Multiple targets detection method in detection of UWB through-wall radar

NASA Astrophysics Data System (ADS)

Yang, Xiuwei; Yang, Chuanfa; Zhao, Xingwen; Tian, Xianzhong

2017-11-01

In this paper, the problems and difficulties encountered in the detection of multiple moving targets by UWB radar are analyzed. The experimental environment and the penetrating radar system are established. An adaptive threshold method based on local area is proposed to effectively filter out clutter interference The objective of the moving target is analyzed, and the false target is further filtered out by extracting the target feature. Based on the correlation between the targets, the target matching algorithm is proposed to improve the detection accuracy. Finally, the effectiveness of the above method is verified by practical experiment.

A fast image retrieval method based on SVM and imbalanced samples in filtering multimedia message spam

NASA Astrophysics Data System (ADS)

Chen, Zhang; Peng, Zhenming; Peng, Lingbing; Liao, Dongyi; He, Xin

2011-11-01

With the swift and violent development of the Multimedia Messaging Service (MMS), it becomes an urgent task to filter the Multimedia Message (MM) spam effectively in real-time. For the fact that most MMs contain images or videos, a method based on retrieving images is given in this paper for filtering MM spam. The detection method used in this paper is a combination of skin-color detection, texture detection, and face detection, and the classifier for this imbalanced problem is a very fast multi-classification combining Support vector machine (SVM) with unilateral binary decision tree. The experiments on 3 test sets show that the proposed method is effective, with the interception rate up to 60% and the average detection time for each image less than 1 second.

Detection of fatigue cracks by nondestructive testing methods

NASA Technical Reports Server (NTRS)

Anderson, R. T.; Delacy, T. J.; Stewart, R. C.

1973-01-01

The effectiveness was assessed of various NDT methods to detect small tight cracks by randomly introducing fatigue cracks into aluminum sheets. The study included optimizing NDT methods calibrating NDT equipment with fatigue cracked standards, and evaluating a number of cracked specimens by the optimized NDT methods. The evaluations were conducted by highly trained personnel, provided with detailed procedures, in order to minimize the effects of human variability. These personnel performed the NDT on the test specimens without knowledge of the flaw locations and reported on the flaws detected. The performance of these tests was measured by comparing the flaws detected against the flaws present. The principal NDT methods utilized were radiographic, ultrasonic, penetrant, and eddy current. Holographic interferometry, acoustic emission monitoring, and replication methods were also applied on a reduced number of specimens. Generally, the best performance was shown by eddy current, ultrasonic, penetrant and holographic tests. Etching provided no measurable improvement, while proof loading improved flaw detectability. Data are shown that quantify the performances of the NDT methods applied.

Study of comparison between Ultra-high Frequency (UHF) method and ultrasonic method on PD detection for GIS

NASA Astrophysics Data System (ADS)

Li, Yanran; Chen, Duo; Li, Li; Zhang, Jiwei; Li, Guang; Liu, Hongxia

2017-11-01

GIS (gas insulated switchgear), is an important equipment in power system. Partial discharge plays an important role in detecting the insulation performance of GIS. UHF method and ultrasonic method frequently used in partial discharge (PD) detection for GIS. However, few studies have been conducted on comparison of this two methods. From the view point of safety, it is necessary to investigate UHF method and ultrasonic method for partial discharge in GIS. This paper presents study aimed at clarifying the effect of UHF method and ultrasonic method for partial discharge caused by free metal particles in GIS. Partial discharge tests were performed in laboratory simulated environment. Obtained results show the ability of anti-interference of signal detection and the accuracy of fault localization for UHF method and ultrasonic method. A new method based on UHF method and ultrasonic method of PD detection for GIS is proposed in order to greatly enhance the ability of anti-interference of signal detection and the accuracy of detection localization.

Infrared small target detection based on directional zero-crossing measure

NASA Astrophysics Data System (ADS)

Zhang, Xiangyue; Ding, Qinghai; Luo, Haibo; Hui, Bin; Chang, Zheng; Zhang, Junchao

2017-12-01

Infrared small target detection under complex background and low signal-to-clutter ratio (SCR) condition is of great significance to the development on precision guidance and infrared surveillance. In order to detect targets precisely and extract targets from intricate clutters effectively, a detection method based on zero-crossing saliency (ZCS) map is proposed. The original map is first decomposed into different first-order directional derivative (FODD) maps by using FODD filters. Then the ZCS map is obtained by fusing all directional zero-crossing points. At last, an adaptive threshold is adopted to segment targets from the ZCS map. Experimental results on a series of images show that our method is effective and robust for detection under complex backgrounds. Moreover, compared with other five state-of-the-art methods, our method achieves better performance in terms of detection rate, SCR gain and background suppression factor.

An improved EMD method for modal identification and a combined static-dynamic method for damage detection

NASA Astrophysics Data System (ADS)

Yang, Jinping; Li, Peizhen; Yang, Youfa; Xu, Dian

2018-04-01

Empirical mode decomposition (EMD) is a highly adaptable signal processing method. However, the EMD approach has certain drawbacks, including distortions from end effects and mode mixing. In the present study, these two problems are addressed using an end extension method based on the support vector regression machine (SVRM) and a modal decomposition method based on the characteristics of the Hilbert transform. The algorithm includes two steps: using the SVRM, the time series data are extended at both endpoints to reduce the end effects, and then, a modified EMD method using the characteristics of the Hilbert transform is performed on the resulting signal to reduce mode mixing. A new combined static-dynamic method for identifying structural damage is presented. This method combines the static and dynamic information in an equilibrium equation that can be solved using the Moore-Penrose generalized matrix inverse. The combination method uses the differences in displacements of the structure with and without damage and variations in the modal force vector. Tests on a four-story, steel-frame structure were conducted to obtain static and dynamic responses of the structure. The modal parameters are identified using data from the dynamic tests and improved EMD method. The new method is shown to be more accurate and effective than the traditional EMD method. Through tests with a shear-type test frame, the higher performance of the proposed static-dynamic damage detection approach, which can detect both single and multiple damage locations and the degree of the damage, is demonstrated. For structures with multiple damage, the combined approach is more effective than either the static or dynamic method. The proposed EMD method and static-dynamic damage detection method offer improved modal identification and damage detection, respectively, in structures.

Analysis of methods commonly used in biomedicine for treatment versus control comparison of very small samples.

PubMed

Ristić-Djurović, Jasna L; Ćirković, Saša; Mladenović, Pavle; Romčević, Nebojša; Trbovich, Alexander M

2018-04-01

A rough estimate indicated that use of samples of size not larger than ten is not uncommon in biomedical research and that many of such studies are limited to strong effects due to sample sizes smaller than six. For data collected from biomedical experiments it is also often unknown if mathematical requirements incorporated in the sample comparison methods are satisfied. Computer simulated experiments were used to examine performance of methods for qualitative sample comparison and its dependence on the effectiveness of exposure, effect intensity, distribution of studied parameter values in the population, and sample size. The Type I and Type II errors, their average, as well as the maximal errors were considered. The sample size 9 and the t-test method with p = 5% ensured error smaller than 5% even for weak effects. For sample sizes 6-8 the same method enabled detection of weak effects with errors smaller than 20%. If the sample sizes were 3-5, weak effects could not be detected with an acceptable error; however, the smallest maximal error in the most general case that includes weak effects is granted by the standard error of the mean method. The increase of sample size from 5 to 9 led to seven times more accurate detection of weak effects. Strong effects were detected regardless of the sample size and method used. The minimal recommended sample size for biomedical experiments is 9. Use of smaller sizes and the method of their comparison should be justified by the objective of the experiment. Copyright © 2018 Elsevier B.V. All rights reserved.

Estimating the Effects of Detection Heterogeneity and Overdispersion on Trends Estimated from Avian Point Counts

EPA Science Inventory

Point counts are a common method for sampling avian distribution and abundance. Though methods for estimating detection probabilities are available, many analyses use raw counts and do not correct for detectability. We use a removal model of detection within an N-mixture approa...

Flexible, multi-measurement guided wave damage detection under varying temperatures

NASA Astrophysics Data System (ADS)

Douglass, Alexander C. S.; Harley, Joel B.

2018-04-01

Temperature compensation in structural health monitoring helps identify damage in a structure by removing data variations due to environmental conditions, such as temperature. Stretch-based methods are one of the most commonly used temperature compensation methods. To account for variations in temperature, stretch-based methods optimally stretch signals in time to optimally match a measurement to a baseline. All of the data is then compared with the single baseline to determine the presence of damage. Yet, for these methods to be effective, the measurement and the baseline must satisfy the inherent assumptions of the temperature compensation method. In many scenarios, these assumptions are wrong, the methods generate error, and damage detection fails. To improve damage detection, a multi-measurement damage detection method is introduced. By using each measurement in the dataset as a baseline, error caused by imperfect temperature compensation is reduced. The multi-measurement method increases the detection effectiveness of our damage metric, or damage indicator, over time and reduces the presence of additional peaks caused by temperature that could be mistaken for damage. By using many baselines, the variance of the damage indicator is reduced and the effects from damage are amplified. Notably, the multi-measurement improves damage detection over single-measurement methods. This is demonstrated through an increase in the maximum of our damage signature from 0.55 to 0.95 (where large values, up to a maximum of one, represent a statistically significant change in the data due to damage).

Method variation in the impact of missing data on response shift detection.

PubMed

Schwartz, Carolyn E; Sajobi, Tolulope T; Verdam, Mathilde G E; Sebille, Veronique; Lix, Lisa M; Guilleux, Alice; Sprangers, Mirjam A G

2015-03-01

Missing data due to attrition or item non-response can result in biased estimates and loss of power in longitudinal quality-of-life (QOL) research. The impact of missing data on response shift (RS) detection is relatively unknown. This overview article synthesizes the findings of three methods tested in this special section regarding the impact of missing data patterns on RS detection in incomplete longitudinal data. The RS detection methods investigated include: (1) Relative importance analysis to detect reprioritization RS in stroke caregivers; (2) Oort's structural equation modeling (SEM) to detect recalibration, reprioritization, and reconceptualization RS in cancer patients; and (3) Rasch-based item-response theory-based (IRT) models as compared to SEM models to detect recalibration and reprioritization RS in hospitalized chronic disease patients. Each method dealt with missing data differently, either with imputation (1), attrition-based multi-group analysis (2), or probabilistic analysis that is robust to missingness due to the specific objectivity property (3). Relative importance analyses were sensitive to the type and amount of missing data and imputation method, with multiple imputation showing the largest RS effects. The attrition-based multi-group SEM revealed differential effects of both the changes in health-related QOL and the occurrence of response shift by attrition stratum, and enabled a more complete interpretation of findings. The IRT RS algorithm found evidence of small recalibration and reprioritization effects in General Health, whereas SEM mostly evidenced small recalibration effects. These differences may be due to differences between the two methods in handling of missing data. Missing data imputation techniques result in different conclusions about the presence of reprioritization RS using the relative importance method, while the attrition-based SEM approach highlighted different recalibration and reprioritization RS effects by attrition group. The IRT analyses detected more recalibration and reprioritization RS effects than SEM, presumably due to IRT's robustness to missing data. Future research should apply simulation techniques in order to make conclusive statements about the impacts of missing data according to the type and amount of RS.

Study of New Method Combined Ultra-High Frequency (UHF) Method and Ultrasonic Method on PD Detection for GIS

NASA Astrophysics Data System (ADS)

Li, Yanran; Chen, Duo; Zhang, Jiwei; Chen, Ning; Li, Xiaoqi; Gong, Xiaojing

2017-09-01

GIS (gas insulated switchgear), is an important equipment in power system. Partial discharge plays an important role in detecting the insulation performance of GIS. UHF method and ultrasonic method frequently used in partial discharge (PD) detection for GIS. It is necessary to investigate UHF method and ultrasonic method for partial discharge in GIS. However, very few studies have been conducted on the method combined this two methods. From the view point of safety, a new method based on UHF method and ultrasonic method of PD detection for GIS is proposed in order to greatly enhance the ability of anti-interference of signal detection and the accuracy of fault localization. This paper presents study aimed at clarifying the effect of the new method combined UHF method and ultrasonic method. Partial discharge tests were performed in laboratory simulated environment. Obtained results show the ability of anti-interference of signal detection and the accuracy of fault localization for this new method combined UHF method and ultrasonic method.

Acquiring information about neutrino parameters by detecting supernova neutrinos

NASA Astrophysics Data System (ADS)

Huang, Ming-Yang; Guo, Xin-Heng; Young, Bing-Lin

2010-08-01

We consider the supernova shock effects, the Mikheyev-Smirnov-Wolfenstein effects, the collective effects, and the Earth matter effects in the detection of type II supernova neutrinos on the Earth. It is found that the event number of supernova neutrinos depends on the neutrino mass hierarchy, the neutrino mixing angle θ13, and neutrino masses. Therefore, we propose possible methods to identify the mass hierarchy and acquire information about θ13 and neutrino masses by detecting supernova neutrinos. We apply these methods to some current neutrino experiments.

Detecting the global and regional effects of sulphate aerosol geoengineering

NASA Astrophysics Data System (ADS)

Lo, Eunice; Charlton-Perez, Andrew; Highwood, Ellie

2017-04-01

Climate warming is unequivocal. In addition to carbon dioxide emission mitigation, some geoengineering ideas have been proposed to reduce future surface temperature rise. One of these proposals involves injecting sulphate aerosols into the stratosphere to increase the planet's albedo. Monitoring the effectiveness of sulphate aerosol injection (SAI) would require us to be able to distinguish and detect its cooling effect from the climate system's internal variability and other externally forced temperature changes. This research uses optimal fingerprinting techniques together with simulations from the GeoMIP data base to estimate the number of years of observations that would be needed to detect SAI's cooling signal in near-surface air temperature, should 5 Tg of sulphur dioxide be injected into the stratosphere per year on top of RCP4.5 from 2020-2070. The first part of the research compares the application of two detection methods that have different null hypotheses to SAI detection in global mean near-surface temperature. The first method assumes climate noise to be dominated by unforced climate variability and attempts to detect the SAI cooling signal and greenhouse gas driven warming signal in the "observations" simultaneously against this noise. The second method considers greenhouse gas driven warming to be a non-stationary background climate and attempts to detect the net cooling effect of SAI against this background. Results from this part of the research show that the conventional multi-variate detection method that has been extensively used to attribute climate warming to anthropogenic sources could also be applied for geoengineering detection. The second part of the research investigates detection of geoengineering effects on the regional scale. The globe is divided into various sub-continental scale regions and the cooling effect of SAI is looked for in the temperature time series in each of these regions using total least squares multi-variate detection. Results show that surface temperature observations would be most useful for SAI detection in the Northern Hemisphere mid-latitudes, especially in East Asia. This can be used to indicate the optimal observational network for monitoring the effectiveness of SAI in the future, should that be needed.

Research on High Accuracy Detection of Red Tide Hyperspecrral Based on Deep Learning Cnn

NASA Astrophysics Data System (ADS)

Hu, Y.; Ma, Y.; An, J.

2018-04-01

Increasing frequency in red tide outbreaks has been reported around the world. It is of great concern due to not only their adverse effects on human health and marine organisms, but also their impacts on the economy of the affected areas. this paper put forward a high accuracy detection method based on a fully-connected deep CNN detection model with 8-layers to monitor red tide in hyperspectral remote sensing images, then make a discussion of the glint suppression method for improving the accuracy of red tide detection. The results show that the proposed CNN hyperspectral detection model can detect red tide accurately and effectively. The red tide detection accuracy of the proposed CNN model based on original image and filter-image is 95.58 % and 97.45 %, respectively, and compared with the SVM method, the CNN detection accuracy is increased by 7.52 % and 2.25 %. Compared with SVM method base on original image, the red tide CNN detection accuracy based on filter-image increased by 8.62 % and 6.37 %. It also indicates that the image glint affects the accuracy of red tide detection seriously.

Real-time method for establishing a detection map for a network of sensors

DOEpatents

Nguyen, Hung D; Koch, Mark W; Giron, Casey; Rondeau, Daniel M; Russell, John L

2012-09-11

A method for establishing a detection map of a dynamically configurable sensor network. This method determines an appropriate set of locations for a plurality of sensor units of a sensor network and establishes a detection map for the network of sensors while the network is being set up; the detection map includes the effects of the local terrain and individual sensor performance. Sensor performance is characterized during the placement of the sensor units, which enables dynamic adjustment or reconfiguration of the placement of individual elements of the sensor network during network set-up to accommodate variations in local terrain and individual sensor performance. The reconfiguration of the network during initial set-up to accommodate deviations from idealized individual sensor detection zones improves the effectiveness of the sensor network in detecting activities at a detection perimeter and can provide the desired sensor coverage of an area while minimizing unintentional gaps in coverage.

Culture-dependent enumeration methods failed to simultaneously detect disinfectant-injured and genetically modified Escherichia coli in drinking water.

PubMed

Li, Jing; Liu, Lu; Yang, Dong; Liu, Wei-Li; Shen, Zhi-Qiang; Qu, Hong-Mei; Qiu, Zhi-Gang; Hou, Ai-Ming; Wang, Da-Ning; Ding, Chen-Shi; Li, Jun-Wen; Guo, Jian-Hua; Jin, Min

2017-05-24

Underestimation of Escherichia coli in drinking water, an indicator microorganism of sanitary risk, may result in potential risks of waterborne diseases. However, the detection of disinfectant-injured or genetically modified (GM) E. coli has been largely overlooked so far. To evaluate the accuracy of culture-dependent enumeration with regard to disinfectant-injured and GM E. coli, chlorine- or ozone-injured wild-type (WT) and GM E. coli were prepared and characterized. Then, water samples contaminated with these E. coli strains were assayed by four widely used methods, including lactose tryptose broth-based multiple-tube fermentation (MTF), m-endo-based membrane filtration method (MFM), an enzyme substrate test (EST) known as Colilert, and Petrifilm-based testing slip method (TSM). It was found that MTF was the most effective method to detect disinfectant-injured WT E. coli (with 76.9% trials detecting all these bacteria), while this method could not effectively detect GM E. coli (with uninjured bacteria undetectable and a maximal detection rate of 21.5% for the injured). The EST was the only method which enabled considerable enumeration of uninjured GM E. coli, with a detection rate of over 93%. However, the detection rate declined to lower than 45.4% once the GM E. coli was injured by disinfectants. The MFM was invalid for both disinfectant-injured and GM E. coli. This is the first study to report the failure of these commonly used enumeration methods to simultaneously detect disinfectant-injured and GM E. coli. Thus, it highlights the urgent requirement for the development of a more accurate and versatile enumeration method which allows the detection of disinfectant-injured and GM E. coli on the assessment of microbial quality of drinking water.

A hybrid method based on Band Pass Filter and Correlation Algorithm to improve debris sensor capacity

NASA Astrophysics Data System (ADS)

Hong, Wei; Wang, Shaoping; Liu, Haokuo; Tomovic, Mileta M.; Chao, Zhang

2017-01-01

The inductive debris detection is an effective method for monitoring mechanical wear, and could be used to prevent serious accidents. However, debris detection during early phase of mechanical wear, when small debris (<100 um) is generated, requires that the sensor has high sensitivity with respect to background noise. In order to detect smaller debris by existing sensors, this paper presents a hybrid method which combines Band Pass Filter and Correlation Algorithm to improve sensor signal-to-noise ratio (SNR). The simulation results indicate that the SNR will be improved at least 2.67 times after signal processing. In other words, this method ensures debris identification when the sensor's SNR is bigger than -3 dB. Thus, smaller debris will be detected in the same SNR. Finally, effectiveness of the proposed method is experimentally validated.

Detecting chaos in particle accelerators through the frequency map analysis method.

PubMed

Papaphilippou, Yannis

2014-06-01

The motion of beams in particle accelerators is dominated by a plethora of non-linear effects, which can enhance chaotic motion and limit their performance. The application of advanced non-linear dynamics methods for detecting and correcting these effects and thereby increasing the region of beam stability plays an essential role during the accelerator design phase but also their operation. After describing the nature of non-linear effects and their impact on performance parameters of different particle accelerator categories, the theory of non-linear particle motion is outlined. The recent developments on the methods employed for the analysis of chaotic beam motion are detailed. In particular, the ability of the frequency map analysis method to detect chaotic motion and guide the correction of non-linear effects is demonstrated in particle tracking simulations but also experimental data.

Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

NASA Astrophysics Data System (ADS)

Abdulbaqi, Hayder Saad; Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin; Abood, Loay Kadom

2015-04-01

Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.

Method for chromium analysis and speciation

DOEpatents

Aiken, Abigail M.; Peyton, Brent M.; Apel, William A.; Petersen, James N.

2004-11-02

A method of detecting a metal in a sample comprising a plurality of metal is disclosed. The method comprises providing the sample comprising a metal to be detected. The sample is added to a reagent solution comprising an enzyme and a substrate, where the enzyme is inhibited by the metal to be detected. An array of chelating agents is used to eliminate the inhibitory effects of additional metals in the sample. An enzymatic activity in the sample is determined and compared to an enzymatic activity in a control solution to detect the metal to be detected. A method of determining a concentration of the metal in the sample is also disclosed. A method of detecting a valence state of a metal is also disclosed.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions.

PubMed

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-06-20

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

An Improved Time-Frequency Analysis Method in Interference Detection for GNSS Receivers

PubMed Central

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-01-01

In this paper, an improved joint time-frequency (TF) analysis method based on a reassigned smoothed pseudo Wigner–Ville distribution (RSPWVD) has been proposed in interference detection for Global Navigation Satellite System (GNSS) receivers. In the RSPWVD, the two-dimensional low-pass filtering smoothing function is introduced to eliminate the cross-terms present in the quadratic TF distribution, and at the same time, the reassignment method is adopted to improve the TF concentration properties of the auto-terms of the signal components. This proposed interference detection method is evaluated by experiments on GPS L1 signals in the disturbing scenarios compared to the state-of-the-art interference detection approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-terms problem and also preserves good TF localization properties, which has been proven to be effective and valid to enhance the interference detection performance of the GNSS receivers, particularly in the jamming environments. PMID:25905704

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

PubMed Central

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-01-01

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions. PMID:28773035

Hybrid approach for detection of dental caries based on the methods FCM and level sets

NASA Astrophysics Data System (ADS)

Chaabene, Marwa; Ben Ali, Ramzi; Ejbali, Ridha; Zaied, Mourad

2017-03-01

This paper presents a new technique for detection of dental caries that is a bacterial disease that destroys the tooth structure. In our approach, we have achieved a new segmentation method that combines the advantages of fuzzy C mean algorithm and level set method. The results obtained by the FCM algorithm will be used by Level sets algorithm to reduce the influence of the noise effect on the working of each of these algorithms, to facilitate level sets manipulation and to lead to more robust segmentation. The sensitivity and specificity confirm the effectiveness of proposed method for caries detection.

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

PubMed

Uddin, M B; Chow, C M; Su, S W

2018-03-26

Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

Bias correction for estimated QTL effects using the penalized maximum likelihood method.

PubMed

Zhang, J; Yue, C; Zhang, Y-M

2012-04-01

A penalized maximum likelihood method has been proposed as an important approach to the detection of epistatic quantitative trait loci (QTL). However, this approach is not optimal in two special situations: (1) closely linked QTL with effects in opposite directions and (2) small-effect QTL, because the method produces downwardly biased estimates of QTL effects. The present study aims to correct the bias by using correction coefficients and shifting from the use of a uniform prior on the variance parameter of a QTL effect to that of a scaled inverse chi-square prior. The results of Monte Carlo simulation experiments show that the improved method increases the power from 25 to 88% in the detection of two closely linked QTL of equal size in opposite directions and from 60 to 80% in the identification of QTL with small effects (0.5% of the total phenotypic variance). We used the improved method to detect QTL responsible for the barley kernel weight trait using 145 doubled haploid lines developed in the North American Barley Genome Mapping Project. Application of the proposed method to other shrinkage estimation of QTL effects is discussed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Yanmei; Li, Xinli; Bai, Yan

The measurement of multiphase flow parameters is of great importance in a wide range of industries. In the measurement of multiphase, the signals from the sensors are extremely weak and often buried in strong background noise. It is thus desirable to develop effective signal processing techniques that can detect the weak signal from the sensor outputs. In this paper, two methods, i.e., lock-in-amplifier (LIA) and improved Duffing chaotic oscillator are compared to detect and process the weak signal. For sinusoidal signal buried in noise, the correlation detection with sinusoidal reference signal is simulated by using LIA. The improved Duffing chaoticmore » oscillator method, which based on the Wigner transformation, can restore the signal waveform and detect the frequency. Two methods are combined to detect and extract the weak signal. Simulation results show the effectiveness and accuracy of the proposed improved method. The comparative analysis shows that the improved Duffing chaotic oscillator method can restrain noise strongly since it is sensitive to initial conditions.« less

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

On detection of median filtering in digital images

NASA Astrophysics Data System (ADS)

Kirchner, Matthias; Fridrich, Jessica

2010-01-01

In digital image forensics, it is generally accepted that intentional manipulations of the image content are most critical and hence numerous forensic methods focus on the detection of such 'malicious' post-processing. However, it is also beneficial to know as much as possible about the general processing history of an image, including content-preserving operations, since they can affect the reliability of forensic methods in various ways. In this paper, we present a simple yet effective technique to detect median filtering in digital images-a widely used denoising and smoothing operator. As a great variety of forensic methods relies on some kind of a linearity assumption, a detection of non-linear median filtering is of particular interest. The effectiveness of our method is backed with experimental evidence on a large image database.

Ensemble empirical mode decomposition based fluorescence spectral noise reduction for low concentration PAHs

NASA Astrophysics Data System (ADS)

Wang, Shu-tao; Yang, Xue-ying; Kong, De-ming; Wang, Yu-tian

2017-11-01

A new noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed to improve the detection effect for fluorescence spectra. Polycyclic aromatic hydrocarbons (PAHs) pollutants, as a kind of important current environmental pollution source, are highly oncogenic. Using the fluorescence spectroscopy method, the PAHs pollutants can be detected. However, instrument will produce noise in the experiment. Weak fluorescent signals can be affected by noise, so we propose a way to denoise and improve the detection effect. Firstly, we use fluorescence spectrometer to detect PAHs to obtain fluorescence spectra. Subsequently, noises are reduced by EEMD algorithm. Finally, the experiment results show the proposed method is feasible.

Underwater Turbulence Detection Using Gated Wavefront Sensing Technique

PubMed Central

Bi, Ying; Xu, Xiping; Chow, Eddy Mun Tik

2018-01-01

Laser sensing has been applied in various underwater applications, ranging from underwater detection to laser underwater communications. However, there are several great challenges when profiling underwater turbulence effects. Underwater detection is greatly affected by the turbulence effect, where the acquired image suffers excessive noise, blurring, and deformation. In this paper, we propose a novel underwater turbulence detection method based on a gated wavefront sensing technique. First, we elaborate on the operating principle of gated wavefront sensing and wavefront reconstruction. We then setup an experimental system in order to validate the feasibility of our proposed method. The effect of underwater turbulence on detection is examined at different distances, and under different turbulence levels. The experimental results obtained from our gated wavefront sensing system indicate that underwater turbulence can be detected and analyzed. The proposed gated wavefront sensing system has the advantage of a simple structure and high detection efficiency for underwater environments. PMID:29518889

Robust vehicle detection under various environmental conditions using an infrared thermal camera and its application to road traffic flow monitoring.

