Fast Extraction Method Research Articles

Fast recognition and classification of static and dynamic signs for Persian sign language

Sign Language (SL) is the most effective way for communication between deaf and hearing-impaired people. Since most non-deaf people are not familiar with SL, a vision-based translator/interpreter can be a very useful tool to enhance their communication. This paper presents a recognition system for Persian static and dynamic signs. The system is designed based on proposed modified non-linear kernel-based fast feature extraction methods, consisting of hybrid kernel principal component analysis and hybrid kernel discriminant analysis. For recognition of dynamic signs, the proposed feature extraction method is employed in association with spatio-temporal approach. The proposed methods are examined and compared with several existing feature extraction methods, including linear and non-linear kernel-based methods. The experiments indicate that our feature extraction methods significantly outperform other methods and reduce computational time while they achieve high recognition rates. Our simulations achieved a promising classification accuracy rate of 96.78% on static and 96.99% on dynamic signs, respectively.

Fast recognition and classification of static and dynamic signs for Persian sign language

Sign Language (SL) is the most effective way for communication between deaf and hearing-impaired people. Since most non-deaf people are not familiar with SL, a vision-based translator/interpreter can be a very useful tool to enhance their communication. This paper presents a recognition system for Persian static and dynamic signs. The system is designed based on proposed modified non-linear kernel-based fast feature extraction methods, consisting of hybrid kernel principal component analysis and hybrid kernel discriminant analysis. For recognition of dynamic signs, the proposed feature extraction method is employed in association with spatio-temporal approach. The proposed methods are examined and compared with several existing feature extraction methods, including linear and non-linear kernel-based methods. The experiments indicate that our feature extraction methods significantly outperform other methods and reduce computational time while they achieve high recognition rates. Our simulations achieved a promising classification accuracy rate of 96.78% on static and 96.99% on dynamic signs, respectively.

Real-Time State-of-Health Estimation of Lithium-Ion Batteries Based on the Equivalent Internal Resistance

Real-time state-of-health (SoH) estimation is often difficult to obtain due to the unavailability of capacity measurements in real-time monitoring. The equivalent internal resistance (EIR), which is easily obtained and closely related to battery deterioration, is studied as a possible solution for achieving real-time and reliable SoH estimation for lithium-ion batteries. A novel real-time SoH estimation method based on the EIR is introduced for lithium-ion batteries. First, an experimental study of the relationship between the EIR and battery degradation is implemented, and this study is used to develop an empirical description of battery degradation using the EIR vector. Second, a fast extraction method for identifying the EIR in real time is proposed by leveraging the relationship between the EIR vector and state of charge (SoC). Third, a support vector regression (SVR)-based method for real-time SoH estimation is introduced by characterizing the hidden relationship between the EIR vector and battery SoH. The proposed method is demonstrated using laboratory test data. The results show that the proposed method can predict the battery SoH in real time with good accuracy and robustness.

A Fast Extraction Method of Energy Distribution of Border Traps in AlGaN/GaN MIS-HEMT

MIS-HEMT is one of the most promising structures to prohibit the unfavorable gate leakage in conventional AlGaN/GaN HEMTs. However, the extra insulator layer introduces massive border traps at insulator/AlGaN interface and results in the poor reliability. In this brief the energy distribution of border traps in AlGaN/GaN MIS-HEMT gate stack is extracted and investigated through a discharging-based trap energy profile technique. The technique adopts spot-Id sense measurement with 1 millisecond measurement time to capture the “whole (both fast and slow)” border traps. The results are beneficial to improve the reliability of AlGaN/GaN MIS-HEMT.

Development of a New and Fast Extraction Method Based on Solvent-Assisted Dispersive Solid-Phase Extraction for Preconcentration and Trace Detection of Atrazine in Real Matrices.

