. The error-reduction algorithm in this study is not limited to any specific decomposition strategy. Rather, we propose it be used for other decomposition methods, especially when analyzing precise motor-unit firing instances, as occurs when measuring synchronization. PMID:25210159

Parallel CE/SE Computations via Domain Decomposition

NASA Technical Reports Server (NTRS)

Himansu, Ananda; Jorgenson, Philip C. E.; Wang, Xiao-Yen; Chang, Sin-Chung

2000-01-01

This paper describes the parallelization strategy and achieved parallel efficiency of an explicit time-marching algorithm for solving conservation laws. The Space-Time Conservation Element and Solution Element (CE/SE) algorithm for solving the 2D and 3D Euler equations is parallelized with the aid of domain decomposition. The parallel efficiency of the resultant algorithm on a Silicon Graphics Origin 2000 parallel computer is checked.

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.

Fast algorithm of adaptive Fourier series

NASA Astrophysics Data System (ADS)

Gao, You; Ku, Min; Qian, Tao

2018-05-01

Adaptive Fourier decomposition (AFD, precisely 1-D AFD or Core-AFD) was originated for the goal of positive frequency representations of signals. It achieved the goal and at the same time offered fast decompositions of signals. There then arose several types of AFDs. AFD merged with the greedy algorithm idea, and in particular, motivated the so-called pre-orthogonal greedy algorithm (Pre-OGA) that was proven to be the most efficient greedy algorithm. The cost of the advantages of the AFD type decompositions is, however, the high computational complexity due to the involvement of maximal selections of the dictionary parameters. The present paper offers one formulation of the 1-D AFD algorithm by building the FFT algorithm into it. Accordingly, the algorithm complexity is reduced, from the original $\\mathcal{O}(M N^2)$ to $\\mathcal{O}(M N\\log_2 N)$, where $N$ denotes the number of the discretization points on the unit circle and $M$ denotes the number of points in $[0,1)$. This greatly enhances the applicability of AFD. Experiments are carried out to show the high efficiency of the proposed algorithm.

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.

Implementing Linear Algebra Related Algorithms on the TI-92+ Calculator.

ERIC Educational Resources Information Center

Alexopoulos, John; Abraham, Paul

2001-01-01

Demonstrates a less utilized feature of the TI-92+: its natural and powerful programming language. Shows how to implement several linear algebra related algorithms including the Gram-Schmidt process, Least Squares Approximations, Wronskians, Cholesky Decompositions, and Generalized Linear Least Square Approximations with QR Decompositions.…

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

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.

Optimization by nonhierarchical asynchronous decomposition

NASA Technical Reports Server (NTRS)

Shankar, Jayashree; Ribbens, Calvin J.; Haftka, Raphael T.; Watson, Layne T.

1992-01-01

Large scale optimization problems are tractable only if they are somehow decomposed. Hierarchical decompositions are inappropriate for some types of problems and do not parallelize well. Sobieszczanski-Sobieski has proposed a nonhierarchical decomposition strategy for nonlinear constrained optimization that is naturally parallel. Despite some successes on engineering problems, the algorithm as originally proposed fails on simple two dimensional quadratic programs. The algorithm is carefully analyzed for quadratic programs, and a number of modifications are suggested to improve its robustness.

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.

Video Shot Boundary Detection Using QR-Decomposition and Gaussian Transition Detection

NASA Astrophysics Data System (ADS)

Amiri, Ali; Fathy, Mahmood

2010-12-01

This article explores the problem of video shot boundary detection and examines a novel shot boundary detection algorithm by using QR-decomposition and modeling of gradual transitions by Gaussian functions. Specifically, the authors attend to the challenges of detecting gradual shots and extracting appropriate spatiotemporal features that affect the ability of algorithms to efficiently detect shot boundaries. The algorithm utilizes the properties of QR-decomposition and extracts a block-wise probability function that illustrates the probability of video frames to be in shot transitions. The probability function has abrupt changes in hard cut transitions, and semi-Gaussian behavior in gradual transitions. The algorithm detects these transitions by analyzing the probability function. Finally, we will report the results of the experiments using large-scale test sets provided by the TRECVID 2006, which has assessments for hard cut and gradual shot boundary detection. These results confirm the high performance of the proposed algorithm.

Domain decomposition: A bridge between nature and parallel computers

NASA Technical Reports Server (NTRS)

Keyes, David E.

1992-01-01

Domain decomposition is an intuitive organizing principle for a partial differential equation (PDE) computation, both physically and architecturally. However, its significance extends beyond the readily apparent issues of geometry and discretization, on one hand, and of modular software and distributed hardware, on the other. Engineering and computer science aspects are bridged by an old but recently enriched mathematical theory that offers the subject not only unity, but also tools for analysis and generalization. Domain decomposition induces function-space and operator decompositions with valuable properties. Function-space bases and operator splittings that are not derived from domain decompositions generally lack one or more of these properties. The evolution of domain decomposition methods for elliptically dominated problems has linked two major algorithmic developments of the last 15 years: multilevel and Krylov methods. Domain decomposition methods may be considered descendants of both classes with an inheritance from each: they are nearly optimal and at the same time efficiently parallelizable. Many computationally driven application areas are ripe for these developments. A progression is made from a mathematically informal motivation for domain decomposition methods to a specific focus on fluid dynamics applications. To be introductory rather than comprehensive, simple examples are provided while convergence proofs and algorithmic details are left to the original references; however, an attempt is made to convey their most salient features, especially where this leads to algorithmic insight.

Path planning of decentralized multi-quadrotor based on fuzzy-cell decomposition algorithm

NASA Astrophysics Data System (ADS)

Iswanto, Wahyunggoro, Oyas; Cahyadi, Adha Imam

2017-04-01

The paper aims to present a design algorithm for multi quadrotor lanes in order to move towards the goal quickly and avoid obstacles in an area with obstacles. There are several problems in path planning including how to get to the goal position quickly and avoid static and dynamic obstacles. To overcome the problem, therefore, the paper presents fuzzy logic algorithm and fuzzy cell decomposition algorithm. Fuzzy logic algorithm is one of the artificial intelligence algorithms which can be applied to robot path planning that is able to detect static and dynamic obstacles. Cell decomposition algorithm is an algorithm of graph theory used to make a robot path map. By using the two algorithms the robot is able to get to the goal position and avoid obstacles but it takes a considerable time because they are able to find the shortest path. Therefore, this paper describes a modification of the algorithms by adding a potential field algorithm used to provide weight values on the map applied for each quadrotor by using decentralized controlled, so that the quadrotor is able to move to the goal position quickly by finding the shortest path. The simulations conducted have shown that multi-quadrotor can avoid various obstacles and find the shortest path by using the proposed algorithms.

Primary decomposition of zero-dimensional ideals over finite fields

NASA Astrophysics Data System (ADS)

Gao, Shuhong; Wan, Daqing; Wang, Mingsheng

2009-03-01

A new algorithm is presented for computing primary decomposition of zero-dimensional ideals over finite fields. Like Berlekamp's algorithm for univariate polynomials, the new method is based on the invariant subspace of the Frobenius map acting on the quotient algebra. The dimension of the invariant subspace equals the number of primary components, and a basis of the invariant subspace yields a complete decomposition. Unlike previous approaches for decomposing multivariate polynomial systems, the new method does not need primality testing nor any generic projection, instead it reduces the general decomposition problem directly to root finding of univariate polynomials over the ground field. Also, it is shown how Groebner basis structure can be used to get partial primary decomposition without any root finding.

Projection decomposition algorithm for dual-energy computed tomography via deep neural network.

PubMed

Xu, Yifu; Yan, Bin; Chen, Jian; Zeng, Lei; Li, Lei

2018-03-15

Dual-energy computed tomography (DECT) has been widely used to improve identification of substances from different spectral information. Decomposition of the mixed test samples into two materials relies on a well-calibrated material decomposition function. This work aims to establish and validate a data-driven algorithm for estimation of the decomposition function. A deep neural network (DNN) consisting of two sub-nets is proposed to solve the projection decomposition problem. The compressing sub-net, substantially a stack auto-encoder (SAE), learns a compact representation of energy spectrum. The decomposing sub-net with a two-layer structure fits the nonlinear transform between energy projection and basic material thickness. The proposed DNN not only delivers image with lower standard deviation and higher quality in both simulated and real data, and also yields the best performance in cases mixed with photon noise. Moreover, DNN costs only 0.4 s to generate a decomposition solution of 360 × 512 size scale, which is about 200 times faster than the competing algorithms. The DNN model is applicable to the decomposition tasks with different dual energies. Experimental results demonstrated the strong function fitting ability of DNN. Thus, the Deep learning paradigm provides a promising approach to solve the nonlinear problem in DECT.

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

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.

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.

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.

Limited-memory adaptive snapshot selection for proper orthogonal decomposition

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

Oxberry, Geoffrey M.; Kostova-Vassilevska, Tanya; Arrighi, Bill

2015-04-02

Reduced order models are useful for accelerating simulations in many-query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, offline training of reduced order models can have prohibitively expensive memory and floating-point operation costs in high-performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits offline training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time-stepping ordinary differential equation solvers. The error estimator used in this work is related to theory boundingmore » the approximation error in time of proper orthogonal decomposition-based reduced order models, and memory usage is minimized by computing the singular value decomposition using a single-pass incremental algorithm. Results for a viscous Burgers’ test problem demonstrate convergence in the limit as the algorithm error tolerances go to zero; in this limit, the full order model is recovered to within discretization error. The resulting method can be used on supercomputers to generate proper orthogonal decomposition-based reduced order models, or as a subroutine within hyperreduction algorithms that require taking snapshots in time, or within greedy algorithms for sampling parameter space.« less

Scenario Decomposition for 0-1 Stochastic Programs: Improvements and Asynchronous Implementation

DOE PAGES

Ryan, Kevin; Rajan, Deepak; Ahmed, Shabbir

2016-05-01

We recently proposed scenario decomposition algorithm for stochastic 0-1 programs finds an optimal solution by evaluating and removing individual solutions that are discovered by solving scenario subproblems. In our work, we develop an asynchronous, distributed implementation of the algorithm which has computational advantages over existing synchronous implementations of the algorithm. Improvements to both the synchronous and asynchronous algorithm are proposed. We also test the results on well known stochastic 0-1 programs from the SIPLIB test library and is able to solve one previously unsolved instance from the test set.

Total variation regularization of the 3-D gravity inverse problem using a randomized generalized singular value decomposition

NASA Astrophysics Data System (ADS)

Vatankhah, Saeed; Renaut, Rosemary A.; Ardestani, Vahid E.

2018-04-01

We present a fast algorithm for the total variation regularization of the 3-D gravity inverse problem. Through imposition of the total variation regularization, subsurface structures presenting with sharp discontinuities are preserved better than when using a conventional minimum-structure inversion. The associated problem formulation for the regularization is nonlinear but can be solved using an iteratively reweighted least-squares algorithm. For small-scale problems the regularized least-squares problem at each iteration can be solved using the generalized singular value decomposition. This is not feasible for large-scale, or even moderate-scale, problems. Instead we introduce the use of a randomized generalized singular value decomposition in order to reduce the dimensions of the problem and provide an effective and efficient solution technique. For further efficiency an alternating direction algorithm is used to implement the total variation weighting operator within the iteratively reweighted least-squares algorithm. Presented results for synthetic examples demonstrate that the novel randomized decomposition provides good accuracy for reduced computational and memory demands as compared to use of classical approaches.

Turbulence Time Series Data Hole Filling using Karhunen-Loeve and ARIMA methods

DTIC Science & Technology

2007-01-01

memory is represented by higher values of d. 4.1. ARIMA and EMD We applied an ARIMA (0,d,0) model to predict the behaviour of the final section of the...to a simplified ARIMA (0,d,0) model , which performed better than the linear interpolant but less effectively than the KL algorithm, disregarding edge...ar X iv :p hy si cs /0 70 12 38 v1 22 J an 2 00 7 Turbulence Time Series Data Hole Filling using Karhunen-Loève and ARIMA methods M P J L

A Parallel Algorithm for Contact in a Finite Element Hydrocode

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

Pierce, Timothy G.

A parallel algorithm is developed for contact/impact of multiple three dimensional bodies undergoing large deformation. As time progresses the relative positions of contact between the multiple bodies changes as collision and sliding occurs. The parallel algorithm is capable of tracking these changes and enforcing an impenetrability constraint and momentum transfer across the surfaces in contact. Portions of the various surfaces of the bodies are assigned to the processors of a distributed-memory parallel machine in an arbitrary fashion, known as the primary decomposition. A secondary, dynamic decomposition is utilized to bring opposing sections of the contacting surfaces together on the samemore » processors, so that opposing forces may be balanced and the resultant deformation of the bodies calculated. The secondary decomposition is accomplished and updated using only local communication with a limited subset of neighbor processors. Each processor represents both a domain of the primary decomposition and a domain of the secondary, or contact, decomposition. Thus each processor has four sets of neighbor processors: (a) those processors which represent regions adjacent to it in the primary decomposition, (b) those processors which represent regions adjacent to it in the contact decomposition, (c) those processors which send it the data from which it constructs its contact domain, and (d) those processors to which it sends its primary domain data, from which they construct their contact domains. The latter three of these neighbor sets change dynamically as the simulation progresses. By constraining all communication to these sets of neighbors, all global communication, with its attendant nonscalable performance, is avoided. A set of tests are provided to measure the degree of scalability achieved by this algorithm on up to 1024 processors. Issues related to the operating system of the test platform which lead to some degradation of the results are analyzed. This algorithm has been implemented as the contact capability of the ALE3D multiphysics code, and is currently in production use.« less

Highly Scalable Matching Pursuit Signal Decomposition Algorithm

NASA Technical Reports Server (NTRS)

Christensen, Daniel; Das, Santanu; Srivastava, Ashok N.

2009-01-01

Matching Pursuit Decomposition (MPD) is a powerful iterative algorithm for signal decomposition and feature extraction. MPD decomposes any signal into linear combinations of its dictionary elements or atoms . A best fit atom from an arbitrarily defined dictionary is determined through cross-correlation. The selected atom is subtracted from the signal and this procedure is repeated on the residual in the subsequent iterations until a stopping criterion is met. The reconstructed signal reveals the waveform structure of the original signal. However, a sufficiently large dictionary is required for an accurate reconstruction; this in return increases the computational burden of the algorithm, thus limiting its applicability and level of adoption. The purpose of this research is to improve the scalability and performance of the classical MPD algorithm. Correlation thresholds were defined to prune insignificant atoms from the dictionary. The Coarse-Fine Grids and Multiple Atom Extraction techniques were proposed to decrease the computational burden of the algorithm. The Coarse-Fine Grids method enabled the approximation and refinement of the parameters for the best fit atom. The ability to extract multiple atoms within a single iteration enhanced the effectiveness and efficiency of each iteration. These improvements were implemented to produce an improved Matching Pursuit Decomposition algorithm entitled MPD++. Disparate signal decomposition applications may require a particular emphasis of accuracy or computational efficiency. The prominence of the key signal features required for the proper signal classification dictates the level of accuracy necessary in the decomposition. The MPD++ algorithm may be easily adapted to accommodate the imposed requirements. Certain feature extraction applications may require rapid signal decomposition. The full potential of MPD++ may be utilized to produce incredible performance gains while extracting only slightly less energy than the standard algorithm. When the utmost accuracy must be achieved, the modified algorithm extracts atoms more conservatively but still exhibits computational gains over classical MPD. The MPD++ algorithm was demonstrated using an over-complete dictionary on real life data. Computational times were reduced by factors of 1.9 and 44 for the emphases of accuracy and performance, respectively. The modified algorithm extracted similar amounts of energy compared to classical MPD. The degree of the improvement in computational time depends on the complexity of the data, the initialization parameters, and the breadth of the dictionary. The results of the research confirm that the three modifications successfully improved the scalability and computational efficiency of the MPD algorithm. Correlation Thresholding decreased the time complexity by reducing the dictionary size. Multiple Atom Extraction also reduced the time complexity by decreasing the number of iterations required for a stopping criterion to be reached. The Course-Fine Grids technique enabled complicated atoms with numerous variable parameters to be effectively represented in the dictionary. Due to the nature of the three proposed modifications, they are capable of being stacked and have cumulative effects on the reduction of the time complexity.

