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Against the backdrop of increasing downward pressure on the global economy, domestic economic development also faces many uncertainties, especially the production of the industrial sector, which responds more quickly and sensitively to external shocks and internal adjustments, making it more difficult to predict industrial added value. Electricity, as an important terminal consumption energy, is a barometer of economic prosperity. Using a decomposition integration approach, a combination prediction method based on LSTM models with electricity consumption data is constructed, and short-term predictions of provincial industrial added value are made. The results show that the EMD-LSTM model with external data can achieve better short-term prediction results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Information hiding is an important technique for security-related applications, and it can be used wildly in many scenarios. Secret data embedding methods are proposed in spatial, frequency, and other domains by researchers in recent years. After investigating the existing information hiding schemes, we find that there is still room to improve the data embedding performance. Inspired by some works of literature, we propose a new scheme based on the modulus function with Minimum Pixel Modification (MPM22) for hiding secret data into carriers. The main idea of the scheme is to embed confidential bits into cover-image pixels while minimizing modification to original pixels. The correctness and high performance of the scheme are proved in mathematics and comprehensive experiments. Experimental result shows MPM22 has the largest embedding capacity (3 < bpp (bit per pixel) less than 7) compared with other schemes while maintaining the best image quality (peak signal-to-noise ratio (PSNR) > 36.1 dB@ k = 3 , PSNR > 30.5 dB @ k = 4)。 The experimental result also confirms that our proposed scheme has higher performance than other schemes. [ABSTRACT FROM AUTHOR]

Background: Despite the large body of evidence on the efficacy of enamel matrix derivative (EMD) in the treatment of periodontal intrabony defects, few studies reported long-term data (≥10-year).Methods: Periodontal patients treated with regenerative surgery with EMD between 1999 and 2012 were invited to participate in a clinical examination. The following clinical parameters were recorded and compared at baseline (T0), 6 months after surgery (T1) and after at least 8 years of follow-up (T2): probing depth (PD), gingival recession (GR), clinical attachment level (CAL), plaque and bleeding scores. The primary outcome variable was CAL change.Results: Forty-one patients with 75 treated teeth were available for analysis. Out of these, 68 (tooth survival rate: 90.7%) reached the latest follow-up with a mean observation period of 10.3 years (range: 8.0 to 21.3). The most frequent reason for tooth loss was recurrence of periodontal disease. Tooth survival curves showed a statistically significant difference between smokers and non-smokers (P = 0.028). Mean CAL changed from 8.43 ± 1.86 (T0) to 6.47 ± 1.70 (T1) (P < 0.001) and to 5.91 ± 1.83 (T2) (P < 0.001). At T1, a CAL gain of ≥3 mm was measured in 35% of the defects whereas at T2 it was detected in 51% of cases.Conclusions: Within their limitations, the present results have shown that in intrabony defects, the clinical improvements obtained following regenerative surgery with EMD can be maintained on a mean period of 10 years. Smoking status and maxillary molars were correlated with an increased risk for tooth and CAL loss, respectively. [ABSTRACT FROM AUTHOR]

Due to the physical characteristics that the magnetic dipole target signal (MDTS) decays with the third power of distance and the fact that the measured data contain usually environmental magnetic noise such as diurnal variation noise and cultural noise, it is very difficult to detect long-distance magnetic targets. In this paper, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm which is an improved version of empirical mode decomposition (EMD) algorithm and pattern recognition algorithm are combined to construct a novel magnetic target detection algorithm(CEEMDAN-PR). CEEMDAN algorithm can effectively decompose the measured magnetic signal into multiple intrinsic mode functions (IMFs), and reduce the aliasing effect between modes. Then, the pattern formed by the characteristics of magnetic dipole signal is used to match the signal reconstructed by the sum of several IMFs to obtain the optimal reconstructed signal. Some simulation tests illustrate that this algorithm has good detection performance. [ABSTRACT FROM AUTHOR]

Alcoholism can be analyzed by Electroencephalogram (EEG) data. Finding an optimal subset of EEG channels for alcoholism detection is a challenging task. The paper reports a new methodology for the detection of optimal channels for alcoholism analysis using EEG data. The proposed technique employs the Empirical Mode Decomposition (EMD) technique to extract the amplitude and frequency modulated bandwidth features from the Intrinsic Mode Function (IMF) and ensemble subspace K-NN as a classifier to classify alcoholics and normal. The optimum channels are selected, using a harmony search algorithm. The fitness value of discrete binary harmony search (DBHS) optimization algorithms is calculated using accuracy and sensitivity achieved by the ensemble subspace K-Nearest Neighbor classifier. Experimental outcomes indicate that the optimal channel selected by the harmony search algorithm has biological inference related to the alcoholic subject. The proposed approach reports a classification accuracy of 93.87%, with only 12 detected EEG channels. [ABSTRACT FROM AUTHOR]

• A classification method of multiple cardiovascular diseases based on modified ECG. • Modified ECG, after EMD analysis, has better denoising performance than the raw ECG. • 1-D CNN learns the inherent features of the modified ECG better than the raw ECG. • Experimented on three public databases and the method outperforms existing methods. • The modified ECG signal is trained faster than any IMF and combinations of IMFs. Multiple cardiovascular disease classification from Electrocardiogram (ECG) signal is necessary for efficient and fast remedial treatment of the patient. This paper presents a method to classify multiple heart diseases using one dimensional deep convolutional neural network (CNN) where a modified ECG signal is given as an input signal to the network. Each ECG signal is first decomposed through Empirical Mode Decomposition (EMD) and higher order Intrinsic Mode Functions (IMFs) are combined to form a modified ECG signal. It is believed that the use of EMD would provide a broader range of information and can provide denoising performance. This processed signal is fed into the CNN architecture that classifies the record according to cardiovascular diseases using softmax regressor at the end of the network. It is observed that the CNN architecture learns the inherent features of the modified ECG signal better in comparison with the raw ECG signal. The method is applied on three publicly available ECG databases and it is found to be superior to other approaches in terms of classification accuracy. In MIT-BIH, St.-Petersberg, PTB databases the proposed method achieves maximum accuracy of 97.70%, 99.71%, and 98.24%, respectively. [ABSTRACT FROM AUTHOR]

