Spike Removal Research Articles

An experimental study on boundary layer transition detection over a pitching supercritical airfoil using hot-film sensors

In the present work, experimental tests are conducted to study boundary layer transition over a supercritical airfoil undergoing pitch oscillations using hot-film sensors. Tests have been undertaken at an incompressible flow. Three reduced frequencies of oscillations and two mean angles of attack are studied and the influences of those parameters on transition location are discussed. Different algorithms are examined on the hot-film signals to detect the transition point. Results show the formation of a laminar separation bubble near the leading edge and at relatively higher angles of attack which leads to the transition of the boundary layer. However, at lower angles of attack, the amplification of the peaks in voltage signal indicate the emergence of the vortical structures within the boundary layer, introducing a different transition mechanism. Moreover, an increase in reduced frequency leads to a delay in transition onset, postponing it to a higher angle of attack, which widens the hysteresis between the upstroke and downstroke motions. Rising the reduced frequency yields in weakening or omission of vortical disturbances ensuing the removal of spikes in the signals. Of the other important results observed, is faster movement of the relaminarization point in the higher mean angle of attack. Finally, a time–frequency analysis of the hot-film signals is performed to investigate evolution of spectral features of the transition due to the pitching motion. An asymmetry is clearly observed in frequency pattern of the signals far from the bubble zone towards the trailing edge; this may reflect the difference between the transition and relaminarization physics. Also, various ranges of frequency were obtained for different transition mechanisms.

Automated weak signal extraction of hyperspectral Raman imaging data by adaptive low‐rank matrix approximation

AbstractHyperspectral Raman imaging has emerged as a promising spectroscopic tool that can provide spatial and molecular information of the sample in a label‐free and noninvasive manner, which is very suitable to the biological and biomedical research. However, the intrinsically weak Raman scattering effect results in the low signal quality of the measured Raman spectra, which has largely limited the application of Raman imaging. In this paper, we develop an adaptive low‐rank matrix approximation method to automatically extract the signal from the noisy hyperspectral Raman imaging data. After spike removal, the hyperspectral Raman imaging data are decomposed into a linear combination of submatrices by singular value decomposition. Next, the submatrices are classified into positive and negative groups according to the SNR contribution. The negative group, reflecting the instrumental noise, is discarded, and the positive group is used to reconstruct the denoised signal. We prove on the simulated data that this algorithm can significantly decrease the normalized mean noise error from 34.35% down to 1.9%. Such a strong denoising performance enables it to efficiently extract the signal from the noisy hyperspectral Raman imaging data, especially under the low SNR condition. We finally apply this algorithm to the fast speed Raman imaging of HeLa cell and surprisingly find that the slight difference of the spectra can be differentiated after signal extraction. This algorithm offers a promising tool in the Raman imaging and also can be extended to other spectroscopic imaging platforms such as fluorescent and IR spectroscopy.

English

Influence of bed roughness on near-bed flow characteristics is of considerable importance, especially in turbulent flows over rough beds. This experimental study is devoted to insight various turbulent flow characteristics such as velocity distributions, Reynolds shear stresses (RSS) and velocity power spectra at the groove, lee and crest level of carrier fluid in an immobile gravel bed stream. Uniform round shape gravels were used in five layers in a laboratory flume. Nortek’s Vectrino four-receiver Acoustic Doppler velocimeter (ADV) probe was used to capture the instantaneous three-dimensional velocity components at groove. The interference between incident and reflected pulses in the ADV data measured in the near-bed flow zone produced spikes. Spikes were detected and filtered with a spike removal algorithm based on the acceleration threshold method. The velocity power spectra provides a satisfactory fit with the Kolmogorov “–5/3 scaling-law” in the inertial subrange. Velocity distributions below the roughness crest level fairly follows a third-degree polynomial series. Key words: Bed roughness, gravel-bed stream, turbulence, Reynolds shear stresses, inertial sub-range.  