Modified truncated randomized singular value decomposition (MTRSVD) algorithms for large scale discrete ill-posed problems with general-form regularization

NASA Astrophysics Data System (ADS)

Jia, Zhongxiao; Yang, Yanfei

2018-05-01

In this paper, we propose new randomization based algorithms for large scale linear discrete ill-posed problems with general-form regularization: subject to , where L is a regularization matrix. Our algorithms are inspired by the modified truncated singular value decomposition (MTSVD) method, which suits only for small to medium scale problems, and randomized SVD (RSVD) algorithms that generate good low rank approximations to A. We use rank-k truncated randomized SVD (TRSVD) approximations to A by truncating the rank- RSVD approximations to A, where q is an oversampling parameter. The resulting algorithms are called modified TRSVD (MTRSVD) methods. At every step, we use the LSQR algorithm to solve the resulting inner least squares problem, which is proved to become better conditioned as k increases so that LSQR converges faster. We present sharp bounds for the approximation accuracy of the RSVDs and TRSVDs for severely, moderately and mildly ill-posed problems, and substantially improve a known basic bound for TRSVD approximations. We prove how to choose the stopping tolerance for LSQR in order to guarantee that the computed and exact best regularized solutions have the same accuracy. Numerical experiments illustrate that the best regularized solutions by MTRSVD are as accurate as the ones by the truncated generalized singular value decomposition (TGSVD) algorithm, and at least as accurate as those by some existing truncated randomized generalized singular value decomposition (TRGSVD) algorithms. This work was supported in part by the National Science Foundation of China (Nos. 11771249 and 11371219).

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.

CP decomposition approach to blind separation for DS-CDMA system using a new performance index

NASA Astrophysics Data System (ADS)

Rouijel, Awatif; Minaoui, Khalid; Comon, Pierre; Aboutajdine, Driss

2014-12-01

In this paper, we present a canonical polyadic (CP) tensor decomposition isolating the scaling matrix. This has two major implications: (i) the problem conditioning shows up explicitly and could be controlled through a constraint on the so-called coherences and (ii) a performance criterion concerning the factor matrices can be exactly calculated and is more realistic than performance metrics used in the literature. Two new algorithms optimizing the CP decomposition based on gradient descent are proposed. This decomposition is illustrated by an application to direct-sequence code division multiplexing access (DS-CDMA) systems; computer simulations are provided and demonstrate the good behavior of these algorithms, compared to others in the literature.

Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

PubMed Central

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms. PMID:24723806

Polar decomposition for attitude determination from vector observations

NASA Technical Reports Server (NTRS)

Bar-Itzhack, Itzhack Y.

1993-01-01

This work treats the problem of weighted least squares fitting of a 3D Euclidean-coordinate transformation matrix to a set of unit vectors measured in the reference and transformed coordinates. A closed-form analytic solution to the problem is re-derived. The fact that the solution is the closest orthogonal matrix to some matrix defined on the measured vectors and their weights is clearly demonstrated. Several known algorithms for computing the analytic closed form solution are considered. An algorithm is discussed which is based on the polar decomposition of matrices into the closest unitary matrix to the decomposed matrix and a Hermitian matrix. A somewhat longer improved algorithm is suggested too. A comparison of several algorithms is carried out using simulated data as well as real data from the Upper Atmosphere Research Satellite. The comparison is based on accuracy and time consumption. It is concluded that the algorithms based on polar decomposition yield a simple although somewhat less accurate solution. The precision of the latter algorithms increase with the number of the measured vectors and with the accuracy of their measurement.

Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

PubMed

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

A novel iterative scheme and its application to differential equations.

PubMed

Khan, Yasir; Naeem, F; Šmarda, Zdeněk

2014-01-01

The purpose of this paper is to employ an alternative approach to reconstruct the standard variational iteration algorithm II proposed by He, including Lagrange multiplier, and to give a simpler formulation of Adomian decomposition and modified Adomian decomposition method in terms of newly proposed variational iteration method-II (VIM). Through careful investigation of the earlier variational iteration algorithm and Adomian decomposition method, we find unnecessary calculations for Lagrange multiplier and also repeated calculations involved in each iteration, respectively. Several examples are given to verify the reliability and efficiency of the method.

A Survey of Singular Value Decomposition Methods and Performance Comparison of Some Available Serial Codes

NASA Technical Reports Server (NTRS)

Plassman, Gerald E.

2005-01-01

This contractor report describes a performance comparison of available alternative complete Singular Value Decomposition (SVD) methods and implementations which are suitable for incorporation into point spread function deconvolution algorithms. The report also presents a survey of alternative algorithms, including partial SVD's special case SVD's, and others developed for concurrent processing systems.

Dual energy computed tomography for the head.

PubMed

Naruto, Norihito; Itoh, Toshihide; Noguchi, Kyo

2018-02-01

Dual energy CT (DECT) is a promising technology that provides better diagnostic accuracy in several brain diseases. DECT can generate various types of CT images from a single acquisition data set at high kV and low kV based on material decomposition algorithms. The two-material decomposition algorithm can separate bone/calcification from iodine accurately. The three-material decomposition algorithm can generate a virtual non-contrast image, which helps to identify conditions such as brain hemorrhage. A virtual monochromatic image has the potential to eliminate metal artifacts by reducing beam-hardening effects. DECT also enables exploration of advanced imaging to make diagnosis easier. One such novel application of DECT is the X-Map, which helps to visualize ischemic stroke in the brain without using iodine contrast medium.

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.

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

A Graph Based Backtracking Algorithm for Solving General CSPs

NASA Technical Reports Server (NTRS)

Pang, Wanlin; Goodwin, Scott D.

2003-01-01

Many AI tasks can be formalized as constraint satisfaction problems (CSPs), which involve finding values for variables subject to constraints. While solving a CSP is an NP-complete task in general, tractable classes of CSPs have been identified based on the structure of the underlying constraint graphs. Much effort has been spent on exploiting structural properties of the constraint graph to improve the efficiency of finding a solution. These efforts contributed to development of a class of CSP solving algorithms called decomposition algorithms. The strength of CSP decomposition is that its worst-case complexity depends on the structural properties of the constraint graph and is usually better than the worst-case complexity of search methods. Its practical application is limited, however, since it cannot be applied if the CSP is not decomposable. In this paper, we propose a graph based backtracking algorithm called omega-CDBT, which shares merits and overcomes the weaknesses of both decomposition and search approaches.

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.

Fast heap transform-based QR-decomposition of real and complex matrices: algorithms and codes

NASA Astrophysics Data System (ADS)

Grigoryan, Artyom M.

2015-03-01

In this paper, we describe a new look on the application of Givens rotations to the QR-decomposition problem, which is similar to the method of Householder transformations. We apply the concept of the discrete heap transform, or signal-induced unitary transforms which had been introduced by Grigoryan (2006) and used in signal and image processing. Both cases of real and complex nonsingular matrices are considered and examples of performing QR-decomposition of square matrices are given. The proposed method of QR-decomposition for the complex matrix is novel and differs from the known method of complex Givens rotation and is based on analytical equations for the heap transforms. Many examples illustrated the proposed heap transform method of QR-decomposition are given, algorithms are described in detail, and MATLAB-based codes are included.

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

Niu, T; Dong, X; Petrongolo, M

Purpose: Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Direct decomposition via matrix inversion suffers from significant degradation of image signal-to-noise ratios, which reduces clinical value. Existing de-noising algorithms achieve suboptimal performance since they suppress image noise either before or after the decomposition and do not fully explore the noise statistical properties of the decomposition process. We propose an iterative image-domain decomposition method for noise suppression in DECT, using the full variance-covariance matrix of the decomposed images. Methods: The proposed algorithm is formulated in the form of least-square estimationmore » with smoothness regularization. It includes the inverse of the estimated variance-covariance matrix of the decomposed images as the penalty weight in the least-square term. Performance is evaluated using an evaluation phantom (Catphan 600) and an anthropomorphic head phantom. Results are compared to those generated using direct matrix inversion with no noise suppression, a de-noising method applied on the decomposed images, and an existing algorithm with similar formulation but with an edge-preserving regularization term. Results: On the Catphan phantom, our method retains the same spatial resolution as the CT images before decomposition while reducing the noise standard deviation of decomposed images by over 98%. The other methods either degrade spatial resolution or achieve less low-contrast detectability. Also, our method yields lower electron density measurement error than direct matrix inversion and reduces error variation by over 97%. On the head phantom, it reduces the noise standard deviation of decomposed images by over 97% without blurring the sinus structures. Conclusion: We propose an iterative image-domain decomposition method for DECT. The method combines noise suppression and material decomposition into an iterative process and achieves both goals simultaneously. The proposed algorithm shows superior performance on noise suppression with high image spatial resolution and low-contrast detectability. This work is supported by a Varian MRA grant.« less

SOI layout decomposition for double patterning lithography on high-performance computer platforms

NASA Astrophysics Data System (ADS)

Verstov, Vladimir; Zinchenko, Lyudmila; Makarchuk, Vladimir

2014-12-01

In the paper silicon on insulator layout decomposition algorithms for the double patterning lithography on high performance computing platforms are discussed. Our approach is based on the use of a contradiction graph and a modified concurrent breadth-first search algorithm. We evaluate our technique on 45 nm Nangate Open Cell Library including non-Manhattan geometry. Experimental results show that our soft computing algorithms decompose layout successfully and a minimal distance between polygons in layout is increased.

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.

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

Implementing dense linear algebra algorithms using multitasking on the CRAY X-MP-4 (or approaching the gigaflop)

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

Dongarra, J.J.; Hewitt, T.

1985-08-01

This note describes some experiments on simple, dense linear algebra algorithms. These experiments show that the CRAY X-MP is capable of small-grain multitasking arising from standard implementations of LU and Cholesky decomposition. The implementation described here provides the ''fastest'' execution rate for LU decomposition, 718 MFLOPS for a matrix of order 1000.

INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization

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

Groer, Christopher S; Sullivan, Blair D; Weerapurage, Dinesh P

2012-10-01

It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some \\emph{NP}-hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms wemore » have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.« less

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.

The design and implementation of signal decomposition system of CL multi-wavelet transform based on DSP builder

NASA Astrophysics Data System (ADS)

Huang, Yan; Wang, Zhihui

2015-12-01

With the development of FPGA, DSP Builder is widely applied to design system-level algorithms. The algorithm of CL multi-wavelet is more advanced and effective than scalar wavelets in processing signal decomposition. Thus, a system of CL multi-wavelet based on DSP Builder is designed for the first time in this paper. The system mainly contains three parts: a pre-filtering subsystem, a one-level decomposition subsystem and a two-level decomposition subsystem. It can be converted into hardware language VHDL by the Signal Complier block that can be used in Quartus II. After analyzing the energy indicator, it shows that this system outperforms Daubenchies wavelet in signal decomposition. Furthermore, it has proved to be suitable for the implementation of signal fusion based on SoPC hardware, and it will become a solid foundation in this new field.

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

A Benders based rolling horizon algorithm for a dynamic facility location problem

DOE PAGES

Marufuzzaman,, Mohammad; Gedik, Ridvan; Roni, Mohammad S.

2016-06-28

This study presents a well-known capacitated dynamic facility location problem (DFLP) that satisfies the customer demand at a minimum cost by determining the time period for opening, closing, or retaining an existing facility in a given location. To solve this challenging NP-hard problem, this paper develops a unique hybrid solution algorithm that combines a rolling horizon algorithm with an accelerated Benders decomposition algorithm. Extensive computational experiments are performed on benchmark test instances to evaluate the hybrid algorithm’s efficiency and robustness in solving the DFLP problem. Computational results indicate that the hybrid Benders based rolling horizon algorithm consistently offers high qualitymore » feasible solutions in a much shorter computational time period than the standalone rolling horizon and accelerated Benders decomposition algorithms in the experimental range.« less

Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

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.

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.

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.

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.

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.

Rank-based decompositions of morphological templates.

PubMed

Sussner, P; Ritter, G X

2000-01-01

Methods for matrix decomposition have found numerous applications in image processing, in particular for the problem of template decomposition. Since existing matrix decomposition techniques are mainly concerned with the linear domain, we consider it timely to investigate matrix decomposition techniques in the nonlinear domain with applications in image processing. The mathematical basis for these investigations is the new theory of rank within minimax algebra. Thus far, only minimax decompositions of rank 1 and rank 2 matrices into outer product expansions are known to the image processing community. We derive a heuristic algorithm for the decomposition of matrices having arbitrary rank.

Novel techniques for data decomposition and load balancing for parallel processing of vision systems: Implementation and evaluation using a motion estimation system

NASA Technical Reports Server (NTRS)

Choudhary, Alok Nidhi; Leung, Mun K.; Huang, Thomas S.; Patel, Janak H.

1989-01-01

Computer vision systems employ a sequence of vision algorithms in which the output of an algorithm is the input of the next algorithm in the sequence. Algorithms that constitute such systems exhibit vastly different computational characteristics, and therefore, require different data decomposition techniques and efficient load balancing techniques for parallel implementation. However, since the input data for a task is produced as the output data of the previous task, this information can be exploited to perform knowledge based data decomposition and load balancing. Presented here are algorithms for a motion estimation system. The motion estimation is based on the point correspondence between the involved images which are a sequence of stereo image pairs. Researchers propose algorithms to obtain point correspondences by matching feature points among stereo image pairs at any two consecutive time instants. Furthermore, the proposed algorithms employ non-iterative procedures, which results in saving considerable amounts of computation time. The system consists of the following steps: (1) extraction of features; (2) stereo match of images in one time instant; (3) time match of images from consecutive time instants; (4) stereo match to compute final unambiguous points; and (5) computation of motion parameters.

Adaptive truncation of matrix decompositions and efficient estimation of NMR relaxation distributions

NASA Astrophysics Data System (ADS)

Teal, Paul D.; Eccles, Craig

2015-04-01

The two most successful methods of estimating the distribution of nuclear magnetic resonance relaxation times from two dimensional data are data compression followed by application of the Butler-Reeds-Dawson algorithm, and a primal-dual interior point method using preconditioned conjugate gradient. Both of these methods have previously been presented using a truncated singular value decomposition of matrices representing the exponential kernel. In this paper it is shown that other matrix factorizations are applicable to each of these algorithms, and that these illustrate the different fundamental principles behind the operation of the algorithms. These are the rank-revealing QR (RRQR) factorization and the LDL factorization with diagonal pivoting, also known as the Bunch-Kaufman-Parlett factorization. It is shown that both algorithms can be improved by adaptation of the truncation as the optimization process progresses, improving the accuracy as the optimal value is approached. A variation on the interior method viz, the use of barrier function instead of the primal-dual approach, is found to offer considerable improvement in terms of speed and reliability. A third type of algorithm, related to the algorithm known as Fast iterative shrinkage-thresholding algorithm, is applied to the problem. This method can be efficiently formulated without the use of a matrix decomposition.

Dictionary-Based Tensor Canonical Polyadic Decomposition

NASA Astrophysics Data System (ADS)

Cohen, Jeremy Emile; Gillis, Nicolas

2018-04-01

To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary. A new formulation of sparse coding is proposed which enables high dimensional tensors dictionary-based canonical polyadic decomposition. The benefits of using a dictionary in tensor decomposition models are explored both in terms of parameter identifiability and estimation accuracy. Performances of the proposed algorithms are evaluated on the decomposition of simulated data and the unmixing of hyperspectral images.

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.

Decomposition of timed automata for solving scheduling problems

NASA Astrophysics Data System (ADS)

Nishi, Tatsushi; Wakatake, Masato

2014-03-01

A decomposition algorithm for scheduling problems based on timed automata (TA) model is proposed. The problem is represented as an optimal state transition problem for TA. The model comprises of the parallel composition of submodels such as jobs and resources. The procedure of the proposed methodology can be divided into two steps. The first step is to decompose the TA model into several submodels by using decomposable condition. The second step is to combine individual solution of subproblems for the decomposed submodels by the penalty function method. A feasible solution for the entire model is derived through the iterated computation of solving the subproblem for each submodel. The proposed methodology is applied to solve flowshop and jobshop scheduling problems. Computational experiments demonstrate the effectiveness of the proposed algorithm compared with a conventional TA scheduling algorithm without decomposition.