Modern control applications justify the need for improved techniques capable of coping with the non-stationary nature of measured signals while being able to monitor systems in real-time. Empirical Mode Decomposition (EMD) is known for its efficiency in time domain analysis of multi-component signals through Intrinsic Mode Functions (IMFs) extraction. Recent years witnessed the introduction of Sliding Window EMD (SWEMD) capable of analyzing signals in real time applications. However, complex signals require several sifting iterations while a rather increased number of IMFs might result in impracticality for on-line applications. This paper introduces a new modified faster SWEMD capable of extracting harmonics from non-stationary signals in real-time operation. The method uses the traditional EMD properties in the first pass for a small number of sifting processes. In addition, a new section is added to the algorithm based on inflection point tracking of the residue derivative from the first pass is added, in order to track low frequency waves and render the analysis faster. The method is validated for non-stationary signals with and without added colored noise and applied on measured turbine side angular velocity for harmonic extraction in wind turbines as an application. The proposed method may well be used for fault detection and disturbance rejection in mechanical systems. [ABSTRACT FROM AUTHOR]

Precipitation is affected by a number of factors and the data may be presented as unsteady nonlinear time series. Through the establishment of nonlinear time series data model to solve the problem of precipitation forecast, the method of Empirical Model Decomposition (EMD) may be employed to the data to generate a series of Intrinsic Mode Functions (IMFs) that reflect the evolution of the time series; upon Hilbert transformation of the IMFs, nonlinear multiscale mathematical models may be established based on the transformed amplitude and frequency of the IMFs components. These procedures were applied to the precipitation prediction in Yellow River Delta that was chosen by this study as a research example. After EMD and Hilbert transformation, two multiscale mathematical models for the precipitation prediction are established, and proved to have the modeling precision, modeling residuals and statistical significance that all passed a validation test. Multiscale precipitation information may be extracted from the modeling results and applied to precipitation forecast with high precision and low relative standard deviation; moreover, the models may be applied to predict the evolution of nonlinear time series. Because of these advantages, the models are expected to find wide applications. [ABSTRACT FROM AUTHOR]

Analysis of long-term change trend of vegetation covers faces several challenges. First, the change of vegetation covers exhibits remarkable cyclical oscillations and disturbances, which often mask the long-term trend. Second, the remote sensing image based time-series analysis is often complicated by the lack of long-series ground reference data. In addition, current remote sensing based vegetation change analysis focuses on continental, national or regional scales. Local scale analysis of vegetation changes is often neglected. In this paper, we developed a synthesized analysis framework, the domain adaptive and classified smoothing - DACS, to tackle these limitations. We integrated domain adaptive learning with the classified maps of vegetation groups (VGs) to derive the ground reference data supporting the image-based time-series analysis and to support local scale spatial analysis. We applied empirical mode decomposition model to extract long-term trends of VGs NDVI changes. We employed univariate polynomial regression to examine the long-term change trends of VGs and to identify their spatial and temporal patterns. We tested this new algorithm by analyzing the SPOT-VEG NDVI 10-days composites from 1999 to 2007 in Wulate, Inner Mongolia, China. The findings confirmed that the newly developed DACS method effectively captured the long-term trends of NDVI change over various VGs and revealed that the regional trend of NDVI change differed from the local trends of change of different VGs. DACS is suitable to other image sources, such as MODIS and Landsat images, and can be applied in other regions for local scale time-series analysis of vegetation cover changes. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a widely employed tool for the analysis of cardiac disorders. A clean ECG is often desired for proper treatment of cardiac ailments. However, in the real scenario, ECG signals are corrupted with various noises during acquisition and transmission. In this article, an efficient ECG denoising methodology using combined empirical mode decomposition (EMD) and adaptive switching mean filter (ASMF) is proposed. The advantages of both EMD and ASMF techniques are exploited to reduce the noises in the ECG signals with minimum distortion. Unlike conventional EMD based techniques, which reject the initial intrinsic mode functions (IMFs) or utilize a window based approach for reducing high-frequency noises, here, a wavelet based soft thresholding scheme is adopted for reduction of high-frequency noises and preservation of QRS complexes. Subsequently, an ASMF operation is performed to enhance the signal quality further. The ECG signals of standard MIT-BIH database are used for the simulation study. Three types of noises in particular white Gaussian noise, Electromyogram (EMG) and power line interference contaminate the test ECG signals. Three standard performance metrics namely output SNR improvement, mean square error, and percentage root mean square difference measure the efficacy of the proposed technique at various signal to noise ratio (SNR). The proposed denoising methodology is compared with other existing ECG denoising approaches. A detail qualitative and quantitative study and analysis indicate that the proposed technique can be used as an effective tool for denoising of ECG signals and hence can serve for better diagnostic in computer-based automated medical system. [ABSTRACT FROM AUTHOR]

The Electroencephalogram signals are characterized by mental status of human body. The electrical activity of this signal is non-linear and non-stationary in nature. In this work, the EEG signal is to undergo empirical mode decomposition which yields a set of intrinsic mode functions. Taking into account the non-linear nature of the EEG signal, state space analysis of the IMFs is carried out, which eventually leads to an outstanding classification into ictal EEG and healthy EEG class. Here Kalman filter is used to take the state estimation of each IMFs. Then temporal and statically features of each IMFs are extracted. The adaptive neuro-fuzzy inference system is used to classify ictal EEG and healthy EEG status. The study shows Teager energy and Kurtosis are 100% accuracy in classification. Apart from this, combination of Kurtosis and standard deviation features shows 100% in sensitivity, specificity and accuracy achieved in classification of different ictal stages. [ABSTRACT FROM AUTHOR]