PubMed

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2013-06-17

We have already proposed a method for detecting vehicle positions and their movements (henceforth referred to as "our previous method") using thermal images taken with an infrared thermal camera. Our experiments have shown that our previous method detects vehicles robustly under four different environmental conditions which involve poor visibility conditions in snow and thick fog. Our previous method uses the windshield and its surroundings as the target of the Viola-Jones detector. Some experiments in winter show that the vehicle detection accuracy decreases because the temperatures of many windshields approximate those of the exterior of the windshields. In this paper, we propose a new vehicle detection method (henceforth referred to as "our new method"). Our new method detects vehicles based on tires' thermal energy reflection. We have done experiments using three series of thermal images for which the vehicle detection accuracies of our previous method are low. Our new method detects 1,417 vehicles (92.8%) out of 1,527 vehicles, and the number of false detection is 52 in total. Therefore, by combining our two methods, high vehicle detection accuracies are maintained under various environmental conditions. Finally, we apply the traffic information obtained by our two methods to traffic flow automatic monitoring, and show the effectiveness of our proposal.

An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3.

PubMed

Liu, Wensong; Yang, Jie; Zhao, Jinqi; Shi, Hongtao; Yang, Le

2018-02-12

The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by R j test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient.

The DNA isolation method has effect on allele drop-out and on the results of fluorescent PCR and DNA fragment analysis.

PubMed

Nagy, Bálint; Bán, Zoltán; Papp, Zoltán

2005-10-01

The quality and the quantity of isolated DNA have an effect on PCR amplifications. The authors studied three DNA isolation protocols (resin binding method using fresh and frozen amniotic fluid samples, and silica adsorption method using fresh samples) on the quantity and on the quality of the isolated DNA. Amniotic fluid samples were obtained from 20 pregnant women. The isolated DNA concentrations were determined by real-time fluorimeter using SYBRGreen I method. Each sample was studied for the presence of 8 STR markers. The authors compared the number of the detected alleles, electrophoretograms and peak areas. There was a significant difference between the concentration of the obtained DNA and in the peak areas between the three isolation protocols. The numbers of detected alleles were different, we observed the most allele drop outs in the resin type DNA isolation protocol from the fresh sample (detected allele numbers 182), followed by resin binding protocol from the frozen samples (detected allele number 243) and by the silica adsorption method (detected allele number 264). The authors demonstrated that the DNA isolation method has an effect on the quantity and quality of the isolated DNA, and on further PCR amplifications.

Evaluation of spectrophotometric and HPLC methods for shikimic acid determination in plants: models in glyphosate-resistant and -susceptible crops.

PubMed

Zelaya, Ian A; Anderson, Jennifer A H; Owen, Micheal D K; Landes, Reid D

2011-03-23

Endogenous shikimic acid determinations are routinely used to assess the efficacy of glyphosate in plants. Numerous analytical methods exist in the public domain for the detection of shikimic acid, yet the most commonly cited comprise spectrophotometric and high-pressure liquid chromatography (HPLC) methods. This paper compares an HPLC and two spectrophotometric methods (Spec 1 and Spec 2) and assesses the effectiveness in the detection of shikimic acid in the tissues of glyphosate-treated plants. Furthermore, the study evaluates the versatility of two acid-based shikimic acid extraction methods and assesses the longevity of plant extract samples under different storage conditions. Finally, Spec 1 and Spec 2 are further characterized with respect to (1) the capacity to discern between shikimic acid and chemically related alicyclic hydroxy acids, (2) the stability of the chromophore (t1/2), (3) the detection limits, and (4) the cost and simplicity of undertaking the analytical procedure. Overall, spectrophotometric methods were more cost-effective and simpler to execute yet provided a narrower detection limit compared to HPLC. All three methods were specific to shikimic acid and detected the compound in the tissues of glyphosate-susceptible crops, increasing exponentially in concentration within 24 h of glyphosate application and plateauing at approximately 72 h. Spec 1 estimated more shikimic acid in identical plant extract samples compared to Spec 2 and, likewise, HPLC detection was more effective than spectrophotometric determinations. Given the unprecedented global adoption of glyphosate-resistant crops and concomitant use of glyphosate, an effective and accurate assessment of glyphosate efficacy is important. Endogenous shikimic acid determinations are instrumental in corroborating the efficacy of glyphosate and therefore have numerous applications in herbicide research and related areas of science as well as resolving many commercial issues as a consequence of glyphosate utilization.

Wavelet threshold method of resolving noise interference in periodic short-impulse signals chaotic detection

NASA Astrophysics Data System (ADS)

Deng, Ke; Zhang, Lu; Luo, Mao-Kang

2010-03-01

The chaotic oscillator has already been considered as a powerful method to detect weak signals, even weak signals accompanied with noises. However, many examples, analyses and simulations indicate that chaotic oscillator detection system cannot guarantee the immunity to noises (even white noise). In fact the randomness of noises has a serious or even a destructive effect on the detection results in many cases. To solve this problem, we present a new detecting method based on wavelet threshold processing that can detect the chaotic weak signal accompanied with noise. All theoretical analyses and simulation experiments indicate that the new method reduces the noise interferences to detection significantly, thereby making the corresponding chaotic oscillator that detects the weak signals accompanied with noises more stable and reliable.

a Method of Time-Series Change Detection Using Full Polsar Images from Different Sensors

NASA Astrophysics Data System (ADS)

Liu, W.; Yang, J.; Zhao, J.; Shi, H.; Yang, L.

2018-04-01

Most of the existing change detection methods using full polarimetric synthetic aperture radar (PolSAR) are limited to detecting change between two points in time. In this paper, a novel method was proposed to detect the change based on time-series data from different sensors. Firstly, the overall difference image of a time-series PolSAR was calculated by ominous statistic test. Secondly, difference images between any two images in different times ware acquired by Rj statistic test. Generalized Gaussian mixture model (GGMM) was used to obtain time-series change detection maps in the last step for the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection by using the time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can detect the time-series change from different sensors.

Dent detection method by high gradation photometric stereo

NASA Astrophysics Data System (ADS)

Hasebe, Akihisa; Kato, Kunihito; Tanahashi, Hideki; Kubota, Naoki

2017-03-01

This paper describes an automatic detection method for small dents on a metal plate. We adopted the photometric stereo as a three-dimensional measurement method, which has advantages in terms of low cost and short measurement time. In addition, a high precision measurement system was realized by using an 18bit camera. Furthermore, the small dent on the surface of the metal plate is detected by the inner product of the measured normal vectors using photometric stereo. Finally, the effectiveness of our method was confirmed by detection experiments.

A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data

NASA Astrophysics Data System (ADS)

Sun, Lin; Liu, Xinyan; Yang, Yikun; Chen, TingTing; Wang, Quan; Zhou, Xueying

2018-04-01

Although enhanced over prior Landsat instruments, Landsat 8 OLI can obtain very high cloud detection precisions, but for the detection of cloud shadows, it still faces great challenges. Geometry-based cloud shadow detection methods are considered the most effective and are being improved constantly. The Function of Mask (Fmask) cloud shadow detection method is one of the most representative geometry-based methods that has been used for cloud shadow detection with Landsat 8 OLI. However, the Fmask method estimates cloud height employing fixed temperature rates, which are highly uncertain, and errors of large area cloud shadow detection can be caused by errors in estimations of cloud height. This article improves the geometry-based cloud shadow detection method for Landsat OLI from the following two aspects. (1) Cloud height no longer depends on the brightness temperature of the thermal infrared band but uses a possible dynamic range from 200 m to 12,000 m. In this case, cloud shadow is not a specific location but a possible range. Further analysis was carried out in the possible range based on the spectrum to determine cloud shadow location. This effectively avoids the cloud shadow leakage caused by the error in the height determination of a cloud. (2) Object-based and pixel spectral analyses are combined to detect cloud shadows, which can realize cloud shadow detection from two aspects of target scale and pixel scale. Based on the analysis of the spectral differences between the cloud shadow and typical ground objects, the best cloud shadow detection bands of Landsat 8 OLI were determined. The combined use of spectrum and shape can effectively improve the detection precision of cloud shadows produced by thin clouds. Several cloud shadow detection experiments were carried out, and the results were verified by the results of artificial recognition. The results of these experiments indicated that this method can identify cloud shadows in different regions with correct accuracy exceeding 80%, approximately 5% of the areas were wrongly identified, and approximately 10% of the cloud shadow areas were missing. The accuracy of this method is obviously higher than the recognition accuracy of Fmask, which has correct accuracy lower than 60%, and the missing recognition is approximately 40%.

Wavelet Fusion for Concealed Object Detection Using Passive Millimeter Wave Sequence Images

NASA Astrophysics Data System (ADS)

Chen, Y.; Pang, L.; Liu, H.; Xu, X.

2018-04-01

PMMW imaging system can create interpretable imagery on the objects concealed under clothing, which gives the great advantage to the security check system. Paper addresses wavelet fusion to detect concealed objects using passive millimeter wave (PMMW) sequence images. According to PMMW real-time imager acquired image characteristics and storage methods firstly, using the sum of squared difference (SSD) as the image-related parameters to screen the sequence images. Secondly, the selected images are optimized using wavelet fusion algorithm. Finally, the concealed objects are detected by mean filter, threshold segmentation and edge detection. The experimental results show that this method improves the detection effect of concealed objects by selecting the most relevant images from PMMW sequence images and using wavelet fusion to enhance the information of the concealed objects. The method can be effectively applied to human body concealed object detection in millimeter wave video.

Matrix-Matching as an Improvement Strategy for the Detection of Pesticide Residues.

PubMed

Giacinti, Géraldine; Raynaud, Christine; Capblancq, Sophie; Simon, Valérie

2016-05-01

More than 90% of the pesticides residues in apples are located in the peel. We developed a gas chromatography/ion trap tandem mass spectrometry method for investigating all detectable residues in the peel of 3 apple varieties. Sample preparation is based on the use of the Quick Easy Cheap Effective Rugged and Safe method on the whole fruit, the flesh, and the peel. Pesticide residues were quantified with solvent-matched and matrix-matched standards, by spiking apple sample extracts. Matrix effects dependent on the type of extract (fruit, flesh, or peel) and the apple variety were detected. The best data processing methods involved normalizing matrix effect rates by matrix-matched internal/external calibration. Boscalid, captan, chlorpyrifos, fludioxonil, and pyraclostrobin were the most frequently detected pesticides. However, their concentrations in the whole fruit were below European maximum residue levels. Despite negative matrix effects, the residues in peel were detected at concentrations up to 10 times higher than those in whole fruits. Consequently, other pesticide residues present at concentrations below the limit of quantification in the whole fruit were detected in the peel. © 2016 Institute of Food Technologists®

A review of Cry protein detection with enzyme-linked immunosorbent assays

USDA-ARS?s Scientific Manuscript database

Several detection methods are available to monitor the fate of Cry proteins in the environment, enzyme-linked immunosorbent assays (ELISAs) have emerged as the preferred detection method, due to their cost-effectiveness, ease of use, and rapid results. Validation of ELISAs is necessary to ensure acc...

[Effect of Parasep® feces centrifuge tube method on detecting schistosome eggs].

PubMed

Ma, Nian; Zhang, Hua-ming; Liu, Xiong; Xiao, Chuan-yun; Wen, Xiao-hong; Li, Xia; Dong, Li-chun; Cui, Cai-xia; Tu, Zu-wu

2014-08-01

To evaluate the effect of the Parasep® feces centrifuge tube method on detecting schistosome eggs. A total of 803 residents aged from 6-65 years were selected in 2 schistosomiasis endemic villages, Jiangling County, Hubei Province, and their stool samples were collected and detected parallelly by the Kato-Katz technique, nylon silk egg hatching method, and Parasep® feces centrifuge tube method at the same time. Among the 803 people, 15 cases were found of schistosome egg positive, and the positive rate was 1.87%. The positive rates of the Kato-Katz technique, nylon silk egg hatching method, and Parasep® feces centrifuge tube method were 0.75%, 1.49% and 1.12%, respectively. The schistosome eggs got with the Parasep® feces centrifuge tube method were clear and easy to identify. In low endemic areas of schistosomiasis, the Parasep® feces centrifuge tube method can be used as schistosomiasis japonica etiology diagnosis method.

Dangerous gas detection based on infrared video

NASA Astrophysics Data System (ADS)

Ding, Kang; Hong, Hanyu; Huang, Likun

2018-03-01

As the gas leak infrared imaging detection technology has significant advantages of high efficiency and remote imaging detection, in order to enhance the detail perception of observers and equivalently improve the detection limit, we propose a new type of gas leak infrared image detection method, which combines background difference methods and multi-frame interval difference method. Compared to the traditional frame methods, the multi-frame interval difference method we proposed can extract a more complete target image. By fusing the background difference image and the multi-frame interval difference image, we can accumulate the information of infrared target image of the gas leak in many aspect. The experiment demonstrate that the completeness of the gas leakage trace information is enhanced significantly, and the real-time detection effect can be achieved.

Measurement of pattern roughness and local size variation using CD-SEM: current status

NASA Astrophysics Data System (ADS)

Fukuda, Hiroshi; Kawasaki, Takahiro; Kawada, Hiroki; Sakai, Kei; Kato, Takashi; Yamaguchi, Satoru; Ikota, Masami; Momonoi, Yoshinori

2018-03-01

Measurement of line edge roughness (LER) is discussed from four aspects: edge detection, PSD prediction, sampling strategy, and noise mitigation, and general guidelines and practical solutions for LER measurement today are introduced. Advanced edge detection algorithms such as wave-matching method are shown effective for robustly detecting edges from low SNR images, while conventional algorithm with weak filtering is still effective in suppressing SEM noise and aliasing. Advanced PSD prediction method such as multi-taper method is effective in suppressing sampling noise within a line edge to analyze, while number of lines is still required for suppressing line to line variation. Two types of SEM noise mitigation methods, "apparent noise floor" subtraction method and LER-noise decomposition using regression analysis are verified to successfully mitigate SEM noise from PSD curves. These results are extended to LCDU measurement to clarify the impact of SEM noise and sampling noise on LCDU.

Effective Heart Disease Detection Based on Quantitative Computerized Traditional Chinese Medicine Using Representation Based Classifiers.

PubMed

Shu, Ting; Zhang, Bob; Tang, Yuan Yan

2017-01-01

At present, heart disease is the number one cause of death worldwide. Traditionally, heart disease is commonly detected using blood tests, electrocardiogram, cardiac computerized tomography scan, cardiac magnetic resonance imaging, and so on. However, these traditional diagnostic methods are time consuming and/or invasive. In this paper, we propose an effective noninvasive computerized method based on facial images to quantitatively detect heart disease. Specifically, facial key block color features are extracted from facial images and analyzed using the Probabilistic Collaborative Representation Based Classifier. The idea of facial key block color analysis is founded in Traditional Chinese Medicine. A new dataset consisting of 581 heart disease and 581 healthy samples was experimented by the proposed method. In order to optimize the Probabilistic Collaborative Representation Based Classifier, an analysis of its parameters was performed. According to the experimental results, the proposed method obtains the highest accuracy compared with other classifiers and is proven to be effective at heart disease detection.

Oil leakage detection for electric power equipment based on ultraviolet fluorescence effect

NASA Astrophysics Data System (ADS)

Zhang, Jing; Wang, Jian-hui; Xu, Bin; Huang, Zhi-dong; Huang, Lan-tao

2018-03-01

This paper presents a method to detect the oil leakage of high voltage power equipment based on ultraviolet fluorescence effect. The method exploits the principle that the insulating oil has the fluorescent effect under the irradiation of specific ultraviolet light. The emission spectrum of insulating oil under excitation light with different wavelengths is measured and analyzed first. On this basis, a portable oil leakage detective device for high voltage power equipment is designed and developed with a selected 365 nm ultraviolet as the excitation light and the low light level camera as the fluorescence image collector. Then, the feasibility of the proposed method and device in different conditions is experimentally verified in the laboratory environment. Finally, the developed oil leakage detective device is applied to 500 kV Xiamen substation and Quanzhou substation. And the results show that the device can detect the oil leakage of high voltage electrical equipment quickly and conveniently even under the condition of a slight oil leakage especially in the low light environment.

Evaluating nanoscale ultra-thin metal films by means of lateral photovoltaic effect in metal-semiconductor structure.

PubMed

Zheng, Diyuan; Yu, Chongqi; Zhang, Qian; Wang, Hui

2017-12-15

Nanoscale metal-semiconductor (MS) structure materials occupy an important position in semiconductor and microelectronic field due to their abundant physical phenomena and effects. The thickness of metal films is a critical factor in determining characteristics of MS devices. How to detect or evaluate the metal thickness is always a key issue for realizing high performance MS devices. In this work, we propose a direct surface detection by use of the lateral photovoltaic effect (LPE) in MS structure, which can not only measure nanoscale thickness, but also detect the fluctuation of metal films. This method is based on the fact that the output of lateral photovoltaic voltage (LPV) is closely linked with the metal thickness at the laser spot. We believe this laser-based contact-free detection is a useful supplement to the traditional methods, such as AFM, SEM, TEM or step profiler. This is because these traditional methods are always incapable of directly detecting ultra-thin metal films in MS structure materials.

Evaluating nanoscale ultra-thin metal films by means of lateral photovoltaic effect in metal-semiconductor structure

NASA Astrophysics Data System (ADS)

Zheng, Diyuan; Yu, Chongqi; Zhang, Qian; Wang, Hui

2017-12-01

Nanoscale metal-semiconductor (MS) structure materials occupy an important position in semiconductor and microelectronic field due to their abundant physical phenomena and effects. The thickness of metal films is a critical factor in determining characteristics of MS devices. How to detect or evaluate the metal thickness is always a key issue for realizing high performance MS devices. In this work, we propose a direct surface detection by use of the lateral photovoltaic effect (LPE) in MS structure, which can not only measure nanoscale thickness, but also detect the fluctuation of metal films. This method is based on the fact that the output of lateral photovoltaic voltage (LPV) is closely linked with the metal thickness at the laser spot. We believe this laser-based contact-free detection is a useful supplement to the traditional methods, such as AFM, SEM, TEM or step profiler. This is because these traditional methods are always incapable of directly detecting ultra-thin metal films in MS structure materials.

Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification

NASA Astrophysics Data System (ADS)

Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang

2017-12-01

To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.

SAR Image Change Detection Based on Fuzzy Markov Random Field Model

NASA Astrophysics Data System (ADS)

Zhao, J.; Huang, G.; Zhao, Z.

2018-04-01

Most existing SAR image change detection algorithms only consider single pixel information of different images, and not consider the spatial dependencies of image pixels. So the change detection results are susceptible to image noise, and the detection effect is not ideal. Markov Random Field (MRF) can make full use of the spatial dependence of image pixels and improve detection accuracy. When segmenting the difference image, different categories of regions have a high degree of similarity at the junction of them. It is difficult to clearly distinguish the labels of the pixels near the boundaries of the judgment area. In the traditional MRF method, each pixel is given a hard label during iteration. So MRF is a hard decision in the process, and it will cause loss of information. This paper applies the combination of fuzzy theory and MRF to the change detection of SAR images. The experimental results show that the proposed method has better detection effect than the traditional MRF method.

A biomolecular detection method based on charge pumping in a nanogap embedded field-effect-transistor biosensor

NASA Astrophysics Data System (ADS)

Kim, Sungho; Ahn, Jae-Hyuk; Park, Tae Jung; Lee, Sang Yup; Choi, Yang-Kyu

2009-06-01

A unique direct electrical detection method of biomolecules, charge pumping, was demonstrated using a nanogap embedded field-effect-transistor (FET). With aid of a charge pumping method, sensitivity can fall below the 1 ng/ml concentration regime in antigen-antibody binding of an avian influenza case. Biomolecules immobilized in the nanogap are mainly responsible for the acute changes of the interface trap density due to modulation of the energy level of the trap. This finding is supported by a numerical simulation. The proposed detection method for biomolecules using a nanogap embedded FET represents a foundation for a chip-based biosensor capable of high sensitivity.

Detection of Moving Targets Using Soliton Resonance Effect

NASA Technical Reports Server (NTRS)

Kulikov, Igor K.; Zak, Michail

2013-01-01

The objective of this research was to develop a fundamentally new method for detecting hidden moving targets within noisy and cluttered data-streams using a novel "soliton resonance" effect in nonlinear dynamical systems. The technique uses an inhomogeneous Korteweg de Vries (KdV) equation containing moving-target information. Solution of the KdV equation will describe a soliton propagating with the same kinematic characteristics as the target. The approach uses the time-dependent data stream obtained with a sensor in form of the "forcing function," which is incorporated in an inhomogeneous KdV equation. When a hidden moving target (which in many ways resembles a soliton) encounters the natural "probe" soliton solution of the KdV equation, a strong resonance phenomenon results that makes the location and motion of the target apparent. Soliton resonance method will amplify the moving target signal, suppressing the noise. The method will be a very effective tool for locating and identifying diverse, highly dynamic targets with ill-defined characteristics in a noisy environment. The soliton resonance method for the detection of moving targets was developed in one and two dimensions. Computer simulations proved that the method could be used for detection of singe point-like targets moving with constant velocities and accelerations in 1D and along straight lines or curved trajectories in 2D. The method also allows estimation of the kinematic characteristics of moving targets, and reconstruction of target trajectories in 2D. The method could be very effective for target detection in the presence of clutter and for the case of target obscurations.

[A review on studies and applications of near infrared spectroscopy technique(NIRS) in detecting quality of hay].

PubMed

Ding, Wu-Rong; Gan, You-Min; Guo, Xu-Sheng; Yang, Fu-Yu

2009-02-01

The quality of hay can directly affect the price of hay and also livestock productivity. Many kinds of methods have been developed for detecting the quality of hay and the method of near infrared spectroscopy (NIRS) has been widely used with consideration of its fast, effective and nondestructive characteristics during detecting process. In the present paper, the feasibility and effectiveness of application of NIRS to detecting hay quality were expounded. Meanwhile, the advance in the study of using NIRS to detect chemical compositions, extent of incursion by epiphyte, amount of toxicant excreted by endogenetic epiphyte and some minim components that can not be detected by using chemical methods were also introduced detailedly. Based on the review of the progresses in using NIRS to detect the quality of hay, it can be concluded that using NIRS to detect hay quality can avoid the disadvantages of time wasting, complication and high cost when using traditional chemical method. And for better utilization of NIRS in practice, some more studies still need to be implemented to further perfect and improve the utilization of NIRS for detecting forage quality, and more accurate modes and systematic analysis software need to be established in times to come.

USEPA Approach for the Detection and Quantification of Enterococcus by qPCR

EPA Science Inventory

The Beach Act 2000 specified that EPA should develop: Appropriate and effective indicators for improviding detection in a timely manner of pathogens in coastal waters Appropriate, accurate, expeditious and cost-effective methods for the timely detection of pathogens in coas...

A method to detect layover and shadow based on distributed spaceborne single-baseline InSAR

NASA Astrophysics Data System (ADS)

Yun, Ren; Huanxin, Zou; Shilin, Zhou; Hao, Sun; Kefeng, Ji

2014-03-01

Layover and Shadow are inevitable phenomenena in InSAR, which seriously destroy the continuity of interferometric phase images and present difficulties in the follow-up phase unwrapping. Thus, it's significant to detect layover and shadow. This paper presents an approach to detect layover and shadow using the auto-correlation matrix and amplitude of the two images. The method can make full use of the spatial information of neighboring pixels and effectively detect layover and shadow regions in the case of low registration accuracy. Experiment result on the simulated data verifies effectiveness of the algorithm.

[Development of a Computer-aided Diagnosis System to Distinguish between Benign and Malignant Mammary Tumors in Dynamic Magnetic Resonance Images: Automatic Detection of the Position with the Strongest Washout Effect in the Tumor].

PubMed

Miyazaki, Yoshiaki; Tabata, Nobuyuki; Taroura, Tomomi; Shinozaki, Kenji; Kubo, Yuichiro; Tokunaga, Eriko; Taguchi, Kenichi

We propose a computer-aided diagnostic (CAD) system that uses time-intensity curves to distinguish between benign and malignant mammary tumors. Many malignant tumors show a washout pattern in time-intensity curves. Therefore, we designed a program that automatically detects the position with the strongest washout effect using the technique, such as the subtraction technique, which extracts only the washout area in the tumor, and by scanning data in 2×2 pixel region of interest (ROI). Operation of this independently developed program was verified using a phantom system that simulated tumors. In three cases of malignant tumors, the washout pattern detection rate in images with manually set ROI was ≤6%, whereas the detection rate with our novel method was 100%. In one case of a benign tumor, when the same method was used, we checked that there was no washout effect and detected the persistent pattern. Thus, the distinction between benign and malignant tumors using our method was completely consistent with the pathological diagnoses made. Our novel method is therefore effective for differentiating between benign and malignant mammary tumors in dynamic magnetic resonance images.

Moving target detection method based on improved Gaussian mixture model

NASA Astrophysics Data System (ADS)

Ma, J. Y.; Jie, F. R.; Hu, Y. J.

2017-07-01

Gaussian Mixture Model is often employed to build background model in background difference methods for moving target detection. This paper puts forward an adaptive moving target detection algorithm based on improved Gaussian Mixture Model. According to the graylevel convergence for each pixel, adaptively choose the number of Gaussian distribution to learn and update background model. Morphological reconstruction method is adopted to eliminate the shadow.. Experiment proved that the proposed method not only has good robustness and detection effect, but also has good adaptability. Even for the special cases when the grayscale changes greatly and so on, the proposed method can also make outstanding performance.

Spatial cluster detection for repeatedly measured outcomes while accounting for residential history.

PubMed

Cook, Andrea J; Gold, Diane R; Li, Yi

2009-10-01

Spatial cluster detection has become an important methodology in quantifying the effect of hazardous exposures. Previous methods have focused on cross-sectional outcomes that are binary or continuous. There are virtually no spatial cluster detection methods proposed for longitudinal outcomes. This paper proposes a new spatial cluster detection method for repeated outcomes using cumulative geographic residuals. A major advantage of this method is its ability to readily incorporate information on study participants relocation, which most cluster detection statistics cannot. Application of these methods will be illustrated by the Home Allergens and Asthma prospective cohort study analyzing the relationship between environmental exposures and repeated measured outcome, occurrence of wheeze in the last 6 months, while taking into account mobile locations.

Traffic Sign Detection Based on Biologically Visual Mechanism

NASA Astrophysics Data System (ADS)

Hu, X.; Zhu, X.; Li, D.

2012-07-01

TSR (Traffic sign recognition) is an important problem in ITS (intelligent traffic system), which is being paid more and more attention for realizing drivers assisting system and unmanned vehicle etc. TSR consists of two steps: detection and recognition, and this paper describe a new traffic sign detection method. The design principle of the traffic sign is comply with the visual attention mechanism of human, so we propose a method using visual attention mechanism to detect traffic sign ,which is reasonable. In our method, the whole scene will firstly be analyzed by visual attention model to acquire the area where traffic signs might be placed. And then, these candidate areas will be analyzed according to the shape characteristics of the traffic sign to detect traffic signs. In traffic sign detection experiments, the result shows the proposed method is effectively and robust than other existing saliency detection method.

