Signal Smoothing Research Articles

REMOVAL OF ELECTRODE MOTION ARTIFACT IN ECG SIGNALS USING WAVELET BASED THRESHOLD METHODS WITH GREY INCIDENCE DEGREE

Cardiovascular diseases are one of the most frequent and dangerous problems in modern society in nowadays. Unfortunately electrocardiograms (ECG) signals, during their acquisition process, are affected by various types of noise and artifacts due to the movement, or breathing of the patient, electrode contact, power-line interferences, etc. The aim of this study was to develop an algorithm to remove electrode motion artifact in ECG signals. Donoho and Johnstone proposed Wavelet thresholding de-noising method based on discrete wavelet transform (DWT) is suitable for non-stationary signals. The wavelet transform coefficient is processed by using grey relation analysis of the grey theory, and a new wavelet threshold method namely wavelet threshold method with grey incidence degree (GID) (or the GID threshold method) based is introduced. It shows that the signal smoothness and similarity of the two signal criteria have been greatly improved by the GID threshold method compared with existing threshold methods. According to the characteristics of different ECG signals, GID threshold method gets better results than it can adaptively deal with noise separation and details remaining of the two opposing signal problems, so as to provide a better choice for wavelet threshold methods of signal processing. Performance analysis was performed by evaluating Mean Square Error (MSE), Signal-to-noise ratio (SNR) and visual inspection over the denoised signal from each algorithm. The experimental result shows that GID hard shrinkage method with sub-band or level dependent thresholding gives the best denoising performance on ECG signal. The result shows that soft threshold not always gives better denoising performance; it depends on which wavelet thresholding algorithm was chosen.

Multi-Channel ARMA Signal Covariance Intersection Fusion Kalman Smoother

For the multi-channel ARMA signal with two sensors, by the classical Kalman filtering method and the covariance intersection (CI) fusion method, a covariance intersection fusion steady-state Kalman signal smoother is presented, which is independent of the unknown cross-covariance. It is proved that its accuracy is higher than that of each local Kalman signal smoother, and is lower than that of the optimal signal fuser weighted by matrices. The geometric interpretation of the above accuracy relations are presented based on the covariance ellipses. A simulation example result shows its effectiveness and correctness.

Carotid Wave Intensity Analysis Reveals Increased Indices of Wave Reflection in Appropriate Weight for Age Ex-Preterm Infants

Background: Themechanism of hypertension in appropriate weight for gestational age (AGA) premature infants is unknown. Common carotid arterial (CCA) wave intensity (WI) analysis may detect vascular changes associated with hypertension prior to conventional methods. Methods: In 47 AGA ex-premature young adults (exprems, 18.6± 0.6 yrs, mean±SD) and 38 age-matched controls, 10 beat cine-loops of CCA velocity (U) and diameter (D) were acquired using echocardiography, with edge-detection, ensemble averaging and smoothing of signals. Diameter was scaled to Sphygmocor-determined CCA pressure (P), and CCA WI was derived as the product of the rates of change of P and U, with separation into forward and backward components. Pressure ( P) and velocity ( U) changes associated with forward and backward waves were calculated after wave separation. Pulse wave velocity (PWV) and augmentation index (AI) were obtained with Sphygmocor. CCA compliance was calculated. Results: Carotid systolic (109± 11mmHg vs 99± 15mmHg, p= 0.001) and mean (85± 8mmHg vs 79± 7mmHg, p= 0.001) blood pressures were greater in ex-prems. WI profiles displayed an initial-systolic forward compression wave (FCW) associated with LV ejection and a mid-systolic backward compression wave (BCW) arising from vascular reflection of FCW. Although FCW area was no different, BCW area was increased in ex-prems (p= 0.05). The P of BCW was increased in ex-prems (p= 0.007), and the BCW:FCW P ratio was 14% higher, indicative of increased wave reflection (p< 0.001). By contrast, PWV,AI, pulse pressure andCCAcompliance were not different. Conclusions: AGA ex-premature adolescents have increased CCA wave reflection indices as determined by WIA, despite normal conventional vascular function.

Discovering Pharmacogenetic Latent Structure Features Using Divergences

Non-linear divergence functions, as sufficient additive contrast-based measures of direct evidence, offer a smooth universal information basis to deconstruct the stochastic question actually being asked in pharmacogenetic experiments. The orthogonal decomposition of individualized marginal divergences is introduced using entropy and commonalities with PLS-DA. Feature selection is shown in examples of the genetic discriminant analysis of:-up to 3-class diseases; gene by drug treatment studies; and drug-induced multiple adverse events. Analysis over multiple data types, aggregates and dummy indicators is presented. Interaction and epistasis analysis is exemplied. Signal stability, smoothing, approximations and permutation based significance tests are discussed.