Because of trace amounts of atrazine in water samples and the complexity of the matrix, direct trace monitoring of atrazine is not feasible by the abovementioned techniques. Hence, an efficient sample pretreatment procedure is necessary for cleanup and preconcentration of atrazine from sample matrices. In the current paper, a new and efficient sample preparation method named solvent-assisted dispersive solid-phase extraction (SA-DSPE), followed by HPLC-UV, was introduced for the monitoring of atrazine at trace levels in environmental water samples. In the present method, benzophenone was used as a sorbent for extraction of target molecules. For dispersing solid sorbents in sample solution, very low milligram amounts of benzophenone and dispersive solvent were mixed and fast-injected into the extraction media. A cloudy solution formed, and after interaction of atrazine and the dispersed solid sorbent, the cloudy solution was centrifuged. The extracted atrazine in the solid phase was dissolved in ethanol and analyzed by HPLC-UV. The introduced method showed a low method detection limit (0.1 μg/L), good precision (relative SD: 3.9-6.9%), and appropriate relative recoveries (95-105%). Within this study, a sensitive and reliable method for the quantification of atrazine in wastewater samples was successfully developed. The obtained figures of merit for the presented sample preparation method were appropriate. The applicability of the method for analysis of atrazine in real matrices was excellent.

Fast unsupervised learning methods for structural health monitoring with large vibration data from dense sensor networks

Data-driven damage localization is an important step of vibration-based structural health monitoring. Statistical pattern recognition based on the prominent steps of feature extraction and statistical decision-making provides an effective and efficient framework for structural health monitoring. However, these steps may become time-consuming or complex when there are large volumes of vibration measurements acquired by dense sensor networks. To deal with this issue, this study proposes fast unsupervised learning methods for feature extraction through autoregressive modeling and damage localization through a new distance measure called Kullback–Leibler divergence with empirical probability measure. The feature extraction approach consists of an iterative algorithm for order selection and parameter estimation aiming to extract residuals in the training phase and another iterative process aiming to extract residuals only in the monitoring phase. The key feature of the proposed approach is the use of correlated residual samples of the autoregressive model as a new time series at each iteration, rather than handling the measured vibration response of the structure. This is shown to highly reduce the computational burden of order selection and feature extraction; moreover, it effectively provides low-order autoregressive models with uncorrelated residuals. The Kullback–Leibler divergence with empirical probability measure method exploits a segmentation technique to subdivide random data into independent sets and provides a distance metric based on the theory of empirical probability measure with no need to explicitly compute the actual probability distributions at the training and monitoring stages. Numerical and experimental benchmarks are then used to assess accuracy and performance of the proposed methods and compare them with some state-of-the-art approaches. Results show that the proposed approaches are successful in feature extraction and damage localization, with a reduced computational burden.

A Fast Positive Sequence Components Extraction Method With Noise Immunity in Unbalanced Grids

Fast and accurate acquisition of voltage components is the basic requirement for grid-tied inverters to realize various control under unbalanced grids. In view of this situation, a delay sampling period filter, which rapidly separates each voltage component by delaying two sampling periods in dq frame, is proposed in this article. In view of the noise interference, a delay operation period filter (DOPF) is proposed to scale the operation period and limit the noise to an acceptable range. The complete positive sequence components (PSC) extraction method based on the developed DOPF+ moving average filter (MAF) algorithm is presented, combing the advantages of DOPF and MAF for eliminating the double-frequency ac components and high-frequency noise, respectively. Finally, the feasibility and priority of the DOPF+MAF algorithm and PSC extraction method have been verified by the physical experimental results.

Investigation on 90Sr concentrations in foods near Ningde Nuclear Power Plant in 2013-2017

Objective To investigate the 90Sr concentration levels in the monitoring areas and the control area at Ningde Nuclear Power Plant (NPP). Methods Food samples were collected from the monitoring areas in 30 km of Ningde NPP and the control area far away. The radioactive levels of 90Sr in food samples were analyzed using the chromatography with 2-(2-ethylhexyl) phosphate fast extraction method. Results In this survey, 90Sr radioactive concentrations in 30 varieties of foods in 6 categories were analyzed. In the monitoring area, 90Sr radioactivity concentration in the samples collected were in the range of 0.017-1.830 Bq/kg for cereals, beans and potatoes, 0.021-0.318 Bq/kg for vegetables, 0.007-7.690 Bq/kg for quatic products, and 0.009-0.184 Bq/kg for meats; whereas, in the control area, cereals, beans and potatoes in 0.017-0.700 Bq/kg, vegetables in 0.034-0.677 Bq/kg, aquatic products in 0.038-3.360 Bq/kg, and meats in 0.019-0.054 Bq/kg. Conclusions The 90Sr radioactive concentrations in foods were far below the food standard limits in this survey. The contribution of evaluated to the public from the 90Sr in foods was neglectable. Key words: Nuclear power plant; Food; Radioactive levels; 90Sr

Electromembrane Extraction of Highly Polar Compounds: Analysis of Cardiovascular Biomarkers in Plasma.

