Spurious Spikes Research Articles

Probability Density Functions of Stationary Surface EMG Signals in Noisy Environments

The probability density function (pdf) of an electromyography (EMG) signal provides useful information for choosing an appropriate feature extraction technique. The pdf is influenced by many factors, including the level of contraction force, muscle type, and noise. In this paper, we investigated the pdfs of noisy EMG signals artificially contaminated with five different noise types: 1) Electrocardiography (ECG) interference; 2) many spurious background spikes; 3) white Gaussian noise; 4) motion artifact; and 5) power line interference at various levels of signal-to-noise ratio (SNR). In addition, we evaluated a set of statistical descriptors for identifying a noisy EMG signal from its pdf, specifically kurtosis, negentropy, L-kurtosis, and robust measures of kurtosis (KR1 and KR2). The results show that at low SNR (<5 dB), all noise types affect the statistical descriptors for the pdf of a noisy EMG signal. In addition, KR2 performs the best among these descriptors in identifying a noisy EMG signal from its pdf, because it is computed based on the quantiles of the data. As a result, it can avoid the effects of outliers resulting in the correct identification of pdf shape of noisy EMGs with all contamination types and all levels of SNR.

The impact of the QNSE‐EDMF scheme and its modifications on boundary layer parameterization in WRF: modelling of CASES‐97

In recent years, many eddy‐diffusivity mass‐flux (EDMF) planetary boundary layer (PBL) parametrizations have been introduced. Yet most validations are based on idealized set‐ups and/or single‐column models. To address this gap, this article focuses on the effect the mass‐flux part has on the performance in the Quasi‐Normal Scale Elimination‐EDMF (QNSE‐EDMF) PBL scheme in the Weather Research and Forecasting (WRF) model by comparing the results to observations from the CASES‐97 field campaign. In addition, two refined versions, one introducing the parametrized clouds to the WRF radiation scheme, and the second adding a different entrainment formulation, have been evaluated.The introduction of mass flux reduced errors in the average moisture profile, but virtual temperature and wind speed profiles did not change as much. The modelled mixed‐layer depth, while still low compared to observations, was closer to observed values with the addition of mass flux. The major changes in the virtual potential temperature flux profiles were an increase in entrainment ratios and a slight decrease in surface values. Allowing for biases in the observed fluxes, modelled and observed flux profiles had a similar shape. However, the agreement among the modelled and between the modelled and observed water‐vapour flux profiles and vertical flux divergence was often poor, a likely result of the model's failure to capture the timing of the morning boundary‐layer water‐vapour maximum. Further, both virtual potential temperature and water‐vapour flux profiles display spurious spikes attributed to the way the non‐local and local terms interact in the model. Adding mass‐flux‐based clouds to the radiation calculation improved the time‐and space‐averaged modelled incoming short‐wave flux. The choice of the representation for entrainment/detrainment often affected the results to the same extent as adding mass flux.

An indirect approach based on Clausius–Clapeyron equation to determine entropy change for the first-order magnetocaloric materials

Taking into account the phase fraction during the structural transition for the first-order magnetocaloric materials, an improved isothermal entropy change (ΔST) determination has been put forward based on the Clausius–Clapeyron (CC) equation. It was found that the ΔST value evaluated by this method is in excellent agreement with those determined from the Maxwell relation (MR) using magnetic measurements for some Heusler alloys with a weak field-induced phase transforming behavior, such as Ni–Mn–Sn Heusler alloys. In comparison with the MR based on isofield magnetization measurements (MRIF), this method is very convenient to obtain the ΔST derived from only few thermomagnetic curves. More importantly, it is quite superior to the MR-based method in eliminating the overestimation of ΔST due to the appearance of the spurious spike derived from MR employing isothermal magnetization measurements (MRIT).