Combined iterative reconstruction and image-domain decomposition for dual energy CT using total-variation regularization

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

Dong, Xue; Niu, Tianye; Zhu, Lei, E-mail: leizhu@gatech.edu

2014-05-15

Purpose: Dual-energy CT (DECT) is being increasingly used for its capability of material decomposition and energy-selective imaging. A generic problem of DECT, however, is that the decomposition process is unstable in the sense that the relative magnitude of decomposed signals is reduced due to signal cancellation while the image noise is accumulating from the two CT images of independent scans. Direct image decomposition, therefore, leads to severe degradation of signal-to-noise ratio on the resultant images. Existing noise suppression techniques are typically implemented in DECT with the procedures of reconstruction and decomposition performed independently, which do not explore the statistical propertiesmore » of decomposed images during the reconstruction for noise reduction. In this work, the authors propose an iterative approach that combines the reconstruction and the signal decomposition procedures to minimize the DECT image noise without noticeable loss of resolution. Methods: The proposed algorithm is formulated as an optimization problem, which balances the data fidelity and total variation of decomposed images in one framework, and the decomposition step is carried out iteratively together with reconstruction. The noise in the CT images from the proposed algorithm becomes well correlated even though the noise of the raw projections is independent on the two CT scans. Due to this feature, the proposed algorithm avoids noise accumulation during the decomposition process. The authors evaluate the method performance on noise suppression and spatial resolution using phantom studies and compare the algorithm with conventional denoising approaches as well as combined iterative reconstruction methods with different forms of regularization. Results: On the Catphan©600 phantom, the proposed method outperforms the existing denoising methods on preserving spatial resolution at the same level of noise suppression, i.e., a reduction of noise standard deviation by one order of magnitude. This improvement is mainly attributed to the high noise correlation in the CT images reconstructed by the proposed algorithm. Iterative reconstruction using different regularization, including quadratic orq-generalized Gaussian Markov random field regularization, achieves similar noise suppression from high noise correlation. However, the proposed TV regularization obtains a better edge preserving performance. Studies of electron density measurement also show that our method reduces the average estimation error from 9.5% to 7.1%. On the anthropomorphic head phantom, the proposed method suppresses the noise standard deviation of the decomposed images by a factor of ∼14 without blurring the fine structures in the sinus area. Conclusions: The authors propose a practical method for DECT imaging reconstruction, which combines the image reconstruction and material decomposition into one optimization framework. Compared to the existing approaches, our method achieves a superior performance on DECT imaging with respect to decomposition accuracy, noise reduction, and spatial resolution.« less

Sensor-Based Vibration Signal Feature Extraction Using an Improved Composite Dictionary Matching Pursuit Algorithm

PubMed Central

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-01-01

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective. PMID:25207870

Sensor-based vibration signal feature extraction using an improved composite dictionary matching pursuit algorithm.

PubMed

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-09-09

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective.

Fat water decomposition using globally optimal surface estimation (GOOSE) algorithm.

PubMed

Cui, Chen; Wu, Xiaodong; Newell, John D; Jacob, Mathews

2015-03-01

This article focuses on developing a novel noniterative fat water decomposition algorithm more robust to fat water swaps and related ambiguities. Field map estimation is reformulated as a constrained surface estimation problem to exploit the spatial smoothness of the field, thus minimizing the ambiguities in the recovery. Specifically, the differences in the field map-induced frequency shift between adjacent voxels are constrained to be in a finite range. The discretization of the above problem yields a graph optimization scheme, where each node of the graph is only connected with few other nodes. Thanks to the low graph connectivity, the problem is solved efficiently using a noniterative graph cut algorithm. The global minimum of the constrained optimization problem is guaranteed. The performance of the algorithm is compared with that of state-of-the-art schemes. Quantitative comparisons are also made against reference data. The proposed algorithm is observed to yield more robust fat water estimates with fewer fat water swaps and better quantitative results than other state-of-the-art algorithms in a range of challenging applications. The proposed algorithm is capable of considerably reducing the swaps in challenging fat water decomposition problems. The experiments demonstrate the benefit of using explicit smoothness constraints in field map estimation and solving the problem using a globally convergent graph-cut optimization algorithm. © 2014 Wiley Periodicals, Inc.

Phase unwrapping with graph cuts optimization and dual decomposition acceleration for 3D high-resolution MRI data.

PubMed

Dong, Jianwu; Chen, Feng; Zhou, Dong; Liu, Tian; Yu, Zhaofei; Wang, Yi

2017-03-01

Existence of low SNR regions and rapid-phase variations pose challenges to spatial phase unwrapping algorithms. Global optimization-based phase unwrapping methods are widely used, but are significantly slower than greedy methods. In this paper, dual decomposition acceleration is introduced to speed up a three-dimensional graph cut-based phase unwrapping algorithm. The phase unwrapping problem is formulated as a global discrete energy minimization problem, whereas the technique of dual decomposition is used to increase the computational efficiency by splitting the full problem into overlapping subproblems and enforcing the congruence of overlapping variables. Using three dimensional (3D) multiecho gradient echo images from an agarose phantom and five brain hemorrhage patients, we compared this proposed method with an unaccelerated graph cut-based method. Experimental results show up to 18-fold acceleration in computation time. Dual decomposition significantly improves the computational efficiency of 3D graph cut-based phase unwrapping algorithms. Magn Reson Med 77:1353-1358, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

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.

Combinatorial algorithms for design of DNA arrays.

PubMed

Hannenhalli, Sridhar; Hubell, Earl; Lipshutz, Robert; Pevzner, Pavel A

2002-01-01

Optimal design of DNA arrays requires the development of algorithms with two-fold goals: reducing the effects caused by unintended illumination (border length minimization problem) and reducing the complexity of masks (mask decomposition problem). We describe algorithms that reduce the number of rectangles in mask decomposition by 20-30% as compared to a standard array design under the assumption that the arrangement of oligonucleotides on the array is fixed. This algorithm produces provably optimal solution for all studied real instances of array design. We also address the difficult problem of finding an arrangement which minimizes the border length and come up with a new idea of threading that significantly reduces the border length as compared to standard designs.

Regularization of nonlinear decomposition of spectral x-ray projection images.

PubMed

Ducros, Nicolas; Abascal, Juan Felipe Perez-Juste; Sixou, Bruno; Rit, Simon; Peyrin, Françoise

2017-09-01

Exploiting the x-ray measurements obtained in different energy bins, spectral computed tomography (CT) has the ability to recover the 3-D description of a patient in a material basis. This may be achieved solving two subproblems, namely the material decomposition and the tomographic reconstruction problems. In this work, we address the material decomposition of spectral x-ray projection images, which is a nonlinear ill-posed problem. Our main contribution is to introduce a material-dependent spatial regularization in the projection domain. The decomposition problem is solved iteratively using a Gauss-Newton algorithm that can benefit from fast linear solvers. A Matlab implementation is available online. The proposed regularized weighted least squares Gauss-Newton algorithm (RWLS-GN) is validated on numerical simulations of a thorax phantom made of up to five materials (soft tissue, bone, lung, adipose tissue, and gadolinium), which is scanned with a 120 kV source and imaged by a 4-bin photon counting detector. To evaluate the method performance of our algorithm, different scenarios are created by varying the number of incident photons, the concentration of the marker and the configuration of the phantom. The RWLS-GN method is compared to the reference maximum likelihood Nelder-Mead algorithm (ML-NM). The convergence of the proposed method and its dependence on the regularization parameter are also studied. We show that material decomposition is feasible with the proposed method and that it converges in few iterations. Material decomposition with ML-NM was very sensitive to noise, leading to decomposed images highly affected by noise, and artifacts even for the best case scenario. The proposed method was less sensitive to noise and improved contrast-to-noise ratio of the gadolinium image. Results were superior to those provided by ML-NM in terms of image quality and decomposition was 70 times faster. For the assessed experiments, material decomposition was possible with the proposed method when the number of incident photons was equal or larger than 10 5 and when the marker concentration was equal or larger than 0.03 g·cm -3 . The proposed method efficiently solves the nonlinear decomposition problem for spectral CT, which opens up new possibilities such as material-specific regularization in the projection domain and a parallelization framework, in which projections are solved in parallel. © 2017 American Association of Physicists in Medicine.

a Novel Two-Component Decomposition for Co-Polar Channels of GF-3 Quad-Pol Data

NASA Astrophysics Data System (ADS)

Kwok, E.; Li, C. H.; Zhao, Q. H.; Li, Y.

2018-04-01

Polarimetric target decomposition theory is the most dynamic and exploratory research area in the field of PolSAR. But most methods of target decomposition are based on fully polarized data (quad pol) and seldom utilize dual-polar data for target decomposition. Given this, we proposed a novel two-component decomposition method for co-polar channels of GF-3 quad-pol data. This method decomposes the data into two scattering contributions: surface, double bounce in dual co-polar channels. To save this underdetermined problem, a criterion for determining the model is proposed. The criterion can be named as second-order averaged scattering angle, which originates from the H/α decomposition. and we also put forward an alternative parameter of it. To validate the effectiveness of proposed decomposition, Liaodong Bay is selected as research area. The area is located in northeastern China, where it grows various wetland resources and appears sea ice phenomenon in winter. and we use the GF-3 quad-pol data as study data, which which is China's first C-band polarimetric synthetic aperture radar (PolSAR) satellite. The dependencies between the features of proposed algorithm and comparison decompositions (Pauli decomposition, An&Yang decomposition, Yamaguchi S4R decomposition) were investigated in the study. Though several aspects of the experimental discussion, we can draw the conclusion: the proposed algorithm may be suitable for special scenes with low vegetation coverage or low vegetation in the non-growing season; proposed decomposition features only using co-polar data are highly correlated with the corresponding comparison decomposition features under quad-polarization data. Moreover, it would be become input of the subsequent classification or parameter inversion.

A fast new algorithm for a robot neurocontroller using inverse QR decomposition

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

Morris, A.S.; Khemaissia, S.

2000-01-01

A new adaptive neural network controller for robots is presented. The controller is based on direct adaptive techniques. Unlike many neural network controllers in the literature, inverse dynamical model evaluation is not required. A numerically robust, computationally efficient processing scheme for neutral network weight estimation is described, namely, the inverse QR decomposition (INVQR). The inverse QR decomposition and a weighted recursive least-squares (WRLS) method for neural network weight estimation is derived using Cholesky factorization of the data matrix. The algorithm that performs the efficient INVQR of the underlying space-time data matrix may be implemented in parallel on a triangular array.more » Furthermore, its systolic architecture is well suited for VLSI implementation. Another important benefit is well suited for VLSI implementation. Another important benefit of the INVQR decomposition is that it solves directly for the time-recursive least-squares filter vector, while avoiding the sequential back-substitution step required by the QR decomposition approaches.« less

Optimal pattern synthesis for speech recognition based on principal component analysis

NASA Astrophysics Data System (ADS)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

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...

Distributed-Memory Breadth-First Search on Massive Graphs

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

Buluc, Aydin; Beamer, Scott; Madduri, Kamesh

This chapter studies the problem of traversing large graphs using the breadth-first search order on distributed-memory supercomputers. We consider both the traditional level-synchronous top-down algorithm as well as the recently discovered direction optimizing algorithm. We analyze the performance and scalability trade-offs in using different local data structures such as CSR and DCSC, enabling in-node multithreading, and graph decompositions such as 1D and 2D decomposition.

Optimum and Heuristic Algorithms for Finite State Machine Decomposition and Partitioning

DTIC Science & Technology

1989-09-01

Heuristic Algorithms for Finite State Machine Decomposition and Partitioning Pravnav Ashar, Srinivas Devadas , and A. Richard Newton , T E’,’ .,jpf~s’!i3...94720. Devadas : Department of Electrical Engineering and Computer Science, Room 36-848, MIT, Cambridge, MA 02139. (617) 253-0454. Copyright* 1989 MIT...and reduction, A finite state miachinie is represenutedl by its State Transition Graphi itodlitied froini two-level B ~oolean imiinimizers. Ilist

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

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

DOE PAGES

Desai, Ajit; Khalil, Mohammad; Pettit, Chris; ...

2017-09-21

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolutionmore » in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.« less

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

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

Desai, Ajit; Khalil, Mohammad; Pettit, Chris

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolutionmore » in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.« less

Multilevel decomposition of complete vehicle configuration in a parallel computing environment

NASA Technical Reports Server (NTRS)

Bhatt, Vinay; Ragsdell, K. M.

1989-01-01

This research summarizes various approaches to multilevel decomposition to solve large structural problems. A linear decomposition scheme based on the Sobieski algorithm is selected as a vehicle for automated synthesis of a complete vehicle configuration in a parallel processing environment. The research is in a developmental state. Preliminary numerical results are presented for several example problems.

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.

Waveform LiDAR processing: comparison of classic approaches and optimized Gold deconvolution to characterize vegetation structure and terrain elevation

NASA Astrophysics Data System (ADS)

Zhou, T.; Popescu, S. C.; Krause, K.

2016-12-01

Waveform Light Detection and Ranging (LiDAR) data have advantages over discrete-return LiDAR data in accurately characterizing vegetation structure. However, we lack a comprehensive understanding of waveform data processing approaches under different topography and vegetation conditions. The objective of this paper is to highlight a novel deconvolution algorithm, the Gold algorithm, for processing waveform LiDAR data with optimal deconvolution parameters. Further, we present a comparative study of waveform processing methods to provide insight into selecting an approach for a given combination of vegetation and terrain characteristics. We employed two waveform processing methods: 1) direct decomposition, 2) deconvolution and decomposition. In method two, we utilized two deconvolution algorithms - the Richardson Lucy (RL) algorithm and the Gold algorithm. The comprehensive and quantitative comparisons were conducted in terms of the number of detected echoes, position accuracy, the bias of the end products (such as digital terrain model (DTM) and canopy height model (CHM)) from discrete LiDAR data, along with parameter uncertainty for these end products obtained from different methods. This study was conducted at three study sites that include diverse ecological regions, vegetation and elevation gradients. Results demonstrate that two deconvolution algorithms are sensitive to the pre-processing steps of input data. The deconvolution and decomposition method is more capable of detecting hidden echoes with a lower false echo detection rate, especially for the Gold algorithm. Compared to the reference data, all approaches generate satisfactory accuracy assessment results with small mean spatial difference (<1.22 m for DTMs, < 0.77 m for CHMs) and root mean square error (RMSE) (<1.26 m for DTMs, < 1.93 m for CHMs). More specifically, the Gold algorithm is superior to others with smaller root mean square error (RMSE) (< 1.01m), while the direct decomposition approach works better in terms of the percentage of spatial difference within 0.5 and 1 m. The parameter uncertainty analysis demonstrates that the Gold algorithm outperforms other approaches in dense vegetation areas, with the smallest RMSE, and the RL algorithm performs better in sparse vegetation areas in terms of RMSE.

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.

Multi-Objectivising Combinatorial Optimisation Problems by Means of Elementary Landscape Decompositions.

PubMed

Ceberio, Josu; Calvo, Borja; Mendiburu, Alexander; Lozano, Jose A

2018-02-15

In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms from this field could be used for solving single-objective problems as well. In this sense, a number of papers have proposed multi-objectivising single-objective problems in order to use multi-objective algorithms in their optimisation. In this article, we follow up this idea by presenting a methodology for multi-objectivising combinatorial optimisation problems based on elementary landscape decompositions of their objective function. Under this framework, each of the elementary landscapes obtained from the decomposition is considered as an independent objective function to optimise. In order to illustrate this general methodology, we consider four problems from different domains: the quadratic assignment problem and the linear ordering problem (permutation domain), the 0-1 unconstrained quadratic optimisation problem (binary domain), and the frequency assignment problem (integer domain). We implemented two widely known multi-objective algorithms, NSGA-II and SPEA2, and compared their performance with that of a single-objective GA. The experiments conducted on a large benchmark of instances of the four problems show that the multi-objective algorithms clearly outperform the single-objective approaches. Furthermore, a discussion on the results suggests that the multi-objective space generated by this decomposition enhances the exploration ability, thus permitting NSGA-II and SPEA2 to obtain better results in the majority of the tested instances.