A New Algorithm for Adaptive Smoothing of Signals in Speech Enhancement

Speech signals are non-stationary and nonlinear in nature. They are affected by background noise and this affects the performance of a speech recognition system. This paper deals with smoothing a signal there by removing noise from speech signals for the development of an efficient speech recognition system for recognizing isolated spoken words in Malayalam. Here, speech signals are pre-processed using wavelet denoising methods which uses Soft Thresholding (ST). A new algorithm is proposed which is used to smooth the signal before applying thresholding. This increases the Signal to Noise Ratio (SNR) indicating that adaptive smoothing improves the quality of the speech signal by reducing more noise.

LA-ICPMS elemental imaging of complex discontinuous carbonates: An example using large benthic foraminifera

Trace element concentrations in biogenic and inorganic carbonates are a valuable source of palaeoenvironmental information. Because laser-ablation spot or 1D track analyses do not fully capture the complex (bio)mineralisation processes, 2D maps are required to arrive at a better understanding of the controls on minor/trace element incorporation. Foraminifera (marine protists) have a complex internal geometry and yield discontinuous sections characterised by calcite–resin–calcite transitions over distances as small as 10 μm. Order of magnitude differences in the total ion beam intensity over such transitions enables effective data exclusion. Inline signal smoothing devices, despite slightly increasing washout time and thus reducing spatial resolution, prevent excessive artificial noise in analyses, of particular concern when utilising small laser spot sizes (<30 μm) in order to maximise spatial resolution. We find surface Al/Ca and Zn/Ca ratios elevated by two and 5–10 times respectively, highlighting the need for pre-acquisition ablation cleaning. Through these analytical considerations we show how the quality of images acquired from discontinuous samples can be maximised, enabling – in this case – seasonality reconstruction in the fossil record from large benthic foraminifera.

A “wire” signal smoothing device for laser ablation inductively coupled plasma mass spectrometry analysis

A novel signal smoothing device for laser ablation inductively coupled plasma mass spectrometry was developed. The “wire” signal smoothing device consists of a copper cylinder filled with steel wire, with an internal volume of approx. 94cm3. The effectiveness of the “wire” signal smoothing device was evaluated with respect to both signal stability and decay time. With the developed “wire” smoothing device, no oscillation of the signal intensity was observed, even at a repetition rate of 1Hz. This finding indicates that this device is well suited for routine optimization of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The signal stability was improved by a factor of 11 compared to the absence of a signal smoothing device at a repetition rate of 1Hz. Another significant advantage of the “wire” smoothing device is that the signal decay time is similar to that without the signal smoothing device. These properties cause the “wire” smoothing device to be well suited for low repetition rate laser ablation analysis, which provides smaller elemental fractionation and better spatial resolution. The proposed “wire” signal smoothing device has been successfully used for high depth resolution zircon dating.

Ceilometer observations of the boundary layer over Warsaw, Poland

Jenoptik’s CHM 15k ceilometer was used to monitor the vertical structure of the atmospheric boundary layer (ABL) over Warsaw, from 2008 until 2011, on Mondays and Thursdays, in 24h periods. Hereby, we present an assessment of the signal-to-noise ratio along with a sensitivity study of signal smoothing methods developed in-house. With the proposed averaging, ceilometer attenuated-backscatter signals reached the high troposphere, which makes this sensor competitive to a single-wavelength elastic lidar. The smoothed signals were employed as an input for algorithms developed to automatically detect the ABL height, clouds, fog, and precipitation in the lower troposphere. The classification of weather conditions was validated by the METAR reports from the Warsaw Airport. The obtained ABL heights were compared to those assessed from radio-soundings from a nearby meteorological station WMO12374 in Legionowo. An inter-comparison of the ABL heights, derived by using the Jenoptik’s automated routine against the in-house developed algorithms, is in favor of the latter. The presented four annual cycles of the ABL height, obtained with various derivative-based methods, are the first such long-term results reported using the CHM 15k sensor in Eastern Europe.

Applying posture identifier in designing an adaptive nonlinear predictive controller for nonholonomic mobile robot

This paper presents a trajectory tracking controller for a nonholonomic mobile robot using an optimization algorithm based predictive feedback control and an adaptive posture identifier model while following a continuous and a non-continuous path. The posture identifier model is a modified Elman neural network that describes the kinematics and dynamics of the mobile robot model. The feedforward neural controller is trained off-line and its adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index prediction algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results and experimental work show the effectiveness of the proposed adaptive nonlinear predictive control algorithm; this is demonstrated by the minimized tracking error and the smoothness of the torque control signal obtained, especially with regards to the external disturbance attenuation problem.