Use of the integrated profile for voluntary muscle activity detection using EMG signals with spurious background spikes: A study with incomplete spinal cord injury

Abstract Following neurological injuries, such as spinal cord injury (SCI), voluntary muscle activity detection from electromyography (EMG) signals can be problematic due to spurious involuntary spikes produced by physiological and extrinsic/accidental origins. The purpose of this study was to demonstrate the usefulness of the integrated profile for voluntary muscle activity detection in the presence of spurious background spikes for SCI patients. Two onset detection methods (the sample entropy analysis and the integrated profile) were evaluated using both semi-synthetic and experimental surface EMG signals with spurious background spikes recorded from SCI patients. For all the examined signal to noise ratios, the integrated profile method was able to achieve comparable performance with the sample entropy analysis-based method but while taking significantly shorter running time (P

Space-Vector-Quantized Dithered Sigma–Delta Modulator for Reducing the Harmonic Noise in Multilevel Converters

Power converters using deterministic switching frequency schemes have switching noise concentrated at harmonic frequencies, resulting in enhanced electromagnetic emission. To suppress the harmonic spikes in three-phase multilevel voltage source converters, a digital control scheme based on dithered sigma-delta modulation (SDM) is proposed in this paper. Introducing a dithered sequence in an SDM varies the switching frequency randomly, resulting in the suppression of the spurious harmonic spikes in the output spectrum even with a regular control input. Although the switching frequency varies randomly, the minimum pulsewidth of the proposed scheme is the sampling time period, avoiding the minimum pulsewidth problem. Instead of a conventional scalar quantizer, the quantizer in the proposed dithered sigma-delta converter uses the principle of space vector quantization. The proposed scheme is experimentally verified on a constant v/f open-loop induction motor drive for a three-level inverter realized by cascading two two-level inverters. The performance of the proposed scheme is compared with different pulsewidth modulation schemes in the entire modulation index, including the overmodulation region.

Effects of radar side-lobes on snow depth retrievals from Operation IceBridge

AbstractArctic snow depth data products from four years (2009–12) of Operation IceBridge (OIB) surveys are examined. In our analysis, we found spurious spikes in the snow depth distributions of both the multi-year and seasonal ice covers. These spikes are artifacts that stem from the incorrect identification of side lobes and main lobes of the impulse response of the snow radar as returns from the air–snow interface. The current OIB snow depth retrieval algorithm does not explicitly account for the presence of these side lobes and main lobes. As a result, overall accuracy of snow depth returns and related statistics is negatively affected. Although the range locations of these side lobes are predictable for each radar installation, they vary with individual airborne campaigns. Comparisons with limited in situ snow surveys show significant differences of &gt;20 cm between OIB and in situ snow surveys. These artifacts affect OIB ice thickness estimates because they rely on estimates of sea-ice freeboard, which are calculated as the differences between coincident snow freeboard from lidar elevations and the retrieved snow depth estimates discussed here. Since these products are widely distributed to the scientific community, our results suggest that earlier geophysical studies based on these products may need to be re-examined.

Miniaturized Lumped-Element LTCC Filter With Spurious Spikes Suppressed Vertically-Interdigital-Capacitors

A miniaturized lumped-element 10-layer low temperature co-fired ceramic (LTCC) filter is proposed for the extremely low center-frequency of 60 MHz. The miniaturization is achieved by using spiral inductors with high-impedance lines and spurious spikes suppressed vertically-interdigital-capacitors (SVICs). By interconnecting the open ends of interval fingers with vertical vias, capacitance increment and spurious spikes suppression of vertically-interdigital-capacitor (VIC) are achieved without Q-factor degradation. The proposed filter has the smallest reported size of only 0.004 ×0.004 ×0.0004 λg. Furthermore, comparisons and discussions are also implemented.

Subspace based adaptive denoising of surface EMG from neurological injury patients