Randomized interpolative decomposition of separated representations

NASA Astrophysics Data System (ADS)

Biagioni, David J.; Beylkin, Daniel; Beylkin, Gregory

2015-01-01

We introduce an algorithm to compute tensor interpolative decomposition (dubbed CTD-ID) for the reduction of the separation rank of Canonical Tensor Decompositions (CTDs). Tensor ID selects, for a user-defined accuracy ɛ, a near optimal subset of terms of a CTD to represent the remaining terms via a linear combination of the selected terms. CTD-ID can be used as an alternative to or in combination with the Alternating Least Squares (ALS) algorithm. We present examples of its use within a convergent iteration to compute inverse operators in high dimensions. We also briefly discuss the spectral norm as a computational alternative to the Frobenius norm in estimating approximation errors of tensor ID. We reduce the problem of finding tensor IDs to that of constructing interpolative decompositions of certain matrices. These matrices are generated via randomized projection of the terms of the given tensor. We provide cost estimates and several examples of the new approach to the reduction of separation rank.

Integrand-level reduction of loop amplitudes by computational algebraic geometry methods

NASA Astrophysics Data System (ADS)

Zhang, Yang

2012-09-01

We present an algorithm for the integrand-level reduction of multi-loop amplitudes of renormalizable field theories, based on computational algebraic geometry. This algorithm uses (1) the Gröbner basis method to determine the basis for integrand-level reduction, (2) the primary decomposition of an ideal to classify all inequivalent solutions of unitarity cuts. The resulting basis and cut solutions can be used to reconstruct the integrand from unitarity cuts, via polynomial fitting techniques. The basis determination part of the algorithm has been implemented in the Mathematica package, BasisDet. The primary decomposition part can be readily carried out by algebraic geometry softwares, with the output of the package BasisDet. The algorithm works in both D = 4 and D = 4 - 2 ɛ dimensions, and we present some two and three-loop examples of applications of this algorithm.

Data decomposition method for parallel polygon rasterization considering load balancing

NASA Astrophysics Data System (ADS)

Zhou, Chen; Chen, Zhenjie; Liu, Yongxue; Li, Feixue; Cheng, Liang; Zhu, A.-xing; Li, Manchun

2015-12-01

It is essential to adopt parallel computing technology to rapidly rasterize massive polygon data. In parallel rasterization, it is difficult to design an effective data decomposition method. Conventional methods ignore load balancing of polygon complexity in parallel rasterization and thus fail to achieve high parallel efficiency. In this paper, a novel data decomposition method based on polygon complexity (DMPC) is proposed. First, four factors that possibly affect the rasterization efficiency were investigated. Then, a metric represented by the boundary number and raster pixel number in the minimum bounding rectangle was developed to calculate the complexity of each polygon. Using this metric, polygons were rationally allocated according to the polygon complexity, and each process could achieve balanced loads of polygon complexity. To validate the efficiency of DMPC, it was used to parallelize different polygon rasterization algorithms and tested on different datasets. Experimental results showed that DMPC could effectively parallelize polygon rasterization algorithms. Furthermore, the implemented parallel algorithms with DMPC could achieve good speedup ratios of at least 15.69 and generally outperformed conventional decomposition methods in terms of parallel efficiency and load balancing. In addition, the results showed that DMPC exhibited consistently better performance for different spatial distributions of polygons.

Domain Decomposition Algorithms for First-Order System Least Squares Methods

NASA Technical Reports Server (NTRS)

Pavarino, Luca F.

1996-01-01

Least squares methods based on first-order systems have been recently proposed and analyzed for second-order elliptic equations and systems. They produce symmetric and positive definite discrete systems by using standard finite element spaces, which are not required to satisfy the inf-sup condition. In this paper, several domain decomposition algorithms for these first-order least squares methods are studied. Some representative overlapping and substructuring algorithms are considered in their additive and multiplicative variants. The theoretical and numerical results obtained show that the classical convergence bounds (on the iteration operator) for standard Galerkin discretizations are also valid for least squares methods.

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.

The Roadmaker's algorithm for the discrete pulse transform.

PubMed

Laurie, Dirk P

2011-02-01

The discrete pulse transform (DPT) is a decomposition of an observed signal into a sum of pulses, i.e., signals that are constant on a connected set and zero elsewhere. Originally developed for 1-D signal processing, the DPT has recently been generalized to more dimensions. Applications in image processing are currently being investigated. The time required to compute the DPT as originally defined via the successive application of LULU operators (members of a class of minimax filters studied by Rohwer) has been a severe drawback to its applicability. This paper introduces a fast method for obtaining such a decomposition, called the Roadmaker's algorithm because it involves filling pits and razing bumps. It acts selectively only on those features actually present in the signal, flattening them in order of increasing size by subtracing an appropriate positive or negative pulse, which is then appended to the decomposition. The implementation described here covers 1-D signal as well as two and 3-D image processing in a single framework. This is achieved by considering the signal or image as a function defined on a graph, with the geometry specified by the edges of the graph. Whenever a feature is flattened, nodes in the graph are merged, until eventually only one node remains. At that stage, a new set of edges for the same nodes as the graph, forming a tree structure, defines the obtained decomposition. The Roadmaker's algorithm is shown to be equivalent to the DPT in the sense of obtaining the same decomposition. However, its simpler operators are not in general equivalent to the LULU operators in situations where those operators are not applied successively. A by-product of the Roadmaker's algorithm is that it yields a proof of the so-called Highlight Conjecture, stated as an open problem in 2006. We pay particular attention to algorithmic details and complexity, including a demonstration that in the 1-D case, and also in the case of a complete graph, the Roadmaker's algorithm has optimal complexity: it runs in time O(m), where m is the number of arcs in the graph.

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…

Reliable and Efficient Parallel Processing Algorithms and Architectures for Modern Signal Processing. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Liu, Kuojuey Ray

1990-01-01

Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered.

Dynamic load balancing algorithm for molecular dynamics based on Voronoi cells domain decompositions

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

Fattebert, J.-L.; Richards, D.F.; Glosli, J.N.

2012-12-01

We present a new algorithm for automatic parallel load balancing in classical molecular dynamics. It assumes a spatial domain decomposition of particles into Voronoi cells. It is a gradient method which attempts to minimize a cost function by displacing Voronoi sites associated with each processor/sub-domain along steepest descent directions. Excellent load balance has been obtained for quasi-2D and 3D practical applications, with up to 440·10 6 particles on 65,536 MPI tasks.

Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section

PubMed Central

Jia, Chaolong; Wei, Lili; Wang, Hanning; Yang, Jiulin

2014-01-01

Focusing on problems existing in track irregularity time series data quality, this paper first presents abnormal data identification, data offset correction algorithm, local outlier data identification, and noise cancellation algorithms. And then proposes track irregularity time series decomposition and reconstruction through the wavelet decomposition and reconstruction approach. Finally, the patterns and features of track irregularity standard deviation data sequence in unit sections are studied, and the changing trend of track irregularity time series is discovered and described. PMID:25435869

Integration of progressive hedging and dual decomposition in stochastic integer programs

DOE PAGES

Watson, Jean -Paul; Guo, Ge; Hackebeil, Gabriel; ...

2015-04-07

We present a method for integrating the Progressive Hedging (PH) algorithm and the Dual Decomposition (DD) algorithm of Carøe and Schultz for stochastic mixed-integer programs. Based on the correspondence between lower bounds obtained with PH and DD, a method to transform weights from PH to Lagrange multipliers in DD is found. Fast progress in early iterations of PH speeds up convergence of DD to an exact solution. As a result, we report computational results on server location and unit commitment instances.

Gold - A novel deconvolution algorithm with optimization for waveform LiDAR processing

NASA Astrophysics Data System (ADS)

Zhou, Tan; Popescu, Sorin C.; Krause, Keith; Sheridan, Ryan D.; Putman, Eric

2017-07-01

Waveform Light Detection and Ranging (LiDAR) data have advantages over discrete-return LiDAR data in accurately characterizing vegetation structure. However, we lack a comprehensive understanding of waveform data processing approaches under different topography and vegetation conditions. The objective of this paper is to highlight a novel deconvolution algorithm, the Gold algorithm, for processing waveform LiDAR data with optimal deconvolution parameters. Further, we present a comparative study of waveform processing methods to provide insight into selecting an approach for a given combination of vegetation and terrain characteristics. We employed two waveform processing methods: (1) direct decomposition, (2) deconvolution and decomposition. In method two, we utilized two deconvolution algorithms - the Richardson-Lucy (RL) algorithm and the Gold algorithm. The comprehensive and quantitative comparisons were conducted in terms of the number of detected echoes, position accuracy, the bias of the end products (such as digital terrain model (DTM) and canopy height model (CHM)) from the corresponding reference data, along with parameter uncertainty for these end products obtained from different methods. This study was conducted at three study sites that include diverse ecological regions, vegetation and elevation gradients. Results demonstrate that two deconvolution algorithms are sensitive to the pre-processing steps of input data. The deconvolution and decomposition method is more capable of detecting hidden echoes with a lower false echo detection rate, especially for the Gold algorithm. Compared to the reference data, all approaches generate satisfactory accuracy assessment results with small mean spatial difference (<1.22 m for DTMs, <0.77 m for CHMs) and root mean square error (RMSE) (<1.26 m for DTMs, <1.93 m for CHMs). More specifically, the Gold algorithm is superior to others with smaller root mean square error (RMSE) (<1.01 m), while the direct decomposition approach works better in terms of the percentage of spatial difference within 0.5 and 1 m. The parameter uncertainty analysis demonstrates that the Gold algorithm outperforms other approaches in dense vegetation areas, with the smallest RMSE, and the RL algorithm performs better in sparse vegetation areas in terms of RMSE. Additionally, the high level of uncertainty occurs more on areas with high slope and high vegetation. This study provides an alternative and innovative approach for waveform processing that will benefit high fidelity processing of waveform LiDAR data to characterize vegetation structures.

Parallelization of PANDA discrete ordinates code using spatial decomposition

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

Humbert, P.

2006-07-01

We present the parallel method, based on spatial domain decomposition, implemented in the 2D and 3D versions of the discrete Ordinates code PANDA. The spatial mesh is orthogonal and the spatial domain decomposition is Cartesian. For 3D problems a 3D Cartesian domain topology is created and the parallel method is based on a domain diagonal plane ordered sweep algorithm. The parallel efficiency of the method is improved by directions and octants pipelining. The implementation of the algorithm is straightforward using MPI blocking point to point communications. The efficiency of the method is illustrated by an application to the 3D-Ext C5G7more » benchmark of the OECD/NEA. (authors)« less

Matching pursuit parallel decomposition of seismic data

NASA Astrophysics Data System (ADS)

Li, Chuanhui; Zhang, Fanchang

2017-07-01

In order to improve the computation speed of matching pursuit decomposition of seismic data, a matching pursuit parallel algorithm is designed in this paper. We pick a fixed number of envelope peaks from the current signal in every iteration according to the number of compute nodes and assign them to the compute nodes on average to search the optimal Morlet wavelets in parallel. With the help of parallel computer systems and Message Passing Interface, the parallel algorithm gives full play to the advantages of parallel computing to significantly improve the computation speed of the matching pursuit decomposition and also has good expandability. Besides, searching only one optimal Morlet wavelet by every compute node in every iteration is the most efficient implementation.

Singular value decomposition for collaborative filtering on a GPU

NASA Astrophysics Data System (ADS)

Kato, Kimikazu; Hosino, Tikara

2010-06-01

A collaborative filtering predicts customers' unknown preferences from known preferences. In a computation of the collaborative filtering, a singular value decomposition (SVD) is needed to reduce the size of a large scale matrix so that the burden for the next phase computation will be decreased. In this application, SVD means a roughly approximated factorization of a given matrix into smaller sized matrices. Webb (a.k.a. Simon Funk) showed an effective algorithm to compute SVD toward a solution of an open competition called "Netflix Prize". The algorithm utilizes an iterative method so that the error of approximation improves in each step of the iteration. We give a GPU version of Webb's algorithm. Our algorithm is implemented in the CUDA and it is shown to be efficient by an experiment.

Accelerated decomposition techniques for large discounted Markov decision processes

NASA Astrophysics Data System (ADS)

Larach, Abdelhadi; Chafik, S.; Daoui, C.

2017-12-01

Many hierarchical techniques to solve large Markov decision processes (MDPs) are based on the partition of the state space into strongly connected components (SCCs) that can be classified into some levels. In each level, smaller problems named restricted MDPs are solved, and then these partial solutions are combined to obtain the global solution. In this paper, we first propose a novel algorithm, which is a variant of Tarjan's algorithm that simultaneously finds the SCCs and their belonging levels. Second, a new definition of the restricted MDPs is presented to ameliorate some hierarchical solutions in discounted MDPs using value iteration (VI) algorithm based on a list of state-action successors. Finally, a robotic motion-planning example and the experiment results are presented to illustrate the benefit of the proposed decomposition algorithms.

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.

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.

Progressive Precision Surface Design

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

Duchaineau, M; Joy, KJ

2002-01-11

We introduce a novel wavelet decomposition algorithm that makes a number of powerful new surface design operations practical. Wavelets, and hierarchical representations generally, have held promise to facilitate a variety of design tasks in a unified way by approximating results very precisely, thus avoiding a proliferation of undergirding mathematical representations. However, traditional wavelet decomposition is defined from fine to coarse resolution, thus limiting its efficiency for highly precise surface manipulation when attempting to create new non-local editing methods. Our key contribution is the progressive wavelet decomposition algorithm, a general-purpose coarse-to-fine method for hierarchical fitting, based in this paper on anmore » underlying multiresolution representation called dyadic splines. The algorithm requests input via a generic interval query mechanism, allowing a wide variety of non-local operations to be quickly implemented. The algorithm performs work proportionate to the tiny compressed output size, rather than to some arbitrarily high resolution that would otherwise be required, thus increasing performance by several orders of magnitude. We describe several design operations that are made tractable because of the progressive decomposition. Free-form pasting is a generalization of the traditional control-mesh edit, but for which the shape of the change is completely general and where the shape can be placed using a free-form deformation within the surface domain. Smoothing and roughening operations are enhanced so that an arbitrary loop in the domain specifies the area of effect. Finally, the sculpting effect of moving a tool shape along a path is simulated.« less

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.

Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

PubMed Central

Zheng, Ming; Wu, Jia-nan; Huang, Yan-xin; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

2012-01-01

Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms. PMID:23226565

Terascale Optimal PDE Simulations (TOPS) Center

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

Professor Olof B. Widlund

2007-07-09

Our work has focused on the development and analysis of domain decomposition algorithms for a variety of problems arising in continuum mechanics modeling. In particular, we have extended and analyzed FETI-DP and BDDC algorithms; these iterative solvers were first introduced and studied by Charbel Farhat and his collaborators, see [11, 45, 12], and by Clark Dohrmann of SANDIA, Albuquerque, see [43, 2, 1], respectively. These two closely related families of methods are of particular interest since they are used more extensively than other iterative substructuring methods to solve very large and difficult problems. Thus, the FETI algorithms are part ofmore » the SALINAS system developed by the SANDIA National Laboratories for very large scale computations, and as already noted, BDDC was first developed by a SANDIA scientist, Dr. Clark Dohrmann. The FETI algorithms are also making inroads in commercial engineering software systems. We also note that the analysis of these algorithms poses very real mathematical challenges. The success in developing this theory has, in several instances, led to significant improvements in the performance of these algorithms. A very desirable feature of these iterative substructuring and other domain decomposition algorithms is that they respect the memory hierarchy of modern parallel and distributed computing systems, which is essential for approaching peak floating point performance. The development of improved methods, together with more powerful computer systems, is making it possible to carry out simulations in three dimensions, with quite high resolution, relatively easily. This work is supported by high quality software systems, such as Argonne's PETSc library, which facilitates code development as well as the access to a variety of parallel and distributed computer systems. The success in finding scalable and robust domain decomposition algorithms for very large number of processors and very large finite element problems is, e.g., illustrated in [24, 25, 26]. This work is based on [29, 31]. Our work over these five and half years has, in our opinion, helped advance the knowledge of domain decomposition methods significantly. We see these methods as providing valuable alternatives to other iterative methods, in particular, those based on multi-grid. In our opinion, our accomplishments also match the goals of the TOPS project quite closely.« less

Long-term power generation expansion planning with short-term demand response: Model, algorithms, implementation, and electricity policies

NASA Astrophysics Data System (ADS)

Lohmann, Timo

Electric sector models are powerful tools that guide policy makers and stakeholders. Long-term power generation expansion planning models are a prominent example and determine a capacity expansion for an existing power system over a long planning horizon. With the changes in the power industry away from monopolies and regulation, the focus of these models has shifted to competing electric companies maximizing their profit in a deregulated electricity market. In recent years, consumers have started to participate in demand response programs, actively influencing electricity load and price in the power system. We introduce a model that features investment and retirement decisions over a long planning horizon of more than 20 years, as well as an hourly representation of day-ahead electricity markets in which sellers of electricity face buyers. This combination makes our model both unique and challenging to solve. Decomposition algorithms, and especially Benders decomposition, can exploit the model structure. We present a novel method that can be seen as an alternative to generalized Benders decomposition and relies on dynamic linear overestimation. We prove its finite convergence and present computational results, demonstrating its superiority over traditional approaches. In certain special cases of our model, all necessary solution values in the decomposition algorithms can be directly calculated and solving mathematical programming problems becomes entirely obsolete. This leads to highly efficient algorithms that drastically outperform their programming problem-based counterparts. Furthermore, we discuss the implementation of all tailored algorithms and the challenges from a modeling software developer's standpoint, providing an insider's look into the modeling language GAMS. Finally, we apply our model to the Texas power system and design two electricity policies motivated by the U.S. Environment Protection Agency's recently proposed CO2 emissions targets for the power sector.