Why conventional detection methods fail in identifying the existence of contamination events.

PubMed

Liu, Shuming; Li, Ruonan; Smith, Kate; Che, Han

2016-04-15

Early warning systems are widely used to safeguard water security, but their effectiveness has raised many questions. To understand why conventional detection methods fail to identify contamination events, this study evaluates the performance of three contamination detection methods using data from a real contamination accident and two artificial datasets constructed using a widely applied contamination data construction approach. Results show that the Pearson correlation Euclidean distance (PE) based detection method performs better for real contamination incidents, while the Euclidean distance method (MED) and linear prediction filter (LPF) method are more suitable for detecting sudden spike-like variation. This analysis revealed why the conventional MED and LPF methods failed to identify existence of contamination events. The analysis also revealed that the widely used contamination data construction approach is misleading. Copyright © 2016 Elsevier Ltd. All rights reserved.

Error Type and Lexical Frequency Effects: Error Detection in Swedish Children with Language Impairment

ERIC Educational Resources Information Center

Hallin, Anna Eva; Reuterskiöld, Christina

2017-01-01

Purpose: The first aim of this study was to investigate if Swedish-speaking school-age children with language impairment (LI) show specific morphosyntactic vulnerabilities in error detection. The second aim was to investigate the effects of lexical frequency on error detection, an overlooked aspect of previous error detection studies. Method:…

Geophysical techniques in detection to river embankments - A case study: To locate sites of potential leaks using surface-wave and electrical methods

USGS Publications Warehouse

Chen, C.; Liu, J.; Xu, S.; Xia, J.; ,

2004-01-01

Geophysical technologies are very effective in environmental, engineering and groundwater applications. Parameters of delineating nature of near-surface materials such as compressional-wave velocity, shear-wave velocity can be obtained using shallow seismic methods. Electric methods are primary approaches for investigating groundwater and detecting leakage. Both of methods are applied to detect embankment in hope of obtaining evidences of the strength and moisture inside the body. A technological experiment has done for detecting and discovering the hidden troubles in the embankment of Yangtze River, Songzi, Hubei, China in 2003. Surface-wave and DC multi-channel array resistivity sounding techniques were used to detect hidden trouble inside and under dike like pipe-seeps. This paper discusses the exploration strategy and the effect of geological characteristics. A practical approach of combining seismic and electric resistivity measurements was applied to locate potential pipe-seeps in embankment in the experiment. The method presents a potential leak factor based on the shear-wave velocity and the resistivity of the medium to evaluate anomalies. An anomaly found in a segment of embankment detected was verified, where occurred a pipe-seep during the 98' flooding.

Detecting brain tumor in computed tomography images using Markov random fields and fuzzy C-means clustering techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abdulbaqi, Hayder Saad; Department of Physics, College of Education, University of Al-Qadisiya, Al-Qadisiya; Jafri, Mohd Zubir Mat

Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introducemore » a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.« less

Detecting the effects of forest harvesting on streamflow using hydrologic model change detection

Treesearch

Nicolas P. Zegre; Nicholas A. Som

2011-01-01

Knowledge of the effects of forest management on hydrology primarily comes from paired-catchment study experiments. This approach has contributed fundamental knowledge of the effects of forest management on hydrology, but results from these studies lack insight into catchment processes. Outlined in this study is an alternative method of change detection that uses a...

Chatter detection in milling process based on VMD and energy entropy

NASA Astrophysics Data System (ADS)

Liu, Changfu; Zhu, Lida; Ni, Chenbing

2018-05-01

This paper presents a novel approach to detect the milling chatter based on Variational Mode Decomposition (VMD) and energy entropy. VMD has already been employed in feature extraction from non-stationary signals. The parameters like number of modes (K) and the quadratic penalty (α) need to be selected empirically when raw signal is decomposed by VMD. Aimed at solving the problem how to select K and α, the automatic selection method of VMD's based on kurtosis is proposed in this paper. When chatter occurs in the milling process, energy will be absorbed to chatter frequency bands. To detect the chatter frequency bands automatically, the chatter detection method based on energy entropy is presented. The vibration signal containing chatter frequency is simulated and three groups of experiments which represent three cutting conditions are conducted. To verify the effectiveness of method presented by this paper, chatter feather extraction has been successfully employed on simulation signals and experimental signals. The simulation and experimental results show that the proposed method can effectively detect the chatter.

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

PubMed Central

Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

2016-01-01

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians. PMID:27548179

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images.

PubMed

Xu, Yongzheng; Yu, Guizhen; Wang, Yunpeng; Wu, Xinkai; Ma, Yalong

2016-08-19

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles' in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians.

Improved wavelet de-noising method of rail vibration signal for wheel tread detection

NASA Astrophysics Data System (ADS)

Zhao, Quan-ke; Zhao, Quanke; Gao, Xiao-rong; Luo, Lin

2011-12-01

The irregularities of wheel tread can be detected by processing acceleration vibration signal of railway. Various kinds of noise from different sources such as wheel-rail resonance, bad weather and artificial reasons are the key factors influencing detection accuracy. A method which uses wavelet threshold de-noising is investigated to reduce noise in the detection signal, and an improved signal processing algorithm based on it has been established. The results of simulations and field experiments show that the proposed method can increase signal-to-noise ratio (SNR) of the rail vibration signal effectively, and improve the detection accuracy.

Optical fiber sensor of partial discharges in High Voltage DC experiments

NASA Astrophysics Data System (ADS)

Búa-Núñez, I.; Azcárraga-Ramos, C. G.; Posada-Román, J. E.; Garcia-Souto, J. A.

2014-05-01

A setup simulating High Voltage DC (HVDC) transformers barriers was developed to demonstrate the effectiveness of an optical fiber (OF) sensor in detecting partial discharges (PD) under these peculiar conditions. Different PD detection techniques were compared: electrical methods, and acoustic methods. Standard piezoelectric sensors (R15i-AST) and the above mentioned OF sensors were used for acoustic detection. The OF sensor was able to detect PD acoustically with a sensitivity better than the other detection methods. The multichannel instrumentation system was tested in real HVDC conditions with the aim of analyzing the behavior of the insulation (mineral oil/pressboard).

Apnea Detection Method for Cheyne-Stokes Respiration Analysis on Newborn

NASA Astrophysics Data System (ADS)

Niimi, Taiga; Itoh, Yushi; Natori, Michiya; Aoki, Yoshimitsu

2013-04-01

Cheyne-Stokes respiration is especially prevalent in preterm newborns, but its severity may not be recognized. It is characterized by apnea and cyclical weakening and strengthening of the breathing. We developed a method for detecting apnea and this abnormal respiration and for estimating its malignancy. Apnea was detected based on a "difference" feature (calculated from wavelet coefficients) and a modified maximum displacement feature (related to the respiratory waveform shape). The waveform is calculated from vertical motion of the thoracic and abdominal region during respiration using a vision sensor. Our proposed detection method effectively detects apnea (sensitivity 88.4%, specificity 99.7%).

Diagnostic Accuracy and Cost-Effectiveness of Alternative Methods for Detection of Soil-Transmitted Helminths in a Post-Treatment Setting in Western Kenya

PubMed Central

Kepha, Stella; Kihara, Jimmy H.; Njenga, Sammy M.; Pullan, Rachel L.; Brooker, Simon J.

2014-01-01

Objectives This study evaluates the diagnostic accuracy and cost-effectiveness of the Kato-Katz and Mini-FLOTAC methods for detection of soil-transmitted helminths (STH) in a post-treatment setting in western Kenya. A cost analysis also explores the cost implications of collecting samples during school surveys when compared to household surveys. Methods Stool samples were collected from children (n = 652) attending 18 schools in Bungoma County and diagnosed by the Kato-Katz and Mini-FLOTAC coprological methods. Sensitivity and additional diagnostic performance measures were analyzed using Bayesian latent class modeling. Financial and economic costs were calculated for all survey and diagnostic activities, and cost per child tested, cost per case detected and cost per STH infection correctly classified were estimated. A sensitivity analysis was conducted to assess the impact of various survey parameters on cost estimates. Results Both diagnostic methods exhibited comparable sensitivity for detection of any STH species over single and consecutive day sampling: 52.0% for single day Kato-Katz; 49.1% for single-day Mini-FLOTAC; 76.9% for consecutive day Kato-Katz; and 74.1% for consecutive day Mini-FLOTAC. Diagnostic performance did not differ significantly between methods for the different STH species. Use of Kato-Katz with school-based sampling was the lowest cost scenario for cost per child tested ($10.14) and cost per case correctly classified ($12.84). Cost per case detected was lowest for Kato-Katz used in community-based sampling ($128.24). Sensitivity analysis revealed the cost of case detection for any STH decreased non-linearly as prevalence rates increased and was influenced by the number of samples collected. Conclusions The Kato-Katz method was comparable in diagnostic sensitivity to the Mini-FLOTAC method, but afforded greater cost-effectiveness. Future work is required to evaluate the cost-effectiveness of STH surveillance in different settings. PMID:24810593

A Precise Visual Method for Narrow Butt Detection in Specular Reflection Workpiece Welding

PubMed Central

Zeng, Jinle; Chang, Baohua; Du, Dong; Hong, Yuxiang; Chang, Shuhe; Zou, Yirong

2016-01-01

During the complex path workpiece welding, it is important to keep the welding torch aligned with the groove center using a visual seam detection method, so that the deviation between the torch and the groove can be corrected automatically. However, when detecting the narrow butt of a specular reflection workpiece, the existing methods may fail because of the extremely small groove width and the poor imaging quality. This paper proposes a novel detection method to solve these issues. We design a uniform surface light source to get high signal-to-noise ratio images against the specular reflection effect, and a double-line laser light source is used to obtain the workpiece surface equation relative to the torch. Two light sources are switched on alternately and the camera is synchronized to capture images when each light is on; then the position and pose between the torch and the groove can be obtained nearly at the same time. Experimental results show that our method can detect the groove effectively and efficiently during the welding process. The image resolution is 12.5 μm and the processing time is less than 10 ms per frame. This indicates our method can be applied to real-time narrow butt detection during high-speed welding process. PMID:27649173

A Precise Visual Method for Narrow Butt Detection in Specular Reflection Workpiece Welding.

PubMed

Zeng, Jinle; Chang, Baohua; Du, Dong; Hong, Yuxiang; Chang, Shuhe; Zou, Yirong

2016-09-13

During the complex path workpiece welding, it is important to keep the welding torch aligned with the groove center using a visual seam detection method, so that the deviation between the torch and the groove can be corrected automatically. However, when detecting the narrow butt of a specular reflection workpiece, the existing methods may fail because of the extremely small groove width and the poor imaging quality. This paper proposes a novel detection method to solve these issues. We design a uniform surface light source to get high signal-to-noise ratio images against the specular reflection effect, and a double-line laser light source is used to obtain the workpiece surface equation relative to the torch. Two light sources are switched on alternately and the camera is synchronized to capture images when each light is on; then the position and pose between the torch and the groove can be obtained nearly at the same time. Experimental results show that our method can detect the groove effectively and efficiently during the welding process. The image resolution is 12.5 μm and the processing time is less than 10 ms per frame. This indicates our method can be applied to real-time narrow butt detection during high-speed welding process.

Eye Tracking and Head Movement Detection: A State-of-Art Survey

PubMed Central

2013-01-01

Eye-gaze detection and tracking have been an active research field in the past years as it adds convenience to a variety of applications. It is considered a significant untraditional method of human computer interaction. Head movement detection has also received researchers' attention and interest as it has been found to be a simple and effective interaction method. Both technologies are considered the easiest alternative interface methods. They serve a wide range of severely disabled people who are left with minimal motor abilities. For both eye tracking and head movement detection, several different approaches have been proposed and used to implement different algorithms for these technologies. Despite the amount of research done on both technologies, researchers are still trying to find robust methods to use effectively in various applications. This paper presents a state-of-art survey for eye tracking and head movement detection methods proposed in the literature. Examples of different fields of applications for both technologies, such as human-computer interaction, driving assistance systems, and assistive technologies are also investigated. PMID:27170851

DNA Extraction Method Affects the Detection of a Fungal Pathogen in Formalin-Fixed Specimens Using qPCR.

PubMed

Adams, Andrea J; LaBonte, John P; Ball, Morgan L; Richards-Hrdlicka, Kathryn L; Toothman, Mary H; Briggs, Cheryl J

2015-01-01

Museum collections provide indispensable repositories for obtaining information about the historical presence of disease in wildlife populations. The pathogenic amphibian chytrid fungus Batrachochytrium dendrobatidis (Bd) has played a significant role in global amphibian declines, and examining preserved specimens for Bd can improve our understanding of its emergence and spread. Quantitative PCR (qPCR) enables Bd detection with minimal disturbance to amphibian skin and is significantly more sensitive to detecting Bd than histology; therefore, developing effective qPCR methodologies for detecting Bd DNA in formalin-fixed specimens can provide an efficient and effective approach to examining historical Bd emergence and prevalence. Techniques for detecting Bd in museum specimens have not been evaluated for their effectiveness in control specimens that mimic the conditions of animals most likely to be encountered in museums, including those with low pathogen loads. We used American bullfrogs (Lithobates catesbeianus) of known infection status to evaluate the success of qPCR to detect Bd in formalin-fixed specimens after three years of ethanol storage. Our objectives were to compare the most commonly used DNA extraction method for Bd (PrepMan, PM) to Macherey-Nagel DNA FFPE (MN), test optimizations for Bd detection with PM, and provide recommendations for maximizing Bd detection. We found that successful detection is relatively high (80-90%) when Bd loads before formalin fixation are high, regardless of the extraction method used; however, at lower infection levels, detection probabilities were significantly reduced. The MN DNA extraction method increased Bd detection by as much as 50% at moderate infection levels. Our results indicate that, for animals characterized by lower pathogen loads (i.e., those most commonly encountered in museum collections), current methods may underestimate the proportion of Bd-infected amphibians. Those extracting DNA from archived museum specimens should ensure that the techniques they are using are known to provide high-quality throughput DNA for later analysis.

The Mediated MIMIC Model for Understanding the Underlying Mechanism of DIF.

PubMed

Cheng, Ying; Shao, Can; Lathrop, Quinn N

2016-02-01

Due to its flexibility, the multiple-indicator, multiple-causes (MIMIC) model has become an increasingly popular method for the detection of differential item functioning (DIF). In this article, we propose the mediated MIMIC model method to uncover the underlying mechanism of DIF. This method extends the usual MIMIC model by including one variable or multiple variables that may completely or partially mediate the DIF effect. If complete mediation effect is found, the DIF effect is fully accounted for. Through our simulation study, we find that the mediated MIMIC model is very successful in detecting the mediation effect that completely or partially accounts for DIF, while keeping the Type I error rate well controlled for both balanced and unbalanced sample sizes between focal and reference groups. Because it is successful in detecting such mediation effects, the mediated MIMIC model may help explain DIF and give guidance in the revision of a DIF item.

The Mediated MIMIC Model for Understanding the Underlying Mechanism of DIF

PubMed Central

Cheng, Ying; Shao, Can; Lathrop, Quinn N.

2015-01-01

Due to its flexibility, the multiple-indicator, multiple-causes (MIMIC) model has become an increasingly popular method for the detection of differential item functioning (DIF). In this article, we propose the mediated MIMIC model method to uncover the underlying mechanism of DIF. This method extends the usual MIMIC model by including one variable or multiple variables that may completely or partially mediate the DIF effect. If complete mediation effect is found, the DIF effect is fully accounted for. Through our simulation study, we find that the mediated MIMIC model is very successful in detecting the mediation effect that completely or partially accounts for DIF, while keeping the Type I error rate well controlled for both balanced and unbalanced sample sizes between focal and reference groups. Because it is successful in detecting such mediation effects, the mediated MIMIC model may help explain DIF and give guidance in the revision of a DIF item.

Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle

PubMed Central

Chen, Long; Li, Qingquan; Li, Ming; Zhang, Liang; Mao, Qingzhou

2012-01-01

This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system.

Android malware detection based on evolutionary super-network

NASA Astrophysics Data System (ADS)

Yan, Haisheng; Peng, Lingling

2018-04-01

In the paper, an android malware detection method based on evolutionary super-network is proposed in order to improve the precision of android malware detection. Chi square statistics method is used for selecting characteristics on the basis of analyzing android authority. Boolean weighting is utilized for calculating characteristic weight. Processed characteristic vector is regarded as the system training set and test set; hyper edge alternative strategy is used for training super-network classification model, thereby classifying test set characteristic vectors, and it is compared with traditional classification algorithm. The results show that the detection method proposed in the paper is close to or better than traditional classification algorithm. The proposed method belongs to an effective Android malware detection means.

Detecting subsurface fluid leaks in real-time using injection and production rates

NASA Astrophysics Data System (ADS)

Singh, Harpreet; Huerta, Nicolas J.

2017-12-01

CO2 injection into geologic formations for either enhanced oil recovery or carbon storage introduces a risk for undesired fluid leakage into overlying groundwater or to the surface. Despite decades of subsurface CO2 production and injection, the technologies and methods for detecting CO2 leaks are still costly and prone to large uncertainties. This is especially true for pressure-based monitoring methods, which require the use of simplified geological and reservoir flow models to simulate the pressure behavior as well as background noise affecting pressure measurements. In this study, we propose a method to detect the time and volume of fluid leakage based on real-time measurements of well injection and production rates. The approach utilizes analogies between fluid flow and capacitance-resistance modeling. Unlike other leak detection methods (e.g. pressure-based), the proposed method does not require geological and reservoir flow models to simulate the behavior that often carry significant sources of uncertainty; therefore, with our approach the leak can be detected with greater certainty. The method can be applied to detect when a leak begins by tracking a departure in fluid production rate from the expected pattern. The method has been tuned to detect the effect of boundary conditions and fluid compressibility on leakage. To highlight the utility of this approach we use our method to detect leaks for two scenarios. The first scenario simulates a fluid leak from the storage formation into an above-zone monitoring interval. The second scenario simulates intra-reservoir migration between two compartments. We illustrate this method to detect fluid leakage in three different reservoirs with varying levels of geological and structural complexity. The proposed leakage detection method has three novelties: i) requires only readily-available data (injection and production rates), ii) accounts for fluid compressibility and boundary effects, and iii) in addition to detecting the time when a leak is activated and the volume of that leakage, this method provides an insight about the leak location, and reservoir connectivity. We are proposing this as a complementary method that can be used with other, more expensive, methods early on in the injection process. This will allow an operator to conduct more expensive surveys less often because the proposed method can show if there are no leaks on a monthly basis that is cheap and fast.

A surface acoustic wave response detection method for passive wireless torque sensor

NASA Astrophysics Data System (ADS)

Fan, Yanping; Kong, Ping; Qi, Hongli; Liu, Hongye; Ji, Xiaojun

2018-01-01

This paper presents an effective surface acoustic wave (SAW) response detection method for the passive wireless SAW torque sensor to improve the measurement accuracy. An analysis was conducted on the relationship between the response energy-entropy and the bandwidth of SAW resonator (SAWR). A self-correlation method was modified to suppress the blurred white noise and highlight the attenuation characteristic of wireless SAW response. The SAW response was detected according to both the variation and the duration of energy-entropy ascension of an acquired RF signal. Numerical simulation results showed that the SAW response can be detected even when the signal-to-noise ratio (SNR) is 6dB. The proposed SAW response detection method was evaluated with several experiments at different conditions. The SAW response can be well distinguished from the sinusoidal signal and the noise. The performance of the SAW torque measurement system incorporating the detection method was tested. The obtained repeatability error was 0.23% and the linearity was 0.9934, indicating the validity of the detection method.

Fault detection of helicopter gearboxes using the multi-valued influence matrix method

NASA Technical Reports Server (NTRS)

Chin, Hsinyung; Danai, Kourosh; Lewicki, David G.

1993-01-01

In this paper we investigate the effectiveness of a pattern classifying fault detection system that is designed to cope with the variability of fault signatures inherent in helicopter gearboxes. For detection, the measurements are monitored on-line and flagged upon the detection of abnormalities, so that they can be attributed to a faulty or normal case. As such, the detection system is composed of two components, a quantization matrix to flag the measurements, and a multi-valued influence matrix (MVIM) that represents the behavior of measurements during normal operation and at fault instances. Both the quantization matrix and influence matrix are tuned during a training session so as to minimize the error in detection. To demonstrate the effectiveness of this detection system, it was applied to vibration measurements collected from a helicopter gearbox during normal operation and at various fault instances. The results indicate that the MVIM method provides excellent results when the full range of faults effects on the measurements are included in the training set.

Comparing scat detection dogs, cameras, and hair snares for surveying carnivores

USGS Publications Warehouse

Long, Robert A.; Donovan, T.M.; MacKay, Paula; Zielinski, William J.; Buzas, Jeffrey S.

2007-01-01

Carnivores typically require large areas of habitat, exist at low natural densities, and exhibit elusive behavior - characteristics that render them difficult to study. Noninvasive survey methods increasingly provide means to collect extensive data on carnivore occupancy, distribution, and abundance. During the summers of 2003-2004, we compared the abilities of scat detection dogs, remote cameras, and hair snares to detect black bears (Ursus americanus), fishers (Martes pennanti), and bobcats (Lynx rufus) at 168 sites throughout Vermont. All 3 methods detected black bears; neither fishers nor bobcats were detected by hair snares. Scat detection dogs yielded the highest raw detection rate and probability of detection (given presence) for each of the target species, as well as the greatest number of unique detections (i.e., occasions when only one method detected the target species). We estimated that the mean probability of detecting the target species during a single visit to a site with a detection dog was 0.87 for black bears, 0.84 for fishers, and 0.27 for bobcats. Although the cost of surveying with detection dogs was higher than that of remote cameras or hair snares, the efficiency of this method rendered it the most cost-effective survey method.

Improved astigmatic focus error detection method

NASA Technical Reports Server (NTRS)

Bernacki, Bruce E.

1992-01-01

All easy-to-implement focus- and track-error detection methods presently used in magneto-optical (MO) disk drives using pre-grooved media suffer from a side effect known as feedthrough. Feedthrough is the unwanted focus error signal (FES) produced when the optical head is seeking a new track, and light refracted from the pre-grooved disk produces an erroneous FES. Some focus and track-error detection methods are more resistant to feedthrough, but tend to be complicated and/or difficult to keep in alignment as a result of environmental insults. The astigmatic focus/push-pull tracking method is an elegant, easy-to-align focus- and track-error detection method. Unfortunately, it is also highly susceptible to feedthrough when astigmatism is present, with the worst effects caused by astigmatism oriented such that the tangential and sagittal foci are at 45 deg to the track direction. This disclosure outlines a method to nearly completely eliminate the worst-case form of feedthrough due to astigmatism oriented 45 deg to the track direction. Feedthrough due to other primary aberrations is not improved, but performance is identical to the unimproved astigmatic method.

An effective hair detection algorithm for dermoscopic melanoma images of skin lesions

NASA Astrophysics Data System (ADS)

Chakraborti, Damayanti; Kaur, Ravneet; Umbaugh, Scott; LeAnder, Robert

2016-09-01

Dermoscopic images are obtained using the method of skin surface microscopy. Pigmented skin lesions are evaluated in terms of texture features such as color and structure. Artifacts, such as hairs, bubbles, black frames, ruler-marks, etc., create obstacles that prevent accurate detection of skin lesions by both clinicians and computer-aided diagnosis. In this article, we propose a new algorithm for the automated detection of hairs, using an adaptive, Canny edge-detection method, followed by morphological filtering and an arithmetic addition operation. The algorithm was applied to 50 dermoscopic melanoma images. In order to ascertain this method's relative detection accuracy, it was compared to the Razmjooy hair-detection method [1], using segmentation error (SE), true detection rate (TDR) and false positioning rate (FPR). The new method produced 6.57% SE, 96.28% TDR and 3.47% FPR, compared to 15.751% SE, 86.29% TDR and 11.74% FPR produced by the Razmjooy method [1]. Because of the 7.27-9.99% improvement in those parameters, we conclude that the new algorithm produces much better results for detecting thick, thin, dark and light hairs. The new method proposed here, shows an appreciable difference in the rate of detecting bubbles, as well.

Delamination Detection Using Guided Wave Phased Arrays

NASA Technical Reports Server (NTRS)

Tian, Zhenhua; Yu, Lingyu; Leckey, Cara

2016-01-01

This paper presents a method for detecting multiple delaminations in composite laminates using non-contact phased arrays. The phased arrays are implemented with a non-contact scanning laser Doppler vibrometer (SLDV). The array imaging algorithm is performed in the frequency domain where both the guided wave dispersion effect and direction dependent wave properties are considered. By using the non-contact SLDV array with a frequency domain imaging algorithm, an intensity image of the composite plate can be generated for delamination detection. For the proof of concept, a laboratory test is performed using a non-contact phased array to detect two delaminations (created through quasi-static impact test) at different locations in a composite plate. Using the non-contact phased array and frequency domain imaging, the two impact-induced delaminations are successfully detected. This study shows that the non-contact phased array method is a potentially effective method for rapid delamination inspection in large composite structures.

Mutation Analysis of SLC26A4 for Pendred Syndrome and Nonsyndromic Hearing Loss by High-Resolution Melting

PubMed Central

Chen, Neng; Tranebjærg, Lisbeth; Rendtorff, Nanna Dahl; Schrijver, Iris

2011-01-01

Pendred syndrome and DFNB4 (autosomal recessive nonsyndromic congenital deafness, locus 4) are associated with autosomal recessive congenital sensorineural hearing loss and mutations in the SLC26A4 gene. Extensive allelic heterogeneity, however, necessitates analysis of all exons and splice sites to identify mutations for individual patients. Although Sanger sequencing is the gold standard for mutation detection, screening methods supplemented with targeted sequencing can provide a cost-effective alternative. One such method, denaturing high-performance liquid chromatography, was developed for clinical mutation detection in SLC26A4. However, this method inherently cannot distinguish homozygous changes from wild-type sequences. High-resolution melting (HRM), on the other hand, can detect heterozygous and homozygous changes cost-effectively, without any post-PCR modifications. We developed a closed-tube HRM mutation detection method specific for SLC26A4 that can be used in the clinical diagnostic setting. Twenty-eight primer pairs were designed to cover all 21 SLC26A4 exons and splice junction sequences. Using the resulting amplicons, initial HRM analysis detected all 45 variants previously identified by sequencing. Subsequently, a 384-well plate format was designed for up to three patient samples per run. Blinded HRM testing on these plates of patient samples collected over 1 year in a clinical diagnostic laboratory accurately detected all variants identified by sequencing. In conclusion, HRM with targeted sequencing is a reliable, simple, and cost-effective method for SLC26A4 mutation screening and detection. PMID:21704276

Financial time series analysis based on effective phase transfer entropy

NASA Astrophysics Data System (ADS)

Yang, Pengbo; Shang, Pengjian; Lin, Aijing

2017-02-01

Transfer entropy is a powerful technique which is able to quantify the impact of one dynamic system on another system. In this paper, we propose the effective phase transfer entropy method based on the transfer entropy method. We use simulated data to test the performance of this method, and the experimental results confirm that the proposed approach is capable of detecting the information transfer between the systems. We also explore the relationship between effective phase transfer entropy and some variables, such as data size, coupling strength and noise. The effective phase transfer entropy is positively correlated with the data size and the coupling strength. Even in the presence of a large amount of noise, it can detect the information transfer between systems, and it is very robust to noise. Moreover, this measure is indeed able to accurately estimate the information flow between systems compared with phase transfer entropy. In order to reflect the application of this method in practice, we apply this method to financial time series and gain new insight into the interactions between systems. It is demonstrated that the effective phase transfer entropy can be used to detect some economic fluctuations in the financial market. To summarize, the effective phase transfer entropy method is a very efficient tool to estimate the information flow between systems.