Role of nonproductive tillers as transient sinks of assimilates in wheat

ABSTRACT Although source–sink relationships in wheat are well studied, the performance of tillers as transient sinks is still unclear, especially under stress conditions. Thus, this work aimed to study the yield relationships of wheat stems in contrasting genotypes as affected by tiller treatment under stress conditions. Two experiments were conducted under greenhouse conditions using BRS Guamirim (high tillering) and BRS Parrudo (low tillering) wheat cultivars. Four tillering treatments were applied on late tillers in the first growing season (2016) and three in the second (2017): (i) free-tillering, (ii) detillering, (iii) physical suppression (only in the first season) and (iv) “spikes removal”. The main stem and the first four emitted tillers were considered as primary tillers. Plants were submitted to drought (2016) and defoliation stress (2017) at preanthesis. Intraspecific competition among tillers increased under drought stress conditions and decreased the plant performance. Nonproductive late tillers do not improve the performance of primary tillers of wheat plants under drought stress, mainly in high-tillering cultivars. The reduction on the thousand grain weight of only ‘Parrudo’ detillered plants indicates that assimilate remobilization from late tillers to primary tillers and main stem seems to be more effective in low-tillering cultivars.

Time-lapse ground penetrating radar difference reflection imaging of saline tracer flow in fractured rock

The characterization of flow and transport processes in fractured rock is challenging because they cannot be observed directly and hydrologic tests can only provide sparse and local data. Time-lapse ground penetrating radar (GPR) can be a valuable tool to monitor such processes in the subsurface, but it requires highly reproducible data. As part of a tracer injection experiment at the Grimsel Test Site (GTS) in Switzerland, borehole reflection GPR data were acquired in a time-lapse survey to monitor saline tracer flow through a fracture network in crystalline rock. Because the reflections from the tracer in the sub-mm fractures appear extremely weak, a differencing approach has been necessary to identify the tracer signal. Furthermore, several processing steps and corrections had to be applied to meet the reproducibility requirements. These steps include (1) single-trace preprocessing, (2) temporal trace alignment, (3) correction of sampling rate fluctuations, (4) spatial trace alignment, (5) spike removal, and (6) postprocessing procedures applied to the difference images. This allowed successful tracer propagation monitoring with a clear signal that revealed two separate tracer flow paths. The GPR results are confirmed by conductivity meters that were placed in boreholes in the GTS. If sufficient data processing is applied, GPR is shown to be capable of resolving tracer flow through sub-mm aperture fractures by difference reflection imaging even in challenging surroundings where many reflectors are present.

Modified locally-adaptive myriad filters

Modified myriad locally-adaptive filters (LAF) in a sliding data window with complex use of local activity indicators (LAIs) of previously designed myriad LAFs are proposed. Improvement of integral indicator for the whole complex one-dimensional signal and local efficiency indicators at segments of linear change, constant level and step edge of signal function was achieved according to criterion of the mean-square error. Based on LAIs comparison, the myriad LAFs switch adaptive myriad filters with adaptation of linearity parameter K depending upon local scale estimates and with different values of window length and coefficient influencing on K. In one of the proposed LAFs, the LAI are also used for approximate estimation of the noise level and for choosing the corresponding set of component filters with parameters more appropriate to the estimated noise level. The preliminary use of the robust Hampel myriad filter is proposed for better spikes removal. The algorithms are implemented in real time.