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

FACETS: multi-faceted functional decomposition of protein interaction networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes

2012-10-15

The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein-protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Supplementary data are available at the Bioinformatics online. Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/~assourav/Facets/

A Domain Decomposition Parallelization of the Fast Marching Method

NASA Technical Reports Server (NTRS)

Herrmann, M.

2003-01-01

In this paper, the first domain decomposition parallelization of the Fast Marching Method for level sets has been presented. Parallel speedup has been demonstrated in both the optimal and non-optimal domain decomposition case. The parallel performance of the proposed method is strongly dependent on load balancing separately the number of nodes on each side of the interface. A load imbalance of nodes on either side of the domain leads to an increase in communication and rollback operations. Furthermore, the amount of inter-domain communication can be reduced by aligning the inter-domain boundaries with the interface normal vectors. In the case of optimal load balancing and aligned inter-domain boundaries, the proposed parallel FMM algorithm is highly efficient, reaching efficiency factors of up to 0.98. Future work will focus on the extension of the proposed parallel algorithm to higher order accuracy. Also, to further enhance parallel performance, the coupling of the domain decomposition parallelization to the G(sub 0)-based parallelization will be investigated.

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.

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.

Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level.

PubMed

Navarro, Pedro J; Fernández-Isla, Carlos; Alcover, Pedro María; Suardíaz, Juan

2016-07-27

This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL), based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS) calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon's entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed.

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.

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

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

Parallel text rendering by a PostScript interpreter

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

Kritskii, S.P.; Zastavnoi, B.A.

1994-11-01

The most radical method of increasing the performance of devices controlled by PostScript interpreters may be the use of multiprocessor controllers. This paper presents a method for parallelizing the operation of a PostScript interpreter for rendering text. The proposed method is based on decomposition of the outlines of letters into horizontal strips covering equal areas. The subroutines thus obtained are distributed to the processors in a network and then filled in by conventional sequential algorithms. A special algorithm has been developed for dividing the outlines of characters into subroutines so that each may be colored independently of the others. Themore » algorithm uses special estimates for estimating the correct partition so that the corresponding outlines are divided into horizontal strips. A method is presented for finding such estimates. Two different processing approaches are presented. In the first, one of the processors performs the decomposition of the outlines and distributes the strips to the remaining processors, which are responsible for the rendering. In the second approach, the decomposition process is itself distributed among the processors in the network.« less

Integrating a Genetic Algorithm Into a Knowledge-Based System for Ordering Complex Design Processes

NASA Technical Reports Server (NTRS)

Rogers, James L.; McCulley, Collin M.; Bloebaum, Christina L.

1996-01-01

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to be able to determine the best ordering of the processes within these subcycles to reduce design cycle time and cost. Many decomposition approaches assume the capability is available to determine what design processes and couplings exist and what order of execution will be imposed during the design cycle. Unfortunately, this is often a complex problem and beyond the capabilities of a human design manager. A new feature, a genetic algorithm, has been added to DeMAID (Design Manager's Aid for Intelligent Decomposition) to allow the design manager to rapidly examine many different combinations of ordering processes in an iterative subcycle and to optimize the ordering based on cost, time, and iteration requirements. Two sample test cases are presented to show the effects of optimizing the ordering with a genetic algorithm.

Effects of image compression and degradation on an automatic diabetic retinopathy screening algorithm

NASA Astrophysics Data System (ADS)

Agurto, C.; Barriga, S.; Murray, V.; Pattichis, M.; Soliz, P.

2010-03-01

Diabetic retinopathy (DR) is one of the leading causes of blindness among adult Americans. Automatic methods for detection of the disease have been developed in recent years, most of them addressing the segmentation of bright and red lesions. In this paper we present an automatic DR screening system that does approach the problem through the segmentation of features. The algorithm determines non-diseased retinal images from those with pathology based on textural features obtained using multiscale Amplitude Modulation-Frequency Modulation (AM-FM) decompositions. The decomposition is represented as features that are the inputs to a classifier. The algorithm achieves 0.88 area under the ROC curve (AROC) for a set of 280 images from the MESSIDOR database. The algorithm is then used to analyze the effects of image compression and degradation, which will be present in most actual clinical or screening environments. Results show that the algorithm is insensitive to illumination variations, but high rates of compression and large blurring effects degrade its performance.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

PubMed

Hong, Xia

2006-07-01

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts

PubMed Central

PONTIFEX, MATTHEW B.; GWIZDALA, KATHRYN L.; PARKS, ANDREW C.; BILLINGER, MARTIN; BRUNNER, CLEMENS

2017-01-01

Despite the growing use of independent component analysis (ICA) algorithms for isolating and removing eyeblink-related activity from EEG data, we have limited understanding of how variability associated with ICA uncertainty may be influencing the reconstructed EEG signal after removing the eyeblink artifact components. To characterize the magnitude of this ICA uncertainty and to understand the extent to which it may influence findings within ERP and EEG investigations, ICA decompositions of EEG data from 32 college-aged young adults were repeated 30 times for three popular ICA algorithms. Following each decomposition, eyeblink components were identified and removed. The remaining components were back-projected, and the resulting clean EEG data were further used to analyze ERPs. Findings revealed that ICA uncertainty results in variation in P3 amplitude as well as variation across all EEG sampling points, but differs across ICA algorithms as a function of the spatial location of the EEG channel. This investigation highlights the potential of ICA uncertainty to introduce additional sources of variance when the data are back-projected without artifact components. Careful selection of ICA algorithms and parameters can reduce the extent to which ICA uncertainty may introduce an additional source of variance within ERP/EEG studies. PMID:28026876

Domain Decomposition: A Bridge between Nature and Parallel Computers

DTIC Science & Technology

1992-09-01

B., "Domain Decomposition Algorithms for Indefinite Elliptic Problems," S"IAM Journal of S; cientific and Statistical (’omputing, Vol. 13, 1992, pp...AD-A256 575 NASA Contractor Report 189709 ICASE Report No. 92-44 ICASE DOMAIN DECOMPOSITION: A BRIDGE BETWEEN NATURE AND PARALLEL COMPUTERS DTIC dE...effectively implemented on dis- tributed memory multiprocessors. In 1990 (as reported in Ref. 38 using the tile algo- rithm), a 103,201-unknown 2D elliptic

Optimal domain decomposition strategies

NASA Technical Reports Server (NTRS)

Yoon, Yonghyun; Soni, Bharat K.

1995-01-01

The primary interest of the authors is in the area of grid generation, in particular, optimal domain decomposition about realistic configurations. A grid generation procedure with optimal blocking strategies has been developed to generate multi-block grids for a circular-to-rectangular transition duct. The focus of this study is the domain decomposition which optimizes solution algorithm/block compatibility based on geometrical complexities as well as the physical characteristics of flow field. The progress realized in this study is summarized in this paper.

Breast density evaluation using spectral mammography, radiologist reader assessment and segmentation techniques: a retrospective study based on left and right breast comparison

PubMed Central

Molloi, Sabee; Ding, Huanjun; Feig, Stephen

2015-01-01

Purpose The purpose of this study was to compare the precision of mammographic breast density measurement using radiologist reader assessment, histogram threshold segmentation, fuzzy C-mean segmentation and spectral material decomposition. Materials and Methods Spectral mammography images from a total of 92 consecutive asymptomatic women (50–69 years old) who presented for annual screening mammography were retrospectively analyzed for this study. Breast density was estimated using 10 radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm and spectral material decomposition. The breast density correlation between left and right breasts was used to assess the precision of these techniques to measure breast composition relative to dual-energy material decomposition. Results In comparison to the other techniques, the results of breast density measurements using dual-energy material decomposition showed the highest correlation. The relative standard error of estimate for breast density measurements from left and right breasts using radiologist reader assessment, standard histogram thresholding, fuzzy C-mean algorithm and dual-energy material decomposition was calculated to be 1.95, 2.87, 2.07 and 1.00, respectively. Conclusion The results indicate that the precision of dual-energy material decomposition was approximately factor of two higher than the other techniques with regard to better correlation of breast density measurements from right and left breasts. PMID:26031229

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.

Hybridization of decomposition and local search for multiobjective optimization.

PubMed

Ke, Liangjun; Zhang, Qingfu; Battiti, Roberto

2014-10-01

Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: memetic algorithm based on decomposition (MOMAD). It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MOMAD evolves three populations: 1) population P(L) for recording the current solution to each subproblem; 2) population P(P) for storing starting solutions for Pareto local search; and 3) an external population P(E) for maintaining all the nondominated solutions found so far during the search. A problem-specific single objective heuristic can be applied to these subproblems to initialize the three populations. At each generation, a Pareto local search method is first applied to search a neighborhood of each solution in P(P) to update P(L) and P(E). Then a single objective local search is applied to each perturbed solution in P(L) for improving P(L) and P(E), and reinitializing P(P). The procedure is repeated until a stopping condition is met. MOMAD provides a generic hybrid multiobjective algorithmic framework in which problem specific knowledge, well developed single objective local search and heuristics and Pareto local search methods can be hybridized. It is a population based iterative method and thus an anytime algorithm. Extensive experiments have been conducted in this paper to study MOMAD and compare it with some other state-of-the-art algorithms on the multiobjective traveling salesman problem and the multiobjective knapsack problem. The experimental results show that our proposed algorithm outperforms or performs similarly to the best so far heuristics on these two problems.

Electrical conductivity imaging using gradient B, decomposition algorithm in magnetic resonance electrical impedance tomography (MREIT).

PubMed

Park, Chunjae; Kwon, Ohin; Woo, Eung Je; Seo, Jin Keun

2004-03-01

In magnetic resonance electrical impedance tomography (MREIT), we try to visualize cross-sectional conductivity (or resistivity) images of a subject. We inject electrical currents into the subject through surface electrodes and measure the z component Bz of the induced internal magnetic flux density using an MRI scanner. Here, z is the direction of the main magnetic field of the MRI scanner. We formulate the conductivity image reconstruction problem in MREIT from a careful analysis of the relationship between the injection current and the induced magnetic flux density Bz. Based on the novel mathematical formulation, we propose the gradient Bz decomposition algorithm to reconstruct conductivity images. This new algorithm needs to differentiate Bz only once in contrast to the previously developed harmonic Bz algorithm where the numerical computation of (inverted delta)2Bz is required. The new algorithm, therefore, has the important advantage of much improved noise tolerance. Numerical simulations with added random noise of realistic amounts show the feasibility of the algorithm in practical applications and also its robustness against measurement noise.

Infrared dim-small target tracking via singular value decomposition and improved Kernelized correlation filter

NASA Astrophysics Data System (ADS)

Qian, Kun; Zhou, Huixin; Rong, Shenghui; Wang, Bingjian; Cheng, Kuanhong

2017-05-01

Infrared small target tracking plays an important role in applications including military reconnaissance, early warning and terminal guidance. In this paper, an effective algorithm based on the Singular Value Decomposition (SVD) and the improved Kernelized Correlation Filter (KCF) is presented for infrared small target tracking. Firstly, the super performance of the SVD-based algorithm is that it takes advantage of the target's global information and obtains a background estimation of an infrared image. A dim target is enhanced by subtracting the corresponding estimated background with update from the original image. Secondly, the KCF algorithm is combined with Gaussian Curvature Filter (GCF) to eliminate the excursion problem. The GCF technology is adopted to preserve the edge and eliminate the noise of the base sample in the KCF algorithm, helping to calculate the classifier parameter for a small target. At last, the target position is estimated with a response map, which is obtained via the kernelized classifier. Experimental results demonstrate that the presented algorithm performs favorably in terms of efficiency and accuracy, compared with several state-of-the-art algorithms.

A new linear back projection algorithm to electrical tomography based on measuring data decomposition

NASA Astrophysics Data System (ADS)

Sun, Benyuan; Yue, Shihong; Cui, Ziqiang; Wang, Huaxiang

2015-12-01

As an advanced measurement technique of non-radiant, non-intrusive, rapid response, and low cost, the electrical tomography (ET) technique has developed rapidly in recent decades. The ET imaging algorithm plays an important role in the ET imaging process. Linear back projection (LBP) is the most used ET algorithm due to its advantages of dynamic imaging process, real-time response, and easy realization. But the LBP algorithm is of low spatial resolution due to the natural ‘soft field’ effect and ‘ill-posed solution’ problems; thus its applicable ranges are greatly limited. In this paper, an original data decomposition method is proposed, and every ET measuring data are decomposed into two independent new data based on the positive and negative sensing areas of the measuring data. Consequently, the number of total measuring data is extended to twice as many as the number of the original data, thus effectively reducing the ‘ill-posed solution’. On the other hand, an index to measure the ‘soft field’ effect is proposed. The index shows that the decomposed data can distinguish between different contributions of various units (pixels) for any ET measuring data, and can efficiently reduce the ‘soft field’ effect of the ET imaging process. In light of the data decomposition method, a new linear back projection algorithm is proposed to improve the spatial resolution of the ET image. A series of simulations and experiments are applied to validate the proposed algorithm by the real-time performances and the progress of spatial resolutions.

Application of modified Martinez-Silva algorithm in determination of net cover

NASA Astrophysics Data System (ADS)

Stefanowicz, Łukasz; Grobelna, Iwona

2016-12-01

In the article we present the idea of modifications of Martinez-Silva algorithm, which allows for determination of place invariants (p-invariants) of Petri net. Their generation time is important in the parallel decomposition of discrete systems described by Petri nets. Decomposition process is essential from the point of view of discrete system design, as it allows for separation of smaller sequential parts. The proposed modifications of Martinez-Silva method concern the net cover by p-invariants and are focused on two important issues: cyclic reduction of invariant matrix and cyclic checking of net cover.

Distributed-Memory Computing With the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA)

NASA Technical Reports Server (NTRS)

Riley, Christopher J.; Cheatwood, F. McNeil

1997-01-01

The Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA), a Navier-Stokes solver, has been modified for use in a parallel, distributed-memory environment using the Message-Passing Interface (MPI) standard. A standard domain decomposition strategy is used in which the computational domain is divided into subdomains with each subdomain assigned to a processor. Performance is examined on dedicated parallel machines and a network of desktop workstations. The effect of domain decomposition and frequency of boundary updates on performance and convergence is also examined for several realistic configurations and conditions typical of large-scale computational fluid dynamic analysis.

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.

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.

Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data.

PubMed

Liu, Yuanyuan; Shang, Fanhua; Jiao, Licheng; Cheng, James; Cheng, Hong

2015-11-01

In recent years, low-rank tensor completion (LRTC) problems have received a significant amount of attention in computer vision, data mining, and signal processing. The existing trace norm minimization algorithms for iteratively solving LRTC problems involve multiple singular value decompositions of very large matrices at each iteration. Therefore, they suffer from high computational cost. In this paper, we propose a novel trace norm regularized CANDECOMP/PARAFAC decomposition (TNCP) method for simultaneous tensor decomposition and completion. We first formulate a factor matrix rank minimization model by deducing the relation between the rank of each factor matrix and the mode- n rank of a tensor. Then, we introduce a tractable relaxation of our rank function, and then achieve a convex combination problem of much smaller-scale matrix trace norm minimization. Finally, we develop an efficient algorithm based on alternating direction method of multipliers to solve our problem. The promising experimental results on synthetic and real-world data validate the effectiveness of our TNCP method. Moreover, TNCP is significantly faster than the state-of-the-art methods and scales to larger problems.