Cardiovascular diseases (CVDs) represent a major concern in today’s society, with more than 17.5 million deaths reported annually worldwide. Recently, five metabolites related to the gut metabolism of phospholipids were identified as promising predictive biomarker candidates for CVD. Validation of those biomarker candidates is crucial for applications to the clinic, showing the need for high-throughput analysis of large numbers of samples. These five compounds, trimethylamine N-oxide (TMAO), choline, betaine, l-carnitine, and deoxy-l-carnitine (4-trimethylammoniobutanoic acid), are highly polar compounds and show poor retention on conventional reversed phase chromatography, which can lead to strong matrix effects when using mass spectrometry detection, especially when high-throughput analysis approaches are used with limited separation of analytes from interferences. In order to reduce the potential matrix effects, we propose a novel fast parallel electromembrane extraction (Pa-EME) method for the analysis of these metabolites in plasma samples. The evaluation of Pa-EME parameters was performed using multi segment injection–capillary electrophoresis–mass spectrometry (MSI-CE-MS). Recoveries up to 100% were achieved, with variability as low as 2%. Overall, this study highlights the necessity of protein precipitation prior to EME for the extraction of highly polar compounds. The developed Pa-EME method was evaluated in terms of concentration range and response function, as well as matrix effects using fast-LC-MS/MS. Finally, the developed workflow was compared to conventional sample pre-treatment, i.e., protein precipitation using methanol, and fast-LC-MS/MS. Data show very strong correlations between both workflows, highlighting the great potential of Pa-EME for high-throughput biological applications.

Liquid chromatographic determination of trace levels of nitrophenols in water samples after dispersive magnetic solid phase extraction.

An efficient and fast dispersive magnetic solid phase extraction method was developed using MIL-101(Cr)/poly (mercaptobenzothiazole)@magnetite nanoparticles for the preconcentration and determination of nitrophenols in river and rain water samples. High-performance liquid chromatography-Ultraviolet instrument was applied for the analysis of target nitrophenols. The effect of several variables on the extraction performance was explored via design of experiment approach. Limits of detection and linear dynamic ranges were attained in the range of 0.05-0.10µg/L and 0.2-250µg/L, respectively. The enrichment factors were in the range of 317-363. The precision (n=3) of dispersive magnetic solid phase extraction method was in the range of 5.3-6.8%. Eventually, the method was utilized for the analysis of target nitrophenols in river and rain water samples.

Based on magnetic beads to develop the kit for extraction of high-quality cell-free DNA from blood of breast cancer patients

Early detection of breast cancer (BC) is very important to avoid mortality caused by it. It is concluded in recent studies that the DNA in patient's serum can be used as biomarker for cancer detection. Therefore, extracting high-quality DNA from patient's serum can achieve this goal. In this work, we compared three methods (phenol-chloroform, column-based and magnetic beads) of extracting DNA from serum by means of purchased commercial kits and concluded that column-based kit is the best one to extract Cell-free DNA (cfDNA). Then we developed a new kit named PuriMag magnetic beads kit to extract DNA from serum of BC patients. Further, we described the properties of this new kit including its recovery capacity and stability. We compared the extraction performances of PuriMag magnetic beads kit and column-based kit and concluded that PuriMag magnetic beads are a better method to extract cfDNA. Surely PuriMag magnetic beads also suit automation and will be a useful method for fast, high-quality and automatic DNA extraction from serum.

Microwave-Assisted Extraction of Ricinine from Ricinus communis Leaves.