Objective: After neurological injuries such as spinal cord injury, voluntary surface electromyogram (EMG) signals recorded from affected muscles are often corrupted by interferences, such as spurious involuntary spikes and background noises produced by physiological and extrinsic/accidental origins, imposing difficulties for signal processing. Conventional methods did not well address the problem caused by interferences. It is difficult to mitigate such interferences using conventional methods. The aim of this study was to develop a subspace-based denoising method to suppress involuntary background spikes contaminating voluntary surface EMG recordings. Approach: The Karhunen–Loeve transform was utilized to decompose a noisy signal into a signal subspace and a noise subspace. An optimal estimate of EMG signal is derived from the signal subspace and the noise power. Specifically, this estimator is capable of making a tradeoff between interference reduction and signal distortion. Since the estimator partially relies on the estimate of noise power, an adaptive method was presented to sequentially track the variation of interference power. The proposed method was evaluated using both semi-synthetic and real surface EMG signals. Main results: The experiments confirmed that the proposed method can effectively suppress interferences while keep the distortion of voluntary EMG signal in a low level. The proposed method can greatly facilitate further signal processing, such as onset detection of voluntary muscle activity. Significance: The proposed method can provide a powerful tool for suppressing background spikes and noise contaminating voluntary surface EMG signals of paretic muscles after neurological injuries, which is of great importance for their multi-purpose applications.

Compressive high‐range‐resolution radar imaging using dynamic dictionaries

Compressive sensing theory suggests that accurate reconstruction of a signal can be achieved using its highly undersampled measurements, provided that the signal is sparse in an a priori known dictionary. For the range imaging problem in wideband radar, this dictionary is typically taken to be a DFT basis. However, since practical target scatterers do not lie exactly in the frequency lattice of the discrete Fourier transform (DFT) basis, there is always mismatch between the assumed DFT basis and the actual dictionary for sparsity. To address this, the authors consider the radar echo sparsifying dictionary as refinable and develop a compressive imaging approach using dynamic dictionaries. The approach treats the frequency gridding points as adjustable parameters of the sparsifying Fourier dictionary and achieves dynamical dictionary refinement via iterative optimisation of these parameters. To achieve joint image formation and dictionary refinement, the approach utilises the variational expectation-maximisation algorithm to iteratively perform a two-step process, that is, estimating sparse backscattering coefficients given a dictionary and then updating the dictionary to better fit the data sparsity model. The experimental results based on both synthetic and anechoic chamber data demonstrate that the approach improves the precision in range estimation and suppresses spurious spikes in the constructed profiles.

Skin self-examination and the ABCDE rule in the early diagnosis of melanoma: is the game over? Reply from author

We thank De Giorgi et al. for their letter,1 and for the opportunity to clarify our findings and conclusions.2 It is challenging to interpret the influence of screening on melanoma incidence and mortality rates. Recent data from developed areas, including the U.S.A. and much of Europe, generally show increases in melanoma incidence, with the greatest increases affecting older age groups.3, 4 Mortality rates have stabilized or decreased in the younger age groups, and increased in the older age groups.3, 4 In Australia, incidence is increasing overall, but stabilizing in the youngest age groups,5 whereas melanoma mortality is decreasing in the youngest age groups, levelling in the middle age groups and increasing in those aged ≥ 80 years.6 Worldwide, mortality rates have not kept pace with increasing incidence, giving rise to the notion of overdiagnosis – the unnecessary removal of indolent melanomas that are not life threatening. In support of overdiagnosis, De Giorgi et al. cite a recent U.S. ecological study showing elevated rates of biopsy and melanoma incidence in older age groups (≥ 65 years).7 Under the assumption that melanoma incidence rates are stable, these authors propose that rising biopsy rates are responsible for a spurious spike in incidence.7 It is impossible, of course, to determine the extent to which increasing melanoma incidence is due to excessive biopsy, or due to true increases in underlying incidence rates. However, we do know that melanoma mortality rates in the U.S.A.3 are increasing sharply in the same ≥ 65 years age group, especially among men, suggesting that true increases of incidence are responsible for the increased biopsy rates, rather than the other way around. Further evidence supporting true elevations in incidence is provided by a recent U.S. study showing rising incidence for deeper tumours, and increasing incidence across all socio‐economic strata, considered a surrogate for access to screening.4 Although the question of overdiagnosis will likely remain controversial, improvements in survival and mortality, where evident, may reflect a true benefit of early detection.4, 5, 8

Fast-gating of single-photon avalanche diodes with 200 ps transitions and 30 ps timing jitter

We present circuits and methods for fast-gating a silicon single-photon avalanche diode (SPAD) in order to attain wide dynamic range in the measurement of very faint and very fast optical signals. A mixed-signal amplifier comprising ECL logic and microwave components allows to achieve turn-ON and turn-OFF transition times below 200ps and gating windows from 10ns down to just few hundreds of picoseconds. A differential front-end electronics reads out the avalanche current pulse while rejecting spurious spikes due to the gate pulse, thus achieving a photon detection timing jitter below 30ps. This paper describes the conceived circuit solutions, the overall instrument development and the results of its characterization and validation.