Calculation of excitation energies from the CC2 linear response theory using Cholesky decomposition

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

Baudin, Pablo, E-mail: baudin.pablo@gmail.com; qLEAP – Center for Theoretical Chemistry, Department of Chemistry, Aarhus University, Langelandsgade 140, DK-8000 Aarhus C; Marín, José Sánchez

2014-03-14

A new implementation of the approximate coupled cluster singles and doubles CC2 linear response model is reported. It employs a Cholesky decomposition of the two-electron integrals that significantly reduces the computational cost and the storage requirements of the method compared to standard implementations. Our algorithm also exploits a partitioning form of the CC2 equations which reduces the dimension of the problem and avoids the storage of doubles amplitudes. We present calculation of excitation energies of benzene using a hierarchy of basis sets and compare the results with conventional CC2 calculations. The reduction of the scaling is evaluated as well asmore » the effect of the Cholesky decomposition parameter on the quality of the results. The new algorithm is used to perform an extrapolation to complete basis set investigation on the spectroscopically interesting benzylallene conformers. A set of calculations on medium-sized molecules is carried out to check the dependence of the accuracy of the results on the decomposition thresholds. Moreover, CC2 singlet excitation energies of the free base porphin are also presented.« less

A copyright protection scheme for digital images based on shuffled singular value decomposition and visual cryptography.

PubMed

Devi, B Pushpa; Singh, Kh Manglem; Roy, Sudipta

2016-01-01

This paper proposes a new watermarking algorithm based on the shuffled singular value decomposition and the visual cryptography for copyright protection of digital images. It generates the ownership and identification shares of the image based on visual cryptography. It decomposes the image into low and high frequency sub-bands. The low frequency sub-band is further divided into blocks of same size after shuffling it and then the singular value decomposition is applied to each randomly selected block. Shares are generated by comparing one of the elements in the first column of the left orthogonal matrix with its corresponding element in the right orthogonal matrix of the singular value decomposition of the block of the low frequency sub-band. The experimental results show that the proposed scheme clearly verifies the copyright of the digital images, and is robust to withstand several image processing attacks. Comparison with the other related visual cryptography-based algorithms reveals that the proposed method gives better performance. The proposed method is especially resilient against the rotation attack.

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

NASA Astrophysics Data System (ADS)

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt; Stuehn, Torsten

2017-11-01

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach, the theoretical modeling and scaling laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. These two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.

Optical systolic solutions of linear algebraic equations

NASA Technical Reports Server (NTRS)

Neuman, C. P.; Casasent, D.

1984-01-01

The philosophy and data encoding possible in systolic array optical processor (SAOP) were reviewed. The multitude of linear algebraic operations achievable on this architecture is examined. These operations include such linear algebraic algorithms as: matrix-decomposition, direct and indirect solutions, implicit and explicit methods for partial differential equations, eigenvalue and eigenvector calculations, and singular value decomposition. This architecture can be utilized to realize general techniques for solving matrix linear and nonlinear algebraic equations, least mean square error solutions, FIR filters, and nested-loop algorithms for control engineering applications. The data flow and pipelining of operations, design of parallel algorithms and flexible architectures, application of these architectures to computationally intensive physical problems, error source modeling of optical processors, and matching of the computational needs of practical engineering problems to the capabilities of optical processors are emphasized.

Algorithms for Spectral Decomposition with Applications to Optical Plume Anomaly Detection

NASA Technical Reports Server (NTRS)

Srivastava, Askok N.; Matthews, Bryan; Das, Santanu

2008-01-01

The analysis of spectral signals for features that represent physical phenomenon is ubiquitous in the science and engineering communities. There are two main approaches that can be taken to extract relevant features from these high-dimensional data streams. The first set of approaches relies on extracting features using a physics-based paradigm where the underlying physical mechanism that generates the spectra is used to infer the most important features in the data stream. We focus on a complementary methodology that uses a data-driven technique that is informed by the underlying physics but also has the ability to adapt to unmodeled system attributes and dynamics. We discuss the following four algorithms: Spectral Decomposition Algorithm (SDA), Non-Negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Principal Components Analysis (PCA) and compare their performance on a spectral emulator which we use to generate artificial data with known statistical properties. This spectral emulator mimics the real-world phenomena arising from the plume of the space shuttle main engine and can be used to validate the results that arise from various spectral decomposition algorithms and is very useful for situations where real-world systems have very low probabilities of fault or failure. Our results indicate that methods like SDA and NMF provide a straightforward way of incorporating prior physical knowledge while NMF with a tuning mechanism can give superior performance on some tests. We demonstrate these algorithms to detect potential system-health issues on data from a spectral emulator with tunable health parameters.

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.

Velocity boundary conditions for vorticity formulations of the incompressible Navier-Stokes equations

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

Kempka, S.N.; Strickland, J.H.; Glass, M.W.

1995-04-01

formulation to satisfy velocity boundary conditions for the vorticity form of the incompressible, viscous fluid momentum equations is presented. The tangential and normal components of the velocity boundary condition are satisfied simultaneously by creating vorticity adjacent to boundaries. The newly created vorticity is determined using a kinematical formulation which is a generalization of Helmholtz` decomposition of a vector field. Though it has not been generally recognized, these formulations resolve the over-specification issue associated with creating voracity to satisfy velocity boundary conditions. The generalized decomposition has not been widely used, apparently due to a lack of a useful physical interpretation. Anmore » analysis is presented which shows that the generalized decomposition has a relatively simple physical interpretation which facilitates its numerical implementation. The implementation of the generalized decomposition is discussed in detail. As an example the flow in a two-dimensional lid-driven cavity is simulated. The solution technique is based on a Lagrangian transport algorithm in the hydrocode ALEGRA. ALEGRA`s Lagrangian transport algorithm has been modified to solve the vorticity transport equation and the generalized decomposition, thus providing a new, accurate method to simulate incompressible flows. This numerical implementation and the new boundary condition formulation allow vorticity-based formulations to be used in a wider range of engineering problems.« less

A parallel domain decomposition-based implicit method for the Cahn–Hilliard–Cook phase-field equation in 3D

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

Zheng, Xiang; Yang, Chao; State Key Laboratory of Computer Science, Chinese Academy of Sciences, Beijing 100190

2015-03-15

We present a numerical algorithm for simulating the spinodal decomposition described by the three dimensional Cahn–Hilliard–Cook (CHC) equation, which is a fourth-order stochastic partial differential equation with a noise term. The equation is discretized in space and time based on a fully implicit, cell-centered finite difference scheme, with an adaptive time-stepping strategy designed to accelerate the progress to equilibrium. At each time step, a parallel Newton–Krylov–Schwarz algorithm is used to solve the nonlinear system. We discuss various numerical and computational challenges associated with the method. The numerical scheme is validated by a comparison with an explicit scheme of high accuracymore » (and unreasonably high cost). We present steady state solutions of the CHC equation in two and three dimensions. The effect of the thermal fluctuation on the spinodal decomposition process is studied. We show that the existence of the thermal fluctuation accelerates the spinodal decomposition process and that the final steady morphology is sensitive to the stochastic noise. We also show the evolution of the energies and statistical moments. In terms of the parallel performance, it is found that the implicit domain decomposition approach scales well on supercomputers with a large number of processors.« less

Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging.

PubMed

Eloyan, Ani; Muschelli, John; Nebel, Mary Beth; Liu, Han; Han, Fang; Zhao, Tuo; Barber, Anita D; Joel, Suresh; Pekar, James J; Mostofsky, Stewart H; Caffo, Brian

2012-01-01

Successful automated diagnoses of attention deficit hyperactive disorder (ADHD) using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc) and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data, including the use of singular value decompositions (SVDs), CUR decompositions, random forest, gradient boosting, bagging, voxel-based morphometry, and support vector machines as well as important insights into the value, and potentially lack thereof, of imaging biomarkers of disease. The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and random forest algorithm. We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion. Of note, a byproduct of this research is a potential automated method for detecting subtle in-scanner motion. The final prediction algorithm, a weighted combination of several algorithms, had an external test set specificity of 94% with sensitivity of 21%. The most promising imaging biomarker was a correlation graph from a motor network parcellation. In summary, we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set. The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD.

Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging

PubMed Central

Eloyan, Ani; Muschelli, John; Nebel, Mary Beth; Liu, Han; Han, Fang; Zhao, Tuo; Barber, Anita D.; Joel, Suresh; Pekar, James J.; Mostofsky, Stewart H.; Caffo, Brian

2012-01-01

Successful automated diagnoses of attention deficit hyperactive disorder (ADHD) using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc) and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data, including the use of singular value decompositions (SVDs), CUR decompositions, random forest, gradient boosting, bagging, voxel-based morphometry, and support vector machines as well as important insights into the value, and potentially lack thereof, of imaging biomarkers of disease. The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and random forest algorithm. We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion. Of note, a byproduct of this research is a potential automated method for detecting subtle in-scanner motion. The final prediction algorithm, a weighted combination of several algorithms, had an external test set specificity of 94% with sensitivity of 21%. The most promising imaging biomarker was a correlation graph from a motor network parcellation. In summary, we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set. The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD. PMID:22969709

The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures

ERIC Educational Resources Information Center

Ceulemans, Eva; Van Mechelen, Iven; Leenen, Iwin

2007-01-01

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite…

Reactivity of fluoroalkanes in reactions of coordinated molecular decomposition

NASA Astrophysics Data System (ADS)

Pokidova, T. S.; Denisov, E. T.

2017-08-01

Experimental results on the coordinated molecular decomposition of RF fluoroalkanes to olefin and HF are analyzed using the model of intersecting parabolas (IPM). The kinetic parameters are calculated to allow estimates of the activation energy ( E) and rate constant ( k) of these reactions, based on enthalpy and IPM algorithms. Parameters E and k are found for the first time for eight RF decomposition reactions. The factors that affect activation energy E of RF decomposition (the enthalpy of the reaction, the electronegativity of the atoms of reaction centers, and the dipole-dipole interaction of polar groups) are determined. The values of E and k for reverse reactions of addition are estimated.

Structural optimization by multilevel decomposition

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.; James, B.; Dovi, A.

1983-01-01

A method is described for decomposing an optimization problem into a set of subproblems and a coordination problem which preserves coupling between the subproblems. The method is introduced as a special case of multilevel, multidisciplinary system optimization and its algorithm is fully described for two level optimization for structures assembled of finite elements of arbitrary type. Numerical results are given for an example of a framework to show that the decomposition method converges and yields results comparable to those obtained without decomposition. It is pointed out that optimization by decomposition should reduce the design time by allowing groups of engineers, using different computers to work concurrently on the same large problem.

Microenvironment temperature prediction between body and seat interface using autoregressive data-driven model.

PubMed

Liu, Zhuofu; Wang, Lin; Luo, Zhongming; Heusch, Andrew I; Cascioli, Vincenzo; McCarthy, Peter W

2015-11-01

There is a need to develop a greater understanding of temperature at the skin-seat interface during prolonged seating from the perspectives of both industrial design (comfort/discomfort) and medical care (skin ulcer formation). Here we test the concept of predicting temperature at the seat surface and skin interface during prolonged sitting (such as required from wheelchair users). As caregivers are usually busy, such a method would give them warning ahead of a problem. This paper describes a data-driven model capable of predicting thermal changes and thus having the potential to provide an early warning (15- to 25-min ahead prediction) of an impending temperature that may increase the risk for potential skin damages for those subject to enforced sitting and who have little or no sensory feedback from this area. Initially, the oscillations of the original signal are suppressed using the reconstruction strategy of empirical mode decomposition (EMD). Consequentially, the autoregressive data-driven model can be used to predict future thermal trends based on a shorter period of acquisition, which reduces the possibility of introducing human errors and artefacts associated with longer duration "enforced" sitting by volunteers. In this study, the method had a maximum predictive error of <0.4 °C when used to predict the temperature at the seat and skin interface 15 min ahead, but required 45 min data prior to give this accuracy. Although the 45 min front loading of data appears large (in proportion to the 15 min prediction), a relative strength derives from the fact that the same algorithm could be used on the other 4 sitting datasets created by the same individual, suggesting that the period of 45 min required to train the algorithm is transferable to other data from the same individual. This approach might be developed (along with incorporation of other measures such as movement and humidity) into a system that can give caregivers prior warning to help avoid exacerbating the skin disorders of patients who suffer from low body insensitivity and disability requiring them to be immobile in seats for prolonged periods. Copyright © 2015 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.

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.

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.

LP and NLP decomposition without a master problem

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

Fuller, D.; Lan, B.

We describe a new algorithm for decomposition of linear programs and a class of convex nonlinear programs, together with theoretical properties and some test results. Its most striking feature is the absence of a master problem; the subproblems pass primal and dual proposals directly to one another. The algorithm is defined for multi-stage LPs or NLPs, in which the constraints link the current stage`s variables to earlier stages` variables. This problem class is general enough to include many problem structures that do not immediately suggest stages, such as block diagonal problems. The basic algorithmis derived for two-stage problems and extendedmore » to more than two stages through nested decomposition. The main theoretical result assures convergence, to within any preset tolerance of the optimal value, in a finite number of iterations. This asymptotic convergence result contrasts with the results of limited tests on LPs, in which the optimal solution is apparently found exactly, i.e., to machine accuracy, in a small number of iterations. The tests further suggest that for LPs, the new algorithm is faster than the simplex method applied to the whole problem, as long as the stages are linked loosely; that the speedup over the simpex method improves as the number of stages increases; and that the algorithm is more reliable than nested Dantzig-Wolfe or Benders` methods in its improvement over the simplex method.« less

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.

Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms

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

Arampatzis, Giorgos, E-mail: garab@math.uoc.gr; Katsoulakis, Markos A., E-mail: markos@math.umass.edu; Plechac, Petr, E-mail: plechac@math.udel.edu

2012-10-01

We present a mathematical framework for constructing and analyzing parallel algorithms for lattice kinetic Monte Carlo (KMC) simulations. The resulting algorithms have the capacity to simulate a wide range of spatio-temporal scales in spatially distributed, non-equilibrium physiochemical processes with complex chemistry and transport micro-mechanisms. Rather than focusing on constructing exactly the stochastic trajectories, our approach relies on approximating the evolution of observables, such as density, coverage, correlations and so on. More specifically, we develop a spatial domain decomposition of the Markov operator (generator) that describes the evolution of all observables according to the kinetic Monte Carlo algorithm. This domain decompositionmore » corresponds to a decomposition of the Markov generator into a hierarchy of operators and can be tailored to specific hierarchical parallel architectures such as multi-core processors or clusters of Graphical Processing Units (GPUs). Based on this operator decomposition, we formulate parallel Fractional step kinetic Monte Carlo algorithms by employing the Trotter Theorem and its randomized variants; these schemes, (a) are partially asynchronous on each fractional step time-window, and (b) are characterized by their communication schedule between processors. The proposed mathematical framework allows us to rigorously justify the numerical and statistical consistency of the proposed algorithms, showing the convergence of our approximating schemes to the original serial KMC. The approach also provides a systematic evaluation of different processor communicating schedules. We carry out a detailed benchmarking of the parallel KMC schemes using available exact solutions, for example, in Ising-type systems and we demonstrate the capabilities of the method to simulate complex spatially distributed reactions at very large scales on GPUs. Finally, we discuss work load balancing between processors and propose a re-balancing scheme based on probabilistic mass transport methods.« less

Wavelet decomposition based principal component analysis for face recognition using MATLAB

NASA Astrophysics Data System (ADS)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

The detection of flaws in austenitic welds using the decomposition of the time-reversal operator

NASA Astrophysics Data System (ADS)

Cunningham, Laura J.; Mulholland, Anthony J.; Tant, Katherine M. M.; Gachagan, Anthony; Harvey, Gerry; Bird, Colin

2016-04-01

The non-destructive testing of austenitic welds using ultrasound plays an important role in the assessment of the structural integrity of safety critical structures. The internal microstructure of these welds is highly scattering and can lead to the obscuration of defects when investigated by traditional imaging algorithms. This paper proposes an alternative objective method for the detection of flaws embedded in austenitic welds based on the singular value decomposition of the time-frequency domain response matrices. The distribution of the singular values is examined in the cases where a flaw exists and where there is no flaw present. A lower threshold on the singular values, specific to austenitic welds, is derived which, when exceeded, indicates the presence of a flaw. The detection criterion is successfully implemented on both synthetic and experimental data. The datasets arising from welds containing a flaw are further interrogated using the decomposition of the time-reversal operator (DORT) method and the total focusing method (TFM), and it is shown that images constructed via the DORT algorithm typically exhibit a higher signal-to-noise ratio than those constructed by the TFM algorithm.