The alkaloid ricinine (3-cyano-4-methoxy-N-methyl-2-pyridone) is found in different parts of the Ricinus communis plant and is known to possess several bioactive properties, including strong antioxidant activity. In this study, a new microwave-assisted extraction (MAE) method was developed for the recovery of ricinine from R. communis leaves. The extraction variables studied were extraction temperature (between 125 °C and 175 °C), microwave power (between 500 W and 1000 W), extraction time (between 5 min and 15 min), extraction solvent (between 10% and 90% of EtOAc in MeOH), and solvent-to-sample ratio (between 25:1 mL and 50:1 mL of solvent per gram of the sample). On studying the effects of extraction variables, both solvent and liquid-to-solid ratio were found to exhibit the highest effects on ricinine recovery. A fast (15 min) microwave-assisted extraction method was developed (high temperatures can be applied because the stability of ricinine is proven in the literature), allowing for the recovery of ricinine from R. communis leaves. The study revealed that R. communis leaves had almost 1.5 mg g−1 (dried weight) of ricinine.

A Fast Signal Estimation Method Based on Probability Density Functions for Fault Feature Extraction of Rolling Bearings

Fault diagnosis of rolling bearings is essential to ensure the efficient and safe operation of mechanical equipment. The extraction of fault features of the repetitive transient component from noisy vibration signals is key to bearing fault diagnosis. However, the bearing fault-induced transients are often submerged by strong background noise and interference. To effectively detect such fault-related transient components, this paper proposes a probability- and statistics-based method. The maximum-a-posteriori (MAP) estimator combined with probability density functions (pdfs) of the repetitive transient component, which is modeled by a mixture of two Laplace pdfs and noise, were used to derive the fast estimation model of the transient component. Subsequently, the LapGauss pdf was adopted to model the noisy coefficients. The parameters of the model derived could then be estimated quickly using the iterative expectation–maximization (EM) algorithm. The main contributions of the proposed statistic-based method are that: (1) transients and their wavelet coefficients are modeled as mixed Laplace pdfs; (2) LapGauss pdf is used to model noisy signals and their wavelet coefficients, facilitating the computation of the proposed method; and (3) computational complexity changes linearly with the size of the dataset and thus contributing to the fast estimation, indicated by analysis of the computational performance of the proposed method. The simulation and experimental vibration signals of faulty bearings were applied to test the effectiveness of the proposed method for fast fault feature extraction. Comparisons of computational complexity between the proposed method and other transient extraction methods were also conducted, showing that the computational complexity of the proposed method is proportional to the size of the dataset, leading to a high computational efficiency.

Identifying Mutations by High Resolution Melting in a TILLING Population of Rice

Target Induced Local Lesions In Genomes (TILLING) is a strategy of reverse genetics for the high-throughput screening of induced mutations. However, the TILLING system has less applicability for insertion/deletion (Indel) detection and traditional TILLING needs more complex steps, like CEL I nuclease digestion and gel electrophoresis. To improve the throughput and selection efficiency, and to make the screening of both Indels and single base substitions (SBSs) possible, a new high-resolution melting (HRM)-based TILLING system is developed. Here, we present a detailed HRM-TILLING protocol and show its application in mutation screening. This method can analyze the mutations of PCR amplicons by measuring the denaturation of double-stranded DNA at high temperatures. HRM analysis is directly performed post-PCR without additional processing. Moreover, a simple, safe and fast (SSF) DNA extraction method is integrated with HRM-TILLING to identify both Indels and SBSs. Its simplicity, robustness and high throughput make it potentially useful for mutation scanning in rice and other crops.