Pornographic information of Internet views detection method based on the connected areas

NASA Astrophysics Data System (ADS)

Wang, Huibai; Fan, Ajie

2017-01-01

Nowadays online porn video broadcasting and downloading is very popular. In view of the widespread phenomenon of Internet pornography, this paper proposed a new method of pornographic video detection based on connected areas. Firstly, decode the video into a serious of static images and detect skin color on the extracted key frames. If the area of skin color reaches a certain threshold, use the AdaBoost algorithm to detect the human face. Judge the connectivity of the human face and the large area of skin color to determine whether detect the sensitive area finally. The experimental results show that the method can effectively remove the non-pornographic videos contain human who wear less. This method can improve the efficiency and reduce the workload of detection.

Robust Vehicle Detection under Various Environmental Conditions Using an Infrared Thermal Camera and Its Application to Road Traffic Flow Monitoring

PubMed Central

Iwasaki, Yoichiro; Misumi, Masato; Nakamiya, Toshiyuki

2013-01-01

We have already proposed a method for detecting vehicle positions and their movements (henceforth referred to as “our previous method”) using thermal images taken with an infrared thermal camera. Our experiments have shown that our previous method detects vehicles robustly under four different environmental conditions which involve poor visibility conditions in snow and thick fog. Our previous method uses the windshield and its surroundings as the target of the Viola-Jones detector. Some experiments in winter show that the vehicle detection accuracy decreases because the temperatures of many windshields approximate those of the exterior of the windshields. In this paper, we propose a new vehicle detection method (henceforth referred to as “our new method”). Our new method detects vehicles based on tires' thermal energy reflection. We have done experiments using three series of thermal images for which the vehicle detection accuracies of our previous method are low. Our new method detects 1,417 vehicles (92.8%) out of 1,527 vehicles, and the number of false detection is 52 in total. Therefore, by combining our two methods, high vehicle detection accuracies are maintained under various environmental conditions. Finally, we apply the traffic information obtained by our two methods to traffic flow automatic monitoring, and show the effectiveness of our proposal. PMID:23774988

Evaluation of two outlier-detection-based methods for detecting tissue-selective genes from microarray data.

PubMed

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-05-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent's non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent's method is not suitable for ROKU.

[Shock shape representation of sinus heart rate based on cloud model].

PubMed

Yin, Wenfeng; Zhao, Jie; Chen, Tiantian; Zhang, Junjian; Zhang, Chunyou; Li, Dapeng; An, Baijing

2014-04-01

The present paper is to analyze the trend of sinus heart rate RR interphase sequence after a single ventricular premature beat and to compare it with the two parameters, turbulence onset (TO) and turbulence slope (TS). Based on the acquisition of sinus rhythm concussion sample, we in this paper use a piecewise linearization method to extract its linear characteristics, following which we describe shock form with natural language through cloud model. In the process of acquisition, we use the exponential smoothing method to forecast the position where QRS wave may appear to assist QRS wave detection, and use template to judge whether current cardiac is sinus rhythm. And we choose some signals from MIT-BIH Arrhythmia Database to detect whether the algorithm is effective in Matlab. The results show that our method can correctly detect the changing trend of sinus heart rate. The proposed method can achieve real-time detection of sinus rhythm shocks, which is simple and easily implemented, so that it is effective as a supplementary method.

Evaluation of different heating methods for the detection of boar taint by means of the human nose.

PubMed

Bekaert, K M; Aluwé, M; Vanhaecke, L; Heres, L; Duchateau, L; Vandendriessche, F; Tuyttens, F A M

2013-05-01

No automated detection system for boar taint detection is currently available, thus boar taint at the slaughterline can currently only be assessed using the singeing method (olfactory scoring). This study compares several heating methods (microwave, soldering iron and pyropen) and evaluates the effect of habituation, cleaning the soldering iron, singeing the fat twice in the same place, and variations in the technical procedures. All methods seem to be suitable for detecting boar taint but the choice of heating method for sensory scoring of boar taint depends on habituation of the trained assessor and specific conditions applied. The pyropen seems to be most suitable because it does not contact the fat and is easy to handle (wireless). Finally, the intensity score may also be influenced by: contamination from not cleaning the soldering iron, singeing the fat twice in the same place, and the effect of habituation. Copyright © 2013 Elsevier Ltd. All rights reserved.

Detection methods and performance criteria for genetically modified organisms.

PubMed

Bertheau, Yves; Diolez, Annick; Kobilinsky, André; Magin, Kimberly

2002-01-01

Detection methods for genetically modified organisms (GMOs) are necessary for many applications, from seed purity assessment to compliance of food labeling in several countries. Numerous analytical methods are currently used or under development to support these needs. The currently used methods are bioassays and protein- and DNA-based detection protocols. To avoid discrepancy of results between such largely different methods and, for instance, the potential resulting legal actions, compatibility of the methods is urgently needed. Performance criteria of methods allow evaluation against a common standard. The more-common performance criteria for detection methods are precision, accuracy, sensitivity, and specificity, which together specifically address other terms used to describe the performance of a method, such as applicability, selectivity, calibration, trueness, precision, recovery, operating range, limit of quantitation, limit of detection, and ruggedness. Performance criteria should provide objective tools to accept or reject specific methods, to validate them, to ensure compatibility between validated methods, and be used on a routine basis to reject data outside an acceptable range of variability. When selecting a method of detection, it is also important to consider its applicability, its field of applications, and its limitations, by including factors such as its ability to detect the target analyte in a given matrix, the duration of the analyses, its cost effectiveness, and the necessary sample sizes for testing. Thus, the current GMO detection methods should be evaluated against a common set of performance criteria.

Non-Destructive Evaluation for Corrosion Monitoring in Concrete: A Review and Capability of Acoustic Emission Technique

PubMed Central

Zaki, Ahmad; Chai, Hwa Kian; Aggelis, Dimitrios G.; Alver, Ninel

2015-01-01

Corrosion of reinforced concrete (RC) structures has been one of the major causes of structural failure. Early detection of the corrosion process could help limit the location and the extent of necessary repairs or replacement, as well as reduce the cost associated with rehabilitation work. Non-destructive testing (NDT) methods have been found to be useful for in-situ evaluation of steel corrosion in RC, where the effect of steel corrosion and the integrity of the concrete structure can be assessed effectively. A complementary study of NDT methods for the investigation of corrosion is presented here. In this paper, acoustic emission (AE) effectively detects the corrosion of concrete structures at an early stage. The capability of the AE technique to detect corrosion occurring in real-time makes it a strong candidate for serving as an efficient NDT method, giving it an advantage over other NDT methods. PMID:26251904

Non-Destructive Evaluation for Corrosion Monitoring in Concrete: A Review and Capability of Acoustic Emission Technique.

PubMed

Zaki, Ahmad; Chai, Hwa Kian; Aggelis, Dimitrios G; Alver, Ninel

2015-08-05

Corrosion of reinforced concrete (RC) structures has been one of the major causes of structural failure. Early detection of the corrosion process could help limit the location and the extent of necessary repairs or replacement, as well as reduce the cost associated with rehabilitation work. Non-destructive testing (NDT) methods have been found to be useful for in-situ evaluation of steel corrosion in RC, where the effect of steel corrosion and the integrity of the concrete structure can be assessed effectively. A complementary study of NDT methods for the investigation of corrosion is presented here. In this paper, acoustic emission (AE) effectively detects the corrosion of concrete structures at an early stage. The capability of the AE technique to detect corrosion occurring in real-time makes it a strong candidate for serving as an efficient NDT method, giving it an advantage over other NDT methods.

PSO Algorithm Particle Filters for Improving the Performance of Lane Detection and Tracking Systems in Difficult Roads

PubMed Central

Cheng, Wen-Chang

2012-01-01

In this paper we propose a robust lane detection and tracking method by combining particle filters with the particle swarm optimization method. This method mainly uses the particle filters to detect and track the local optimum of the lane model in the input image and then seeks the global optimal solution of the lane model by a particle swarm optimization method. The particle filter can effectively complete lane detection and tracking in complicated or variable lane environments. However, the result obtained is usually a local optimal system status rather than the global optimal system status. Thus, the particle swarm optimization method is used to further refine the global optimal system status in all system statuses. Since the particle swarm optimization method is a global optimization algorithm based on iterative computing, it can find the global optimal lane model by simulating the food finding way of fish school or insects under the mutual cooperation of all particles. In verification testing, the test environments included highways and ordinary roads as well as straight and curved lanes, uphill and downhill lanes, lane changes, etc. Our proposed method can complete the lane detection and tracking more accurately and effectively then existing options. PMID:23235453

Diagnostic accuracy and cost-effectiveness of alternative methods for detection of soil-transmitted helminths in a post-treatment setting in western Kenya.

PubMed

Assefa, Liya M; Crellen, Thomas; Kepha, Stella; Kihara, Jimmy H; Njenga, Sammy M; Pullan, Rachel L; Brooker, Simon J

2014-05-01

This study evaluates the diagnostic accuracy and cost-effectiveness of the Kato-Katz and Mini-FLOTAC methods for detection of soil-transmitted helminths (STH) in a post-treatment setting in western Kenya. A cost analysis also explores the cost implications of collecting samples during school surveys when compared to household surveys. Stool samples were collected from children (n = 652) attending 18 schools in Bungoma County and diagnosed by the Kato-Katz and Mini-FLOTAC coprological methods. Sensitivity and additional diagnostic performance measures were analyzed using Bayesian latent class modeling. Financial and economic costs were calculated for all survey and diagnostic activities, and cost per child tested, cost per case detected and cost per STH infection correctly classified were estimated. A sensitivity analysis was conducted to assess the impact of various survey parameters on cost estimates. Both diagnostic methods exhibited comparable sensitivity for detection of any STH species over single and consecutive day sampling: 52.0% for single day Kato-Katz; 49.1% for single-day Mini-FLOTAC; 76.9% for consecutive day Kato-Katz; and 74.1% for consecutive day Mini-FLOTAC. Diagnostic performance did not differ significantly between methods for the different STH species. Use of Kato-Katz with school-based sampling was the lowest cost scenario for cost per child tested ($10.14) and cost per case correctly classified ($12.84). Cost per case detected was lowest for Kato-Katz used in community-based sampling ($128.24). Sensitivity analysis revealed the cost of case detection for any STH decreased non-linearly as prevalence rates increased and was influenced by the number of samples collected. The Kato-Katz method was comparable in diagnostic sensitivity to the Mini-FLOTAC method, but afforded greater cost-effectiveness. Future work is required to evaluate the cost-effectiveness of STH surveillance in different settings.

Leak detection by mass balance effective for Norman Wells line

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liou, J.C.P.

Mass-balance calculations for leak detection have been shown as effective as a leading software system, in a comparison based on a major Canadian crude-oil pipeline. The calculations and NovaCorp`s Leakstop software each detected 4% (approximately) or greater leaks on Interprovincial Pipe Line (IPL) Inc.`s Norman Wells pipeline. Insufficient data exist to assess performances of the two methods for leaks smaller than 4%. Pipeline leak detection using such software-based systems are common. Their effectiveness is measured by how small and how quickly a leak can be detected. Algorithms used and measurement uncertainties determine leak detectability.

A novel method for overlapping community detection using Multi-objective optimization

NASA Astrophysics Data System (ADS)

Ebrahimi, Morteza; Shahmoradi, Mohammad Reza; Heshmati, Zainabolhoda; Salehi, Mostafa

2018-09-01

The problem of community detection as one of the most important applications of network science can be addressed effectively by multi-objective optimization. In this paper, we aim to present a novel efficient method based on this approach. Also, in this study the idea of using all Pareto fronts to detect overlapping communities is introduced. The proposed method has two main advantages compared to other multi-objective optimization based approaches. The first advantage is scalability, and the second is the ability to find overlapping communities. Despite most of the works, the proposed method is able to find overlapping communities effectively. The new algorithm works by extracting appropriate communities from all the Pareto optimal solutions, instead of choosing the one optimal solution. Empirical experiments on different features of separated and overlapping communities, on both synthetic and real networks show that the proposed method performs better in comparison with other methods.

Multi person detection and tracking based on hierarchical level-set method

NASA Astrophysics Data System (ADS)

Khraief, Chadia; Benzarti, Faouzi; Amiri, Hamid

2018-04-01

In this paper, we propose an efficient unsupervised method for mutli-person tracking based on hierarchical level-set approach. The proposed method uses both edge and region information in order to effectively detect objects. The persons are tracked on each frame of the sequence by minimizing an energy functional that combines color, texture and shape information. These features are enrolled in covariance matrix as region descriptor. The present method is fully automated without the need to manually specify the initial contour of Level-set. It is based on combined person detection and background subtraction methods. The edge-based is employed to maintain a stable evolution, guide the segmentation towards apparent boundaries and inhibit regions fusion. The computational cost of level-set is reduced by using narrow band technique. Many experimental results are performed on challenging video sequences and show the effectiveness of the proposed method.

A high-throughput multiplex method adapted for GMO detection.

PubMed

Chaouachi, Maher; Chupeau, Gaëlle; Berard, Aurélie; McKhann, Heather; Romaniuk, Marcel; Giancola, Sandra; Laval, Valérie; Bertheau, Yves; Brunel, Dominique

2008-12-24

A high-throughput multiplex assay for the detection of genetically modified organisms (GMO) was developed on the basis of the existing SNPlex method designed for SNP genotyping. This SNPlex assay allows the simultaneous detection of up to 48 short DNA sequences (approximately 70 bp; "signature sequences") from taxa endogenous reference genes, from GMO constructions, screening targets, construct-specific, and event-specific targets, and finally from donor organisms. This assay avoids certain shortcomings of multiplex PCR-based methods already in widespread use for GMO detection. The assay demonstrated high specificity and sensitivity. The results suggest that this assay is reliable, flexible, and cost- and time-effective for high-throughput GMO detection.

Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks

PubMed Central

Chen, Xiaoguang; Jiang, Xu; Wu, Zhilu; Zhuang, Shufeng

2017-01-01

High throughput, low latency and reliable communication has always been a hot topic for wireless sensor networks (WSNs) in various applications. Multiuser detection is widely used to suppress the bad effect of multiple access interference in WSNs. In this paper, a novel multiuser detection method based on differential characteristics is proposed to suppress multiple access interference. The proposed iterative receive method consists of three stages. Firstly, a differential characteristics function is presented based on the optimal multiuser detection decision function; then on the basis of differential characteristics, a preliminary threshold detection is utilized to find the potential wrongly received bits; after that an error bit corrector is employed to correct the wrong bits. In order to further lower the bit error ratio (BER), the differential characteristics calculation, threshold detection and error bit correction process described above are iteratively executed. Simulation results show that after only a few iterations the proposed multiuser detection method can achieve satisfactory BER performance. Besides, BER and near far resistance performance are much better than traditional suboptimal multiuser detection methods. Furthermore, the proposed iterative multiuser detection method also has a large system capacity. PMID:28212328

Detection of honeycomb cell walls from measurement data based on Harris corner detection algorithm

NASA Astrophysics Data System (ADS)

Qin, Yan; Dong, Zhigang; Kang, Renke; Yang, Jie; Ayinde, Babajide O.

2018-06-01

A honeycomb core is a discontinuous material with a thin-wall structure—a characteristic that makes accurate surface measurement difficult. This paper presents a cell wall detection method based on the Harris corner detection algorithm using laser measurement data. The vertexes of honeycomb cores are recognized with two different methods: one method is the reduction of data density, and the other is the optimization of the threshold of the Harris corner detection algorithm. Each cell wall is then identified in accordance with the neighboring relationships of its vertexes. Experiments were carried out for different types and surface shapes of honeycomb cores, where the proposed method was proved effective in dealing with noise due to burrs and/or deformation of cell walls.

A Sea-Sky Line Detection Method for Unmanned Surface Vehicles Based on Gradient Saliency.

PubMed

Wang, Bo; Su, Yumin; Wan, Lei

2016-04-15

Special features in real marine environments such as cloud clutter, sea glint and weather conditions always result in various kinds of interference in optical images, which make it very difficult for unmanned surface vehicles (USVs) to detect the sea-sky line (SSL) accurately. To solve this problem a saliency-based SSL detection method is proposed. Through the computation of gradient saliency the line features of SSL are enhanced effectively, while other interference factors are relatively suppressed, and line support regions are obtained by a region growing method on gradient orientation. The SSL identification is achieved according to region contrast, line segment length and orientation features, and optimal state estimation of SSL detection is implemented by introducing a cubature Kalman filter (CKF). In the end, the proposed method is tested on a benchmark dataset from the "XL" USV in a real marine environment, and the experimental results demonstrate that the proposed method is significantly superior to other state-of-the-art methods in terms of accuracy rate and real-time performance, and its accuracy and stability are effectively improved by the CKF.

Smear plus Detect-TB for a sensitive diagnosis of pulmonary tuberculosis: a cost-effectiveness analysis in an incarcerated population.

PubMed

Schmid, Karen Barros; Scherer, Luciene; Barcellos, Regina Bones; Kuhleis, Daniele; Prestes, Isaías Valente; Steffen, Ricardo Ewbank; Dalla Costa, Elis Regina; Rossetti, Maria Lucia Rosa

2014-12-16

Prison conditions can favor the spread of tuberculosis (TB). This study aimed to evaluate in a Brazilian prison: the performance and accuracy of smear, culture and Detect-TB; performance of smear plus culture and smear plus Detect-TB, according to different TB prevalence rates; and the cost-effectiveness of these procedures for pulmonary tuberculosis (PTB) diagnosis. This paper describes a cost-effectiveness study. A decision analytic model was developed to estimate the costs and cost-effectiveness of five routine diagnostic procedures for diagnosis of PTB using sputum specimens: a) Smear alone, b) Culture alone, c) Detect-TB alone, d) Smear plus culture and e) Smear plus Detect-TB. The cost-effectiveness ratio of costs were evaluated per correctly diagnosed TB case and all procedures costs were attributed based on the procedure costs adopted by the Brazilian Public Health System. A total of 294 spontaneous sputum specimens from patients suspected of having TB were analyzed. The sensibility and specificity were calculated to be 47% and 100% for smear; 93% and 100%, for culture; 74% and 95%, for Detect-TB; 96% and 100%, for smear plus culture; and 86% and 95%, for smear plus Detect-TB. The negative and positive predictive values for smear plus Detect-TB, according to different TB prevalence rates, ranged from 83 to 99% and 48 to 96%, respectively. In a cost-effectiveness analysis, smear was both less costly and less effective than the other strategies. Culture and smear plus culture were more effective but more costly than the other strategies. Smear plus Detect-TB was the most cost-effective method. The Detect-TB evinced to be sensitive and effective for the PTB diagnosis when applied with smear microscopy. Diagnostic methods should be improved to increase TB case detection. To support rational decisions about the implementation of such techniques, cost-effectiveness studies are essential, including in prisons, which are known for health care assessment problems.

Effect Size Calculations and Single Subject Designs

ERIC Educational Resources Information Center

Olive, Melissa L.; Smith, Benjamin W.

2005-01-01

This study compared visual analyses with five alternative methods for assessing the magnitude of effect with single subject designs. Each method was successful in detecting intervention effect. When rank ordered, each method was consistent in identifying the participants with the largest effect. We recommend the use of the standard mean difference…

Application of virtual phase-shifting speckle-interferometry for detection of polymorphism in the Chlamydia trachomatis omp1 gene

NASA Astrophysics Data System (ADS)

Feodorova, Valentina A.; Saltykov, Yury V.; Zaytsev, Sergey S.; Ulyanov, Sergey S.; Ulianova, Onega V.

2018-04-01

Method of phase-shifting speckle-interferometry has been used as a new tool with high potency for modern bioinformatics. Virtual phase-shifting speckle-interferometry has been applied for detection of polymorphism in the of Chlamydia trachomatis omp1 gene. It has been shown, that suggested method is very sensitive to natural genetic mutations as single nucleotide polymorphism (SNP). Effectiveness of proposed method has been compared with effectiveness of the newest bioinformatic tools, based on nucleotide sequence alignment.

The effect of pit and fissure sealants on the detection of occlusal caries in vitro.

PubMed

Manton, D J; Messer, L B

2007-03-01

To compare, in vitro, the effect of placing opaque (OPS) and clear fluorescing (CFS) pit and fissure sealants (PFS) on the detection of occlusal caries (OCD). Occlusal surfaces of 67 extracted molars were examined under standardised conditions by 6 final year undergraduate dental students, using visual, bitewing radiography, transillumination (FOTI), laser fluorescence (LF) and tactile methods of caries detection. The teeth were then assigned randomly to two groups for PFS placement: OPS and CFS; then the OCD methods were repeated. Caries presence/absence was determined histologically on serial sections examined under stereo-microscopy (10x). Before PFS placement the sensitivity and specificity for the OCD methods were: visual: 68%, 71%; radiographic: 15%, 95%; FOTI: 36%, 93%; LF: 49%, 83% and tactile: 39%, 67%, respectively. After placement of OPS, the sensitivity of LF (20%) and visual (13%) methods decreased and specificity increased (93%, 98% respectively). Placement of CFS resulted in minor changes in sensitivity and specificity. Correlation (Spearman's Rho coefficients) between OCD methods and histological intra-dentinal caries for pre- PFS, OPS, and CFS were: visual: 0.38, 0.34, 0.33; FOTI: 0.42, 0.35, 0.43; and LF: 0.41, 0.30, and 0.45 respectively. The sensitivity of all OCD methods was low, as well as their correlation to the histological gold standard. Placing OPS further decreased the sensitivity of LF and visual methods, whereas placing CFS had little effect on all OCD methods. It is recommended that tactile detection of occlusal caries should be discontinued, and the probe used only to clean the pits and fissures gently for more accurate visual detection, or prior to pit and fissure sealant placement. Further research into the development of an affordable, robust, accurate and easy to use method for OCD is required.

Simultaneous detection of perchlorate and bromate using rapid high-performance ion exchange chromatography-tandem mass spectrometry and perchlorate removal in drinking water.

PubMed

West, Danielle M; Mu, Ruipu; Gamagedara, Sanjeewa; Ma, Yinfa; Adams, Craig; Eichholz, Todd; Burken, Joel G; Shi, Honglan

2015-06-01

Perchlorate and bromate occurrence in drinking water causes health concerns due to their effects on thyroid function and carcinogenicity, respectively. The purpose of this study was threefold: (1) to advance a sensitive method for simultaneous rapid detection of perchlorate and bromate in drinking water system, (2) to systematically study the occurrence of these two contaminants in Missouri drinking water treatment systems, and (3) to examine effective sorbents for minimizing perchlorate in drinking water. A rapid high-performance ion exchange chromatography-tandem mass spectrometry (HPIC-MS/MS) method was advanced for simultaneous detection of perchlorate and bromate in drinking water. The HPIC-MS/MS method was rapid, required no preconcentration of the water samples, and had detection limits for perchlorate and bromate of 0.04 and 0.01 μg/L, respectively. The method was applied to determine perchlorate and bromate concentrations in total of 23 selected Missouri drinking water treatment systems during differing seasons. The water systems selected include different source waters: groundwater, lake water, river water, and groundwater influenced by surface water. The concentrations of perchlorate and bromate were lower than or near to method detection limits in most of the drinking water samples monitored. The removal of perchlorate by various adsorbents was studied. A cationic organoclay (TC-99) exhibited effective removal of perchlorate from drinking water matrices.

Detection method of flexion relaxation phenomenon based on wavelets for patients with low back pain

NASA Astrophysics Data System (ADS)

Nougarou, François; Massicotte, Daniel; Descarreaux, Martin

2012-12-01

The flexion relaxation phenomenon (FRP) can be defined as a reduction or silence of myoelectric activity of the lumbar erector spinae muscle during full trunk flexion. It is typically absent in patients with chronic low back pain (LBP). Before any broad clinical utilization of this neuromuscular response can be made, effective, standardized, and accurate methods of identifying FRP limits are needed. However, this phenomenon is clearly more difficult to detect for LBP patients than for healthy patients. The main goal of this study is to develop an automated method based on wavelet transformation that would improve time point limits detection of surface electromyography signals of the FRP in case of LBP patients. Conventional visual identification and proposed automated methods of time point limits detection of relaxation phase were compared on experimental data using criteria of accuracy and repeatability based on physiological properties. The evaluation demonstrates that the use of wavelet transform (WT) yields better results than methods without wavelet decomposition. Furthermore, methods based on wavelet per packet transform are more effective than algorithms employing discrete WT. Compared to visual detection, in addition to demonstrating an obvious saving of time, the use of wavelet per packet transform improves the accuracy and repeatability in the detection of the FRP limits. These results clearly highlight the value of the proposed technique in identifying onset and offset of the flexion relaxation response in LBP subjects.

Evaluation of efficiency of nested multiplex allele-specific PCR assay for detection of multidrug resistant tuberculosis directly from sputum samples.

PubMed

Mistri, S K; Sultana, M; Kamal, S M M; Alam, M M; Irin, F; Nessa, J; Ahsan, C R; Yasmin, M

2016-05-01

For an effective control of tuberculosis, rapid detection of multidrug resistant tuberculosis (MDR-TB) is necessary. Therefore, we developed a modified nested multiplex allele-specific polymerase chain reaction (MAS-PCR) method that enables rapid MDR-TB detection directly from sputum samples. The efficacy of this method was evaluated using 79 sputum samples collected from suspected tuberculosis patients. The performance of nested MAS-PCR method was compared with other MDR-TB detection methods like drug susceptibility testing (DST) and DNA sequencing. As rifampicin (RIF) resistance conforms to MDR-TB in greater than 90% cases, only the presence of RIF-associated mutations in rpoB gene was determined by DNA sequencing and nested MAS-PCR to detect MDR-TB. The concordance between nested MAS-PCR and DNA sequencing results was found to be 96·3%. When compared with DST, the sensitivity and specificity of nested MAS-PCR for RIF-resistance detection were determined to be 92·9 and 100% respectively. For developing- and high-TB burden countries, molecular-based tests have been recommended by the World Health Organization for rapid detection of MDR-TB. The results of this study indicate that, nested MAS-PCR assay might be a practical and relatively cost effective molecular method for rapid detection of MDR-TB from suspected sputum samples in developing countries with resource poor settings. © 2016 The Society for Applied Microbiology.