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

In order to analyze the combustion characteristic of the internal-combustion engine, combustion noise signal was collected, replacing the cylinder pressure signal in this paper. An acoustic signal feature extraction method using empirical mode decomposition (EMD) and wavelet analysis (WA) was proposed. Time synchronous average (TSA) is used for filtering interference noise to enhance the signal-to-noise ratio (SNR) of the measured acoustic signal as noise will affect the decomposition process resulting in over decomposition. The processed acoustic signals were decomposed into a series of intrinsic mode functions (IMF) using the method of empirical mode decomposition (EMD) in the time domain. Then wavelet analysis which has good time-frequency localization feature was applied on useful IMFs containing the information of diesel engine combustion. The root mean square (RMS) value of wavelet analysis results was calculated to achieve linear state monitoring. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine distinctive features related to sleep stages (wake, light sleep, deep sleep) from heart rate variability (HRV) and evaluate their usefulness in classifying sleep stages. To this end, we looked into DFA alpha1 sequence (DFAseq) in which DFA (Detrended fluctuation analysis) alpha1 values are calculated for every R points of HRV signal over time. This DFAseq can be interpreted as a variation of the fractal property of HRV over time. To investigate the DFAseq data, we reduced noise component from them using the empirical mode decomposition (EMD) method. For experiments, we used the CAP-sleep database from PhysioNet and evaluated the relevance between the noise-reduced DFA alpha1 sequence (NR-DFAseq) and the sleep stage sequence. Our results on 13 subjects showed that the correlation coefficient between sleep stage sequence and NR-DFAseq are 0.65 on the average, while the correlation coefficient without noise reduction is 0.42. For sleep stage classification, we constructed a prediction model that distinguish wake stage from the other sleep stages based on NR-DFAseq, and obtained 77% of sensitivity and 73% of specificity from the model. In addition, we constructed a model that distinguishes deep sleep from light sleep, and obtained 72% of the classification accuracy from the model. Interestingly, our prediction results only with NR-DFAseq are better than some recent study that employed multiple features extracted from heart beat signals. Therefore, it might be worthwhile to see that the noised-reduced fractal property of HRV could be highly correlated with the sleep stage. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signal serves is a powerful tool in epilepsy detection. This study decomposes intrinsic mode functions (IMFs) into amplitude envelope and frequency functions on a time-scale basis using the analytic function of Hilbert transform. IMFs results from the empirical mode decomposition of EEG signals. Features such as energy and entropy parameters were calculated from the amplitude contour of each IMF. Other features, such as interquartile range, mean absolute deviation and standard deviation are also computed for their instantaneous frequencies. Discriminative features were extracted using a large database to classify healthy and ictal EEG signals. Normal EEG segments were differentiated from the seizure attack in individual IMF features, multiple features with individual IMF, and individual features with multiple IMFs. Discriminating capability of three Cases was tested. (i) Artificial neural network and (ii) adaptive neuro-fuzzy inference system classification were used to identify EEG segments with seizure attacks. ANOVA was used to analyze statistical performance. Energy and entropy-based features of instantaneous amplitude and standard deviation of instantaneous frequency of IMF2 and IMF1 have 100% accuracy, sensitivity, and specificity. Good performance with a single feature that represents information of the whole data was obtained. The result involved less complicated computation than other time–frequency analysis techniques. [ABSTRACT FROM AUTHOR]

Denoising of electrooculography (EOG) signals is a challenging task as the noise and signal share the same frequency band. This paper proposes a two-stage framework for denoising EOG signals. The first stage approach is based on preserving the nature of eye movements while the second stage is based on the nature of noise (Gaussian or not). In the first stage, denoising is carried out using one out of four filtering methods, each filter being optimal for a particular EOG pattern. The four methods used in the first stage are linear bandpass filtering, stationary wavelet transform (SWT), empirical mode decomposition (EMD) and median filtering. The Stage I framework selects the output that provides the highest estimated signal to noise ratio (SNR). In case, the Stage I filtering does not provide a significant SNR, the system uses Stage II filtering. In the second stage, we use two recursive state estimators, i.e. a Kalman filter and a particle filter for further denoising. The two-stage method is found to provide a better SNR as compared to a single stage method. [ABSTRACT FROM AUTHOR]

Deep learning has achieved promising results on a broad spectrum of tasks using an end-to-end approach, and domain-specific knowledge can be used to supplement it by either constraining the solution-space, or to transform data such that relevant signals are more influential during the training process. This is especially important in the context of continuous recordings or wearable-based monitoring, where signal quality may be constrained by hardware, such as a limited number of available electrodes, or by external perturbations. In this study, a global feature extraction method for multi-periodic signals is proposed, which effectively classifies multi-cycle normal and abnormal heartbeats. The abnormal heartbeats are classed as the Left Bundle Branch Block beat (LBBB), Right Bundle Branch Block beat (RBBB), and Paced beat (P). The signal analysis process of the electrocardiogram (ECG) recordings consist of three main stages: (I) segmentation of the ECG data and partitioning of the data set; (II) generation of an overall feature map representing the "heartbeat condition" based on Empirical Mode Decomposition (EMD); and (III) a classification stage for determining the patient's heartbeat condition. The extracted feature image is used to classify the heartbeat condition using a two-dimensional Convolutional Neural Network (CNN). This method is applied to the publicly available MIT-BIH arrhythmia database. Experimental results show that the reconstructed ECG features outperform use of the raw ECG signals. Under self-test conditions of 3 min of ECG signal, the Total Classification Accuracy (TCA) was approximately 99.01%, while the TCA without the proposed method was approximately 90.69%. • Necessary operations related to partial filtering and denoising can be omitted. • Preprocessing operations are reduced and the difficulty of preprocessing is reduced. • Overcome the shortcoming that instantaneous heart rate is easy to be misclassified. • Improve the classification accuracy of personal ECG. • Helps reduce the false positive rate of ECG recognition of wearable detection devices. [ABSTRACT FROM AUTHOR]