Decoction as an Alternative to Producing Indigo Tarum Areuy (Marsdenia tinctoria) Dyes and Its Role on Fiber Coloration from Cellulose

Indigo natural dyes are generally made by maceration-fermentation for 24 - 72 hours impacting on productivity. A faster extraction method is needed by decoction methods. The faster production of indigo tarum areuy dyes is done by decoction at temperatures of 60ºC, 70ºC and 80ºC for 5 minutes, 10 minutes and 15 minutes. The extract was adjusted to reach pH 11. Aeration was carried out 30 minutes and then deposited for 24 hours. Indigo dyes are determined yield, purity of indigo, and evaluation of the quality of the staining results using spectrophotometric methods and color fastness testing of washing. Increased yield of indigo dyes occurs at temperatures of 60ºC and 70ºC. At 80ºC, the yield decreases with increasing decoction time. The best temperature and decoction time for pure indigo yield and the quality of the dyeing results in cotton were at 70ºC for 15 minutes with a yield of 3.625 grams per 500 grams of leaves. The dyeing color of the cotton fabric has increased along with the increase in yield and the purity of the dye. The highest color determination is achieved by a cloth dyed by indigo dyes from the decoction process at 70ºC for 15 minutes, with a<em> </em>K / S value of 3.8754. The extraction time and temperature did not affect the washing fastness.

Extraction of Earthquake-Induced Collapsed Buildings From Bi-Temporal VHR Images Using Object-Level Homogeneity Index and Histogram

The availability of very high resolution (VHR) satellite images has made the analysis of remotely sensed data an increasingly effective tool for the extraction of urban building damage caused by earthquakes. In this study, we proposed a novel method to extract collapsed buildings from bi-temporal VHR images using object-level homogeneity index (OHI) and object histogram. The OHI, which is calculated using an improved spatial homogeneity index of pixel, quantifies internal homogeneity of image object generated from image segmentation. The object histogram quantifies the spectral variability of image object. Ground objects that are intact and significantly different from collapsed buildings, such as vegetation and non-vegetated homogeneous areas, were first extracted from post-event VHR image using OHI and normalized difference vegetation index and were masked out. Collapsed buildings were then extracted from bi-temporal images of the remaining areas using object histogram and a curve matching method, multi-reference spectral angle mapper. The proposed method was evaluated and compared to two existing methods using bi-temporal QuickBird images over Bam, Iran, which was heavily hit by an earthquake in 2003. The experimental results showed that the proposed method outperformed the two comparative methods, with the increase of 11.38 and 5.65% in overall accuracy, and 14.27 and 7.83% in F-score, respectively. The proposed method provides a fast and reliable method for extraction of collapsed buildings.

Research on Fast Extraction Method of Sea Ice Boundary in Arctic Channel Based on MODIS Data

In order to study the distribution of sea ice in the Arctic Channel, sea ice along the Arctic Channel was extracted based on MODIS remote sensing data. Supervised classification method is used to establish a noise removal method for short time series to obtain the sea ice distribution boundary. From this, the navigability of the Arctic waterway is analyzed. The results show that although MODIS data contain more noise effects, more accurate sea ice distribution information can be obtained by effective noise removal methods, which provides support for the judgment of navigation capacity of Arctic waterway.

Development and Validation of a Fecal Extraction Procedure for the Assessment of Multiple Fecal Biomarkers of Intestinal Inflammation (P13-025-19)

ObjectivesFecal biomarkers have emerged as an important tool to assess intestinal inflammation and permeability. Commonly measured biomarkers include calprotectin (CP), myeloperoxidase (MPO), alpha-1-antitrypsin (AAT) and neopterin (NEO). We sought to develop a simple, fast and cost-effective single extraction procedure for use in determining all four biomarkers of interest. The applicability and sensitivity of this procedure for use in healthy adults was examined. MethodsSample extraction buffers and methods including sample weight, dilution, homogenization and centrifugation were all considered in the development of a single extraction procedure. An extraction buffer that included phosphate-buffered saline, phenylmethylsulfonyl fluoride, bovine serum albumin and Tween 20 was used to extract fecal samples. To assess the applicability and sensitivity of the single extraction procedure, concentrations of CP, MPO, AAT and NEO were measured using commercially available sandwich ELISA kits, according to manufacturer’s instructions. ResultsCP, MPO and AAT concentrations were measured in fecal samples of healthy adults (aged 50–80 years, n = 85) and found to be comparable to findings of previously published studies in healthy populations. Mean concentrations of CP and AAT were 3.6 ± 3.8 μg/g of wet weight (range 0.14–18.0 μg/g) and 2.3 ± 0.73 μg/g (range 0.76–5.2 μg/g), respectively. Mean fecal MPO concentrations were 135 ± 24 ng/g (range 3–1290 ng/g). NEO concentrations were examined in a subset of healthy adults (n = 10), with mean concentrations of 18 ± 1 ng/g (range 17–20 ng/g). ConclusionsWe demonstrated the efficacy of a single extraction procedure used to assess multiple fecal biomarkers of intestinal inflammation. This simple, fast and inexpensive extraction method will facilitate the determination of multiple fecal biomarkers which is critical in validating their use as clinical or predictive biomarkers of intestinal inflammation. Funding SourcesSupported by the U.S. Department of Agriculture – Agricultural Research Service (ARS), under Agreement No. 58–1950-4–003, the Bill & Melinda Gates Foundation and Canadian Institutes of Health Research Postdoctoral Fellowship.