A Pulse Rate Detection Method for Mouse Application Based on Multi-PPG Sensors

PubMed Central

Chen, Wei-Hao

2017-01-01

Heart rate is an important physiological parameter for healthcare. Among measurement methods, photoplethysmography (PPG) is an easy and convenient method for pulse rate detection. However, as the PPG signal faces the challenge of motion artifacts and is constrained by the position chosen, the purpose of this paper is to implement a comfortable and easy-to-use multi-PPG sensor module combined with a stable and accurate real-time pulse rate detection method on a computer mouse. A weighted average method for multi-PPG sensors is used to adjust the weight of each signal channel in order to raise the accuracy and stability of the detected signal, therefore reducing the disturbance of noise under the environment of moving effectively and efficiently. According to the experiment results, the proposed method can increase the usability and probability of PPG signal detection on palms. PMID:28708112

Wildfire Detection using by Multi Dimensional Histogram in Boreal Forest

NASA Astrophysics Data System (ADS)

Honda, K.; Kimura, K.; Honma, T.

2008-12-01

Early detection of wildfires is an issue for reduction of damage to environment and human. There are some attempts to detect wildfires by using satellite imagery, which are mainly classified into three methods: Dozier Method(1981-), Threshold Method(1986-) and Contextual Method(1994-). However, the accuracy of these methods is not enough: some commission and omission errors are included in the detected results. In addition, it is not so easy to analyze satellite imagery with high accuracy because of insufficient ground truth data. Kudoh and Hosoi (2003) developed the detection method by using three-dimensional (3D) histogram from past fire data with the NOAA-AVHRR imagery. But their method is impractical because their method depends on their handworks to pick up past fire data from huge data. Therefore, the purpose of this study is to collect fire points as hot spots efficiently from satellite imagery and to improve the method to detect wildfires with the collected data. As our method, we collect past fire data with the Alaska Fire History data obtained by the Alaska Fire Service (AFS). We select points that are expected to be wildfires, and pick up the points inside the fire area of the AFS data. Next, we make 3D histogram with the past fire data. In this study, we use Bands 1, 21 and 32 of MODIS. We calculate the likelihood to detect wildfires with the three-dimensional histogram. As our result, we select wildfires with the 3D histogram effectively. We can detect the troidally spreading wildfire. This result shows the evidence of good wildfire detection. However, the area surrounding glacier tends to rise brightness temperature. It is a false alarm. Burnt area and bare ground are sometimes indicated as false alarms, so that it is necessary to improve this method. Additionally, we are trying various combinations of MODIS bands as the better method to detect wildfire effectively. So as to adjust our method in another area, we are applying our method to tropical forest in Kalimantan, Indonesia and around Chiang Mai, Thailand. But the ground truth data in these areas is lesser than the one in Alaska. Our method needs lots of accurate observed data to make multi-dimensional histogram in the same area. In this study, we can show the system to select wildfire data efficiently from satellite imagery. Furthermore, the development of multi-dimensional histogram from past fire data makes it possible to detect wildfires accurately.

A Lateral Flow Biosensor for the Detection of Single Nucleotide Polymorphisms.

PubMed

Zeng, Lingwen; Xiao, Zhuo

2017-01-01

A lateral flow biosensor (LFB) is introduced for the detection of single nucleotide polymorphisms (SNPs). The assay is composed of two steps: circular strand displacement reaction and lateral flow biosensor detection. In step 1, the nucleotide at SNP site is recognized by T4 DNA ligase and the signal is amplified by strand displacement DNA polymerase, which can be accomplished at a constant temperature. In step 2, the reaction product of step 1 is detected by a lateral flow biosensor, which is a rapid and cost effective tool for nuclei acid detection. Comparing with conventional methods, it requires no complicated machines. It is suitable for the use of point of care diagnostics. Therefore, this simple, cost effective, robust, and promising LFB detection method of SNP has great potential for the detection of genetic diseases, personalized medicine, cancer related mutations, and drug-resistant mutations of infectious agents.

Infrared video based gas leak detection method using modified FAST features

NASA Astrophysics Data System (ADS)

Wang, Min; Hong, Hanyu; Huang, Likun

2018-03-01

In order to detect the invisible leaking gas that is usually dangerous and easily leads to fire or explosion in time, many new technologies have arisen in the recent years, among which the infrared video based gas leak detection is widely recognized as a viable tool. However, all the moving regions of a video frame can be detected as leaking gas regions by the existing infrared video based gas leak detection methods, without discriminating the property of each detected region, e.g., a walking person in a video frame may be also detected as gas by the current gas leak detection methods.To solve this problem, we propose a novel infrared video based gas leak detection method in this paper, which is able to effectively suppress strong motion disturbances.Firstly, the Gaussian mixture model(GMM) is used to establish the background model.Then due to the observation that the shapes of gas regions are different from most rigid moving objects, we modify the Features From Accelerated Segment Test (FAST) algorithm and use the modified FAST (mFAST) features to describe each connected component. In view of the fact that the statistical property of the mFAST features extracted from gas regions is different from that of other motion regions, we propose the Pixel-Per-Points (PPP) condition to further select candidate connected components.Experimental results show that the algorithm is able to effectively suppress most strong motion disturbances and achieve real-time leaking gas detection.

Optimizing occupancy surveys by maximizing detection probability: application to amphibian monitoring in the Mediterranean region.

PubMed

Petitot, Maud; Manceau, Nicolas; Geniez, Philippe; Besnard, Aurélien

2014-09-01

Setting up effective conservation strategies requires the precise determination of the targeted species' distribution area and, if possible, its local abundance. However, detection issues make these objectives complex for most vertebrates. The detection probability is usually <1 and is highly dependent on species phenology and other environmental variables. The aim of this study was to define an optimized survey protocol for the Mediterranean amphibian community, that is, to determine the most favorable periods and the most effective sampling techniques for detecting all species present on a site in a minimum number of field sessions and a minimum amount of prospecting effort. We visited 49 ponds located in the Languedoc region of southern France on four occasions between February and June 2011. Amphibians were detected using three methods: nighttime call count, nighttime visual encounter, and daytime netting. The detection nondetection data obtained was then modeled using site-occupancy models. The detection probability of amphibians sharply differed between species, the survey method used and the date of the survey. These three covariates also interacted. Thus, a minimum of three visits spread over the breeding season, using a combination of all three survey methods, is needed to reach a 95% detection level for all species in the Mediterranean region. Synthesis and applications: detection nondetection surveys combined to site occupancy modeling approach are powerful methods that can be used to estimate the detection probability and to determine the prospecting effort necessary to assert that a species is absent from a site.

A label-free amplified fluorescence DNA detection based on isothermal circular strand-displacement polymerization reaction and graphene oxide.

PubMed

Li, Zhen; Zhu, Wenping; Zhang, Jinwen; Jiang, Jianhui; Shen, Guoli; Yu, Ruqin

2013-07-07

A label-free fluorescent DNA biosensor has been presented based on isothermal circular strand-displacement polymerization reaction (ICSDPR) combined with graphene oxide (GO) binding. The proposed method is simple and cost-effective with a low detection limit of 4 pM, which compares favorably with other GO-based homogenous DNA detection methods.

Proactive malware detection

NASA Astrophysics Data System (ADS)

Gloster, Jonathan; Diep, Michael; Dredden, David; Mix, Matthew; Olsen, Mark; Price, Brian; Steil, Betty

2014-06-01

Small-to-medium sized businesses lack resources to deploy and manage high-end advanced solutions to deter sophisticated threats from well-funded adversaries, but evidence shows that these types of businesses are becoming key targets. As malicious code and network attacks become more sophisticated, classic signature-based virus and malware detection methods are less effective. To augment the current malware methods of detection, we developed a proactive approach to detect emerging malware threats using open source tools and intelligence to discover patterns and behaviors of malicious attacks and adversaries. Technical and analytical skills are combined to track adversarial behavior, methods and techniques. We established a controlled (separated domain) network to identify, monitor, and track malware behavior to increase understanding of the methods and techniques used by cyber adversaries. We created a suite of tools that observe the network and system performance looking for anomalies that may be caused by malware. The toolset collects information from open-source tools and provides meaningful indicators that the system was under or has been attacked. When malware is discovered, we analyzed and reverse engineered it to determine how it could be detected and prevented. Results have shown that with minimum resources, cost effective capabilities can be developed to detect abnormal behavior that may indicate malicious software.

Biomedical journals lack a consistent method to detect outcome reporting bias: a cross-sectional analysis.

PubMed

Huan, L N; Tejani, A M; Egan, G

2014-10-01

An increasing amount of recently published literature has implicated outcome reporting bias (ORB) as a major contributor to skewing data in both randomized controlled trials and systematic reviews; however, little is known about the current methods in place to detect ORB. This study aims to gain insight into the detection and management of ORB by biomedical journals. This was a cross-sectional analysis involving standardized questions via email or telephone with the top 30 biomedical journals (2012) ranked by impact factor. The Cochrane Database of Systematic Reviews was excluded leaving 29 journals in the sample. Of 29 journals, 24 (83%) responded to our initial inquiry of which 14 (58%) answered our questions and 10 (42%) declined participation. Five (36%) of the responding journals indicated they had a specific method to detect ORB, whereas 9 (64%) did not have a specific method in place. The prevalence of ORB in the review process seemed to differ with 4 (29%) journals indicating ORB was found commonly, whereas 7 (50%) indicated ORB was uncommon or never detected by their journal previously. The majority (n = 10/14, 72%) of journals were unwilling to report or make discrepancies found in manuscripts available to the public. Although the minority, there were some journals (n = 4/14, 29%) which described thorough methods to detect ORB. Many journals seemed to lack a method with which to detect ORB and its estimated prevalence was much lower than that reported in literature suggesting inadequate detection. There exists a potential for overestimation of treatment effects of interventions and unclear risks. Fortunately, there are journals within this sample which appear to utilize comprehensive methods for detection of ORB, but overall, the data suggest improvements at the biomedical journal level for detecting and minimizing the effect of this bias are needed. © 2014 John Wiley & Sons Ltd.

Convolutional neural network features based change detection in satellite images

NASA Astrophysics Data System (ADS)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Smoke regions extraction based on two steps segmentation and motion detection in early fire

NASA Astrophysics Data System (ADS)

Jian, Wenlin; Wu, Kaizhi; Yu, Zirong; Chen, Lijuan

2018-03-01

Aiming at the early problems of video-based smoke detection in fire video, this paper proposes a method to extract smoke suspected regions by combining two steps segmentation and motion characteristics. Early smoldering smoke can be seen as gray or gray-white regions. In the first stage, regions of interests (ROIs) with smoke are obtained by using two step segmentation methods. Then, suspected smoke regions are detected by combining the two step segmentation and motion detection. Finally, morphological processing is used for smoke regions extracting. The Otsu algorithm is used as segmentation method and the ViBe algorithm is used to detect the motion of smoke. The proposed method was tested on 6 test videos with smoke. The experimental results show the effectiveness of our proposed method over visual observation.

A modified anomaly detection method for capsule endoscopy images using non-linear color conversion and Higher-order Local Auto-Correlation (HLAC).

PubMed

Hu, Erzhong; Nosato, Hirokazu; Sakanashi, Hidenori; Murakawa, Masahiro

2013-01-01

Capsule endoscopy is a patient-friendly endoscopy broadly utilized in gastrointestinal examination. However, the efficacy of diagnosis is restricted by the large quantity of images. This paper presents a modified anomaly detection method, by which both known and unknown anomalies in capsule endoscopy images of small intestine are expected to be detected. To achieve this goal, this paper introduces feature extraction using a non-linear color conversion and Higher-order Local Auto Correlation (HLAC) Features, and makes use of image partition and subspace method for anomaly detection. Experiments are implemented among several major anomalies with combinations of proposed techniques. As the result, the proposed method achieved 91.7% and 100% detection accuracy for swelling and bleeding respectively, so that the effectiveness of proposed method is demonstrated.

[A cloud detection algorithm for MODIS images combining Kmeans clustering and multi-spectral threshold method].

PubMed

Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei

2011-04-01

An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.

Quantification of Humic Substances in Natural Water Using Nitrogen-Doped Carbon Dots.

PubMed

Guan, Yan-Fang; Huang, Bao-Cheng; Qian, Chen; Yu, Han-Qing

2017-12-19

Dissolved organic matter (DOM) is ubiquitous in aqueous environments and plays a significant role in pollutant mitigation, transformation and organic geochemical circulation. DOM is also capable of forming carcinogenic byproducts in the disinfection treatment processes of drinking water. Thus, efficient methods for DOM quantification are highly desired. In this work, a novel sensor for rapid and selective detection of humic substances (HS), a key component of DOM, based on fluorescence quenching of nitrogen-doped carbon quantum dots was developed. The experimental results show that the HS detection range could be broadened to 100 mg/L with a detection limit of 0.2 mg/L. Moreover, the detection was effective within a wide pH range of 3.0 to 12.0, and the interferences of ions on the HS measurement were negligible. A good detection result for real surface water samples further validated the feasibility of the developed detection method. Furthermore, a nonradiation electron transfer mechanism for quenching the nitrogen-doped carbon-dots fluorescence by HS was elucidated. In addition, we prepared a test paper and proved its effectiveness. This work provides a new efficient method for the HS quantification than the frequently used modified Lowry method in terms of sensitivity and detection range.

Experimental Investigation on the Detection of Multiple Surface Cracks Using Vibrothermography with a Low-Power Piezoceramic Actuator.

PubMed

Xu, Changhang; Xie, Jing; Zhang, Wuyang; Kong, Qingzhao; Chen, Guoming; Song, Gangbing

2017-11-23

Vibrothermography often employs a high-power actuator to generate heat on a specimen to reveal damage, however, the high-power actuator brings inconvenience to the application and possibly introduces additional damage to the inspected objects. This study uses a low-power piezoceramic transducer as the actuator of vibrothermography and explores its ability to detect multiple surface cracks in a metal part. Experiments were conducted on a thin aluminum beam with three cracks in different orientations. Detailed analyses of both thermograms and temperature data are presented to validate the proposed vibrothermography method. To further investigate the performance of the proposed vibrothermography method, we experimentally studied the effects of several critical factors, including the amplitude of excitation signal, specimen constraints, relative position between the transducer and cracks (the transducer is mounted on the same or the opposite side with the cracks). The results demonstrate that all cracks can be detected conveniently and simultaneously by using the proposed low-power vibrothermography. We also found that the magnitude of excitation signal and the specimen constraints have a great influence on detection results. Combined with effective data processing methods, such as Fourier transformation employed in this study, the proposed method provides a promising potential to detect multiple cracks on a metal surface in a safe and effective manner.

Space moving target detection using time domain feature

NASA Astrophysics Data System (ADS)

Wang, Min; Chen, Jin-yong; Gao, Feng; Zhao, Jin-yu

2018-01-01

The traditional space target detection methods mainly use the spatial characteristics of the star map to detect the targets, which can not make full use of the time domain information. This paper presents a new space moving target detection method based on time domain features. We firstly construct the time spectral data of star map, then analyze the time domain features of the main objects (target, stars and the background) in star maps, finally detect the moving targets using single pulse feature of the time domain signal. The real star map target detection experimental results show that the proposed method can effectively detect the trajectory of moving targets in the star map sequence, and the detection probability achieves 99% when the false alarm rate is about 8×10-5, which outperforms those of compared algorithms.

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data.

PubMed

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-05-15

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection.

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data

PubMed Central

Han, Yanling; Li, Jue; Zhang, Yun; Hong, Zhonghua; Wang, Jing

2017-01-01

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection. PMID:28505135

MPAI (mass probes aided ionization) method for total analysis of biomolecules by mass spectrometry.

PubMed

Honda, Aki; Hayashi, Shinichiro; Hifumi, Hiroki; Honma, Yuya; Tanji, Noriyuki; Iwasawa, Naoko; Suzuki, Yoshio; Suzuki, Koji

2007-01-01

We have designed and synthesized various mass probes, which enable us to effectively ionize various molecules to be detected with mass spectrometry. We call the ionization method using mass probes the "MPAI (mass probes aided ionization)" method. We aim at the sensitive detection of various biological molecules, and also the detection of bio-molecules by a single mass spectrometry serially without changing the mechanical settings. Here, we review mass probes for small molecules with various functional groups and mass probes for proteins. Further, we introduce newly developed mass probes for proteins for highly sensitive detection.

Detection of β-Lactams and Chloramphenicol Residues in Raw Milk-Development and Application of an HPLC-DAD Method in Comparison with Microbial Inhibition Assays.

PubMed

Karageorgou, Eftychia; Christoforidou, Sofia; Ioannidou, Maria; Psomas, Evdoxios; Samouris, Georgios

2018-06-01

The present study was carried out to assess the detection sensitivity of four microbial inhibition assays (MIAs) in comparison with the results obtained by the High Performance Liquid Chromatography with Diode-Array Detection (HPLC-DAD) method for antibiotics of the β-lactam group and chloramphenicol in fortified raw milk samples. MIAs presented fairly good results when detecting β-lactams, whereas none were able to detect chloramphenicol at or above the permissible limits. HPLC analysis revealed high recoveries of examined compounds, whereas all detection limits observed were lower than their respective maximum residue limits (MRL) values. The extraction and clean-up procedure of antibiotics was performed by a modified matrix solid phase dispersion procedure using a mixture of Plexa by Agilent and QuEChERS as a sorbent. The HPLC method developed was validated, determining the accuracy, precision, linearity, decision limit, and detection capability. Both methods were used to monitor raw milk samples of several cows and sheep, obtained from producers in different regions of Greece, for the presence of examined antibiotic residues. Results obtained showed that MIAs could be used effectively and routinely to detect antibiotic residues in several milk types. However, in some cases, spoilage of milk samples revealed that the kits' sensitivity could be strongly affected, whereas this fact does not affect the effectiveness of HPLC-DAD analysis.

Recombinase Polymerase Amplification (RPA) of CaMV-35S Promoter and nos Terminator for Rapid Detection of Genetically Modified Crops

PubMed Central

Xu, Chao; Li, Liang; Jin, Wujun; Wan, Yusong

2014-01-01

Recombinase polymerase amplification (RPA) is a novel isothermal DNA amplification and detection technology that enables the amplification of DNA within 30 min at a constant temperature of 37–42 °C by simulating in vivo DNA recombination. In this study, based on the regulatory sequence of the cauliflower mosaic virus 35S (CaMV-35S) promoter and the Agrobacterium tumefaciens nopaline synthase gene (nos) terminator, which are widely incorporated in genetically modified (GM) crops, we designed two sets of RPA primers and established a real-time RPA detection method for GM crop screening and detection. This method could reliably detect as few as 100 copies of the target molecule in a sample within 15–25 min. Furthermore, the real-time RPA detection method was successfully used to amplify and detect DNA from samples of four major GM crops (maize, rice, cotton, and soybean). With this novel amplification method, the test time was significantly shortened and the reaction process was simplified; thus, this method represents an effective approach to the rapid detection of GM crops. PMID:25310647

Infrared small target detection technology based on OpenCV

NASA Astrophysics Data System (ADS)

Liu, Lei; Huang, Zhijian

2013-05-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

Infrared small target detection technology based on OpenCV

NASA Astrophysics Data System (ADS)

Liu, Lei; Huang, Zhijian

2013-09-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

Recombinase polymerase amplification (RPA) of CaMV-35S promoter and nos terminator for rapid detection of genetically modified crops.

PubMed

Xu, Chao; Li, Liang; Jin, Wujun; Wan, Yusong

2014-10-10

Recombinase polymerase amplification (RPA) is a novel isothermal DNA amplification and detection technology that enables the amplification of DNA within 30 min at a constant temperature of 37-42 °C by simulating in vivo DNA recombination. In this study, based on the regulatory sequence of the cauliflower mosaic virus 35S (CaMV-35S) promoter and the Agrobacterium tumefaciens nopaline synthase gene (nos) terminator, which are widely incorporated in genetically modified (GM) crops, we designed two sets of RPA primers and established a real-time RPA detection method for GM crop screening and detection. This method could reliably detect as few as 100 copies of the target molecule in a sample within 15-25 min. Furthermore, the real-time RPA detection method was successfully used to amplify and detect DNA from samples of four major GM crops (maize, rice, cotton, and soybean). With this novel amplification method, the test time was significantly shortened and the reaction process was simplified; thus, this method represents an effective approach to the rapid detection of GM crops.

Evaluation of seven aquatic sampling methods for amphibians and other aquatic fauna

USGS Publications Warehouse

Gunzburger, M.S.

2007-01-01

To design effective and efficient research and monitoring programs researchers must have a thorough understanding of the capabilities and limitations of their sampling methods. Few direct comparative studies exist for aquatic sampling methods for amphibians. The objective of this study was to simultaneously employ seven aquatic sampling methods in 10 wetlands to compare amphibian species richness and number of individuals detected with each method. Four sampling methods allowed counts of individuals (metal dipnet, D-frame dipnet, box trap, crayfish trap), whereas the other three methods allowed detection of species (visual encounter, aural, and froglogger). Amphibian species richness was greatest with froglogger, box trap, and aural samples. For anuran species, the sampling methods by which each life stage was detected was related to relative length of larval and breeding periods and tadpole size. Detection probability of amphibians varied across sampling methods. Box trap sampling resulted in the most precise amphibian count, but the precision of all four count-based methods was low (coefficient of variation > 145 for all methods). The efficacy of the four count sampling methods at sampling fish and aquatic invertebrates was also analyzed because these predatory taxa are known to be important predictors of amphibian habitat distribution. Species richness and counts were similar for fish with the four methods, whereas invertebrate species richness and counts were greatest in box traps. An effective wetland amphibian monitoring program in the southeastern United States should include multiple sampling methods to obtain the most accurate assessment of species community composition at each site. The combined use of frogloggers, crayfish traps, and dipnets may be the most efficient and effective amphibian monitoring protocol. ?? 2007 Brill Academic Publishers.

Quantitation of heparosan with heparin lyase III and spectrophotometry.

PubMed

Huang, Haichan; Zhao, Yingying; Lv, Shencong; Zhong, Weihong; Zhang, Fuming; Linhardt, Robert J

2014-02-15

Heparosan is Escherichia coli K5 capsule polysaccharide, which is the key precursor for preparing bioengineered heparin. A rapid and effective quantitative method for detecting heparosan is important in the large-scale production of heparosan. Heparin lyase III (Hep III) effectively catalyzes the heparosan depolymerization, forming unsaturated disaccharides that are measurable using a spectrophotometer at 232 nm. We report a new method for the quantitative detection of heparosan with heparin lyase III and spectrophotometry that is safer and more specific than the traditional carbazole assay. In an optimized detection system, heparosan at a minimum concentration of 0.60 g/L in fermentation broth can be detected. Copyright © 2013 Elsevier Inc. All rights reserved.

Improvement of charge-pumping electrically detected magnetic resonance and its application to silicon metal-oxide-semiconductor field-effect transistor

NASA Astrophysics Data System (ADS)

Hori, Masahiro; Tsuchiya, Toshiaki; Ono, Yukinori

2017-01-01

Charge-pumping electrically detected magnetic resonance (CP EDMR), or EDMR in the CP mode, is improved and applied to a silicon metal-oxide-semiconductor field-effect transistor (MOSFET). Real-time monitoring of the CP process reveals that high-frequency transient currents are an obstacle to signal amplification for EDMR. Therefore, we introduce cutoff circuitry, leading to a detection limit for the number of spins as low as 103 for Si MOS interface defects. With this improved method, we demonstrate that CP EDMR inherits one of the most important features of the CP method: the gate control of the energy window of the detectable interface defects for spectroscopy.

A Comparative Study of Test Data Dimensionality Assessment Procedures Under Nonparametric IRT Models

ERIC Educational Resources Information Center

van Abswoude, Alexandra A. H.; van der Ark, L. Andries; Sijtsma, Klaas

2004-01-01

In this article, an overview of nonparametric item response theory methods for determining the dimensionality of item response data is provided. Four methods were considered: MSP, DETECT, HCA/CCPROX, and DIMTEST. First, the methods were compared theoretically. Second, a simulation study was done to compare the effectiveness of MSP, DETECT, and…

Rapid detection of Ganoderma-infected oil palms by microwave ergosterol extraction with HPLC and TLC.

PubMed

Muniroh, M S; Sariah, M; Zainal Abidin, M A; Lima, N; Paterson, R R M

2014-05-01

Detection of basal stem rot (BSR) by Ganoderma of oil palms was based on foliar symptoms and production of basidiomata. Enzyme-Linked Immunosorbent Assays-Polyclonal Antibody (ELISA-PAB) and PCR have been proposed as early detection methods for the disease. These techniques are complex, time consuming and have accuracy limitations. An ergosterol method was developed which correlated well with the degree of infection in oil palms, including samples growing in plantations. However, the method was capable of being optimised. This current study was designed to develop a simpler, more rapid and efficient ergosterol method with utility in the field that involved the use of microwave extraction. The optimised procedure involved extracting a small amount of Ganoderma, or Ganoderma-infected oil palm suspended in low volumes of solvent followed by irradiation in a conventional microwave oven at 70°C and medium high power for 30s, resulting in simultaneous extraction and saponification. Ergosterol was detected by thin layer chromatography (TLC) and quantified using high performance liquid chromatography with diode array detection. The TLC method was novel and provided a simple, inexpensive method with utility in the field. The new method was particularly effective at extracting high yields of ergosterol from infected oil palm and enables rapid analysis of field samples on site, allowing infected oil palms to be treated or culled very rapidly. Some limitations of the method are discussed herein. The procedures lend themselves to controlling the disease more effectively and allowing more effective use of land currently employed to grow oil palms, thereby reducing pressure to develop new plantations. Copyright © 2014 Elsevier B.V. All rights reserved.

Characterisation of a new, highly effective method for detecting nematode eggs (Ascaris spp., Toxocara spp., Trichuris spp.) in sewage sludge containing flocculants.

PubMed

Zdybel, Jolanta; Karamon, Jacek; Różycki, Mirosław; Bilska-Zając, Ewa; Kłapeć, Teresa; Cencek, Tomasz

2016-11-01

Because traditional methods used for sewage sludge parasitological examinations have low sensitivity, a new, highly effective method (own method - OM) was devised. The principle of this method is to eliminate the flocculent effect on the structure of sewage sludge by mechanically damaging floccules in the presence of surfactants and to increase the effectiveness of egg isolation processes in large volumes of liquids. The objective of this study was to estimate the effectiveness of the OM in detecting nematode eggs in sewage sludge samples containing flocculants. In the first stage, the effectiveness of the OM was compared to 4 other methods routinely used in parasitological examinations of dehydrated sewage sludge. Next, method standardisation was performed using sewage sludge samples supplemented with eggs from 3 parasite species (Ascaris suum, Toxocara canis and Trichuris vulpis). The study demonstrated that OM efficiency was 6-65 times greater than other methods, depending on the method and type of detected eggs. Limit of detection (LOD) calculations for the OM were performed on samples supplemented with a known number of parasite eggs resulting in 10, 5 and 3 eggs/50 g of sample for A. suum, T. vulpis and T. canis eggs, respectively. The limits of quantification (LOQ) of the OM were established as 200 eggs/50 g of sample for A. suum and T. vulpis eggs and 50 eggs/50 g of sample for T. canis eggs. The rectilinear regression functions, which determined the relationship between the number of eggs detected in OM measurements and the number of eggs contained in the samples, were characterised by high and statistically significant coefficients of determination (r 2 ). The slopes of the trend lines were 0.3188, 0.3821 and 0.3276, and the intercepts were -11.223, -9.0261 and -23.15 for A. suum, T. canis and T. vulpis eggs, respectively. Method sensitivity, calculated as the slope coefficient of the regression function and expressed as a percentage, ranged from 32% to 38% depending on egg type. The study confirmed that the OM may be applied to quantify parasite eggs in dehydrated sewage sludge containing polyelectrolytes. Copyright © 2016 Elsevier Inc. All rights reserved.