Evaluation of an algorithm based on single-condition decision rules for binary classification of 12-lead ambulatory ECG recording quality

A new algorithm for classifying ECG recording quality based on the detection of commonly observed ECG contaminants which often render the ECG unusable for diagnostic purposes was evaluated. Contaminants (baseline drift, flat line, QRS-artefact, spurious spikes, amplitude stepwise changes, noise) were detected on individual leads from joint time-frequency analysis and QRS amplitude. Classification was based on cascaded single-condition decision rules (SCDR) that tested levels of contaminants against classification thresholds. A supervised learning classifier (SLC) was implemented for comparison. The SCDR and SLC algorithms were trained on an annotated database (Set A, PhysioNet Challenge 2011) of ‘acceptable’ versus ‘unacceptable’ quality recordings using the ‘leave M out’ approach with repeated random partitioning and cross-validation. Two training approaches were considered: (i) balanced, in which training records had equal numbers of ‘acceptable’ and ‘unacceptable’ recordings, (ii) unbalanced, in which the ratio of ‘acceptable’ to ‘unacceptable’ recordings from Set A was preserved. For each training approach, thresholds were calculated, and classification accuracy of the algorithm compared to other rule based algorithms and the SLC using a database for which classifications were unknown (Set B PhysioNet Challenge 2011). The SCDR algorithm achieved the highest accuracy (91.40%) compared to the SLC (90.40%) in spite of its simple logic. It also offers the advantage that it facilitates reporting of meaningful causes of poor signal quality to users.

Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes

Voluntary surface electromyogram (EMG) signal is sometimes contaminated by spurious background spikes of both physiological and extrinsic or accidental origins. A novel method of muscle activity onset detection against such spurious spikes was proposed in this study based primarily on the sample entropy (SampEn) analysis of the surface EMG. The method takes advantage of the nonlinear properties of the SampEn analysis to distinguish voluntary surface EMG signals from spurious background spikes in the complexity domain. To facilitate muscle activity onset detection, the SampEn analysis of surface EMG was first performed to highlight voluntary EMG activity while suppressing spurious background spikes. Then, a SampEn threshold was optimized for muscle activity onset detection. The performance of the proposed method was examined using both semi-synthetic and experimental surface EMG signals. The SampEn based methods effectively reduced the detection error induced by spurious background spikes and achieved improved performance over the methods relying on conventional amplitude thresholding or its extended version in the Teager Kaiser Energy domain.

DESPIKING OF SPACECRAFT ENERGETIC PROTON FLUX TO STUDY GALACTIC COSMIC-RAY MODULATION

Galactic cosmic ray (GCR) is usually assumed as a stable background, with solar influence considered as a modulation. The violent solar energetic particle (SEP) events associated with solar activities change particle fluxes by several orders of magnitude in a few minutes. Thus, the flux observation of GCR provided by satellites may be heavily contaminated by spurious spikes due to SEPs, and that provided by ground-based neutron monitors (NMs) may be contaminated by the system error spikes and the ground level enhancement effect. To obtain the pure background GCR flux for modulation research, the removal of multifarious spikes is necessary. In this article, we use a robust automatic despiking algorithm based on the Poincare map thresholding method provided by Goring and Nikora for purification of the time-series GCR flux observations. We can show that the algorithm is good at cleaning up the heavily contaminated GCR intensity rates measured by both spacecraft and NMs without artificial parameters. In addition, using the algorithm to despike the spacecraft observations of relatively lower energetic proton flux, we get both 11 year and 27 day period cycles comparable to the much higher energy GCR flux data measured by the ground-based NMs.