Deep learning has achieved promising results on a broad spectrum of tasks using an end-to-end approach, and domain-specific knowledge can be used to supplement it by either constraining the solution-space, or to transform data such that relevant signals are more influential during the training process. This is especially important in the context of continuous recordings or wearable-based monitoring, where signal quality may be constrained by hardware, such as a limited number of available electrodes, or by external perturbations. In this study, a global feature extraction method for multi-periodic signals is proposed, which effectively classifies multi-cycle normal and abnormal heartbeats. The abnormal heartbeats are classed as the Left Bundle Branch Block beat (LBBB), Right Bundle Branch Block beat (RBBB), and Paced beat (P). The signal analysis process of the electrocardiogram (ECG) recordings consist of three main stages: (I) segmentation of the ECG data and partitioning of the data set; (II) generation of an overall feature map representing the "heartbeat condition" based on Empirical Mode Decomposition (EMD); and (III) a classification stage for determining the patient's heartbeat condition. The extracted feature image is used to classify the heartbeat condition using a two-dimensional Convolutional Neural Network (CNN). This method is applied to the publicly available MIT-BIH arrhythmia database. Experimental results show that the reconstructed ECG features outperform use of the raw ECG signals. Under self-test conditions of 3 min of ECG signal, the Total Classification Accuracy (TCA) was approximately 99.01%, while the TCA without the proposed method was approximately 90.69%. • Necessary operations related to partial filtering and denoising can be omitted. • Preprocessing operations are reduced and the difficulty of preprocessing is reduced. • Overcome the shortcoming that instantaneous heart rate is easy to be misclassified. • Improve the classification accuracy of personal ECG. • Helps reduce the false positive rate of ECG recognition of wearable detection devices. [ABSTRACT FROM AUTHOR]

In this paper, we aim to analyse the impact of training on improvement in cognitive abilities and performance of the subjects playing single player action video game. Recent research indicates that playing Action video games improve cognitive abilities. However no study has exploited the novel technique, Empirical Mode Decomposition in the field of action video games. Empirical mode decomposition was used to extract various features by decomposing EEG data into intrinsic mode functions. Intrinsic mode functions were used to calculate linear features like standard deviation, phase and energy. K- Nearest Neighbour & Linear Discriminant Analysis classifiers were used to classify the subject based on the changes in features extracted due to the impact of training. Psychological tests conducted before and after the training, positively affirm that training improves cognitive abilities like reaction time and reduces stress level. [ABSTRACT FROM AUTHOR]

Microwave Doppler radar has received considerable attention as a non-contact form of measuring human respiration; in particular for long term monitoring. One of the main challenges in converting this into a viable application is to suppress or separate the artefacts and other interfering signals from the desired respiration signal using a less complex and practically feasible design for regular and potentially real time use. Existing systems either require complex experimental setups or multiple Doppler radar modules to achieve this. In this paper, we propose an approach based on EMD-ICA and approximate entropy ideas to systematically separate received Doppler shifted signal into distinct components and reconstruct the desired respiration pattern pertaining to respective physiological activity. Indeed this allows suppression of the undesirable artefacts and interference from other competing signals. Practical experiments confirmed comparable performance of the proposed method to the measurements obtained through chest straps which are widely used clinically for monitoring respiration. [ABSTRACT FROM AUTHOR]

An automatic sleep scoring method based on single channel electroencephalogram (EEG) is essential not only for alleviating the burden of the clinicians of analyzing a high volume of data but also for making a low-power wearable sleep monitoring system feasible. However, most of the existing works are either multichannel or multiple physiological signal based or yield poor algorithmic performance. In this study, we propound a data-driven and robust automatic sleep staging scheme that uses single channel EEG signal. Decomposing the EEG signal segments using Empirical Mode Decomposition (EMD), we extract various statistical moment based features. The effectiveness of statistical features in the EMD domain is inspected. Statistical analysis is performed for feature selection. We then employ Adaptive Boosting and decision trees to perform classification. The performance of our feature extraction scheme is studied for various choices of classification models. Experimental outcomes manifest that the performance of the proposed sleep staging algorithm is better than that of the state-of-the-art ones. Furthermore, the proposed method's non-REM 1 stage detection accuracy is better than most of the existing works. [ABSTRACT FROM AUTHOR]

Site productivity, as affected by soil nutrients and available moisture is often characterized using an edatopic grid. A challenge for forest ecologists and managers working across large areas and in complex landscapes is the need to identify spatially different ecological environments that follow an edatopic classification. Recent advances in remote sensing offer some potential approaches for mapping ecological environments and landscape conditions. It is now feasible to compile long temporal image sequences using Landsat imagery for reconstruction of forest stands and derivation of long term indices of landscape productivity; and the increasing proliferation of airborne laser scanning (ALS) technology also allows for the acquisition of detailed information on topographic elevation and vegetation structure with sub-meter accuracy. In a large area of boreal mixedwood forest in northwestern Alberta, Canada, we examined the utility of using Landsat-derived vegetation greenness (indicating productivity), and ALS-derived cartographic depth-to-water (indicating moisture), to determine forest cover type and vegetation responses following variable retention harvesting. Our results demonstrate that both long-term image sequences from Landsat and ALS-derived topography and vegetation structure act as proxies for edatopic grid components, and are well-suited to differentiating forest cover types. Deciduous-dominated, deciduous-dominated with conifer understory, mixed, and conifer-dominated forests were generally distributed (in order) across a gradient of increasing moisture and decreasing greenness. Landscape greenness was the strongest predictor of vegetation regrowth after disturbance, followed by depth to water and other terrain factors, such as elevation and slope. New advances in, and complementary use of, different remotely sensed information provides a better understanding of both the landscape-scale distribution of forest cover types and patterns of vegetation regrowth following disturbance. [ABSTRACT FROM AUTHOR]