Unsupervised malaria parasite detection based on phase spectrum.

PubMed

Fang, Yuming; Xiong, Wei; Lin, Weisi; Chen, Zhenzhong

2011-01-01

In this paper, we propose a novel method for malaria parasite detection based on phase spectrum. The method first obtains the amplitude spectrum and phase spectrum for blood smear images through Quaternion Fourier Transform (QFT). Then it gets the reconstructed image based on Inverse Quaternion Fourier transform (IQFT) on a constant amplitude spectrum and the original phase spectrum. The malaria parasite areas can be detected easily from the reconstructed blood smear images. Extensive experiments have demonstrated the effectiveness of this novel method.

Application of Time-Frequency Domain Transform to Three-Dimensional Interpolation of Medical Images.

PubMed

Lv, Shengqing; Chen, Yimin; Li, Zeyu; Lu, Jiahui; Gao, Mingke; Lu, Rongrong

2017-11-01

Medical image three-dimensional (3D) interpolation is an important means to improve the image effect in 3D reconstruction. In image processing, the time-frequency domain transform is an efficient method. In this article, several time-frequency domain transform methods are applied and compared in 3D interpolation. And a Sobel edge detection and 3D matching interpolation method based on wavelet transform is proposed. We combine wavelet transform, traditional matching interpolation methods, and Sobel edge detection together in our algorithm. What is more, the characteristics of wavelet transform and Sobel operator are used. They deal with the sub-images of wavelet decomposition separately. Sobel edge detection 3D matching interpolation method is used in low-frequency sub-images under the circumstances of ensuring high frequency undistorted. Through wavelet reconstruction, it can get the target interpolation image. In this article, we make 3D interpolation of the real computed tomography (CT) images. Compared with other interpolation methods, our proposed method is verified to be effective and superior.

Delving into α-stable distribution in noise suppression for seizure detection from scalp EEG

NASA Astrophysics Data System (ADS)

Wang, Yueming; Qi, Yu; Wang, Yiwen; Lei, Zhen; Zheng, Xiaoxiang; Pan, Gang

2016-10-01

Objective. There is serious noise in EEG caused by eye blink and muscle activities. The noise exhibits similar morphologies to epileptic seizure signals, leading to relatively high false alarms in most existing seizure detection methods. The objective in this paper is to develop an effective noise suppression method in seizure detection and explore the reason why it works. Approach. Based on a state-space model containing a non-linear observation function and multiple features as the observations, this paper delves deeply into the effect of the α-stable distribution in the noise suppression for seizure detection from scalp EEG. Compared with the Gaussian distribution, the α-stable distribution is asymmetric and has relatively heavy tails. These properties make it more powerful in modeling impulsive noise in EEG, which usually can not be handled by the Gaussian distribution. Specially, we give a detailed analysis in the state estimation process to show the reason why the α-stable distribution can suppress the impulsive noise. Main results. To justify each component in our model, we compare our method with 4 different models with different settings on a collected 331-hour epileptic EEG data. To show the superiority of our method, we compare it with the existing approaches on both our 331-hour data and 892-hour public data. The results demonstrate that our method is most effective in both the detection rate and the false alarm. Significance. This is the first attempt to incorporate the α-stable distribution to a state-space model for noise suppression in seizure detection and achieves the state-of-the-art performance.

Diffraction analysis and evaluation of several focus- and track-error detection schemes for magneto-optical disk systems

NASA Technical Reports Server (NTRS)

Bernacki, Bruce E.; Mansuripur, M.

1992-01-01

A commonly used tracking method on pre-grooved magneto-optical (MO) media is the push-pull technique, and the astigmatic method is a popular focus-error detection approach. These two methods are analyzed using DIFFRACT, a general-purpose scalar diffraction modeling program, to observe the effects on the error signals due to focusing lens misalignment, Seidel aberrations, and optical crosstalk (feedthrough) between the focusing and tracking servos. Using the results of the astigmatic/push-pull system as a basis for comparison, a novel focus/track-error detection technique that utilizes a ring toric lens is evaluated as well as the obscuration method (focus error detection only).

A Novel Method for Block Size Forensics Based on Morphological Operations

NASA Astrophysics Data System (ADS)

Luo, Weiqi; Huang, Jiwu; Qiu, Guoping

Passive forensics analysis aims to find out how multimedia data is acquired and processed without relying on pre-embedded or pre-registered information. Since most existing compression schemes for digital images are based on block processing, one of the fundamental steps for subsequent forensics analysis is to detect the presence of block artifacts and estimate the block size for a given image. In this paper, we propose a novel method for blind block size estimation. A 2×2 cross-differential filter is first applied to detect all possible block artifact boundaries, morphological operations are then used to remove the boundary effects caused by the edges of the actual image contents, and finally maximum-likelihood estimation (MLE) is employed to estimate the block size. The experimental results evaluated on over 1300 nature images show the effectiveness of our proposed method. Compared with existing gradient-based detection method, our method achieves over 39% accuracy improvement on average.

Evaluation of Two Outlier-Detection-Based Methods for Detecting Tissue-Selective Genes from Microarray Data

PubMed Central

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-01-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent’s non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent’s method is not suitable for ROKU. PMID:19936074

Detection of Abnormal Events via Optical Flow Feature Analysis

PubMed Central

Wang, Tian; Snoussi, Hichem

2015-01-01

In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm. PMID:25811227

Indirect fluorometric detection techniques on thin layer chromatography and effect of ultrasound on gel electrophoresis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yinfa, Ma.

Thin-layer chromatography (TLC) is a broadly applicable separation technique. It offers many advantages over high performance liquid chromatography (HPLC), such as easily adapted for two-dimensional separation, for whole-column'' detection and for handling multiple samples, etc. However, due to its draggy development of detection techniques comparing with HPLC, TLC has not received the attention it deserves. Therefore, exploring new detection techniques is very important to the development of TLC. It is the principal of this dissertation to present a new detection method for TLC -- indirect fluorometric detection method. This detection technique is universal sensitive, nondestructive, and simple. This will bemore » described in detail from Sections 1 through Section 5. Section 1 and 3 describe the indirect fluorometric detection of anions and nonelectrolytes in TLC. In Section 2, a detection method for cations based on fluorescence quenching of ethidium bromide is presented. In Section 4, a simple and interesting TLC experiment is designed, three different fluorescence detection principles are used for the determination of caffeine, saccharin and sodium benzoate in beverages. A laser-based indirect fluorometric detection technique in TLC is developed in Section 5. Section 6 is totally different from Sections 1 through 5. An ultrasonic effect on the separation of DNA fragments in agarose gel electrophoresis is investigated. 262 refs.« less

Effects of Average Signed Area Between Two Item Characteristic Curves and Test Purification Procedures on the DIF Detection via the Mantel-Haenszel Method

ERIC Educational Resources Information Center

Wang, Wen-Chung; Su, Ya-Hui

2004-01-01

In this study we investigated the effects of the average signed area (ASA) between the item characteristic curves of the reference and focal groups and three test purification procedures on the uniform differential item functioning (DIF) detection via the Mantel-Haenszel (M-H) method through Monte Carlo simulations. The results showed that ASA,…

Real-time PCR-based method for the rapid detection of extended RAS mutations using bridged nucleic acids in colorectal cancer.

PubMed

Iida, Takao; Mizuno, Yukie; Kaizaki, Yasuharu

2017-10-27

Mutations in RAS and BRAF are predictors of the efficacy of anti-epidermal growth factor receptor (EGFR) therapy in patients with metastatic colorectal cancer (mCRC). Therefore, simple, rapid, cost-effective methods to detect these mutations in the clinical setting are greatly needed. In the present study, we evaluated BNA Real-time PCR Mutation Detection Kit Extended RAS (BNA Real-time PCR), a real-time PCR method that uses bridged nucleic acid clamping technology to rapidly detect mutations in RAS exons 2-4 and BRAF exon 15. Genomic DNA was extracted from 54 formalin-fixed paraffin-embedded (FFPE) tissue samples obtained from mCRC patients. Among the 54 FFPE samples, BNA Real-time PCR detected 21 RAS mutations (38.9%) and 5 BRAF mutations (9.3%), and the reference assay (KRAS Mutation Detection Kit and MEBGEN™ RASKET KIT) detected 22 RAS mutations (40.7%). The concordance rate of detected RAS mutations between the BNA Real-time PCR assay and the reference assays was 98.2% (53/54). The BNA Real-time PCR assay proved to be a more simple, rapid, and cost-effective method for detecting KRAS and RAS mutations compared with existing assays. These findings suggest that BNA Real-time PCR is a valuable tool for predicting the efficacy of early anti-EGFR therapy in mCRC patients. Copyright © 2017 Elsevier B.V. All rights reserved.

An Effective Method for Substance Detection Using the Broad Spectrum THz Signal: A “Terahertz Nose”

PubMed Central

Trofimov, Vyacheslav A.; Varentsova, Svetlana A.

2015-01-01

We propose an effective method for the detection and identification of dangerous substances by using the broadband THz pulse. This pulse excites, for example, many vibrational or rotational energy levels of molecules simultaneously. By analyzing the time-dependent spectrum of the THz pulse transmitted through or reflected from a substance, we follow the average response spectrum dynamics. Comparing the absorption and emission spectrum dynamics of a substance under analysis with the corresponding data for a standard substance, one can detect and identify the substance under real conditions taking into account the influence of packing material, water vapor and substance surface. For quality assessment of the standard substance detection in the signal under analysis, we propose time-dependent integral correlation criteria. Restrictions of usually used detection and identification methods, based on a comparison between the absorption frequencies of a substance under analysis and a standard substance, are demonstrated using a physical experiment with paper napkins. PMID:26020281

Road traffic sign detection and classification from mobile LiDAR point clouds

NASA Astrophysics Data System (ADS)

Weng, Shengxia; Li, Jonathan; Chen, Yiping; Wang, Cheng

2016-03-01

Traffic signs are important roadway assets that provide valuable information of the road for drivers to make safer and easier driving behaviors. Due to the development of mobile mapping systems that can efficiently acquire dense point clouds along the road, automated detection and recognition of road assets has been an important research issue. This paper deals with the detection and classification of traffic signs in outdoor environments using mobile light detection and ranging (Li- DAR) and inertial navigation technologies. The proposed method contains two main steps. It starts with an initial detection of traffic signs based on the intensity attributes of point clouds, as the traffic signs are always painted with highly reflective materials. Then, the classification of traffic signs is achieved based on the geometric shape and the pairwise 3D shape context. Some results and performance analyses are provided to show the effectiveness and limits of the proposed method. The experimental results demonstrate the feasibility and effectiveness of the proposed method in detecting and classifying traffic signs from mobile LiDAR point clouds.

Highly Sensitive Detection of Low-Abundance White Spot Syndrome Virus by a Pre-Amplification PCR Method.

PubMed

Pan, Xiaoming; Zhang, Yanfang; Sha, Xuejiao; Wang, Jing; Li, Jing; Dong, Ping; Liang, Xingguo

2017-03-28

White spot syndrome virus (WSSV) is a major threat to the shrimp farming industry and so far there is no effective therapy for it, and thus early diagnostic of WSSV is of great importance. However, at the early stage of infection, the extremely low-abundance of WSSV DNA challenges the detection sensitivity and accuracy of PCR. To effectively detect low-abundance WSSV, here we developed a pre-amplification PCR (pre-amp PCR) method to amplify trace amounts of WSSV DNA from massive background genomic DNA. Combining with normal specific PCR, 10 copies of target WSSV genes were detected from ~10 10 magnitude of backgrounds. In particular, multiple target genes were able to be balanced amplified with similar efficiency due to the usage of the universal primer. The efficiency of the pre-amp PCR was validated by nested-PCR and quantitative PCR, and pre-amp PCR showed higher efficiency than nested-PCR when multiple targets were detected. The developed method is particularly suitable for the super early diagnosis of WSSV, and has potential to be applied in other low-abundance sample detection cases.

Quantitative Detection of Cracks in Steel Using Eddy Current Pulsed Thermography.

PubMed

Shi, Zhanqun; Xu, Xiaoyu; Ma, Jiaojiao; Zhen, Dong; Zhang, Hao

2018-04-02

Small cracks are common defects in steel and often lead to catastrophic accidents in industrial applications. Various nondestructive testing methods have been investigated for crack detection; however, most current methods focus on qualitative crack identification and image processing. In this study, eddy current pulsed thermography (ECPT) was applied for quantitative crack detection based on derivative analysis of temperature variation. The effects of the incentive parameters on the temperature variation were analyzed in the simulation study. The crack profile and position are identified in the thermal image based on the Canny edge detection algorithm. Then, one or more trajectories are determined through the crack profile in order to determine the crack boundary through its temperature distribution. The slope curve along the trajectory is obtained. Finally, quantitative analysis of the crack sizes was performed by analyzing the features of the slope curves. The experimental verification showed that the crack sizes could be quantitatively detected with errors of less than 1%. Therefore, the proposed ECPT method was demonstrated to be a feasible and effective nondestructive approach for quantitative crack detection.

Small threat and contraband detection with TNA-based systems.

PubMed

Shaw, T J; Brown, D; D'Arcy, J; Liu, F; Shea, P; Sivakumar, M; Gozani, T

2005-01-01

The detection of small threats, such as explosives, drugs, and chemical weapons, concealed or encased in surrounding material, is a major concern in areas from security checkpoints to UneXploded Ordnance (UXO) clearance. Techniques such as X-ray and trace detection are often ineffectual in these applications. Thermal neutron analysis (TNA) provides an effective method for detecting concealed threats. This paper shows the effectiveness of Ancore's SPEDS, based on TNA, in detecting concealed liquid threats and differentiating live from inert mortar shells.

[A research in speech endpoint detection based on boxes-coupling generalization dimension].

PubMed

Wang, Zimei; Yang, Cuirong; Wu, Wei; Fan, Yingle

2008-06-01

In this paper, a new calculating method of generalized dimension, based on boxes-coupling principle, is proposed to overcome the edge effects and to improve the capability of the speech endpoint detection which is based on the original calculating method of generalized dimension. This new method has been applied to speech endpoint detection. Firstly, the length of overlapping border was determined, and through calculating the generalized dimension by covering the speech signal with overlapped boxes, three-dimension feature vectors including the box dimension, the information dimension and the correlation dimension were obtained. Secondly, in the light of the relation between feature distance and similarity degree, feature extraction was conducted by use of common distance. Lastly, bi-threshold method was used to classify the speech signals. The results of experiment indicated that, by comparison with the original generalized dimension (OGD) and the spectral entropy (SE) algorithm, the proposed method is more robust and effective for detecting the speech signals which contain different kinds of noise in different signal noise ratio (SNR), especially in low SNR.

Balancing precision and risk: should multiple detection methods be analyzed separately in N-mixture models?

USGS Publications Warehouse

Graves, Tabitha A.; Royle, J. Andrew; Kendall, Katherine C.; Beier, Paul; Stetz, Jeffrey B.; Macleod, Amy C.

2012-01-01

Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic) and bear rubs (opportunistic). We used hierarchical abundance models (N-mixture models) with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1) lead to the selection of the same variables as important and (2) provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3) yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight), and (4) improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed against those risks. The analysis framework presented here will be useful for other species exhibiting heterogeneity by detection method.

Robust obstacle detection for unmanned surface vehicles

NASA Astrophysics Data System (ADS)

Qin, Yueming; Zhang, Xiuzhi

2018-03-01

Obstacle detection is of essential importance for Unmanned Surface Vehicles (USV). Although some obstacles (e.g., ships, islands) can be detected by Radar, there are many other obstacles (e.g., floating pieces of woods, swimmers) which are difficult to be detected via Radar because these obstacles have low radar cross section. Therefore, detecting obstacle from images taken onboard is an effective supplement. In this paper, a robust vision-based obstacle detection method for USVs is developed. The proposed method employs the monocular image sequence captured by the camera on the USVs and detects obstacles on the sea surface from the image sequence. The experiment results show that the proposed scheme is efficient to fulfill the obstacle detection task.

Deep Constrained Siamese Hash Coding Network and Load-Balanced Locality-Sensitive Hashing for Near Duplicate Image Detection.

PubMed

Hu, Weiming; Fan, Yabo; Xing, Junliang; Sun, Liang; Cai, Zhaoquan; Maybank, Stephen

2018-09-01

We construct a new efficient near duplicate image detection method using a hierarchical hash code learning neural network and load-balanced locality-sensitive hashing (LSH) indexing. We propose a deep constrained siamese hash coding neural network combined with deep feature learning. Our neural network is able to extract effective features for near duplicate image detection. The extracted features are used to construct a LSH-based index. We propose a load-balanced LSH method to produce load-balanced buckets in the hashing process. The load-balanced LSH significantly reduces the query time. Based on the proposed load-balanced LSH, we design an effective and feasible algorithm for near duplicate image detection. Extensive experiments on three benchmark data sets demonstrate the effectiveness of our deep siamese hash encoding network and load-balanced LSH.

Development of capillary electrophoresis methods for quantitative determination of taurine in vehicle system and biological media.

PubMed

da Silva, Dayse L P; Rüttinger, Hans H; Mrestani, Yahia; Baum, Walter F; Neubert, Reinhard H H

2006-06-01

CE methods have been developed for the determination of taurine in pharmaceutical formulation (microemulsion) and in biological media such as sweat. The CE system with end-column pulsed amperometric detection has been found to be an interesting method in comparison with UV and fluorescence detection for its simplicity and rapidity. A gold-disk electrode of 100 mm diameter was used as the working electrode. The effects of a field decoupler at the end of the capillary, separation voltage, injection and pressure times were investigated. A detection limit of 4 x 10(-5) mol/L was reached using integrated pulsed amperometric detection, a method successfully applied to taurine analysis of the biological samples such as sweat. For taurine analysis of oil-in-water microemulsion, fluorescence detector was the favored method, the detection limit of which was 4 x 10(-11) mol/L.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines.

PubMed

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-12-13

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines

PubMed Central

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-01-01

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods. PMID:27983577

Salient target detection based on pseudo-Wigner-Ville distribution and Rényi entropy.

PubMed

Xu, Yuannan; Zhao, Yuan; Jin, Chenfei; Qu, Zengfeng; Liu, Liping; Sun, Xiudong

2010-02-15

We present what we believe to be a novel method based on pseudo-Wigner-Ville distribution (PWVD) and Rényi entropy for salient targets detection. In the foundation of studying the statistical property of Rényi entropy via PWVD, the residual entropy-based saliency map of an input image can be obtained. From the saliency map, target detection is completed by the simple and convenient threshold segmentation. Experimental results demonstrate the proposed method can detect targets effectively in complex ground scenes.

Utility of gold nanoparticles in luminescence determination of trovafloxacin: comparison of chemiluminescence and fluorescence detection.

PubMed

Alarfaj, Nawal A; El-Tohamy, Maha F

2015-12-01

Two novel sensitive sequential injection chemiluminescence analysis and fluorescence methods for trovafloxacin mesylate detection have been developed. The methods were based on the enhancement effect of gold nanoparticles on luminol-ferricyanide-trovafloxacin and europium(III)-trovafloxacin complex systems. The optimum conditions for both detection methods were investigated. The chemiluminescence signal was emitted due to the enhanced effect of gold nanoparticles on the reaction of luminol-ferricyanide-trovafloxacin in an alkaline medium. The response was linear over a concentration range of 1.0 × 10(-9) to 1.0 × 10(-2) mol/L (%RSD = 1.3), (n = 9, r = 0.9991) with a detection limit of 1.7 × 10(-10) mol/L (S/N = 3). The weak fluorescence intensity signal of the oxidation complex of europium(III)-trovafloxacin was strongly enhanced by gold nanoparticles and detected at λex = 330 and λem = 540 nm. Fluorescence detection enabled the determination of trovafloxacin mesylate over a linear range of 1.0 × 10(-8) to 1.0 × 10(-3) mol/L (%RSD = 1.2), (n = 6, r = 0.9993) with a detection limit of 3.3 × 10(-9) mol/L. The proposed methods were successfully applied to the determination of the studied drug in its bulk form and in pharmaceutical preparations. The results were treated statistically and compared with those obtained from other reported methods. Copyright © 2015 John Wiley & Sons, Ltd.

Development of a Tandem Repeat-Based Polymerase Chain Displacement Reaction Method for Highly Sensitive Detection of 'Candidatus Liberibacter asiaticus'.

PubMed

Lou, Binghai; Song, Yaqin; RoyChowdhury, Moytri; Deng, Chongling; Niu, Ying; Fan, Qijun; Tang, Yan; Zhou, Changyong

2018-02-01

Huanglongbing (HLB) is one of the most destructive diseases in citrus production worldwide. Early detection of HLB pathogens can facilitate timely removal of infected citrus trees in the field. However, low titer and uneven distribution of HLB pathogens in host plants make reliable detection challenging. Therefore, the development of effective detection methods with high sensitivity is imperative. This study reports the development of a novel method, tandem repeat-based polymerase chain displacement reaction (TR-PCDR), for the detection of 'Candidatus Liberibacter asiaticus', a widely distributed HLB-associated bacterium. A uniquely designed primer set (TR2-PCDR-F/TR2-PCDR-1R) and a thermostable Taq DNA polymerase mutant with strand displacement activity were used for TR-PCDR amplification. Performed in a regular thermal cycler, TR-PCDR could produce more than two amplicons after each amplification cycle. Sensitivity of the developed TR-PCDR was 10 copies of target DNA fragment. The sensitive level was proven to be 100× higher than conventional PCR and similar to real-time PCR. Data from the detection of 'Ca. L. asiaticus' with filed samples using the above three methods also showed similar results. No false-positive TR-PCDR amplification was observed from healthy citrus samples and water controls. These results thereby illustrated that the developed TR-PCDR method can be applied to the reliable, highly sensitive, and cost-effective detection of 'Ca. L. asiaticus'.

Comparison of point counts and territory mapping for detecting effects of forest management on songbirds

USGS Publications Warehouse

Newell, Felicity L.; Sheehan, James; Wood, Petra Bohall; Rodewald, Amanda D.; Buehler, David A.; Keyser, Patrick D.; Larkin, Jeffrey L.; Beachy, Tiffany A.; Bakermans, Marja H.; Boves, Than J.; Evans, Andrea; George, Gregory A.; McDermott, Molly E.; Perkins, Kelly A.; White, Matthew; Wigley, T. Bently

2013-01-01

Point counts are commonly used to assess changes in bird abundance, including analytical approaches such as distance sampling that estimate density. Point-count methods have come under increasing scrutiny because effects of detection probability and field error are difficult to quantify. For seven forest songbirds, we compared fixed-radii counts (50 m and 100 m) and density estimates obtained from distance sampling to known numbers of birds determined by territory mapping. We applied point-count analytic approaches to a typical forest management question and compared results to those obtained by territory mapping. We used a before–after control impact (BACI) analysis with a data set collected across seven study areas in the central Appalachians from 2006 to 2010. Using a 50-m fixed radius, variance in error was at least 1.5 times that of the other methods, whereas a 100-m fixed radius underestimated actual density by >3 territories per 10 ha for the most abundant species. Distance sampling improved accuracy and precision compared to fixed-radius counts, although estimates were affected by birds counted outside 10-ha units. In the BACI analysis, territory mapping detected an overall treatment effect for five of the seven species, and effects were generally consistent each year. In contrast, all point-count methods failed to detect two treatment effects due to variance and error in annual estimates. Overall, our results highlight the need for adequate sample sizes to reduce variance, and skilled observers to reduce the level of error in point-count data. Ultimately, the advantages and disadvantages of different survey methods should be considered in the context of overall study design and objectives, allowing for trade-offs among effort, accuracy, and power to detect treatment effects.

Single-copy gene detection using branched DNA (bDNA) in situ hybridization.

PubMed

Player, A N; Shen, L P; Kenny, D; Antao, V P; Kolberg, J A

2001-05-01

We have developed a branched DNA in situ hybridization (bDNA ISH) method for detection of human papillomavirus (HPV) DNA in whole cells. Using human cervical cancer cell lines with known copies of HPV DNA, we show that the bDNA ISH method is highly sensitive, detecting as few as one or two copies of HPV DNA per cell. By modifying sample pretreatment, viral mRNA or DNA sequences can be detected using the same set of oligonucleotide probes. In experiments performed on mixed populations of cells, the bDNA ISH method is highly specific and can distinguish cells with HPV-16 from cells with HPV-18 DNA. Furthermore, we demonstrate that the bDNA ISH method provides precise localization, yielding positive signals retained within the subcellular compartments in which the target nucleic acid sequences are localized. As an effective and convenient means for nucleic acid detection, the bDNA ISH method is applicable to the detection of cancers and infectious agents. (J Histochem Cytochem 49:603-611, 2001)

Infrared target tracking via weighted correlation filter

NASA Astrophysics Data System (ADS)

He, Yu-Jie; Li, Min; Zhang, JinLi; Yao, Jun-Ping

2015-11-01

Design of an effective target tracker is an important and challenging task for many applications due to multiple factors which can cause disturbance in infrared video sequences. In this paper, an infrared target tracking method under tracking by detection framework based on a weighted correlation filter is presented. This method consists of two parts: detection and filtering. For the detection stage, we propose a sequential detection method for the infrared target based on low-rank representation. For the filtering stage, a new multi-feature weighted function which fuses different target features is proposed, which takes the importance of the different regions into consideration. The weighted function is then incorporated into a correlation filter to compute a confidence map more accurately, in order to indicate the best target location based on the detection results obtained from the first stage. Extensive experimental results on different video sequences demonstrate that the proposed method performs favorably for detection and tracking compared with baseline methods in terms of efficiency and accuracy.

Pavement crack detection combining non-negative feature with fast LoG in complex scene

NASA Astrophysics Data System (ADS)

Wang, Wanli; Zhang, Xiuhua; Hong, Hanyu

2015-12-01

Pavement crack detection is affected by much interference in the realistic situation, such as the shadow, road sign, oil stain, salt and pepper noise etc. Due to these unfavorable factors, the exist crack detection methods are difficult to distinguish the crack from background correctly. How to extract crack information effectively is the key problem to the road crack detection system. To solve this problem, a novel method for pavement crack detection based on combining non-negative feature with fast LoG is proposed. The two key novelties and benefits of this new approach are that 1) using image pixel gray value compensation to acquisit uniform image, and 2) combining non-negative feature with fast LoG to extract crack information. The image preprocessing results demonstrate that the method is indeed able to homogenize the crack image with more accurately compared to existing methods. A large number of experimental results demonstrate the proposed approach can detect the crack regions more correctly compared with traditional methods.