Sub-Gaussian model of processes with heavy-tailed distributions applied to air permeabilities of fractured tuff

Earth and environmental variables are commonly taken to have multivariate Gaussian or heavy-tailed distributions in space and/or time. This is based on the observation that univariate frequency distributions of corresponding samples appear to be Gaussian or heavy-tailed. Of particular interest to us is the well-documented but heretofore little noticed and unexplained phenomenon that whereas the frequency distribution of log permeability data often seems to be Gaussian, that of corresponding increments tends to exhibit heavy tails. The tails decay as powers of −\( \alpha \) where 1 < \( \alpha \) < 2 is either constant or grows monotonically toward an asymptote with increasing separation distance or lag. We illustrate the latter phenomenon on 1-m scale log air permeabilities from pneumatic tests in 6 vertical and inclined boreholes completed in unsaturated fractured tuff near Superior, Arizona. We then show theoretically and demonstrate numerically, on synthetically generated signals, that whereas the case of constant \( \alpha \) is consistent with a collection of samples from truncated sub-Gaussian fractional Levy noise, a random field (or process) subordinated to truncated fractional Gaussian noise, the case of variable \( \alpha \) is consistent with a collection of samples from truncated sub-Gaussian fractional Levy motion (tfLm), a random field subordinated to truncated fractional Brownian motion. Whereas the first type of signal is relatively regular and characterized by Levy index \( \alpha \), the second is highly irregular (punctuated by spurious spikes) and characterized by the asymptote of \( \alpha \) values associated with its increments. We describe a procedure to estimate the parameters of univariate distributions characterizing such signals and apply it to our log air permeability data. The latter are found to be consistent with a collection of samples from tfLm with \( \alpha \) slightly smaller than 2, which is easily confused with a Gaussian field (characterized by constant \( \alpha \) = 2). The irregular (spiky) nature of this signal is typical of observed fractured rock properties. We propose that distributions of earth and environmental variable be inferred jointly from measured values and their increments in a way that insures consistency between these two sets of data.

Calculating Frequency at Loads in Simulations of Electro-Mechanical Transients

This paper introduces a new method for calculating frequency at an electrical load in simulations of electro-mechanical transients. The method is designed for simulation studies that require accurate models of sensors such as phasor measurement units and F-Net devices, which measure frequency at locations away from generating plants. These sensors are poised to become critical components in the control systems of electrical power grids, and therefore simulation tools that incorporate accurate models of these devices are essential. The method proposed here corrects two drawbacks of using numerically computed phase angle derivatives to approximate frequency. First, it eliminates spurious spikes in frequencies calculated at loads. Second, it eliminates instabilities induced by the simulator in studies of frequency responsive loads. The proposed method is derived from a simplified model of the generators and loads in an electrical system, but in the final analysis does not depend critically on these simplifications and is therefore applicable to more sophisticated models. The method is demonstrated with the simplified model applied to the IEEE 14 and 300 bus systems.

Numerical modeling of shock-to-detonation transition in energetic materials

Determining the hazard classification of energetic materials is important for transportation safety and storage concerns. To avoid costly grain redesign and additional testing, a model that adequately predicts the shock sensitivity of energetic materials is required, particularly the outcome of the Naval Ordnance Laboratory Large Scale Gap Test. The goals of this effort are to develop and validate computational tools that predict the shock sensitivity of energetic materials. Specifically, to use our packing code, Rocpack, to generate morphologies of interest for shock sensitivity assessments, and to use our CFD code, RocSDT, to propagate shocks of various strengths through the pack to predict the onset of detonation.Dealing accurately with the material interfaces in this problem is a long-standing challenge, as familiar strategies lead to spurious temperature spikes, and therefore spurious reaction rate spikes. We describe a new strategy, which does not generate spurious spikes, and demonstrate via a number of test problems that numerical convergence can be achieved. We also examine two problems that are stepping stones to a complete simulation; both are planar. In the first, we consider the passage of a shock wave through pure HMX in which a line of hot spots of the kind generated by void collapse are located a short distance behind the shock. When the hot spot spacing is large, the shock remains a shock; when small, transition to detonation occurs. In the second problem we also insert hot spots, but into a matrix of HMX particles and binder.