Fast Spatiotemporal Smoothing of Calcium Measurements in Dendritic Trees

We discuss methods for fast spatiotemporal smoothing of calcium signals in dendritic trees, given single-trial, spatially localized imaging data obtained via multi-photon microscopy. By analyzing the dynamics of calcium binding to probe molecules and the effects of the imaging procedure, we show that calcium concentration can be estimated up to an affine transformation, i.e., an additive and multiplicative constant. To obtain a full spatiotemporal estimate, we model calcium dynamics within the cell using a functional approach. The evolution of calcium concentration is represented through a smaller set of hidden variables that incorporate fast transients due to backpropagating action potentials (bAPs), or other forms of stimulation. Because of the resulting state space structure, inference can be done in linear time using forward-backward maximum-a-posteriori methods. Non-negativity constraints on the calcium concentration can also be incorporated using a log-barrier method that does not affect the computational scaling. Moreover, by exploiting the neuronal tree structure we show that the cost of the algorithm is also linear in the size of the dendritic tree, making the approach applicable to arbitrarily large trees. We apply this algorithm to data obtained from hippocampal CA1 pyramidal cells with experimentally evoked bAPs, some of which were paired with excitatory postsynaptic potentials (EPSPs). The algorithm recovers the timing of the bAPs and provides an estimate of the induced calcium transient throughout the tree. The proposed methods could be used to further understand the interplay between bAPs and EPSPs in synaptic strength modification. More generally, this approach allows us to infer the concentration on intracellular calcium across the dendritic tree from noisy observations at a discrete set of points in space.

Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information

We propose a joint segmentation and groupwise registration method for dynamic cardiac perfusion images that uses temporal information. The nature of perfusion images makes groupwise registration especially attractive as the temporal information from the entire image sequence can be used. Registration aims to maximize the smoothness of the intensity signal while segmentation minimizes a pixel's dissimilarity with other pixels having the same segmentation label. The cost function is optimized in an iterative fashion using B-splines. Tests on real patient datasets show that compared with two other methods, our method shows lower registration error and higher segmentation accuracy. This is attributed to the use of temporal information for groupwise registration and mutual complementary registration and segmentation information in one framework while other methods solve the two problems separately.

Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information

We propose a joint segmentation and groupwise registration method for cardiac perfusion images by using temporal information. The nature of perfusion images makes groupwise registration especially attractive as the temporal information from the entire image sequence can be used. Registration aims to maximize the smoothness of the intensity signal, while segmentation minimizes a pixel’s dissimilarity with other pixels having the same segmentation label. The cost function is optimized in an iterative fashion using B-splines. Tests on real patient datasets show that compared to two other methods, our method shows lower registration error and higher segmentation accuracy. This is attributed to the use of temporal information for groupwise registration and mutually complementary registration and segmentation information in one framework, while other methods solve the two problems separately.

Design of optimization-based controllers applied to an ethanol steam reformer for hydrogen production

This paper focuses on the control of a low temperature ethanol steam reformer for in-situ hydrogen production. For this purpose, three optimization-based control configurations are proposed, namely, a linear model-based predictive controller, a linear quadratic regulator with output error integral action and a cascade control combining the two previous configurations. In all cases, control objectives aim at obtaining the desired flow of hydrogen while keeping the carbon monoxide at its nominal working point under input and output operational constraints. Output tracking and robustness of each configuration are compared using two key performance indicators that evaluate the output errors and the smoothness of the control signals. Simulation results allow to compare the characteristics of each control configuration when applied to the non-linear model of the ethanol steam reformer.

Sigseg: a tool for the detection of position and velocity discontinuities in geodetic time-series

The detection of discontinuities in geodetic data is an ever more important topic due to both the influence of discontinuities on the results of models and analyses, and to the very meaning of discontinuities in physical phenomena. We consider and describe a mathematical model, originally formulated for the approximation of images by smooth functions, in one dimension (1D). The model had been designed to smooth the data while preserving and detecting its discontinuities following a variational approach. A second and more complex model for the approximation of images by functions with smooth first derivatives is also available. This second model had been designed to smooth the data while preserving and detecting the discontinuities of the data, but also those of the first derivative. Such interesting features suggest the chance to apply this second model to 1D geodetic data, in particular for the detection of discontinuities and velocity changes in position time-series rather than for signal smoothing. The sigseg (signal segmentation) program implements the two variational models in 1D and is presented with applications to geodetic data. The essential mathematical elements are sketched, and some details on the numerical implementation are given.