ЗАСТОСУВАННЯ ЛОКАЛЬНО-АДАПТИВНОЇ МІРІАДНОЇ ФІЛЬТРАЦІЇ ДЛЯ КОМПЛЕКСНОЇ МОДЕЛІ ОДНОМІРНОГО СИГНАЛУ

Local adaptive algorithms of myriad filtering with adaptation of a sample myriad linearity parameter K depending upon local estimates of signal scale and "hard" switching of parameters of sliding window lengths and coefficients that influence on parameter K are proposed. Statistical estimates of filters quality are obtained using criterion of minimum mean-square error for a complex model of one-dimensional signal that includes elementary signals such as a constant signal, step edge, piecewise linear fragments, linearly increasing and decreasing signals, a peak-like maximum, a piecewise function that consists of constant signal and polynomial curve, a parabolic maximum under conditions of additive Gaussian noise with zero mean and different variances and possible spikes presence. It is shown that the proposed myriad locally-adaptive filters can preserve fragments of rapid changing of signal as step edge and other discontinues due to high dynamic properties in nonlinear mode of myriad filtering and small length of the sliding window and can effectively suppress noise while processing fragments of linear signal behavior and polynomial curves by adjusting the parameter K to a linear mode and increasing the window length. Having high efficiency for all fragments of the complex signal, one of the proposed algorithms provides practically optimal noise suppression at the fragment of linear change of the signal, the other one provides higher quality of step-like and constant signals processing. As a result of application of the proposed myriad locally-adaptive filters, improvement of integral and local performance indicators is shown in comparison to the high effective non-linear locally adaptive algorithm that adaptively switches the output signals between median filter with small window length and alpha-trimmed filters with middle and large windows and local-adaptive myriad algorithm with adaptation of the linearity parameter K which are used for the considered test complex signal. Due to the use of multithreading in programing for parallel calculations, all the considered nonlinear algorithms have possibility to be implemented in real time. The more appropriate algorithm for calculating a sample myriad is the algorithm of minimization of myriad cost function based on a numerical Newton technique because of its best performance in step-like signal fragments and best robustness. In order to ensure better spike removal, it is expedient to pre-process the signal by robust myriad filter.

An EigenECG Network Approach Based on PCANet for Personal Identification from ECG Signal.

We herein propose an EigenECG Network (EECGNet) based on the principal component analysis network (PCANet) for the personal identification of electrocardiogram (ECG) from human biosignal data. The EECGNet consists of three stages. In the first stage, ECG signals are preprocessed by normalization and spike removal. The R peak points in the preprocessed ECG signals are detected. Subsequently, ECG signals are transformed into two-dimensional images to use as the input to the EECGNet. Further, we perform patch-mean removal and PCA algorithm similar to the PCANet from the transformed two-dimensional images. The second stage is almost the same as the first stage, where the mean removal and PCA process are repeatedly performed in the cascaded network. In the final stage, the binary quantization, block sliding, and histogram computation are performed. Thus, this EECGNet performs well without the use of back-propagation to obtain features from the visual content. We constructed a Chosun University (CU)-ECG database from an ECG sensor implemented by ourselves. Further, we used the well-known MIT Beth Israel Hospital (BIH) ECG database. The experimental results clearly reveal the good performance and effectiveness of the proposed method compared with conventional algorithms such as PCA, auto-encoder (AE), extreme learning machine (ELM), and ensemble extreme learning machine (EELM).

Influence of Spike Removal and NPK Levels on Corm Growth and Development of Gladiolus

Influence of Spike Removal and NPK Levels on Corm Growth and Development of Gladiolus

A simple algorithm for despiking Raman spectra

Raman Spectroscopy is a widely used analytical technique, favoured when molecular specificity with minimal sample preparation is required. The majority of Raman instruments use charge-coupled device (ccd) detectors, these are susceptible to cosmic rays and as such multiple spurious spikes can occur in the measurement. These spikes are problematic as they may hinder subsequent analysis, particularly if multivariate data analysis is required. In this work we present a new algorithm to remove these spikes from spectra after acquisition. Specifically, our algorithm uses modified Z scores calculated from the once-differenced detrended spectrum to locate the offending spikes, followed by a simple moving average to remove them. The algorithm is very simple and its execution is essentially instantaneous, resulting in spike-free spectra with minimal distortion of actual Raman data. The presented algorithm represents an improvement on existing spike removal methods by utilising simple, easy to understand mathematical concepts, making it ideal for experts and non-experts alike.