This paper proposes a novel progressive secret image-hiding scheme based on the inpainting technique, the vector quantization technique (VQ) and the exploiting modification direction (EMD) technique. The proposed scheme first divides the secret image into non-overlapping blocks and categorizes the blocks into two groups: complex and smooth. The blocks in the complex group are compressed by VQ with PCA sorted codebook to obtain the VQ index table. Instead of embedding the original secret image, the proposed method progressively embeds the VQ index table into the cover images by using the EMD technique. After the receiver recovers the complex parts of the secret image by decoding the VQ index table from the shadow images, the smooth parts can be reconstructed by using the inpainting technique based on the content of the complex parts. The experimental results demonstrate that the proposed scheme not only has the advantage of progressive data hiding, which involves more shadow images joining to recover the secret image so as to produce a higher quality steganography image, but also can achieve high hiding capacity with acceptable recovered image quality. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent diagnosis technique for wind turbine imbalance fault identification based on generator current signals. For this aim, Probabilistic Neural Network (PNN), which is powerful algorithm for classification problems that needs small training time in solving nonlinear problems and applicable to high dimension applications, is employed. The complete dynamics of a permanent magnet synchronous generator (PMSG) based wind-turbine (WTG) model are imitated in an amalgamated domain of Simulink, FAST and TurbSim under six distinct conditions, i.e., aerodynamic asymmetry, rotor furl imbalance, tail furl imbalance, blade imbalance, nacelle-yaw imbalance and normal operating scenarios. The simulation results in time domain of the PMSG stator current are decomposed into the Intrinsic Mode Frequency (IMF) using EMD method, which are utilized as input variable in PNN. The analyzed results proclaim the effectiveness of the proposed approach to identify the healthy condition from imbalance faults in WTG. The presented work renders initial results that are helpful for online condition monitoring and health assessment of WTG. [ABSTRACT FROM AUTHOR]

This paper proposes a simple and reliable Field Programmable Gate Array (FPGA) based ECG Analysis system. Electrocardiogram (ECG) is important biomedical signal, which show the electrical activity of the heart. ECG waveform provides the valuable information for detection of abnormal heart diseases. For accurate analysis, ECG signal must be processed to remove the noise signal. Also, various features of ECG must be extracted for diagnosis of cardiac disorders. Thus, ECG signal processing includes two stages: Preprocessing and Feature Extraction. Preprocessing stage removes noise from the raw ECG signal and the feature extraction stage extracts diagnostic information from the ECG signal. The main objectives of the work are (i) ECG signal enhancement using Empirical mode decomposition (EMD) based method. (ii) Detection of R peaks which is the first step towards automatic detection of cardiac arrhythmias in ECG signal. The proposed system can detect three different arrhythmias. 94.76% accuracy of the proposed method is achieved in detecting the different heart Arrhythmias correctly by using subset of data records from the MIT-BIH database . The system is implemented using Verilog HDL and Xilinx Spartan 3E FPGA respectively. [ABSTRACT FROM AUTHOR]

The random ocean waves have a natural tendencyto form groups of waves that produced due to the interaction of lower and higher frequency wave components. In this paper H ilbert H uang T ransformationis considered, which is the result of the empirical mode decomposition (EMD) and the Hilbert spectral analysis (HSA). HHT uses the EMD method to decompose a signal into so-called intrinsic mode functions, and uses the HSA method to obtain instantaneous frequency data. Using EMD method, any non-stationary data set can be decomposed into a finite and often small number of components, which are intrinsic mode functions ( IMF ). This decomposition method operating in the time domain is adaptive and highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it can be applied to nonlinear and nonstationaryprocesses. This method is applied to the field data collected in the Natural Ocean Engineering Laboratory (NOEL, Reggio Calabria, Italy) and results on the wave groups in crossing sea are discussed. [ABSTRACT FROM AUTHOR]

Understanding landscape patterns that result from natural disturbances in the mixedwood boreal forests of Canada is a critical precursor to advancing sustainable forestry practices and ecosystem-based land management initiatives. However, monitoring changes in boreal forest structure following disturbance is difficult due to restricted access and the spatial scale at which these disturbances occur. Airborne Laser Scanning (ALS) measures the three-dimensional distribution of vegetation across large areas with high sampling intensities, enabling the detection of small changes in vegetation structure in stands of otherwise similar composition and age. In this paper we compare the suitability of three suites of ALS metrics to discriminate changes in vegetation structure across a gradient of forest harvest retention levels (100% retention (uncut control), 75%, 50%, 20%, 10%, and 0% (clear cut)) for four boreal forest stand types: conifer, deciduous, mixed, and deciduous with conifer understorey. Specifically, we focus on three key types of ALS metric: plot-based point cloud metrics, canopy volumes, and curve-fitting approaches; and evaluate the sensitivity of these metrics to changes in forest stand architecture in response to the harvest treatments or lack thereof. From the three types of metrics, height based point cloud metrics show the most significant separation by treatments and stand-types, followed by canopy volume profiles. Airborne laser scanning has strong utility for distinguishing responses in silvicultural treatments and cover types, revealing characteristics not captured by traditional measurements like crown closure or basal area. [ABSTRACT FROM AUTHOR]

Now a days Empirical Mode Decomposition (EMD) is an important tool for image analyzing. Optimizing threshold value of Bidimensional Intrinsic Mode Function (BIMF) is one of the important tasks in speckle noise reduction in the Bidimensional Empirical Mode Decomposition (BEMD) domain. Without proper selection of threshold value image information may be lost, which is unwanted. In this paper we proposed optimum threshold parameter using Fisher Discriminant Analysis (FDA) for determining the optimum threshold value of the Intrinsic Mode Functions (IMF) for the best speckle noise reduction. In the mean time, we used the optimal threshold value for separating the higher frequency signal from BIMF to calculate the mean of these separated signals for alleviating speckle noise. It also preserves edges without loss of important image information. The method is compared with the several other classical thresholding methods on variety of images and the experimental results confirm significant improvement over existing methods. [ABSTRACT FROM AUTHOR]