Cloud parallel processing of tandem mass spectrometry based proteomics data.

PubMed

Mohammed, Yassene; Mostovenko, Ekaterina; Henneman, Alex A; Marissen, Rob J; Deelder, André M; Palmblad, Magnus

2012-10-05

Data analysis in mass spectrometry based proteomics struggles to keep pace with the advances in instrumentation and the increasing rate of data acquisition. Analyzing this data involves multiple steps requiring diverse software, using different algorithms and data formats. Speed and performance of the mass spectral search engines are continuously improving, although not necessarily as needed to face the challenges of acquired big data. Improving and parallelizing the search algorithms is one possibility; data decomposition presents another, simpler strategy for introducing parallelism. We describe a general method for parallelizing identification of tandem mass spectra using data decomposition that keeps the search engine intact and wraps the parallelization around it. We introduce two algorithms for decomposing mzXML files and recomposing resulting pepXML files. This makes the approach applicable to different search engines, including those relying on sequence databases and those searching spectral libraries. We use cloud computing to deliver the computational power and scientific workflow engines to interface and automate the different processing steps. We show how to leverage these technologies to achieve faster data analysis in proteomics and present three scientific workflows for parallel database as well as spectral library search using our data decomposition programs, X!Tandem and SpectraST.

Generalized neurofuzzy network modeling algorithms using Bézier-Bernstein polynomial functions and additive decomposition.

PubMed

Hong, X; Harris, C J

2000-01-01

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bézier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bézier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bézier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bézier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

The detection of flaws in austenitic welds using the decomposition of the time-reversal operator

PubMed Central

Cunningham, Laura J.; Mulholland, Anthony J.; Gachagan, Anthony; Harvey, Gerry; Bird, Colin

2016-01-01

The non-destructive testing of austenitic welds using ultrasound plays an important role in the assessment of the structural integrity of safety critical structures. The internal microstructure of these welds is highly scattering and can lead to the obscuration of defects when investigated by traditional imaging algorithms. This paper proposes an alternative objective method for the detection of flaws embedded in austenitic welds based on the singular value decomposition of the time-frequency domain response matrices. The distribution of the singular values is examined in the cases where a flaw exists and where there is no flaw present. A lower threshold on the singular values, specific to austenitic welds, is derived which, when exceeded, indicates the presence of a flaw. The detection criterion is successfully implemented on both synthetic and experimental data. The datasets arising from welds containing a flaw are further interrogated using the decomposition of the time-reversal operator (DORT) method and the total focusing method (TFM), and it is shown that images constructed via the DORT algorithm typically exhibit a higher signal-to-noise ratio than those constructed by the TFM algorithm. PMID:27274683

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.

FACETS: multi-faceted functional decomposition of protein interaction networks

PubMed Central

Seah, Boon-Siew; Bhowmick, Sourav S.; Forbes Dewey, C.

2012-01-01

Motivation: The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein–protein interaction (PPI) network using graph theoretic analysis. Despite the recent progress, systems level analysis of high-throughput PPIs remains a daunting task because of the amount of data they present. In this article, we propose a novel PPI network decomposition algorithm called FACETS in order to make sense of the deluge of interaction data using Gene Ontology (GO) annotations. FACETS finds not just a single functional decomposition of the PPI network, but a multi-faceted atlas of functional decompositions that portray alternative perspectives of the functional landscape of the underlying PPI network. Each facet in the atlas represents a distinct interpretation of how the network can be functionally decomposed and organized. Our algorithm maximizes interpretative value of the atlas by optimizing inter-facet orthogonality and intra-facet cluster modularity. Results: We tested our algorithm on the global networks from IntAct, and compared it with gold standard datasets from MIPS and KEGG. We demonstrated the performance of FACETS. We also performed a case study that illustrates the utility of our approach. Contact: seah0097@ntu.edu.sg or assourav@ntu.edu.sg Supplementary information: Supplementary data are available at the Bioinformatics online. Availability: Our software is available freely for non-commercial purposes from: http://www.cais.ntu.edu.sg/∼assourav/Facets/ PMID:22908217

Statistical iterative material image reconstruction for spectral CT using a semi-empirical forward model

NASA Astrophysics Data System (ADS)

Mechlem, Korbinian; Ehn, Sebastian; Sellerer, Thorsten; Pfeiffer, Franz; Noël, Peter B.

2017-03-01

In spectral computed tomography (spectral CT), the additional information about the energy dependence of attenuation coefficients can be exploited to generate material selective images. These images have found applications in various areas such as artifact reduction, quantitative imaging or clinical diagnosis. However, significant noise amplification on material decomposed images remains a fundamental problem of spectral CT. Most spectral CT algorithms separate the process of material decomposition and image reconstruction. Separating these steps is suboptimal because the full statistical information contained in the spectral tomographic measurements cannot be exploited. Statistical iterative reconstruction (SIR) techniques provide an alternative, mathematically elegant approach to obtaining material selective images with improved tradeoffs between noise and resolution. Furthermore, image reconstruction and material decomposition can be performed jointly. This is accomplished by a forward model which directly connects the (expected) spectral projection measurements and the material selective images. To obtain this forward model, detailed knowledge of the different photon energy spectra and the detector response was assumed in previous work. However, accurately determining the spectrum is often difficult in practice. In this work, a new algorithm for statistical iterative material decomposition is presented. It uses a semi-empirical forward model which relies on simple calibration measurements. Furthermore, an efficient optimization algorithm based on separable surrogate functions is employed. This partially negates one of the major shortcomings of SIR, namely high computational cost and long reconstruction times. Numerical simulations and real experiments show strongly improved image quality and reduced statistical bias compared to projection-based material decomposition.

Parallel deterministic transport sweeps of structured and unstructured meshes with overloaded mesh decompositions

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

Pautz, Shawn D.; Bailey, Teresa S.

Here, the efficiency of discrete ordinates transport sweeps depends on the scheduling algorithm, the domain decomposition, the problem to be solved, and the computational platform. Sweep scheduling algorithms may be categorized by their approach to several issues. In this paper we examine the strategy of domain overloading for mesh partitioning as one of the components of such algorithms. In particular, we extend the domain overloading strategy, previously defined and analyzed for structured meshes, to the general case of unstructured meshes. We also present computational results for both the structured and unstructured domain overloading cases. We find that an appropriate amountmore » of domain overloading can greatly improve the efficiency of parallel sweeps for both structured and unstructured partitionings of the test problems examined on up to 10 5 processor cores.« less

Convergence issues in domain decomposition parallel computation of hovering rotor

NASA Astrophysics Data System (ADS)

Xiao, Zhongyun; Liu, Gang; Mou, Bin; Jiang, Xiong

2018-05-01

Implicit LU-SGS time integration algorithm has been widely used in parallel computation in spite of its lack of information from adjacent domains. When applied to parallel computation of hovering rotor flows in a rotating frame, it brings about convergence issues. To remedy the problem, three LU factorization-based implicit schemes (consisting of LU-SGS, DP-LUR and HLU-SGS) are investigated comparatively. A test case of pure grid rotation is designed to verify these algorithms, which show that LU-SGS algorithm introduces errors on boundary cells. When partition boundaries are circumferential, errors arise in proportion to grid speed, accumulating along with the rotation, and leading to computational failure in the end. Meanwhile, DP-LUR and HLU-SGS methods show good convergence owing to boundary treatment which are desirable in domain decomposition parallel computations.

Parallel deterministic transport sweeps of structured and unstructured meshes with overloaded mesh decompositions

DOE PAGES

Pautz, Shawn D.; Bailey, Teresa S.

2016-11-29

Here, the efficiency of discrete ordinates transport sweeps depends on the scheduling algorithm, the domain decomposition, the problem to be solved, and the computational platform. Sweep scheduling algorithms may be categorized by their approach to several issues. In this paper we examine the strategy of domain overloading for mesh partitioning as one of the components of such algorithms. In particular, we extend the domain overloading strategy, previously defined and analyzed for structured meshes, to the general case of unstructured meshes. We also present computational results for both the structured and unstructured domain overloading cases. We find that an appropriate amountmore » of domain overloading can greatly improve the efficiency of parallel sweeps for both structured and unstructured partitionings of the test problems examined on up to 10 5 processor cores.« less

High-performance computing on GPUs for resistivity logging of oil and gas wells

NASA Astrophysics Data System (ADS)

Glinskikh, V.; Dudaev, A.; Nechaev, O.; Surodina, I.

2017-10-01

We developed and implemented into software an algorithm for high-performance simulation of electrical logs from oil and gas wells using high-performance heterogeneous computing. The numerical solution of the 2D forward problem is based on the finite-element method and the Cholesky decomposition for solving a system of linear algebraic equations (SLAE). Software implementations of the algorithm used the NVIDIA CUDA technology and computing libraries are made, allowing us to perform decomposition of SLAE and find its solution on central processor unit (CPU) and graphics processor unit (GPU). The calculation time is analyzed depending on the matrix size and number of its non-zero elements. We estimated the computing speed on CPU and GPU, including high-performance heterogeneous CPU-GPU computing. Using the developed algorithm, we simulated resistivity data in realistic models.

Reduced Order Model Basis Vector Generation: Generates Basis Vectors fro ROMs

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

Arrighi, Bill

2016-03-03

libROM is a library that implements order reduction via singular value decomposition (SVD) of sampled state vectors. It implements 2 parallel, incremental SVD algorithms and one serial, non-incremental algorithm. It also provides a mechanism for adaptive sampling of basis vectors.

Data decomposition of Monte Carlo particle transport simulations via tally servers

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

Romano, Paul K.; Siegel, Andrew R.; Forget, Benoit

An algorithm for decomposing large tally data in Monte Carlo particle transport simulations is developed, analyzed, and implemented in a continuous-energy Monte Carlo code, OpenMC. The algorithm is based on a non-overlapping decomposition of compute nodes into tracking processors and tally servers. The former are used to simulate the movement of particles through the domain while the latter continuously receive and update tally data. A performance model for this approach is developed, suggesting that, for a range of parameters relevant to LWR analysis, the tally server algorithm should perform with minimal overhead on contemporary supercomputers. An implementation of the algorithmmore » in OpenMC is then tested on the Intrepid and Titan supercomputers, supporting the key predictions of the model over a wide range of parameters. We thus conclude that the tally server algorithm is a successful approach to circumventing classical on-node memory constraints en route to unprecedentedly detailed Monte Carlo reactor simulations.« less

Efficient Delaunay Tessellation through K-D Tree Decomposition

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

Morozov, Dmitriy; Peterka, Tom

Delaunay tessellations are fundamental data structures in computational geometry. They are important in data analysis, where they can represent the geometry of a point set or approximate its density. The algorithms for computing these tessellations at scale perform poorly when the input data is unbalanced. We investigate the use of k-d trees to evenly distribute points among processes and compare two strategies for picking split points between domain regions. Because resulting point distributions no longer satisfy the assumptions of existing parallel Delaunay algorithms, we develop a new parallel algorithm that adapts to its input and prove its correctness. We evaluatemore » the new algorithm using two late-stage cosmology datasets. The new running times are up to 50 times faster using k-d tree compared with regular grid decomposition. Moreover, in the unbalanced data sets, decomposing the domain into a k-d tree is up to five times faster than decomposing it into a regular grid.« less

GPU-accelerated computing for Lagrangian coherent structures of multi-body gravitational regimes

NASA Astrophysics Data System (ADS)

Lin, Mingpei; Xu, Ming; Fu, Xiaoyu

2017-04-01

Based on a well-established theoretical foundation, Lagrangian Coherent Structures (LCSs) have elicited widespread research on the intrinsic structures of dynamical systems in many fields, including the field of astrodynamics. Although the application of LCSs in dynamical problems seems straightforward theoretically, its associated computational cost is prohibitive. We propose a block decomposition algorithm developed on Compute Unified Device Architecture (CUDA) platform for the computation of the LCSs of multi-body gravitational regimes. In order to take advantage of GPU's outstanding computing properties, such as Shared Memory, Constant Memory, and Zero-Copy, the algorithm utilizes a block decomposition strategy to facilitate computation of finite-time Lyapunov exponent (FTLE) fields of arbitrary size and timespan. Simulation results demonstrate that this GPU-based algorithm can satisfy double-precision accuracy requirements and greatly decrease the time needed to calculate final results, increasing speed by approximately 13 times. Additionally, this algorithm can be generalized to various large-scale computing problems, such as particle filters, constellation design, and Monte-Carlo simulation.

Scalable parallel elastic-plastic finite element analysis using a quasi-Newton method with a balancing domain decomposition preconditioner

NASA Astrophysics Data System (ADS)

Yusa, Yasunori; Okada, Hiroshi; Yamada, Tomonori; Yoshimura, Shinobu

2018-04-01

A domain decomposition method for large-scale elastic-plastic problems is proposed. The proposed method is based on a quasi-Newton method in conjunction with a balancing domain decomposition preconditioner. The use of a quasi-Newton method overcomes two problems associated with the conventional domain decomposition method based on the Newton-Raphson method: (1) avoidance of a double-loop iteration algorithm, which generally has large computational complexity, and (2) consideration of the local concentration of nonlinear deformation, which is observed in elastic-plastic problems with stress concentration. Moreover, the application of a balancing domain decomposition preconditioner ensures scalability. Using the conventional and proposed domain decomposition methods, several numerical tests, including weak scaling tests, were performed. The convergence performance of the proposed method is comparable to that of the conventional method. In particular, in elastic-plastic analysis, the proposed method exhibits better convergence performance than the conventional method.

Repeated decompositions reveal the stability of infomax decomposition of fMRI data

PubMed Central

Duann, Jeng-Ren; Jung, Tzyy-Ping; Sejnowski, Terrence J.; Makeig, Scott

2010-01-01

In this study, we decomposed 12 fMRI data sets from six subjects each 101 times using the infomax algorithm. The first decomposition was taken as a reference decomposition; the others were used to form a component matrix of 100 by 100 components. Equivalence relations between components in this matrix, defined as maximum spatial correlations to the components of the reference decomposition, were found by the Hungarian sorting method and used to form 100 equivalence classes for each data set. We then tested the reproducibility of the matched components in the equivalence classes using uncertainty measures based on component distributions, time courses, and ROC curves. Infomax ICA rarely failed to derive nearly the same components in different decompositions. Very few components per data set were poorly reproduced, even using vector angle uncertainty measures stricter than correlation and detection theory measures. PMID:17281453

Computational mechanics analysis tools for parallel-vector supercomputers

NASA Technical Reports Server (NTRS)

Storaasli, O. O.; Nguyen, D. T.; Baddourah, M. A.; Qin, J.

1993-01-01

Computational algorithms for structural analysis on parallel-vector supercomputers are reviewed. These parallel algorithms, developed by the authors, are for the assembly of structural equations, 'out-of-core' strategies for linear equation solution, massively distributed-memory equation solution, unsymmetric equation solution, general eigen-solution, geometrically nonlinear finite element analysis, design sensitivity analysis for structural dynamics, optimization algorithm and domain decomposition. The source code for many of these algorithms is available from NASA Langley.

GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations.

PubMed

Jung, Jaewoon; Mori, Takaharu; Kobayashi, Chigusa; Matsunaga, Yasuhiro; Yoda, Takao; Feig, Michael; Sugita, Yuji

2015-07-01

GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310-323. doi: 10.1002/wcms.1220.