Functional growth inhibition of influenza A and B viruses by liquid and powder components of leaves from the subtropical plant Melia azedarach L.

We evaluated the anti-influenza-virus effects of Melia components and discuss the utility of these components. The effects of leaf components of Melia azedarach L. on viruses were examined, and plaque inhibition tests were performed. The in vivo efficacy of M. azedarach L. was tested in a mouse model. Leaf components of Melia azedarach L. markedly inhibited the growth of various influenza viruses. In an initial screening, multiplication and haemagglutination (HA) activities of H1N1, H3N2, H5, and B influenza viruses were inactivated by the liquid extract of leaves of M. azedarach L. (MLE). Furthermore, plaque inhibition titres of H1N1, H3N2, and B influenza viruses treated with MLE ranged from 103.7 to 104.2. MLE possessed high plaque-inhibitory activity against pandemic avian H5N1, H7N9, and H9N2 vaccine candidate strains, with a plaque inhibition titre of more than 104.2. Notably, the buoyant density decreased from 1.175 to 1.137 g/cm3, and spikeless particles appeared. We identified four anti-influenza virus substances: pheophorbide b, pheophorbide a, pyropheophorbide a, and pheophytin a. Photomorphogenesis inside the envelope may lead to removal of HA and neuraminidase spikes from viruses. Thus, MLE could efficiently remove floating influenza virus in the air space without toxicity. Consistent with this finding, intranasal administration of MLE in mice significantly decreased the occurrence of pneumonia. Additionally, leaf powder of Melia (MLP) inactivated influenza viruses and viruses in the intestines of chickens. MLE and MLP may have applications as novel, safe biological disinfectants for use in humans and poultry.

Effects of source/sink manipulation on grain zinc accumulation by winter wheat genotypes

Agronomy practices aimed at wheat (Triticum aestivum L.) grain Zn biofortification are important to alleviate Zn deficiency in humans, especially for those resource-poor people owing to subsistence on diets dominated by cereal-based foods with low concentrations and bioavailability of Zn. To promote understanding of source-sink flow processes affecting biofortification of wheat with Zn, effects of different source/sink manipulations on grain Zn accumulation by wheat were examined in pot and field at two locations. Treatments included foliar applications of deionized water, sucrose, ZnSO4, sucrose + Zn, defoliation, spike shading or partial removal of spikes. Results showed sucrose + Zn significantly increased grain Zn concentration more than Zn alone. Grain Zn concentration with sucrose + Zn averaged 66 mg kg-1 for ‘Kenong 9204’ and 59 mg kg-1 for ‘Liangxing 99’ in pot, and ranged from 42 to 58 mg kg-1 for ‘Liangxing 99’, ‘Jinan 17’, ‘Jimai 20’, ‘Jimai 22’, and ‘Luyuan 502’ under field, approaching the field-grown target 60 mg kg-1 proposed by World Health Organization. Molar ratios of phytic acid/Zn and phytic acid × Ca/Zn of ‘Jinan 17’, ‘Jimai 20’ or ‘Luyuan 502’ with sucrose + Zn were reduced to be < 15 and < 200, respectively, suggesting higher Zn bioavailability. Defoliation or spike shading decreased grain weight, Zn concentration and content. It is concluded that grain Zn accumulation of wheat can be affected by the source-sink relationship of Zn and/or carbohydrate, and the foliar spray ‘sucrose + Zn’ is recommended for increasing concentration and bioavailability of Zn in wheat grains.