Because of the chaotic nature and intrinsic complexity of wind speed, it is difficult to describe the moving tendency of wind speed and accurately forecast it. In our study, a novel EMD-ENN approach, a hybrid of empirical mode decomposition (EMD) and Elman neural network (ENN), is proposed to forecast wind speed. First, the original wind speed datasets are decomposed into a collection of intrinsic mode functions (IMFs) and a residue by EMD, yielding relatively stationary sub-series that can be readily modeled by neural networks. Second, both IMF components and residue are applied to establish the corresponding ENN models. Then, each sub-series is predicted using the corresponding ENN. Finally, the prediction values of the original wind speed datasets are calculated by the sum of the forecasting values of every sub-series. Moreover, in the ENN modeling process, the neuron number of the input layer is determined by a partial autocorrelation function. Four prediction cases of wind speed are used to test the performance of the proposed hybrid approach. Compared with the persistent model, back-propagation neural network, and ENN, the simulation results show that the proposed EMD-ENN model consistently has the minimum statistical error of the mean absolute error, mean square error, and mean absolute percentage error. Thus, it is concluded that the proposed approach is suitable for wind speed prediction. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this study was to evaluate the effect of Emdogain (Enamel Matrix Derivative, EMD) and Bone Morphogenetic Protein-2 (BMP-2), either solely or in combination, on the gene expression and mineralized nodule formation of alveolar bone proper-derived stem/progenitor cells. Stem/progenitor cells were isolated from human alveolar bone proper, magnetically sorted using STRO-1 antibodies, characterized flowcytometrically for their surface markers' expression, and examined for colony formation and multilineage differentiation potential. Subsequently, cells were treated over three weeks with 100 μg/ml Emdogain (EMD-Group), or 100 ng/ml BMP-2 (BMP-Group), or a combination of 100 ng/ml BMP-2 and 100 μg/ml Emdogain (BMP/EMD-Group). Unstimulated stem/progenitor cells (MACS+-Group) and osteoblasts (OB-Group) served as controls. Osteogenic gene expression was analyzed using RTq-PCR after 1, 2 and 3 weeks (N = 3/group). Mineralized nodule formation was evaluated by Alizarin-Red staining. BMP and EMD up-regulated the osteogenic gene expression. The BMP Group showed significantly higher expression of Collagen-I, III, and V, Alkaline phosphatase and Osteonectin compared to MACS+- and OB-Group (p < 0.05; Two-way ANOVA/Bonferroni) with no mineralized nodule formation. Under in-vitro conditions, Emdogain and BMP-2 up-regulate the osteogenic gene expression of stem/progenitor cells. The combination of BMP-2 and Emdogain showed no additive effect and would not be recommended for a combined clinical stimulation. [Copyright &y& Elsevier]

The goal of the present clinical study was to evaluate new bone formation in human extraction sockets augmented with enamel matrix derivatives (EMD) and Bio-Oss Collagen. Patients with symmetrical single-rooted teeth in the bilateral quadrants of the upper jaw condemned for extraction participated in this study. Following extraction, the sockets (20 sockets) were randomly augmented using either EMD or Bio-Oss Collagen. After 3 months of healing, bone biopsies were obtained and prepared for histological analyses. Dental implants were then placed. Implant stability quotient (ISQ) readings were obtained for each implant at the time of surgery and at 1 and 3 months postoperatively. The mean new bone formation was 34.57±25.67% in the EMD sites and 28.80±16.14% in the Bio-Oss Collagen sites. There was no significant difference between the groups. The ISQ values were significantly higher for the implants placed in the EMD sites at the first and third months, but no significant differences were observed in the ISQ values for the implants placed in the Bio-Oss Collagen sites. The augmentation of the extraction sockets with EMD or Bio-Oss Collagen leads to similar behaviour in bone regeneration. [ABSTRACT FROM AUTHOR]

• The proposed method presents a study with an EMD approach aimed at automatically diagnosing migraine from EEG signals. • Testing the classification performance for electrode-based migraine diagnosis with the proposed method. • Testing the distinctiveness of the obtained features by applying different statistical tests (Kruskal Wallis-Wilcoxon). • Comparison of the computational complexity of the proposed method with different decomposition methods. • Increasing classification performance in EEG-based migraine diagnosis by considering existing CAD studies. This study presents an Empirical Mode Decomposition (EMD) approach that aims to automatically detect migraine disease (MD) from electroencephalogram (EEG) recordings of migraine patient (MP) and healthy control (HC) subjects. First, the Multiscale Principal Component Analysis (MSPCA) method was applied to remove the noise on the raw EEG signals. Later, EEG signals were separated into intrinsic mode functions (IMF) components by EMD method. Statistical features were calculated and extracted from each IMF component. By applying the Kruskal Wallis (KW) test, the ability to distinguish these features in classification was tested. Classification performances are tested by classifying the features of each IMF component with a few leading ensemble algorithms. The highest classification accuracy of 92.47% was achieved by classifying the features of the IMF1 component with the Random Forest learning algorithm. At the last stage of the study, a comparative analysis with different time–frequency analysis methods is presented. As a result of the experimental comparison of our proposed method, it has been observed that it has a higher classification performance than other studies that detect EEG-based MD. With these aspects, our study reveals that it has the potential to be used as a computer aided diagnosis system that will support expert opinion in the detection of MD. [ABSTRACT FROM AUTHOR]

Using EMD signal decomposition method to decompose the data of material test and comparing the difference of Hilbert spectrum in signal components on the different stages in the paper,the amplitude and frequency variation in the test process is calculated so as to determine the strength and reliability of the material as well as from the obtained experience to judge the health of the material used. It innovatively applies the segment data to make comparisons, accurately locates the specific data segment with structural damage and provides the simultaneous monitoring scheme while reducing the amount of calculation. [ABSTRACT FROM AUTHOR]