Cepstral smoothing of separated signals for underdetermined speech separation

Cepstral smoothing of separated signals for underdetermined speech separation

Development of Standard Device for Resistive Current Tester for Zinc Oxide Surge Arrester

The topic research, standard device for resistive current tester for zinc oxide surge arrester, can be used for calibration for resistive current tester for zinc oxide surge arrester. The equip functions involve total current, resistive component of current, capacitance component of current, active power, reference voltage, phase angle, harmonic component of current. The topic introduces the working principle and the core technology of the standard device, among them; the most innovative points are the “smoothing signal generation” and “traceability design”.

Signal quality and Bayesian signal processing in neurofeedback based on real-time fMRI

Real-time fMRI allows analysis and visualization of the brain activity online, i.e. within one repetition time. It can be used in neurofeedback applications where subjects attempt to control an activation level in a specified region of interest (ROI) of their brain. The signal derived from the ROI is contaminated with noise and artifacts, namely with physiological noise from breathing and heart beat, scanner drift, motion-related artifacts and measurement noise. We developed a Bayesian approach to reduce noise and to remove artifacts in real-time using a modified Kalman filter. The system performs several signal processing operations: subtraction of constant and low-frequency signal components, spike removal and signal smoothing. Quantitative feedback signal quality analysis was used to estimate the quality of the neurofeedback time series and performance of the applied signal processing on different ROIs. The signal-to-noise ratio (SNR) across the entire time series and the group event-related SNR (eSNR) were significantly higher for the processed time series in comparison to the raw data. Applied signal processing improved the t-statistic increasing the significance of blood oxygen level-dependent (BOLD) signal changes. Accordingly, the contrast-to-noise ratio (CNR) of the feedback time series was improved as well. In addition, the data revealed increase of localized self-control across feedback sessions. The new signal processing approach provided reliable neurofeedback, performed precise artifacts removal, reduced noise, and required minimal manual adjustments of parameters. Advanced and fast online signal processing algorithms considerably increased the quality as well as the information content of the control signal which in turn resulted in higher contingency in the neurofeedback loop.

Discussion on the Wavelet Adjustment Factor Threshold Noise Reduction Technology Based on DSP

Because of discontinuity at threshold, hard threshold de-noising leads to the additional oscillation at threshold when reconstructing the signal, so that the smoothness of the de-noised signal becomes weak; Soft threshold de-noising method can guarantee the good continuity of the signal. However, for the existence of the fixed bias between the estimated threshold and actual threshold, there are also deviations between the reconstructed signal and the actual signal existing deviations too. In this work, by using the high performance DSP as a signal processing tool, we reduced the noise of actual measured vibration signal collected from headstock of CA6140 lathe. By comparing, the result is the same as the noise reduction simulation calculated from MATLAB, which demonstrates the noise reduction effect of wavelet adjustment factor threshold de-noising apparently.

Regularization Method for System Error Estimation Based on One Measurement Element

Error exists in almost every measurement, which often affects the application of measurement. In traditional method, system error is estimated by combine several measurements, but in some cases, we can get only one measurement and should estimate the system error of this measurement. This paper studies the estimation of high frequency system error of one measurement element. In this method, we assume the frequency of signal is lower than the frequency of system error. Under this assumption, we can constrain the derivative of the signal to be small because of its low frequency, and this we help us to establish a model to separate system error with high frequency from signal. According to this thought, this paper gives a regularization model for system error estimation based on signal smoothness constraint. Simulated results demonstrated the efficiency of new method.

Robust visual speakingness detection using bi-level HMM

Visual voice activity detection (V-VAD) plays an important role in both HCI and HRI, affecting both the conversation strategy and sync between humans and robots/computers. The typical speakingness decision of V-VAD consists of post-processing for signal smoothing and classification using thresholding. Several parameters, ensuring a good trade-off between hit rate and false alarm, are usually heuristically defined. This makes the V-VAD approaches vulnerable to noisy observation and changes of environment conditions, resulting in poor performance and robustness to undesired frequent speaking state changes. To overcome those difficulties, this paper proposes a new probabilistic approach, naming bi-level HMM and analyzing lip activity energy for V-VAD in HRI. The designing idea is based on lip movement and speaking assumptions, embracing two essential procedures into a single model. A bi-level HMM is an HMM with two state variables in different levels, where state occurrence in a lower level conditionally depends on the state in an upper level. The approach works online with low-resolution image and in various lighting conditions, and has been successfully tested in 21 image sequences (22,927 frames). It achieved over 90% of probabilities of detection, in which it brought improvements of almost 20% compared to four other V-VAD approaches.