High throughput hardware architecture for accurate semi-global matching

As the most important step of a stereo vision system, stereo matching, which finds the correspondences in stereo image pairs, requires high-quality real-time depth computation. In this paper, a high throughput and high accuracy full-pipeline hardware architecture with disparity and row parallelism is proposed. In the semi-global aggregation stage, to improve the accuracy in discontinuous regions, adaptive weighted path costs are adopted, and, five aggregation paths are used without consuming external memory resources. Finally, L-R check, spike removal and disparity confidence check handle the outlier regions to enhance the disparity map. Disparity map quality evaluations on Middlebury benchmark and real-world outdoor scenery show that the proposed implementation is able to generate high quality disparity maps. The proposed hardware architecture is implemented on a Stratix V FPGA, which results in a throughput of 1280 × 960/197fps with 64 disparity levels at 156MHz. Compared with the latest work, we can get 1.48 times speedup and use 19% more memory with much less computation resources (34.5% less ALUTs and 46.1% less Regis-ters).

Novel Cascaded Switched-Diode Multilevel Inverter for Renewable Energy Integration

In this paper, a new topology of two-stage cascaded switched-diode (CSD) multilevel inverter is proposed for medium-voltage renewable energy integration. First, it aims to reduce the number of switches along with its gate drivers. Thus, the installation space and cost of a multilevel inverter are reduced. The spike removal switch added in the first stage of the inverter provides a flowing path for the reverse load current, and as a result, high voltage spikes occurring at the base of the stepped output voltage based upon conventional CSD multilevel inverter topologies are removed. Moreover, to resolve the problems related to dc source fluctuations of multilevel inverter used for renewable energy integration, the clock phase-shifting (CPS) one-cycle control (OCC) is developed to control the two-stage CSD multilevel inverter. By shifting the clock pulse phase of every cascaded unit, the staircase-like output voltage waveforms are obtained and a strong suppression ability against fluctuations in dc sources is achieved. Simulation and experimental results are discussed to verify the feasibility and performances of the two-stage CSD multilevel inverter controlled by the CPS OCC method.

A single spike deteriorates synaptic conductance estimation

We investigated the estimation accuracy of synaptic conductances by analyzing simulated voltage traces generated by a Hodgkin–Huxley type model. We show that even a single spike substantially deteriorates the estimation. We also demonstrate that two approaches, namely, negative current injection and spike removal, can ameliorate this deterioration.

Spectral restoration method for spatial heterodyne Raman spectrometer

Spatial heterodyne Raman spectrometer (SHRS) is a new developing spectrometer to detect Raman spectra with many advantages over traditional instruments. Several research teams have demonstrated the detection ability of SHRS by experiments and theoretical estimation. However, there still lack descriptions of the spectral restoration method for SHRS. The article provides a spectral restoration method made of two parts, processing method for raw interferogram and processing method for raw Raman spectrum. In the first part, the article describes the processing methods for one‐dimensional and two‐dimensional interferograms. Using flatfielding, filling zeroes, Fourier transform and row averaging for one‐dimensional SHRS or finding spectral row for two‐dimensional SHRS, the raw interferogram achieved by SHRS can be translated to raw Raman spectrum. In another part, the authors provide the spike removal method for raw Raman spectrum. The processing results show that the restoration method has a good performance for the real Raman spectra achieved by an SHRS breadboard. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Determination of a Section of Woji Riverbed Depths for Safe Navigation

The depths of the oceans, seas, rivers, etc. and charting the shape and topography of the ocean floor are only determined though bathymetric processes. And mariners primarily depend on water depths beneath their vessels for safe navigation. Bathymetry also goes a long way to enhance the identification of possible features on the seabed such as elevation changes, rock outcrop, wreckages, sunken vessels, pipeline, or any other obstructions that could cause hazard to navigation. This study employs the bathymetric principles to determine river-bed depths of a section of the Woji River for the purpose of creating a two-dimensional view of the underlying sub-surface and a threedimensional model of the river-bed, to enhance safe navigation. Data acquisition was done using satellite imagery from Google Earth, total tide prediction data, sounding depths using Midas echo sounder and GPS data. Data processing performed on observed bathymetric data include spike removal, tidal correction on instantaneous depths. The processed depths were analysed and presented in form of profile view, graphs, and charts. A flow and sedimentation simulation was carried out using ArcGIS 10.3, to show the nature of the river. The tidal data aided in sounding reduction, and river average depth of 6.022m was obtained. The levelling mis-closure was 0.001m and the average variation in echo sounder calibration was 0.08m. Based on the acquired and output data, the produced chart revealed sedimentation effect on the convex coastline, uneven riverbed topography possibly due to sand excavation, which could pose serious danger to the safety of navigation. Based on these results, the use of Sub-Bottom Profiler and a Multi-Beam Echo Sounder (MBES) is therefore recommended in order to improve the quality of data obtained.

Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

Drought Monitoring by remotely sensed moisture vegetation indexes is being an active research subject as the vegetation spectral responses are showed to be highly correlated to water content. The MODIS (MODerate resolution Imaging Spectro-radiometer) sensor of the Terra satellite provides MOD09A1 product of BRDF (Bidirectional Reflectance Distribution Function) used in computing moisture vegetation indexes (MVI). The exploration of an MVI time-series in the Kroumirie forest in Northern Tunisia showed important noise due to both clouds contamination and sensor defaults that had to be removed. Amongst methods for removing these imperfections, TIMESAT tool was designed for correcting time-series of satellite data and also to retrieve seasonal parameters from smoothed vegetation indexes. The methodology of smoothing functions to fit the time series data is based on two stages. First, a least square fit to the upper envelope of the vegetation indices series is applied. The second stage is achieved by local and adaptive fitting functions. The corrections have been made by spikes removal due to abrupt change of MVI variations and by fitting the MVI time-series to the upper envelop to correct the negative biases of remote sensing vegetation indexes. The adaptive Savitsky-Golay function filter compared to local filtering process produces variations that conserve local variations for all the tested MVI. Seasonal vegetation parameters were extracted for each year of the time-series analysis and compared to the Standardized Precipitation Index (SPI) calculated at meteorological station level and for different time scales. Positive relations were found between SPI and the seasonal parameters expressed by the length and the amplitude of the season, indicating MODIS derived MVI sensitivity to water deficit or surplus conditions. The 6-month SPI showed the best performance when related to water sensitive indexes suggesting that MODIS derived indexes are more correlated to the precipitation variations over seasons.

Nighttime atmospheric chemistry of iodine

Abstract. Little attention has so far been paid to the nighttime atmospheric chemistry of iodine species. Current atmospheric models predict a buildup of HOI and I2 during the night that leads to a spike of IO at sunrise, which is not observed by measurements. In this work, electronic structure calculations are used to survey possible reactions that HOI and I2 could undergo at night in the lower troposphere, and hence reduce their nighttime accumulation. The new reaction NO3+ HOI → IO + HNO3 is proposed, with a rate coefficient calculated from statistical rate theory over the temperature range 260–300 K and at a pressure of 1000 hPa to be k(T) = 2.7 × 10−12 (300 K/T)2.66 cm3 molecule−1 s−1. This reaction is included in two atmospheric models, along with the known reaction between I2 and NO3, to explore a new nocturnal iodine radical activation mechanism. The results show that this iodine scheme leads to a considerable reduction of nighttime HOI and I2, which results in the enhancement of more than 25 % of nighttime ocean emissions of HOI + I2 and the removal of the anomalous spike of IO at sunrise. We suggest that active nighttime iodine can also have a considerable, so far unrecognized, impact on the reduction of the NO3 radical levels in the marine boundary layer (MBL) and hence upon the nocturnal oxidizing capacity of the marine atmosphere. The effect of this is exemplified by the indirect effect on dimethyl sulfide (DMS) oxidation.

Removing Cosmic Spikes Using a Hyperspectral Upper-Bound Spectrum Method.

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in a hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. A comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.