Abstract: Automatic detection of different cardiac abnormalities is an emerging field of study in assistive diagnosis technology for ca rdiac diseases. Atrial fibrillation (AF) is a kind of arrhythmia which increases risk of heart attack especially to the older people. Detection of AF at the early stage may cause prevention of serious stoke. This paper presents a method of automatic detection of AF by using higher order statistical moments of ECG signal in Empirical Mode Decomposition (EMD) domain. The proposed technique operates in two stages. First stage consists of decomposition of denoised ECG into intrinsic mode functions (IMF) and derives the statistical parameters like variance and standard deviation for classification from each IMF. In the second s tage these features are used by a supervised classifier to distinguish between normal and AF ECG rhythms. The performance of this method is tested with the MIT-BIH arrhythmia data base. Possibility to use different IMFs is also tested and 96% sensitivity is achieved in IMF 4. [Copyright &y& Elsevier]

Abstract: The adult periodontal ligament (PDL) is considered to contain progenitor cells that are involved in the healing of periodontal wounds. Treatment with enamel matrix derivative (EMD), a heat-treated preparation derived from enamel matrix proteins (EMPs), has been shown to be of some clinical benefit in eliciting periodontal regeneration in vivo. Although there is extensive information available about the effects of EMD on periodontal regeneration, the precise influence of this material on alveolar bone and the formation of blood vessels and proprioceptive sensory nerves, prominent features of functionally active periodontal tissue, remain unclear. The aim of the present study was therefore to examine the effects of EMD on the ability of human periodontal ligament cells (HPCs) to undergo multi-lineage differentiation in vitro. Our results showed that HPCs treated with EMD under non-selective growth conditions did not show any evidence of osteogenic, adipogenic, chondrogenic, neovasculogenic, neurogenic and gliogenic “terminal” differentiation. In contrast, under selective lineage-specific culture conditions, EMD up-regulated osteogenic, chondrogenic and neovasculogenic genes and “terminal” differentiation, but suppressed adipogenesis, neurogenesis and gliogenesis. These findings thus demonstrate for the first time that EMD can differentially modulate the multi-lineage differentiation of HPCs in vitro. [Copyright &y& Elsevier]

Abstract: This paper presents a new ECG denoising approach based on noise reduction algorithms in empirical mode decomposition (EMD) and discrete wavelet transform (DWT) domains. Unlike the conventional EMD based ECG denoising approaches that neglect a number of initial intrinsic mode functions (IMFs) containing the QRS complex as well as noise, we propose to perform windowing in the EMD domain in order to reduce the noise from the initial IMFs instead of discarding them completely thus preserving the QRS complex and yielding a relatively cleaner ECG signal. The signal thus obtained is transformed in the DWT domain, where an adaptive soft thresholding based noise reduction algorithm is employed considering the advantageous properties of the DWT compared to that of the EMD in preserving the energy in the presence of noise and in reconstructing the original ECG signal with a better time resolution. Extensive simulations are carried out using the MIT-BIH arrythmia database and the performance of the proposed method is evaluated in terms of several standard metrics. The simulation results show that the proposed method is able to reduce noise from the noisy ECG signals more accurately and consistently in comparison to some of the stateof-the-art methods. [Copyright &y& Elsevier]

Abstract: Newly acquired fingerprint images have a lot of noise. This article use BEMD decomposes the fingerprint image. Each of the IMF obtained represents a particular scale. To some extent, it reflects the image texture information at different scales. The proposed algorithm use two-dimensional empirical mode decomposition to resolve the fingerprint image into a series of IMF and the residual. Take into account the distribution of noise between each component, the method use selected components to synthesis fingerprint image. It eliminates the effect of noise and enhance fingerprint image effectively. [Copyright &y& Elsevier]

Abstract: With the application of wide-area measurement system (WAMS), the analysis of dynamic behavior of power system based on swing tracks acts as an important research field and is different from the past methods based on a given mathematical models. In this paper,EMD-based eigensystem realization algorithm is proposed for identifying frequency, damping and vibration in the low frequency oscillationidentify for low frequency oscillation frequency, damping and vibration. Through a example of a four-machine two-region system, it is proved that this method on distinguishing the oscillation frequency, damping ratio and mode shapes is validity. This method can be minimal realization of the system, damping controller for the design conditions.this method can achieve the simplest state space modal of system. It is important for designer to invent advanced damping controller. [Copyright &y& Elsevier]

Abstract: The measurement of power parameters is one of the most basic calculation in power systems, it plays a key role on running power systems in security, economy and reliability. This thesis studys the application of the Hilbert-Huang transform in the field of nonstationary electric power signal in the aspects of amplitude nonstationary, on which the traditional methods work not very well. Much MATLAB simulation shows that HHT is more suitable than DFT to analyze the nonstationary electric power signal. HHT is excellent in the amplitude nonstationary signal analysis. In this thesis, the structure, the function, the communication mode, the application and the trend of development of Power Analyzer are analyzed. Finally, a self-developed chip named Power Analyzer used for measuring of power parameter based on TMS320F2812 processor is designed. The instrument can attain 0.2 grade of RMS, 0.2 grade of power and high-precision measurement of 2 to 31 harmonics. Its real-time performance is very good. [Copyright &y& Elsevier]

Abstract: This paper introduce EMD as a useful measurement of success in selection of the LDPC code from selected ensemble. When EMD metric associated bad with other cycles in the code graph, which is unavoidable in constructing LDPC codes, we introduce cycle remove in order to improve LDPC codes’ performance. Experimental results show that the algorithm lower error floor and improve LDPC codes’ performance. [Copyright &y& Elsevier]

Abstract: This paper investigates two multi-resolution noise reduction models for Inertial Navigation System(INS) raw data denoising. Wavelet multi-resolution analysis is firstly introduced and EMD multi-resolution is defined. To define new noise reduction models, Kalman filter is used to filter high frequency band of the multi-resolution decomposition signal and Wavelet-Kalman filter and EMD-Kalman filter are developed. Real INS raw data of Gyro-x and Gyro-z direction are applied to test the proposed models. Quantity evaluation indexes show that the EMD-Kalman has the largest signal-to-noise ratio(SNR) but lowest RMSE,EBias and ENAE which verify the merits of EMD-Kalman model. [Copyright &y& Elsevier]