Evaluation of Polymerase Chain Reaction for Detecting Coliform Bacteria in Drinking Water Sources.

PubMed

Isfahani, Bahram Nasr; Fazeli, Hossein; Babaie, Zeinab; Poursina, Farkhondeh; Moghim, Sharareh; Rouzbahani, Meisam

2017-01-01

Coliform bacteria are used as indicator organisms for detecting fecal pollution in water. Traditional methods including microbial culture tests in lactose-containing media and enzyme-based tests for the detection of β-galactosidase; however, these methods are time-consuming and less specific. The aim of this study was to evaluate polymerase chain reaction (PCR) for detecting coliform. Totally, 100 of water samples from Isfahan drinking water source were collected. Coliform bacteria and Escherichia coli were detected in drinking water using LacZ and LamB genes in PCR method performed in comparison with biochemical tests for all samples. Using phenotyping, 80 coliform isolates were found. The results of the biochemical tests illustrated 78.7% coliform bacteria and 21.2% E. coli . PCR results for LacZ and LamB genes were 67.5% and 17.5%, respectively. The PCR method was shown to be an effective, sensitive, and rapid method for detecting coliform and E. coli in drinking water from the Isfahan drinking water sources.

Selective detection of cavitation bubbles by triplet pulse sequence in high-intensity focused ultrasound treatment

NASA Astrophysics Data System (ADS)

Iwasaki, Ryosuke; Nagaoka, Ryo; Yoshizawa, Shin; Umemura, Shin-ichiro

2018-07-01

Acoustic cavitation bubbles are known to enhance the heating effect in high-intensity focused ultrasound (HIFU) treatment. The detection of cavitation bubbles with high sensitivity and selectivity is required to predict the therapeutic and side effects of cavitation, and ensure the efficacy and safety of the treatment. A pulse inversion (PI) technique has been widely used for imaging microbubbles through enhancing the second-harmonic component of echo signals. However, it has difficulty in separating the nonlinear response of microbubbles from that due to nonlinear propagation. In this study, a triplet pulse (3P) method was investigated to specifically image cavitation bubbles by extracting the 1.5th fractional harmonic component. The proposed 3P method depicted cavitation bubbles with a contrast ratio significantly higher than those in conventional imaging methods with and without PI. The results suggest that the 3P method is effective for specifically detecting microbubbles in cavitation-enhanced HIFU treatment.

Camouflage target detection via hyperspectral imaging plus information divergence measurement

NASA Astrophysics Data System (ADS)

Chen, Yuheng; Chen, Xinhua; Zhou, Jiankang; Ji, Yiqun; Shen, Weimin

2016-01-01

Target detection is one of most important applications in remote sensing. Nowadays accurate camouflage target distinction is often resorted to spectral imaging technique due to its high-resolution spectral/spatial information acquisition ability as well as plenty of data processing methods. In this paper, hyper-spectral imaging technique together with spectral information divergence measure method is used to solve camouflage target detection problem. A self-developed visual-band hyper-spectral imaging device is adopted to collect data cubes of certain experimental scene before spectral information divergences are worked out so as to discriminate target camouflage and anomaly. Full-band information divergences are measured to evaluate target detection effect visually and quantitatively. Information divergence measurement is proved to be a low-cost and effective tool for target detection task and can be further developed to other target detection applications beyond spectral imaging technique.

Multisignal detecting system of pile integrity testing

NASA Astrophysics Data System (ADS)

Liu, Zuting; Luo, Ying; Yu, Shihai

2002-05-01

The low strain reflection wave method plays a principal rule in the integrating detection of base piles. However, there are some deficiencies with this method. For example, there is a blind area of detection on top of the tested pile; it is difficult to recognize the defects at deep-seated parts of the pile; there is still the planar of 3D domino effect, etc. It is very difficult to solve these problems only with the single-transducer pile integrity testing system. A new multi-signal piles integrity testing system is proposed in this paper, which is able to impulse and collect signals on multiple points on top of the pile. By using the multiple superposition data processing method, the detecting system can effectively restrain the interference and elevate the precision and SNR of pile integrity testing. The system can also be applied to the evaluation of engineering structure health.

Likelihood of Brine and CO 2 Leak Detection using Magnetotellurics and Electrical Resistivity Tomography Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, X.; Buscheck, T. A.; Mansoor, K.

The US DOE National Risk Assessment Partnership (NRAP), funded through the Office of Fossil Energy and NETL, is developing methods to evaluate the effectiveness of monitoring techniques to detect brine and CO 2 leakage from legacy wells into underground sources of drinking water (USDW) overlying a CO 2 storage reservoir. As part of the NRAP Strategic Monitoring group, we have generated 140 simulations of aquifer impact data based on the Kimberlina site in California’s southern San Joaquin Basin, Kimberlina Rev. 1.1. CO 2 buoyancy allows some of the stored CO 2 to reach shallower permeable zones and is detectable withmore » surface geophysical sensors. We are using this simulated data set to evaluate effectiveness of electrical resistivity tomography (ERT) and magnetotellurics (MT) for leak detection. The evaluation of additional monitoring methods such as pressure, seismic and gravity is underway through a multi-lab collaboration.« less

Incorrect Match Detection Method for Arctic Sea-Ice Reconstruction Using Uav Images

NASA Astrophysics Data System (ADS)

Kim, J.-I.; Kim, H.-C.

2018-05-01

Shapes and surface roughness, which are considered as key indicators in understanding Arctic sea-ice, can be measured from the digital surface model (DSM) of the target area. Unmanned aerial vehicle (UAV) flying at low altitudes enables theoretically accurate DSM generation. However, the characteristics of sea-ice with textureless surface and incessant motion make image matching difficult for DSM generation. In this paper, we propose a method for effectively detecting incorrect matches before correcting a sea-ice DSM derived from UAV images. The proposed method variably adjusts the size of search window to analyze the matching results of DSM generated and distinguishes incorrect matches. Experimental results showed that the sea-ice DSM produced large errors along the textureless surfaces, and that the incorrect matches could be effectively detected by the proposed method.

Airborne rhinovirus detection and effect of ultraviolet irradiation on detection by a semi-nested RT-PCR assay

PubMed Central

Myatt, Theodore A; Johnston, Sebastian L; Rudnick, Stephen; Milton, Donald K

2003-01-01

Background Rhinovirus, the most common cause of upper respiratory tract infections, has been implicated in asthma exacerbations and possibly asthma deaths. Although the method of transmission of rhinoviruses is disputed, several studies have demonstrated that aerosol transmission is a likely method of transmission among adults. As a first step in studies of possible airborne rhinovirus transmission, we developed methods to detect aerosolized rhinovirus by extending existing technology for detecting infectious agents in nasal specimens. Methods We aerosolized rhinovirus in a small aerosol chamber. Experiments were conducted with decreasing concentrations of rhinovirus. To determine the effect of UV irradiation on detection of rhinoviral aerosols, we also conducted experiments in which we exposed aerosols to a UV dose of 684 mJ/m2. Aerosols were collected on Teflon filters and rhinovirus recovered in Qiagen AVL buffer using the Qiagen QIAamp Viral RNA Kit (Qiagen Corp., Valencia, California) followed by semi-nested RT-PCR and detection by gel electrophoresis. Results We obtained positive results from filter samples that had collected at least 1.3 TCID50 of aerosolized rhinovirus. Ultraviolet irradiation of airborne virus at doses much greater than those used in upper-room UV germicidal irradiation applications did not inhibit subsequent detection with the RT-PCR assay. Conclusion The air sampling and extraction methodology developed in this study should be applicable to the detection of rhinovirus and other airborne viruses in the indoor air of offices and schools. This method, however, cannot distinguish UV inactivated virus from infectious viral particles. PMID:12525263

Detection of Suspicious Persons using Internet Camera

NASA Astrophysics Data System (ADS)

Terada, Kenji; Kamogashira, Daisuke

Recently, many brutal crimes have shocked us. Therefore, the importance of security and self-defense have increased more and more. It is necessary to develop an automatic method of detecting suspicious persons. In this paper, we propose a method of detecting suspicious persons using the internet camera. An image sequence is obtained by the internet camera. By using these images, the recognition of suspicious persons is carried out. Our method classifies the condition of the target person into 3 postures: walking, staying and sitting. The system employs the subspace method which uses three features: the value of movement, the number of looking around restlessly, and the rate of stopping and going. Some experimental results using a simple experimental system are also reported, which indicate effectiveness of the proposed method. In most scenes, the suspicious persons are able to be detected by the proposed method.

Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches

PubMed Central

Choi, Ted; Eskin, Eleazar

2013-01-01

Gene expression data, in conjunction with information on genetic variants, have enabled studies to identify expression quantitative trait loci (eQTLs) or polymorphic locations in the genome that are associated with expression levels. Moreover, recent technological developments and cost decreases have further enabled studies to collect expression data in multiple tissues. One advantage of multiple tissue datasets is that studies can combine results from different tissues to identify eQTLs more accurately than examining each tissue separately. The idea of aggregating results of multiple tissues is closely related to the idea of meta-analysis which aggregates results of multiple genome-wide association studies to improve the power to detect associations. In principle, meta-analysis methods can be used to combine results from multiple tissues. However, eQTLs may have effects in only a single tissue, in all tissues, or in a subset of tissues with possibly different effect sizes. This heterogeneity in terms of effects across multiple tissues presents a key challenge to detect eQTLs. In this paper, we develop a framework that leverages two popular meta-analysis methods that address effect size heterogeneity to detect eQTLs across multiple tissues. We show by using simulations and multiple tissue data from mouse that our approach detects many eQTLs undetected by traditional eQTL methods. Additionally, our method provides an interpretation framework that accurately predicts whether an eQTL has an effect in a particular tissue. PMID:23785294

Edge detection of optical subaperture image based on improved differential box-counting method

NASA Astrophysics Data System (ADS)

Li, Yi; Hui, Mei; Liu, Ming; Dong, Liquan; Kong, Lingqin; Zhao, Yuejin

2018-01-01

Optical synthetic aperture imaging technology is an effective approach to improve imaging resolution. Compared with monolithic mirror system, the image of optical synthetic aperture system is often more complex at the edge, and as a result of the existence of gap between segments, which makes stitching becomes a difficult problem. So it is necessary to extract the edge of subaperture image for achieving effective stitching. Fractal dimension as a measure feature can describe image surface texture characteristics, which provides a new approach for edge detection. In our research, an improved differential box-counting method is used to calculate fractal dimension of image, then the obtained fractal dimension is mapped to grayscale image to detect edges. Compared with original differential box-counting method, this method has two improvements as follows: by modifying the box-counting mechanism, a box with a fixed height is replaced by a box with adaptive height, which solves the problem of over-counting the number of boxes covering image intensity surface; an image reconstruction method based on super-resolution convolutional neural network is used to enlarge small size image, which can solve the problem that fractal dimension can't be calculated accurately under the small size image, and this method may well maintain scale invariability of fractal dimension. The experimental results show that the proposed algorithm can effectively eliminate noise and has a lower false detection rate compared with the traditional edge detection algorithms. In addition, this algorithm can maintain the integrity and continuity of image edge in the case of retaining important edge information.

Image processing based detection of lung cancer on CT scan images

NASA Astrophysics Data System (ADS)

Abdillah, Bariqi; Bustamam, Alhadi; Sarwinda, Devvi

2017-10-01

In this paper, we implement and analyze the image processing method for detection of lung cancer. Image processing techniques are widely used in several medical problems for picture enhancement in the detection phase to support the early medical treatment. In this research we proposed a detection method of lung cancer based on image segmentation. Image segmentation is one of intermediate level in image processing. Marker control watershed and region growing approach are used to segment of CT scan image. Detection phases are followed by image enhancement using Gabor filter, image segmentation, and features extraction. From the experimental results, we found the effectiveness of our approach. The results show that the best approach for main features detection is watershed with masking method which has high accuracy and robust.

A Reference-Free and Non-Contact Method for Detecting and Imaging Damage in Adhesive-Bonded Structures Using Air-Coupled Ultrasonic Transducers.

PubMed

Yonathan Sunarsa, Timotius; Aryan, Pouria; Jeon, Ikgeun; Park, Byeongjin; Liu, Peipei; Sohn, Hoon

2017-12-08

Adhesive bonded structures have been widely used in aerospace, automobile, and marine industries. Due to the complex nature of the failure mechanisms of bonded structures, cost-effective and reliable damage detection is crucial for these industries. Most of the common damage detection methods are not adequately sensitive to the presence of weakened bonding. This paper presents an experimental and analytical method for the in-situ detection of damage in adhesive-bonded structures. The method is fully non-contact, using air-coupled ultrasonic transducers (ACT) for ultrasonic wave generation and sensing. The uniqueness of the proposed method relies on accurate detection and localization of weakened bonding in complex adhesive bonded structures. The specimens tested in this study are parts of real-world structures with critical and complex damage types, provided by Hyundai Heavy Industries ® and IKTS Fraunhofer ® . Various transmitter and receiver configurations, including through transmission, pitch-catch scanning, and probe holder angles, were attempted, and the obtained results were analyzed. The method examines the time-of-flight of the ultrasonic waves over a target inspection area, and the spatial variation of the time-of-flight information was examined to visualize and locate damage. The proposed method works without relying on reference data obtained from the pristine condition of the target specimen. Aluminum bonded plates and triplex adhesive layers with debonding and weakened bonding were used to examine the effectiveness of the method.

A Reference-Free and Non-Contact Method for Detecting and Imaging Damage in Adhesive-Bonded Structures Using Air-Coupled Ultrasonic Transducers

PubMed Central

Yonathan Sunarsa, Timotius; Aryan, Pouria; Jeon, Ikgeun; Park, Byeongjin; Liu, Peipei

2017-01-01

Adhesive bonded structures have been widely used in aerospace, automobile, and marine industries. Due to the complex nature of the failure mechanisms of bonded structures, cost-effective and reliable damage detection is crucial for these industries. Most of the common damage detection methods are not adequately sensitive to the presence of weakened bonding. This paper presents an experimental and analytical method for the in-situ detection of damage in adhesive-bonded structures. The method is fully non-contact, using air-coupled ultrasonic transducers (ACT) for ultrasonic wave generation and sensing. The uniqueness of the proposed method relies on accurate detection and localization of weakened bonding in complex adhesive bonded structures. The specimens tested in this study are parts of real-world structures with critical and complex damage types, provided by Hyundai Heavy Industries® and IKTS Fraunhofer®. Various transmitter and receiver configurations, including through transmission, pitch-catch scanning, and probe holder angles, were attempted, and the obtained results were analyzed. The method examines the time-of-flight of the ultrasonic waves over a target inspection area, and the spatial variation of the time-of-flight information was examined to visualize and locate damage. The proposed method works without relying on reference data obtained from the pristine condition of the target specimen. Aluminum bonded plates and triplex adhesive layers with debonding and weakened bonding were used to examine the effectiveness of the method. PMID:29292752

Microelectrode array recordings of cardiac action potentials as a high throughput method to evaluate pesticide toxicity.

PubMed

Natarajan, A; Molnar, P; Sieverdes, K; Jamshidi, A; Hickman, J J

2006-04-01

The threat of environmental pollution, biological warfare agent dissemination and new diseases in recent decades has increased research into cell-based biosensors. The creation of this class of sensors could specifically aid the detection of toxic chemicals and their effects in the environment, such as pyrethroid pesticides. Pyrethroids are synthetic pesticides that have been used increasingly over the last decade to replace other pesticides like DDT. In this study we used a high-throughput method to detect pyrethroids by using multielectrode extracellular recordings from cardiac cells. The data from this cell-electrode hybrid system was compared to published results obtained with patch-clamp electrophysiology and also used as an alternative method to further understand pyrethroid effects. Our biosensor consisted of a confluent monolayer of cardiac myocytes cultured on microelectrode arrays (MEA) composed of 60 substrate-integrated electrodes. Spontaneous activity of these beating cells produced extracellular field potentials in the range of 100 microV to nearly 1200 microV with a beating frequency of 0.5-4 Hz. All of the tested pyrethroids; alpha-Cypermethrin, Tetramethrin and Tefluthrin, produced similar changes in the electrophysiological properties of the cardiac myocytes, namely reduced beating frequency and amplitude. The sensitivity of our toxin detection method was comparable to earlier patch-clamp studies, which indicates that, in specific applications, high-throughput extracellular methods can replace single-cell studies. Moreover, the similar effect of all three pyrethroids on the measured parameters suggests, that not only detection of the toxins but, their classification might also be possible with this method. Overall our results support the idea that whole cell biosensors might be viable alternatives when compared to current toxin detection methods.

Fluorescein Diacetate Microplate Assay in Cell Viability Detection.

PubMed

Chen, Xi; Yang, Xiu-Ying; Fang, Lian-Hua; DU, Guan-Hua

2016-12-20

Objective To investigate the application of the fluorescein diacetate (FDA) microplate assay in cell viability detection. Methods Cells were seeded in a 96-well culture plate until detection. After incubated with FDA,the plate was detected by fluorescence microplate analyzer. The effects of FDA incubation duration,concentration,and other factors on the assay's accuracy and stability were assessed. We also compared the results of FDA with methyl thiazolyl(MTT) in terms of cell numbers and H 2 O 2 injury. Results Within 0-30 minutes,the fluorescence-cell number coefficient of FDA assay increased with duration and reached 0.99 in 27-30 minutes. The optimum concentration of final FDA in this study was 10-30 μg/ml. On cell viability detection,the result of FDA method was equivalent to MTT method. As to H 2 O 2 injury assay,the sensitivity of FDA method was superior to MTT on the higher concentration H 2 O 2 treatment due to a relative shorter duration for detection. Conclusion As a stable and reliable method,FDA is feasible for cell variability detection under varied conditions.

Gastric cancer screening of a high-risk population in Japan using serum pepsinogen and barium digital radiography.

PubMed

Ohata, Hiroshi; Oka, Masashi; Yanaoka, Kimihiko; Shimizu, Yasuhito; Mukoubayashi, Chizu; Mugitani, Kouichi; Iwane, Masataka; Nakamura, Hideya; Tamai, Hideyuki; Arii, Kenji; Nakata, Hiroya; Yoshimura, Noriko; Takeshita, Tetsuya; Miki, Kazumasa; Mohara, Osamu; Ichinose, Masao

2005-10-01

With the aim of developing more efficient gastric cancer screening programs for use in Japan, we studied a new screening program that combines serum pepsinogen (PG) testing and barium digital radiography (DR). A total of 17 647 middle-aged male subjects underwent workplace screening over a 7-year period using a combination of PG testing and DR. This program's effectiveness, as well as other characteristics of the program, was analyzed. Forty-nine cases of gastric cancer were detected (comprising 88% early cancer cases). The detection rate was 0.28%, and the positive predictive value was 0.85%. The PG test detected 63.3% of cases, DR detected 69.4% of cases, and both tests were positive in 32.7% of cancer cases. The two methods were almost equally effective, and were considerably more effective than conventional screening using photofluorography. Each screening method detected a distinct gastric cancer subgroup; the PG test efficiently detected asymptomatic small early cancer with intestinal type histology, while DR was efficient at detecting cancers with depressed or ulcerated morphology and diffuse type histology. The cost for the detection of a single cancer was much less than that for conventional screening. In fact, it is possible to further reduce the cost of detecting a single cancer to a cost comparable to that of surgically resecting a single gastric cancer. Thus, it is probable that a highly efficient gastric cancer screening system can be implemented by combining the two screening methods. Such a screening program would be beneficial in a population at high risk for gastric cancer.

Joint detection and tracking of size-varying infrared targets based on block-wise sparse decomposition

NASA Astrophysics Data System (ADS)

Li, Miao; Lin, Zaiping; Long, Yunli; An, Wei; Zhou, Yiyu

2016-05-01

The high variability of target size makes small target detection in Infrared Search and Track (IRST) a challenging task. A joint detection and tracking method based on block-wise sparse decomposition is proposed to address this problem. For detection, the infrared image is divided into overlapped blocks, and each block is weighted on the local image complexity and target existence probabilities. Target-background decomposition is solved by block-wise inexact augmented Lagrange multipliers. For tracking, label multi-Bernoulli (LMB) tracker tracks multiple targets taking the result of single-frame detection as input, and provides corresponding target existence probabilities for detection. Unlike fixed-size methods, the proposed method can accommodate size-varying targets, due to no special assumption for the size and shape of small targets. Because of exact decomposition, classical target measurements are extended and additional direction information is provided to improve tracking performance. The experimental results show that the proposed method can effectively suppress background clutters, detect and track size-varying targets in infrared images.

Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

PubMed

Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

2017-11-01

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

Colorimetric detection of uranium in water

DOEpatents

DeVol, Timothy A [Clemson, SC; Hixon, Amy E [Piedmont, SC; DiPrete, David P [Evans, GA

2012-03-13

Disclosed are methods, materials and systems that can be used to determine qualitatively or quantitatively the level of uranium contamination in water samples. Beneficially, disclosed systems are relatively simple and cost-effective. For example, disclosed systems can be utilized by consumers having little or no training in chemical analysis techniques. Methods generally include a concentration step and a complexation step. Uranium concentration can be carried out according to an extraction chromatographic process and complexation can chemically bind uranium with a detectable substance such that the formed substance is visually detectable. Methods can detect uranium contamination down to levels even below the MCL as established by the EPA.

Damage Detection Based on Static Strain Responses Using FBG in a Wind Turbine Blade.

PubMed

Tian, Shaohua; Yang, Zhibo; Chen, Xuefeng; Xie, Yong

2015-08-14

The damage detection of a wind turbine blade enables better operation of the turbines, and provides an early alert to the destroyed events of the blade in order to avoid catastrophic losses. A new non-baseline damage detection method based on the Fiber Bragg grating (FBG) in a wind turbine blade is developed in this paper. Firstly, the Chi-square distribution is proven to be an effective damage-sensitive feature which is adopted as the individual information source for the local decision. In order to obtain the global and optimal decision for the damage detection, the feature information fusion (FIF) method is proposed to fuse and optimize information in above individual information sources, and the damage is detected accurately through of the global decision. Then a 13.2 m wind turbine blade with the distributed strain sensor system is adopted to describe the feasibility of the proposed method, and the strain energy method (SEM) is used to describe the advantage of the proposed method. Finally results show that the proposed method can deliver encouraging results of the damage detection in the wind turbine blade.

Evaluation of Polymerase Chain Reaction for Detecting Coliform Bacteria in Drinking Water Sources

PubMed Central

Isfahani, Bahram Nasr; Fazeli, Hossein; Babaie, Zeinab; Poursina, Farkhondeh; Moghim, Sharareh; Rouzbahani, Meisam

2017-01-01

Background: Coliform bacteria are used as indicator organisms for detecting fecal pollution in water. Traditional methods including microbial culture tests in lactose-containing media and enzyme-based tests for the detection of β-galactosidase; however, these methods are time-consuming and less specific. The aim of this study was to evaluate polymerase chain reaction (PCR) for detecting coliform. Materials and Methods: Totally, 100 of water samples from Isfahan drinking water source were collected. Coliform bacteria and Escherichia coli were detected in drinking water using LacZ and LamB genes in PCR method performed in comparison with biochemical tests for all samples. Results: Using phenotyping, 80 coliform isolates were found. The results of the biochemical tests illustrated 78.7% coliform bacteria and 21.2% E. coli. PCR results for LacZ and LamB genes were 67.5% and 17.5%, respectively. Conclusion: The PCR method was shown to be an effective, sensitive, and rapid method for detecting coliform and E. coli in drinking water from the Isfahan drinking water sources. PMID:29142893

Application of fluorescence spectroscopy and imaging in the detection of a photosensitizer in photodynamic therapy

NASA Astrophysics Data System (ADS)

Zang, Lixin; Zhao, Huimin; Zhang, Zhiguo; Cao, Wenwu

2017-02-01

Photodynamic therapy (PDT) is currently an advanced optical technology in medical applications. However, the application of PDT is limited by the detection of photosensitizers. This work focuses on the application of fluorescence spectroscopy and imaging in the detection of an effective photosenzitizer, hematoporphyrin monomethyl ether (HMME). Optical properties of HMME were measured and analyzed based on its absorption and fluorescence spectra. The production mechanism of its fluorescence emission was analyzed. The detection device for HMME based on fluorescence spectroscopy was designed. Ratiometric method was applied to eliminate the influence of intensity change of excitation sources, fluctuates of excitation sources and photo detectors, and background emissions. The detection limit of this device is 6 μg/L, and it was successfully applied to the diagnosis of the metabolism of HMME in the esophageal cancer cells. To overcome the limitation of the point measurement using fluorescence spectroscopy, a two-dimensional (2D) fluorescence imaging system was established. The algorithm of the 2D fluorescence imaging system is deduced according to the fluorescence ratiometric method using bandpass filters. The method of multiple pixel point addition (MPPA) was used to eliminate fluctuates of signals. Using the method of MPPA, SNR was improved by about 30 times. The detection limit of this imaging system is 1.9 μg/L. Our systems can be used in the detection of porphyrins to improve the PDT effect.

Effective PCR detection of animal species in highly processed animal byproducts and compound feeds.

PubMed

Fumière, Olivier; Dubois, Marc; Baeten, Vincent; von Holst, Christoph; Berben, Gilbert

2006-07-01

In this paper we present a polymerase chain reaction (PCR)-based method for detecting meat and bone meal (MBM) in compound feedingstuffs. By choosing adequate DNA targets from an appropriate localisation in the genome, the real-time PCR method developed here proved to be robust to severe heat treatment of the MBM, showing high sensitivity in the detection of MBM. The method developed here permits the specific detection of processed pig and cattle materials treated at 134 degrees C in various feed matrices down to a limit of detection of about 0.1%. This technique has also been successfully applied to well-characterised MBM samples heated to as high as 141 degrees C, as well as to various blind feed samples with very low MBM contents. Finally, the method also passed several official European ring trials.

Illumination Invariant Change Detection (iicd): from Earth to Mars

NASA Astrophysics Data System (ADS)

Wan, X.; Liu, J.; Qin, M.; Li, S. Y.

2018-04-01

Multi-temporal Earth Observation and Mars orbital imagery data with frequent repeat coverage provide great capability for planetary surface change detection. When comparing two images taken at different times of day or in different seasons for change detection, the variation of topographic shades and shadows caused by the change of sunlight angle can be so significant that it overwhelms the real object and environmental changes, making automatic detection unreliable. An effective change detection algorithm therefore has to be robust to the illumination variation. This paper presents our research on developing and testing an Illumination Invariant Change Detection (IICD) method based on the robustness of phase correlation (PC) to the variation of solar illumination for image matching. The IICD is based on two key functions: i) initial change detection based on a saliency map derived from pixel-wise dense PC matching and ii) change quantization which combines change type identification, motion estimation and precise appearance change identification. Experiment using multi-temporal Landsat 7 ETM+ satellite images, Rapid eye satellite images and Mars HiRiSE images demonstrate that our frequency based image matching method can reach sub-pixel accuracy and thus the proposed IICD method can effectively detect and precisely segment large scale change such as landslide as well as small object change such as Mars rover, under daily and seasonal sunlight changes.

Mean grain size detection of DP590 steel plate using a corrected method with electromagnetic acoustic resonance.