An Efficient R-peak Detection Based on New Nonlinear Transformation and First-Order Gaussian Differentiator

In this paper, we present a reliable and efficient automatic R-wave detection based on new nonlinear transformation and simple peak-finding strategy. The detection algorithm consists of four stages. In the first stage, the bandpass filtering and differentiation operations are used to enhance QRS complexes and to reduce out-of-band noise. In the second stage, we introduce a new nonlinear transformation based on energy thresholding, Shannon energy computation, and smoothing processes to obtain a positive-valued feature signal which includes large candidate peaks corresponding to the QRS complex regions. The energy thresholding reduces the effect of spurious noise spikes from muscle artifacts. The Shannon energy transformation amplifies medium amplitudes and results in small deviations between successive peaks. Therefore, the proposed nonlinear transformation is capable of reducing the number of false-positives and false-negatives under small-QRS and wide-QRS complexes and noisy ECG signals. In the third stage, we propose a simple peak-finding strategy based on the first-order Gaussian differentiator (FOGD) that accurately identifies locations of candidate R-peaks in a feature signal. This stage computes convolution of the smooth feature signal and FOGD operator. The resultant convolution output has negative zero-crossings (ZCs) around the candidate peaks of feature signal due to the anti-symmetric nature of the FOGD operator. Thus, these negative ZCS are detected and used as guides to find locations of real R-peaks in an original signal at the fourth stage. Unlike other existing algorithms, the proposed algorithm does not use search back algorithm and learning phase. The proposed algorithm is validated using the standard MIT-BIH arrhythmia database and achieves an average sensitivity of 99.94% and a positive predictivity of 99.96%. Experimental results show that the proposed algorithm outperforms other existing algorithms in case of different QRS complex morphologies (negative, low-amplitude, wide), very big change in amplitudes of adjacent R-peaks, irregular heart rates, and noisy ECG signals.

Advantages and limitations of the fiber‐optic probe hydrophone for characterization of shock waves in water.

The fiber‐optic probe hydrophone (FOPH) (RP Acoustics, Leutenbach, Germany) is the standard for shock wave measurement, as it is omnidirectional with a flat frequency response ranging from static pressure to several megahertz. The FOPH calibration is determined from the equation of state of water, the optical refractive index of the glass/water interface, and the dc level of reflected light. We tested the accuracy of this calibration by placing the sensitive tip of the FOPH under static pressure up to 140 MPa. The FOPH gave accurate readings of applied static pressures provided there were no defects in the fiber. Defects (cracks and chips) in the glass fiber were difficult to control and could occur during routine handling: stripping, cleaving, or mounting. Such defects led to spurious spikes in measured waveforms. Defects were also caused by cavitation damage to the fiber. In addition, cavitation bubbles on the fiber compressed the fiber and resulted in distorted waveform measurement. Thus, although the FOPH is omnidirectional and accurate from zero to tens of megahertz, it is also susceptible to minute defects in the fiber and to cavitation bubble collapse along the fiber. [This work was supported in part by NIH‐DK‐43881 and by Impulse ACPT Contract No. W9113M‐07‐C‐0178.]

Median filtering algorithms for multichannel detectors

Particle detectors of worldwide networks are continuously measuring various secondary particle fluxes incident on Earth surface. At the Aragats Space Environmental Center (ASEC), the data of 12 cosmic ray particle detectors with a total of ∼280 measuring channels (count rates of electrons, muons and neutrons channels) are sent each minute via wireless bridges to a MySQL database. These time series are used for the different tasks of off-line physical analysis and for online forewarning services. Usually long time series contain several types of errors (gaps due to failures of high or low voltage power supply, spurious spikes due to radio interferences, abrupt changes of mean values of several channels or/and slowly trends in mean values due to aging of electronics components, etc.). To avoid erroneous physical inference and false alarms of alerting systems we introduce offline and online filters to “purify” multiple time-series. In the presented paper we classify possible mistakes in time series and introduce median filtering algorithms for online and off-line “purification” of multiple time-series.