Abstract: The features of non-stationary multi-component signals are often difficult to be extracted for expert systems. In this paper, a new method for feature extraction that is based on maximization of local Gaussian correlation function of wavelet coefficients and signal is presented. The effect of empirical mode decomposition (EMD) to decompose multi-component signals to intrinsic mode functions (IMFs), before using of local Gaussian correlation is discussed. The experimental vibration signals from two gearbox systems are used to show the efficiency of the presented method. Linear support vector machine (SVM) is utilized to classify feature sets extracted with the presented method. The obtained results show that the features extracted in this method have excellent ability to classify faults without any additional feature selection; it is also shown that EMD can improve or degrade features according to the utilized feature reduction method. [Copyright &y& Elsevier]

Abstract: In this paper, the empirical mode decomposition (EMD) of the wavelet transformation is introduced into the processing of financial time series with high frequency. The high-frequency data are decomposed with EMD at first. Then the evolutionary law and development trend of each component of intrinsic mode function (IMF) are explored in different time scales. Finally, forecast model are reconstructed by using the IMF components. Using this forecast model, the time series of oil futures at 5minute intervals as samples is analyzed. The results showed that there are quasi-cycles with 10, 27, 80, 150, 370, 860, 1290 points of data in the financial time series. Furthermore, we can make more accurate forecast with EMD by extracting the IMF components with the different volatility cycle in thefinancial time series. [Copyright &y& Elsevier]

To identify the number of modes of vibration in the random theory results for wind vibration, the empirical mode decomposition (EMD) method and wavelet transform method are used to analyze the joint characteristic for self-induced response signals of aeroelastic model. First, the intrinsic mode functions (IMF) are directly decomposed from original signal with EMD, and then the signal characteristic parameters of every IMF can be extracted with wavelet transform, which are compared with the results by traditional spectrum analysis method and wavelet transform on original signal. The combination of EMD and wavelet transform method can identify the high-order mode of vibration ; the main characteristics of signals in arbitrary frequence band can be obtained ; and the distribution of signal energy with time and frequence can be displayed more clearly. The numerical simulation and the analysis of response signal date show that this method is effective and practical. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) signal is popular and extensively used as a diagnostic tool for revealing several diseases related to the heart. Unfortunately, its sensitive to noises from various sources. Denoising these noises remains a hard and major challenge task, although there are some existing methods for denoising, they remain limited to a few types of noises with significant distortion after the filtering process. In this paper, we investigated a hybrid system that can deal with different types of noises, with different levels of SNR, conserving all the signal features with the minimum distortion. The hybrid system consists of a decomposition method followed by local means (LM) filtering. The best candidates used for the signal decomposition are the Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Discrete Wavelet Transform (DWT), and Stationary Wavelet Transform (SWT). To demonstrate the effectiveness of the proposed method, several real ECG recordings and synthetic ECG infected by a different type of noise (i.e., baseline wander, muscle artifacts, electrode motion, mix noise, white noise, colored noise) with different levels of signal to noise ratios (24, 18, 6, 0, −6) dB are used in our numerical analysis. The performance evaluation of our proposed system is made using the following metrics: SNR improvement, signal-to-noise and distortion ratio (SINAD), the approximate entropy, and the fuzzy entropy. The experimental results emphasized that the Ensemble Empirical Mode Decomposition (EEMD) followed by local means (LM) outperforms all the comparison methods, in terms of all the metrics used, with few exceptions where the Empirical Mode Decomposition (EMD) followed by local means (LM) and Discrete Wavelet Transform (DWT) followed by local means (LM) performed well. [ABSTRACT FROM AUTHOR]

The detection and classification of Mysticetes' vocalisations have evoked the attention of researchers over the years because of their relevance to the marine ecosystem. These vocalisations are gathered employing different passive acoustic monitoring techniques. The vocalisation datasets are accumulated over a period; thus, they are large and cannot be easily analysed manually. Consequently, efficient machine learning (ML) tools such as the hidden Markov models (HMMs) are used extensively to automatically detect and classify these huge vocalisation datasets. As with most ML tools, the detection efficiency of the HMMs depend on the adopted feature extraction technique. Feature extraction techniques such as the Mel-scale frequency cepstral coefficient (MFCC), linear predictive coefficient (LPC), and empirical mode decomposition (EMD) have been adopted with the HMMs to detect different Mysticetes' vocalisations. This article introduces the method of dynamic mode decomposition (DMD) as a feature extraction technique that can be adopted with the HMMs for the detection of Mysticetes' vocalisations. The DMD has emerged as a robust tool for analysing the dynamics of non-stationary and non-linear signals. It is a completely data-driven tool that decomposes a signal over a certain period of time into relevant modes. Here, these spatial-temporal modes are reconstructed mathematically to form reliable feature vectors of the decomposed vocalisation signals. The performance of the proposed DMD-HMM detection technique is demonstrated using the acoustic datasets of two different Mysticetes' vocalisations: Humpback whale songs and Inshore Bryde's whale short pulse calls. In both species, the proposed DMD-HMM exhibits superior sensitivity and false discovery rate performances as compared to the MFCC-HMM, LPC-HMM, and EMD-HMM detection methods. Likewise, this proposed DMD-HMM detection method can be extended to other Mysticetes' that produce characteristics sounds. • A dynamic mode decomposition approach for Mysticetes sound detection is described • The technique is adapted with the hidden Markov model. • The performance of the technique is verified with two Mysticetes species: Humpback whale song and Bryde's whale pulse call. • The performance of the DMD is accessed against the MFCC, LPC and EMD methods. • The technique is robust and exhibited superior sensitivity and false discovery rate results. [ABSTRACT FROM AUTHOR]