A projection method for low speed flows

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

Colella, P.; Pao, K.

The authors propose a decomposition applicable to low speed, inviscid flows of all Mach numbers less than 1. By using the Hodge decomposition, they may write the velocity field as the sum of a divergence-free vector field and a gradient of a scalar function. Evolution equations for these parts are presented. A numerical procedure based on this decomposition is designed, using projection methods for solving the incompressible variables and a backward-Euler method for solving the potential variables. Numerical experiments are included to illustrate various aspects of the algorithm.

Self-similar pyramidal structures and signal reconstruction

NASA Astrophysics Data System (ADS)

Benedetto, John J.; Leon, Manuel; Saliani, Sandra

1998-03-01

Pyramidal structures are defined which are locally a combination of low and highpass filtering. The structures are analogous to but different from wavelet packet structures. In particular, new frequency decompositions are obtained; and these decompositions can be parameterized to establish a correspondence with a large class of Cantor sets. Further correspondences are then established to relate such frequency decompositions with more general self- similarities. The role of the filters in defining these pyramidal structures gives rise to signal reconstruction algorithms, and these, in turn, are used in the analysis of speech data.

Implementation of High Time Delay Accuracy of Ultrasonic Phased Array Based on Interpolation CIC Filter

PubMed Central

Liu, Peilu; Li, Xinghua; Li, Haopeng; Su, Zhikun; Zhang, Hongxu

2017-01-01

In order to improve the accuracy of ultrasonic phased array focusing time delay, analyzing the original interpolation Cascade-Integrator-Comb (CIC) filter, an 8× interpolation CIC filter parallel algorithm was proposed, so that interpolation and multichannel decomposition can simultaneously process. Moreover, we summarized the general formula of arbitrary multiple interpolation CIC filter parallel algorithm and established an ultrasonic phased array focusing time delay system based on 8× interpolation CIC filter parallel algorithm. Improving the algorithmic structure, 12.5% of addition and 29.2% of multiplication was reduced, meanwhile the speed of computation is still very fast. Considering the existing problems of the CIC filter, we compensated the CIC filter; the compensated CIC filter’s pass band is flatter, the transition band becomes steep, and the stop band attenuation increases. Finally, we verified the feasibility of this algorithm on Field Programming Gate Array (FPGA). In the case of system clock is 125 MHz, after 8× interpolation filtering and decomposition, time delay accuracy of the defect echo becomes 1 ns. Simulation and experimental results both show that the algorithm we proposed has strong feasibility. Because of the fast calculation, small computational amount and high resolution, this algorithm is especially suitable for applications with high time delay accuracy and fast detection. PMID:29023385

Implementation of High Time Delay Accuracy of Ultrasonic Phased Array Based on Interpolation CIC Filter.

PubMed

Liu, Peilu; Li, Xinghua; Li, Haopeng; Su, Zhikun; Zhang, Hongxu

2017-10-12

In order to improve the accuracy of ultrasonic phased array focusing time delay, analyzing the original interpolation Cascade-Integrator-Comb (CIC) filter, an 8× interpolation CIC filter parallel algorithm was proposed, so that interpolation and multichannel decomposition can simultaneously process. Moreover, we summarized the general formula of arbitrary multiple interpolation CIC filter parallel algorithm and established an ultrasonic phased array focusing time delay system based on 8× interpolation CIC filter parallel algorithm. Improving the algorithmic structure, 12.5% of addition and 29.2% of multiplication was reduced, meanwhile the speed of computation is still very fast. Considering the existing problems of the CIC filter, we compensated the CIC filter; the compensated CIC filter's pass band is flatter, the transition band becomes steep, and the stop band attenuation increases. Finally, we verified the feasibility of this algorithm on Field Programming Gate Array (FPGA). In the case of system clock is 125 MHz, after 8× interpolation filtering and decomposition, time delay accuracy of the defect echo becomes 1 ns. Simulation and experimental results both show that the algorithm we proposed has strong feasibility. Because of the fast calculation, small computational amount and high resolution, this algorithm is especially suitable for applications with high time delay accuracy and fast detection.

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

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

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach and paper, the theoretical modeling and scalingmore » laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. Finally, these two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.« less

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

DOE PAGES

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt; ...

2017-11-27

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach and paper, the theoretical modeling and scalingmore » laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. Finally, these two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.« less

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

Approximating the 0-1 Multiple Knapsack Problem with Agent Decomposition and Market Negotiation

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

Smolinski, B.

The 0-1 multiple knapsack problem appears in many domains from financial portfolio management to cargo ship stowing. Methods for solving it range from approximate algorithms, such as greedy algorithms, to exact algorithms, such as branch and bound. Approximate algorithms have no bounds on how poorly they perform and exact algorithms can suffer from exponential time and space complexities with large data sets. This paper introduces a market model based on agent decomposition and market auctions for approximating the 0-1 multiple knapsack problem, and an algorithm that implements the model (M(x)). M(x) traverses the solution space rather than getting caught inmore » a local maximum, overcoming an inherent problem of many greedy algorithms. The use of agents ensures that infeasible solutions are not considered while traversing the solution space and that traversal of the solution space is not just random, but is also directed. M(x) is compared to a bound and bound algorithm (BB) and a simple greedy algorithm with a random shuffle (G(x)). The results suggest that M(x) is a good algorithm for approximating the 0-1 Multiple Knapsack problem. M(x) almost always found solutions that were close to optimal in a fraction of the time it took BB to run and with much less memory on large test data sets. M(x) usually performed better than G(x) on hard problems with correlated data.« less

The Exact Solution to Rank-1 L1-Norm TUCKER2 Decomposition

NASA Astrophysics Data System (ADS)

Markopoulos, Panos P.; Chachlakis, Dimitris G.; Papalexakis, Evangelos E.

2018-04-01

We study rank-1 {L1-norm-based TUCKER2} (L1-TUCKER2) decomposition of 3-way tensors, treated as a collection of $N$ $D \\times M$ matrices that are to be jointly decomposed. Our contributions are as follows. i) We prove that the problem is equivalent to combinatorial optimization over $N$ antipodal-binary variables. ii) We derive the first two algorithms in the literature for its exact solution. The first algorithm has cost exponential in $N$; the second one has cost polynomial in $N$ (under a mild assumption). Our algorithms are accompanied by formal complexity analysis. iii) We conduct numerical studies to compare the performance of exact L1-TUCKER2 (proposed) with standard HOSVD, HOOI, GLRAM, PCA, L1-PCA, and TPCA-L1. Our studies show that L1-TUCKER2 outperforms (in tensor approximation) all the above counterparts when the processed data are outlier corrupted.

Continuous-variable quantum Gaussian process regression and quantum singular value decomposition of nonsparse low-rank matrices

NASA Astrophysics Data System (ADS)

Das, Siddhartha; Siopsis, George; Weedbrook, Christian

2018-02-01

With the significant advancement in quantum computation during the past couple of decades, the exploration of machine-learning subroutines using quantum strategies has become increasingly popular. Gaussian process regression is a widely used technique in supervised classical machine learning. Here we introduce an algorithm for Gaussian process regression using continuous-variable quantum systems that can be realized with technology based on photonic quantum computers under certain assumptions regarding distribution of data and availability of efficient quantum access. Our algorithm shows that by using a continuous-variable quantum computer a dramatic speedup in computing Gaussian process regression can be achieved, i.e., the possibility of exponentially reducing the time to compute. Furthermore, our results also include a continuous-variable quantum-assisted singular value decomposition method of nonsparse low rank matrices and forms an important subroutine in our Gaussian process regression algorithm.

Optimal Wavelengths Selection Using Hierarchical Evolutionary Algorithm for Prediction of Firmness and Soluble Solids Content in Apples

USDA-ARS?s Scientific Manuscript database

Hyperspectral scattering is a promising technique for rapid and noninvasive measurement of multiple quality attributes of apple fruit. A hierarchical evolutionary algorithm (HEA) approach, in combination with subspace decomposition and partial least squares (PLS) regression, was proposed to select o...

Recursive inverse factorization.

PubMed

Rubensson, Emanuel H; Bock, Nicolas; Holmström, Erik; Niklasson, Anders M N

2008-03-14

A recursive algorithm for the inverse factorization S(-1)=ZZ(*) of Hermitian positive definite matrices S is proposed. The inverse factorization is based on iterative refinement [A.M.N. Niklasson, Phys. Rev. B 70, 193102 (2004)] combined with a recursive decomposition of S. As the computational kernel is matrix-matrix multiplication, the algorithm can be parallelized and the computational effort increases linearly with system size for systems with sufficiently sparse matrices. Recent advances in network theory are used to find appropriate recursive decompositions. We show that optimization of the so-called network modularity results in an improved partitioning compared to other approaches. In particular, when the recursive inverse factorization is applied to overlap matrices of irregularly structured three-dimensional molecules.

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.

Orthogonal recursive bisection data decomposition for high performance computing in cardiac model simulations: dependence on anatomical geometry.

PubMed

Reumann, Matthias; Fitch, Blake G; Rayshubskiy, Aleksandr; Keller, David U J; Seemann, Gunnar; Dossel, Olaf; Pitman, Michael C; Rice, John J

2009-01-01

Orthogonal recursive bisection (ORB) algorithm can be used as data decomposition strategy to distribute a large data set of a cardiac model to a distributed memory supercomputer. It has been shown previously that good scaling results can be achieved using the ORB algorithm for data decomposition. However, the ORB algorithm depends on the distribution of computational load of each element in the data set. In this work we investigated the dependence of data decomposition and load balancing on different rotations of the anatomical data set to achieve optimization in load balancing. The anatomical data set was given by both ventricles of the Visible Female data set in a 0.2 mm resolution. Fiber orientation was included. The data set was rotated by 90 degrees around x, y and z axis, respectively. By either translating or by simply taking the magnitude of the resulting negative coordinates we were able to create 14 data set of the same anatomy with different orientation and position in the overall volume. Computation load ratios for non - tissue vs. tissue elements used in the data decomposition were 1:1, 1:2, 1:5, 1:10, 1:25, 1:38.85, 1:50 and 1:100 to investigate the effect of different load ratios on the data decomposition. The ten Tusscher et al. (2004) electrophysiological cell model was used in monodomain simulations of 1 ms simulation time to compare performance using the different data sets and orientations. The simulations were carried out for load ratio 1:10, 1:25 and 1:38.85 on a 512 processor partition of the IBM Blue Gene/L supercomputer. Th results show that the data decomposition does depend on the orientation and position of the anatomy in the global volume. The difference in total run time between the data sets is 10 s for a simulation time of 1 ms. This yields a difference of about 28 h for a simulation of 10 s simulation time. However, given larger processor partitions, the difference in run time decreases and becomes less significant. Depending on the processor partition size, future work will have to consider the orientation of the anatomy in the global volume for longer simulation runs.

Task Decomposition Module For Telerobot Trajectory Generation

NASA Astrophysics Data System (ADS)

Wavering, Albert J.; Lumia, Ron

1988-10-01

A major consideration in the design of trajectory generation software for a Flight Telerobotic Servicer (FTS) is that the FTS will be called upon to perform tasks which require a diverse range of manipulator behaviors and capabilities. In a hierarchical control system where tasks are decomposed into simpler and simpler subtasks, the task decomposition module which performs trajectory planning and execution should therefore be able to accommodate a wide range of algorithms. In some cases, it will be desirable to plan a trajectory for an entire motion before manipulator motion commences, as when optimizing over the entire trajectory. Many FTS motions, however, will be highly sensory-interactive, such as moving to attain a desired position relative to a non-stationary object whose position is periodically updated by a vision system. In this case, the time-varying nature of the trajectory may be handled either by frequent replanning using updated sensor information, or by using an algorithm which creates a less specific state-dependent plan that determines the manipulator path as the trajectory is executed (rather than a priori). This paper discusses a number of trajectory generation techniques from these categories and how they may be implemented in a task decompo-sition module of a hierarchical control system. The structure, function, and interfaces of the proposed trajectory gener-ation module are briefly described, followed by several examples of how different algorithms may be performed by the module. The proposed task decomposition module provides a logical structure for trajectory planning and execution, and supports a large number of published trajectory generation techniques.

Finding Hierarchical and Overlapping Dense Subgraphs using Nucleus Decompositions

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

Seshadhri, Comandur; Pinar, Ali; Sariyuce, Ahmet Erdem

Finding dense substructures in a graph is a fundamental graph mining operation, with applications in bioinformatics, social networks, and visualization to name a few. Yet most standard formulations of this problem (like clique, quasiclique, k-densest subgraph) are NP-hard. Furthermore, the goal is rarely to nd the \\true optimum", but to identify many (if not all) dense substructures, understand their distribution in the graph, and ideally determine a hierarchical structure among them. Current dense subgraph nding algorithms usually optimize some objective, and only nd a few such subgraphs without providing any hierarchy. It is also not clear how to account formore » overlaps in dense substructures. We de ne the nucleus decomposition of a graph, which represents the graph as a forest of nuclei. Each nucleus is a subgraph where smaller cliques are present in many larger cliques. The forest of nuclei is a hierarchy by containment, where the edge density increases as we proceed towards leaf nuclei. Sibling nuclei can have limited intersections, which allows for discovery of overlapping dense subgraphs. With the right parameters, the nuclear decomposition generalizes the classic notions of k-cores and k-trusses. We give provable e cient algorithms for nuclear decompositions, and empirically evaluate their behavior in a variety of real graphs. The tree of nuclei consistently gives a global, hierarchical snapshot of dense substructures, and outputs dense subgraphs of higher quality than other state-of-theart solutions. Our algorithm can process graphs with tens of millions of edges in less than an hour.« less

[Detection of constitutional types of EEG using the orthogonal decomposition method].

PubMed

Kuznetsova, S M; Kudritskaia, O V

1987-01-01

The authors present an algorithm of investigation into the processes of brain bioelectrical activity with the help of an orthogonal decomposition device intended for the identification of constitutional types of EEGs. The method has helped to effectively solve the task of the diagnosis of constitutional types of EEGs, which are determined by a variable degree of hereditary predisposition for longevity or cerebral stroke.

The development of a scalable parallel 3-D CFD algorithm for turbomachinery. M.S. Thesis Final Report

NASA Technical Reports Server (NTRS)

Luke, Edward Allen

1993-01-01

Two algorithms capable of computing a transonic 3-D inviscid flow field about rotating machines are considered for parallel implementation. During the study of these algorithms, a significant new method of measuring the performance of parallel algorithms is developed. The theory that supports this new method creates an empirical definition of scalable parallel algorithms that is used to produce quantifiable evidence that a scalable parallel application was developed. The implementation of the parallel application and an automated domain decomposition tool are also discussed.

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.

Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences. The LLSG algorithm

NASA Astrophysics Data System (ADS)

Gomez Gonzalez, C. A.; Absil, O.; Absil, P.-A.; Van Droogenbroeck, M.; Mawet, D.; Surdej, J.

2016-05-01

Context. Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or a wavefront control system, and it is intertwined with the chosen observing strategy. Among the data processing techniques for angular differential imaging (ADI), the most recent is the family of principal component analysis (PCA) based algorithms. It is a widely used statistical tool developed during the first half of the past century. PCA serves, in this case, as a subspace projection technique for constructing a reference point spread function (PSF) that can be subtracted from the science data for boosting the detectability of potential companions present in the data. Unfortunately, when building this reference PSF from the science data itself, PCA comes with certain limitations such as the sensitivity of the lower dimensional orthogonal subspace to non-Gaussian noise. Aims: Inspired by recent advances in machine learning algorithms such as robust PCA, we aim to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys. Methods: We used randomized low-rank approximation methods recently proposed in the machine learning literature, coupled with entry-wise thresholding to decompose an ADI image sequence locally into low-rank, sparse, and Gaussian noise components (LLSG). This local three-term decomposition separates the starlight and the associated speckle noise from the planetary signal, which mostly remains in the sparse term. We tested the performance of our new algorithm on a long ADI sequence obtained on β Pictoris with VLT/NACO. Results: Compared to a standard PCA approach, LLSG decomposition reaches a higher signal-to-noise ratio and has an overall better performance in the receiver operating characteristic space. This three-term decomposition brings a detectability boost compared to the full-frame standard PCA approach, especially in the small inner working angle region where complex speckle noise prevents PCA from discerning true companions from noise.