Application of a new sample preparation method based on surfactant-assisted dispersive micro solid phase extraction coupled with ultrasonic power for easy and fast simultaneous preconcentration of toluene and xylene biomarkers from human urine samples

A fast and easy surfactant-assisted dispersive micro-solid phase extraction method coupled with ultrasonic power was used for the simultaneous preconcentration and determination of low levels of hippuric and methyl hippuric acid in human urine samples. In the first step, magnetic nanoparticles was coated by titanium oxide nanoparticles (Fe3O4/TiO2 NPs), afterward characterized by the scanning electron microscopy, energy-dispersive X-ray spectroscopy, and Fourier-transform infrared spectroscopy. In the next step, anionic surfactants were thus combined with the synthesized magnetic nanoparticles to create a new adsorbent for increasing the simultaneous extraction of hippuric and methyl hippuric acid. After elution of extracted target molecules by the sorbent, the concentration of these molecules were measured by high-performance liquid chromatography (HPLC–UV). Meanwhile, a statistical approach known as Box–Behnken design was applied for optimizing significant parameters. With the optimum parameters anticipated by the experimental design, the limit of quantification (LOQ) acquired was reported to be 3 µg L− 1, and the calibration curve was linear within the concentrations of 3–1000 µg L− 1. Finally, the method was effectively implemented for the determination of low levels of hippuric and methyl hippuric acid in human urine samples.

High-Resolution Urban Land Mapping in China from Sentinel 1A/2 Imagery Based on Google Earth Engine

Accurate and timely urban land mapping is fundamental to supporting large area environmental and socio-economic research. Most of the available large-area urban land products are limited to a spatial resolution of 30 m. The fusion of optical and synthetic aperture radar (SAR) data for large-area high-resolution urban land mapping has not yet been widely explored. In this study, we propose a fast and effective urban land extraction method using ascending/descending orbits of Sentinel-1A SAR data and Sentinel-2 MSI (MultiSpectral Instrument, Level 1C) optical data acquired from 1 January 2015 to 30 June 2016. Potential urban land (PUL) was identified first through logical operations on yearly mean and standard deviation composites from a time series of ascending/descending orbits of SAR data. A Yearly Normalized Difference Vegetation Index (NDVI) maximum and modified Normalized Difference Water Index (MNDWI) mean composite were generated from Sentinel-2 imagery. The slope image derived from SRTM DEM data was used to mask mountain pixels and reduce the false positives in SAR data over these regions. We applied a region-specific threshold on PUL to extract the target urban land (TUL) and a global threshold on the MNDWI mean, and slope image to extract water bodies and high-slope regions. A majority filter with a three by three window was applied on previously extracted results and the main processing was carried out on the Google Earth Engine (GEE) platform. China was chosen as the testing region to validate the accuracy and robustness of our proposed method through 224,000 validation points randomly selected from high-resolution Google Earth imagery. Additionally, a total of 735 blocks with a size of 900 × 900 m were randomly selected and used to compare our product’s accuracy with the global human settlement layer (GHSL, 2014), GlobeLand30 (2010), and Liu (2015) products. Our method demonstrated the effectiveness of using a fusion of optical and SAR data for large area urban land extraction especially in areas where optical data fail to distinguish urban land from spectrally similar objects. Results show that the average overall, producer’s and user’s accuracies are 88.03%, 94.50% and 82.22%, respectively.