PubMed

Wang, Bin; Wang, Xiaokai; Hua, Lin; Li, Juanjuan; Xiang, Qing

2017-04-01

Electromagnetic acoustic resonance (EMAR) is a considerable method to determine the mean grain size of the metal material with a high precision. The basic ultrasonic attenuation theory used for the mean grain size detection of EMAR is come from the single phase theory. In this paper, the EMAR testing was carried out based on the ultrasonic attenuation theory. The detection results show that the double peaks phenomenon occurs in the EMAR testing of DP590 steel plate. The dual phase structure of DP590 steel is the inducement of the double peaks phenomenon in the EMAR testing. In reaction to the phenomenon, a corrected method with EMAR was put forward to detect the mean grain size of dual phase steel. Compared with the traditional attenuation evaluation method and the uncorrected method with EMAR, the corrected method with EMAR shows great effectiveness and superiority for the mean grain size detection of DP590 steel plate. Copyright © 2016. Published by Elsevier B.V.

Experimental Investigation on the Detection of Multiple Surface Cracks Using Vibrothermography with a Low-Power Piezoceramic Actuator

PubMed Central

Xu, Changhang; Xie, Jing; Zhang, Wuyang; Kong, Qingzhao; Chen, Guoming; Song, Gangbing

2017-01-01

Vibrothermography often employs a high-power actuator to generate heat on a specimen to reveal damage, however, the high-power actuator brings inconvenience to the application and possibly introduces additional damage to the inspected objects. This study uses a low-power piezoceramic transducer as the actuator of vibrothermography and explores its ability to detect multiple surface cracks in a metal part. Experiments were conducted on a thin aluminum beam with three cracks in different orientations. Detailed analyses of both thermograms and temperature data are presented to validate the proposed vibrothermography method. To further investigate the performance of the proposed vibrothermography method, we experimentally studied the effects of several critical factors, including the amplitude of excitation signal, specimen constraints, relative position between the transducer and cracks (the transducer is mounted on the same or the opposite side with the cracks). The results demonstrate that all cracks can be detected conveniently and simultaneously by using the proposed low-power vibrothermography. We also found that the magnitude of excitation signal and the specimen constraints have a great influence on detection results. Combined with effective data processing methods, such as Fourier transformation employed in this study, the proposed method provides a promising potential to detect multiple cracks on a metal surface in a safe and effective manner. PMID:29168759

Varying face occlusion detection and iterative recovery for face recognition

NASA Astrophysics Data System (ADS)

Wang, Meng; Hu, Zhengping; Sun, Zhe; Zhao, Shuhuan; Sun, Mei

2017-05-01

In most sparse representation methods for face recognition (FR), occlusion problems were usually solved via removing the occlusion part of both query samples and training samples to perform the recognition process. This practice ignores the global feature of facial image and may lead to unsatisfactory results due to the limitation of local features. Considering the aforementioned drawback, we propose a method called varying occlusion detection and iterative recovery for FR. The main contributions of our method are as follows: (1) to detect an accurate occlusion area of facial images, an image processing and intersection-based clustering combination method is used for occlusion FR; (2) according to an accurate occlusion map, the new integrated facial images are recovered iteratively and put into a recognition process; and (3) the effectiveness on recognition accuracy of our method is verified by comparing it with three typical occlusion map detection methods. Experiments show that the proposed method has a highly accurate detection and recovery performance and that it outperforms several similar state-of-the-art methods against partial contiguous occlusion.

Minimum detectable gas concentration performance evaluation method for gas leak infrared imaging detection systems.

PubMed

Zhang, Xu; Jin, Weiqi; Li, Jiakun; Wang, Xia; Li, Shuo

2017-04-01

Thermal imaging technology is an effective means of detecting hazardous gas leaks. Much attention has been paid to evaluation of the performance of gas leak infrared imaging detection systems due to several potential applications. The minimum resolvable temperature difference (MRTD) and the minimum detectable temperature difference (MDTD) are commonly used as the main indicators of thermal imaging system performance. This paper establishes a minimum detectable gas concentration (MDGC) performance evaluation model based on the definition and derivation of MDTD. We proposed the direct calculation and equivalent calculation method of MDGC based on the MDTD measurement system. We build an experimental MDGC measurement system, which indicates the MDGC model can describe the detection performance of a thermal imaging system to typical gases. The direct calculation, equivalent calculation, and direct measurement results are consistent. The MDGC and the minimum resolvable gas concentration (MRGC) model can effectively describe the performance of "detection" and "spatial detail resolution" of thermal imaging systems to gas leak, respectively, and constitute the main performance indicators of gas leak detection systems.

Thin Cloud Detection Method by Linear Combination Model of Cloud Image

NASA Astrophysics Data System (ADS)

Liu, L.; Li, J.; Wang, Y.; Xiao, Y.; Zhang, W.; Zhang, S.

2018-04-01

The existing cloud detection methods in photogrammetry often extract the image features from remote sensing images directly, and then use them to classify images into cloud or other things. But when the cloud is thin and small, these methods will be inaccurate. In this paper, a linear combination model of cloud images is proposed, by using this model, the underlying surface information of remote sensing images can be removed. So the cloud detection result can become more accurate. Firstly, the automatic cloud detection program in this paper uses the linear combination model to split the cloud information and surface information in the transparent cloud images, then uses different image features to recognize the cloud parts. In consideration of the computational efficiency, AdaBoost Classifier was introduced to combine the different features to establish a cloud classifier. AdaBoost Classifier can select the most effective features from many normal features, so the calculation time is largely reduced. Finally, we selected a cloud detection method based on tree structure and a multiple feature detection method using SVM classifier to compare with the proposed method, the experimental data shows that the proposed cloud detection program in this paper has high accuracy and fast calculation speed.

Improvement of automatic hemorrhage detection methods using brightness correction on fundus images

NASA Astrophysics Data System (ADS)

Hatanaka, Yuji; Nakagawa, Toshiaki; Hayashi, Yoshinori; Kakogawa, Masakatsu; Sawada, Akira; Kawase, Kazuhide; Hara, Takeshi; Fujita, Hiroshi

2008-03-01

We have been developing several automated methods for detecting abnormalities in fundus images. The purpose of this study is to improve our automated hemorrhage detection method to help diagnose diabetic retinopathy. We propose a new method for preprocessing and false positive elimination in the present study. The brightness of the fundus image was changed by the nonlinear curve with brightness values of the hue saturation value (HSV) space. In order to emphasize brown regions, gamma correction was performed on each red, green, and blue-bit image. Subsequently, the histograms of each red, blue, and blue-bit image were extended. After that, the hemorrhage candidates were detected. The brown regions indicated hemorrhages and blood vessels and their candidates were detected using density analysis. We removed the large candidates such as blood vessels. Finally, false positives were removed by using a 45-feature analysis. To evaluate the new method for the detection of hemorrhages, we examined 125 fundus images, including 35 images with hemorrhages and 90 normal images. The sensitivity and specificity for the detection of abnormal cases was were 80% and 88%, respectively. These results indicate that the new method may effectively improve the performance of our computer-aided diagnosis system for hemorrhages.

Automatic Building Damage Detection Method Using High-Resolution Remote Sensing Images and 3d GIS Model

NASA Astrophysics Data System (ADS)

Tu, Jihui; Sui, Haigang; Feng, Wenqing; Song, Zhina

2016-06-01

In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

Detecting population-environmental interactions with mismatched time series data.

PubMed

Ferguson, Jake M; Reichert, Brian E; Fletcher, Robert J; Jager, Henriëtte I

2017-11-01

Time series analysis is an essential method for decomposing the influences of density and exogenous factors such as weather and climate on population regulation. However, there has been little work focused on understanding how well commonly collected data can reconstruct the effects of environmental factors on population dynamics. We show that, analogous to similar scale issues in spatial data analysis, coarsely sampled temporal data can fail to detect covariate effects when interactions occur on timescales that are fast relative to the survey period. We propose a method for modeling mismatched time series data that couples high-resolution environmental data to low-resolution abundance data. We illustrate our approach with simulations and by applying it to Florida's southern Snail kite population. Our simulation results show that our method can reliably detect linear environmental effects and that detecting nonlinear effects requires high-resolution covariate data even when the population turnover rate is slow. In the Snail kite analysis, our approach performed among the best in a suite of previously used environmental covariates explaining Snail kite dynamics and was able to detect a potential phenological shift in the environmental dependence of Snail kites. Our work provides a statistical framework for reliably detecting population-environment interactions from coarsely surveyed time series. An important implication of this work is that the low predictability of animal population growth by weather variables found in previous studies may be due, in part, to how these data are utilized as covariates. © 2017 by the Ecological Society of America.

Detecting population–environmental interactions with mismatched time series data

PubMed Central

Ferguson, Jake M.; Reichert, Brian E.; Fletcher, Robert J.; Jager, Henriëtte I.

2017-01-01

Time series analysis is an essential method for decomposing the influences of density and exogenous factors such as weather and climate on population regulation. However, there has been little work focused on understanding how well commonly collected data can reconstruct the effects of environmental factors on population dynamics. We show that, analogous to similar scale issues in spatial data analysis, coarsely sampled temporal data can fail to detect covariate effects when interactions occur on timescales that are fast relative to the survey period. We propose a method for modeling mismatched time series data that couples high-resolution environmental data to low-resolution abundance data. We illustrate our approach with simulations and by applying it to Florida’s southern Snail kite population. Our simulation results show that our method can reliably detect linear environmental effects and that detecting nonlinear effects requires high-resolution covariate data even when the population turnover rate is slow. In the Snail kite analysis, our approach performed among the best in a suite of previously used environmental covariates explaining Snail kite dynamics and was able to detect a potential phenological shift in the environmental dependence of Snail kites. Our work provides a statistical framework for reliably detecting population–environment interactions from coarsely surveyed time series. An important implication of this work is that the low predictability of animal population growth by weather variables found in previous studies may be due, in part, to how these data are utilized as covariates. PMID:28759123

Recombinant Temporal Aberration Detection Algorithms for Enhanced Biosurveillance

PubMed Central

Murphy, Sean Patrick; Burkom, Howard

2008-01-01

Objective Broadly, this research aims to improve the outbreak detection performance and, therefore, the cost effectiveness of automated syndromic surveillance systems by building novel, recombinant temporal aberration detection algorithms from components of previously developed detectors. Methods This study decomposes existing temporal aberration detection algorithms into two sequential stages and investigates the individual impact of each stage on outbreak detection performance. The data forecasting stage (Stage 1) generates predictions of time series values a certain number of time steps in the future based on historical data. The anomaly measure stage (Stage 2) compares features of this prediction to corresponding features of the actual time series to compute a statistical anomaly measure. A Monte Carlo simulation procedure is then used to examine the recombinant algorithms’ ability to detect synthetic aberrations injected into authentic syndromic time series. Results New methods obtained with procedural components of published, sometimes widely used, algorithms were compared to the known methods using authentic datasets with plausible stochastic injected signals. Performance improvements were found for some of the recombinant methods, and these improvements were consistent over a range of data types, outbreak types, and outbreak sizes. For gradual outbreaks, the WEWD MovAvg7+WEWD Z-Score recombinant algorithm performed best; for sudden outbreaks, the HW+WEWD Z-Score performed best. Conclusion This decomposition was found not only to yield valuable insight into the effects of the aberration detection algorithms but also to produce novel combinations of data forecasters and anomaly measures with enhanced detection performance. PMID:17947614

Surface-enhanced Raman spectroscopy detection of polybrominated diphenylethers using a portable Raman spectrometer.

PubMed

Jiang, Xiaohong; Lai, Yongchao; Wang, Wei; Jiang, Wei; Zhan, Jinhua

2013-11-15

Polybrominated diphenylethers (PBDEs), one of the most common brominated flame retardants, are toxic and persistent, generally detected by the chromatographic method. In this work, qualitative and quantitative detection of PBDEs were explored based on surface-enhanced Raman spectroscopy (SERS) technique using a portable Raman spectrometer. Alkanethiol modified silver nanoparticle aggregates were used as the substrate and PBDEs could be pre-concentrated close to the substrate surface through their hydrophobic interactions with alkanethiol. The effect of alkanethiols with different chain length on the SERS detection of PBDEs was evaluated. It was shown that 1-hexanethiol (HT) modified substrate has higher sensitivity, good stability and reusability. Qualitative and quantitative SERS detection of PBDEs in real sea water was accomplished, with the measured detection limits at 1.2×10(2) μg L(-1). These results illustrate SERS could be used as an effective method for the detection of PBDEs. Copyright © 2013 Elsevier B.V. All rights reserved.

[Real-time PCR kits for the detection of the African Swine Fever virus].

PubMed

Latyshev, O E; Eliseeva, O V; Grebennikova, T V; Verkhovskiĭ, O A; Tsibezov, V V; Chernykh, O Iu; Dzhailidi, G A; Aliper, T I

2014-01-01

The results obtained using the diagnostic kit based on real-time polymerase chain reaction to detect the DNA of the African Swine Fever in the pathological material, as well as in the culture fluid, are presented. A high sensitivity and specificity for detection of the DNA in the organs and tissues of animals was shown to be useful for detection in the European Union referentiality reagent kits for DNA detection by real time PCR of ASFV. More rapid and effective method of DNA extraction using columns mini spin Quick gDNA(TM) MiniPrep was suggested and compared to the method of DNA isolation on the inorganic sorbent. High correlation of the results of the DNA detection of ASFV by real-time PCR and antigen detection results ASFV by competitive ELISA obtained with the ELISA SEROTEST/INGEZIM COMRAC PPA was demonstrated. The kit can be used in the veterinary services for effective monitoring of ASFV to contain, eliminate and prevent further spread of the disease.

Wireless sensor networks for heritage object deformation detection and tracking algorithm.

PubMed

Xie, Zhijun; Huang, Guangyan; Zarei, Roozbeh; He, Jing; Zhang, Yanchun; Ye, Hongwu

2014-10-31

Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT). In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection.

Diagnosing and ranking retinopathy disease level using diabetic fundus image recuperation approach.

PubMed

Somasundaram, K; Rajendran, P Alli

2015-01-01

Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy. To categorize the exudates, Diabetic Fundus Image Recuperation (DFIR) method based on sliding window approach is developed in this work to select the features of optic cup in digital retinal fundus images. The DFIR feature selection uses collection of sliding windows with varying range to obtain the features based on the histogram value using Group Sparsity Nonoverlapping Function. Using support vector model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy disease level. The ranking of disease level on each candidate set provides a much promising result for developing practically automated and assisted diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, ranking efficiency, and feature selection time.

Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image Recuperation Approach

PubMed Central

Somasundaram, K.; Alli Rajendran, P.

2015-01-01

Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy. To categorize the exudates, Diabetic Fundus Image Recuperation (DFIR) method based on sliding window approach is developed in this work to select the features of optic cup in digital retinal fundus images. The DFIR feature selection uses collection of sliding windows with varying range to obtain the features based on the histogram value using Group Sparsity Nonoverlapping Function. Using support vector model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy disease level. The ranking of disease level on each candidate set provides a much promising result for developing practically automated and assisted diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, ranking efficiency, and feature selection time. PMID:25945362

Wireless Sensor Networks for Heritage Object Deformation Detection and Tracking Algorithm

PubMed Central

Xie, Zhijun; Huang, Guangyan; Zarei, Roozbeh; He, Jing; Zhang, Yanchun; Ye, Hongwu

2014-01-01

Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT). In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection. PMID:25365458

Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio Transients

NASA Technical Reports Server (NTRS)

Thompson, David R.; Majid, Walid A.; Reed, Colorado J.; Wagstaff, Kiri L.

2011-01-01

We present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses adaptive eigenbases to combine 1) prior knowledge about uninteresting signals with 2) online estimation of the current data properties to enable highly sensitive and precise detection of novel signals. We apply the method to the problem of detecting fast transient radio anomalies and compare it to current alternative algorithms. Tests based on observations from the Parkes Multibeam Survey show both effective detection of interesting rare events and robustness to known false alarm anomalies.

Automatic background updating for video-based vehicle detection

NASA Astrophysics Data System (ADS)

Hu, Chunhai; Li, Dongmei; Liu, Jichuan

2008-03-01

Video-based vehicle detection is one of the most valuable techniques for the Intelligent Transportation System (ITS). The widely used video-based vehicle detection technique is the background subtraction method. The key problem of this method is how to subtract and update the background effectively. In this paper an efficient background updating scheme based on Zone-Distribution for vehicle detection is proposed to resolve the problems caused by sudden camera perturbation, sudden or gradual illumination change and the sleeping person problem. The proposed scheme is robust and fast enough to satisfy the real-time constraints of vehicle detection.

Multi-scale occupancy estimation and modelling using multiple detection methods

USGS Publications Warehouse

Nichols, James D.; Bailey, Larissa L.; O'Connell, Allan F.; Talancy, Neil W.; Grant, Evan H. Campbell; Gilbert, Andrew T.; Annand, Elizabeth M.; Husband, Thomas P.; Hines, James E.

2008-01-01

Occupancy estimation and modelling based on detection–nondetection data provide an effective way of exploring change in a species’ distribution across time and space in cases where the species is not always detected with certainty. Today, many monitoring programmes target multiple species, or life stages within a species, requiring the use of multiple detection methods. When multiple methods or devices are used at the same sample sites, animals can be detected by more than one method.We develop occupancy models for multiple detection methods that permit simultaneous use of data from all methods for inference about method-specific detection probabilities. Moreover, the approach permits estimation of occupancy at two spatial scales: the larger scale corresponds to species’ use of a sample unit, whereas the smaller scale corresponds to presence of the species at the local sample station or site.We apply the models to data collected on two different vertebrate species: striped skunks Mephitis mephitis and red salamanders Pseudotriton ruber. For striped skunks, large-scale occupancy estimates were consistent between two sampling seasons. Small-scale occupancy probabilities were slightly lower in the late winter/spring when skunks tend to conserve energy, and movements are limited to males in search of females for breeding. There was strong evidence of method-specific detection probabilities for skunks. As anticipated, large- and small-scale occupancy areas completely overlapped for red salamanders. The analyses provided weak evidence of method-specific detection probabilities for this species.Synthesis and applications. Increasingly, many studies are utilizing multiple detection methods at sampling locations. The modelling approach presented here makes efficient use of detections from multiple methods to estimate occupancy probabilities at two spatial scales and to compare detection probabilities associated with different detection methods. The models can be viewed as another variation of Pollock's robust design and may be applicable to a wide variety of scenarios where species occur in an area but are not always near the sampled locations. The estimation approach is likely to be especially useful in multispecies conservation programmes by providing efficient estimates using multiple detection devices and by providing device-specific detection probability estimates for use in survey design.

Fundamentals, achievements and challenges in the electrochemical sensing of pathogens.

PubMed

Monzó, Javier; Insua, Ignacio; Fernandez-Trillo, Francisco; Rodriguez, Paramaconi

2015-11-07

Electrochemical sensors are powerful tools widely used in industrial, environmental and medical applications. The versatility of electrochemical methods allows for the investigation of chemical composition in real time and in situ. Electrochemical detection of specific biological molecules is a powerful means for detecting disease-related markers. In the last 10 years, highly-sensitive and specific methods have been developed to detect waterborne and foodborne pathogens. In this review, we classify the different electrochemical techniques used for the qualitative and quantitative detection of pathogens. The robustness of electrochemical methods allows for accurate detection even in heterogeneous and impure samples. We present a fundamental description of the three major electrochemical sensing methods used in the detection of pathogens and the advantages and disadvantages of each of these methods. In each section, we highlight recent breakthroughs, including the utilisation of microfluidics, immunomagnetic separation and multiplexing for the detection of multiple pathogens in a single device. We also include recent studies describing new strategies for the design of future immunosensing systems and protocols. The high sensitivity and selectivity, together with the portability and the cost-effectiveness of the instrumentation, enhances the demand for further development in the electrochemical detection of microbes.

Effective method for detecting regions of given colors and the features of the region surfaces

NASA Astrophysics Data System (ADS)

Gong, Yihong; Zhang, HongJiang

1994-03-01

Color can be used as a very important cue for image recognition. In industrial and commercial areas, color is widely used as a trademark or identifying feature in objects, such as packaged goods, advertising signs, etc. In image database systems, one may retrieve an image of interest by specifying prominent colors and their locations in the image (image retrieval by contents). These facts enable us to detect or identify a target object using colors. However, this task depends mainly on how effectively we can identify a color and detect regions of the given color under possibly non-uniform illumination conditions such as shade, highlight, and strong contrast. In this paper, we present an effective method to detect regions matching given colors, along with the features of the region surfaces. We adopt the HVC color coordinates in the method because of its ability of completely separating the luminant and chromatic components of colors. Three basis functions functionally serving as the low-pass, high-pass, and band-pass filters, respectively, are introduced.

Automatic Fringe Detection for Oil Film Interferometry Measurement of Skin Friction

NASA Technical Reports Server (NTRS)

Naughton, Jonathan W.; Decker, Robert K.; Jafari, Farhad

2001-01-01

This report summarizes two years of work on investigating algorithms for automatically detecting fringe patterns in images acquired using oil-drop interferometry for the determination of skin friction. Several different analysis methods were tested, and a combination of a windowed Fourier transform followed by a correlation was found to be most effective. The implementation of this method is discussed and details of the process are described. The results indicate that this method shows promise for automating the fringe detection process, but further testing is required.

The development of a super-fine-grained nuclear emulsion

NASA Astrophysics Data System (ADS)

Asada, Takashi; Naka, Tatsuhiro; Kuwabara, Ken-ichi; Yoshimoto, Masahiro

2017-06-01

A nuclear emulsion with micronized crystals is required for the tracking detection of submicron ionizing particles, which are one of the targets of dark-matter detection and other techniques. We found that a new production method, called the PVA—gelatin mixing method (PGMM), could effectively control crystal size from 20 nm to 50 nm. We called the two types of emulsion produced with the new method the nano imaging tracker and the ultra-nano imaging tracker. Their composition and spatial resolution were measured, and the results indicate that these emulsions detect extremely short tracks.

Effects of Environmental Toxicants on Metabolic Activity of Natural Microbial Communities

PubMed Central

Barnhart, Carole L. H.; Vestal, J. Robie

1983-01-01

Two methods of measuring microbial activity were used to study the effects of toxicants on natural microbial communities. The methods were compared for suitability for toxicity testing, sensitivity, and adaptability to field applications. This study included measurements of the incorporation of 14C-labeled acetate into microbial lipids and microbial glucosidase activity. Activities were measured per unit biomass, determined as lipid phosphate. The effects of various organic and inorganic toxicants on various natural microbial communities were studied. Both methods were useful in detecting toxicity, and their comparative sensitivities varied with the system studied. In one system, the methods showed approximately the same sensitivities in testing the effects of metals, but the acetate incorporation method was more sensitive in detecting the toxicity of organic compounds. The incorporation method was used to study the effects of a point source of pollution on the microbiota of a receiving stream. Toxic doses were found to be two orders of magnitude higher in sediments than in water taken from the same site, indicating chelation or adsorption of the toxicant by the sediment. The microbiota taken from below a point source outfall was 2 to 100 times more resistant to the toxicants tested than was that taken from above the outfall. Downstream filtrates in most cases had an inhibitory effect on the natural microbiota taken from above the pollution source. The microbial methods were compared with commonly used bioassay methods, using higher organisms, and were found to be similar in ability to detect comparative toxicities of compounds, but were less sensitive than methods which use standard media because of the influences of environmental factors. PMID:16346432

Development of Decision Making Algorithm for Control of Sea Cargo Containers by ``TAGGED'' Neutron Method

NASA Astrophysics Data System (ADS)

Anan'ev, A. A.; Belichenko, S. G.; Bogolyubov, E. P.; Bochkarev, O. V.; Petrov, E. V.; Polishchuk, A. M.; Udaltsov, A. Yu.

2009-12-01

Nowadays in Russia and abroad there are several groups of scientists, engaged in development of systems based on "tagged" neutron method (API method) and intended for detection of dangerous materials, including high explosives (HE). Particular attention is paid to possibility of detection of dangerous objects inside a sea cargo container. Energy gamma-spectrum, registered from object under inspection is used for determination of oxygen/carbon and nitrogen/carbon chemical ratios, according to which dangerous object is distinguished from not dangerous one. Material of filled container, however, gives rise to additional effects of rescattering and moderation of 14 MeV primary neutrons of generator, attenuation of secondary gamma-radiation from reactions of inelastic neutron scattering on objects under inspection. These effects lead to distortion of energy gamma-response from examined object and therefore prevent correct recognition of chemical ratios. These difficulties are taken into account in analytical method, presented in the paper. Method has been validated against experimental data, obtained by the system for HE detection in sea cargo, based on API method and developed in VNIIA. Influence of shielding materials on results of HE detection and identification is considered. Wood and iron were used as shielding materials. Results of method application for analysis of experimental data on HE simulator measurement (tetryl, trotyl, hexogen) are presented.

Cloud point extraction and determination of trace trichlorfon by high performance liquid chromatography with ultraviolet-detection based on its catalytic effect on benzidine oxidizing.

PubMed

Zhu, Hai-Zhen; Liu, Wei; Mao, Jian-Wei; Yang, Ming-Min

2008-04-28

4-Amino-4'-nitrobiphenyl, which is formed by catalytic effect of trichlorfon on sodium perborate oxidizing benzidine, is extracted with a cloud point extraction method and then detected using a high performance liquid chromatography with ultraviolet detection (HPLC-UV). Under the optimum experimental conditions, there was a linear relationship between trichlorfon in the concentration range of 0.01-0.2 mgL(-1) and the peak areas of 4-amino-4'-nitrobiphenyl (r=0.996). Limit of detection was 2.0 microgL(-1), recoveries of spiked water and cabbage samples ranged between 95.4-103 and 85.2-91.2%, respectively. It was proved that the cloud point extraction (CPE) method was simple, cheap, and environment friendly than extraction with organic solvents and had more effective extraction yield.

An effective method on pornographic images realtime recognition

NASA Astrophysics Data System (ADS)

Wang, Baosong; Lv, Xueqiang; Wang, Tao; Wang, Chengrui

2013-03-01

In this paper, skin detection, texture filtering and face detection are used to extract feature on an image library, training them with the decision tree arithmetic to create some rules as a decision tree classifier to distinguish an unknown image. Experiment based on more than twenty thousand images, the precision rate can get 76.21% when testing on 13025 pornographic images and elapsed time is less than 0.2s. This experiment shows it has a good popularity. Among the steps mentioned above, proposing a new skin detection model which called irregular polygon region skin detection model based on YCbCr color space. This skin detection model can lower the false detection rate on skin detection. A new method called sequence region labeling on binary connected area can calculate features on connected area, it is faster and needs less memory than other recursive methods.

Detecting Signage and Doors for Blind Navigation and Wayfinding

PubMed Central

Wang, Shuihua; Yang, Xiaodong; Tian, Yingli

2013-01-01

Signage plays a very important role to find destinations in applications of navigation and wayfinding. In this paper, we propose a novel framework to detect doors and signage to help blind people accessing unfamiliar indoor environments. In order to eliminate the interference information and improve the accuracy of signage detection, we first extract the attended areas by using a saliency map. Then the signage is detected in the attended areas by using a bipartite graph matching. The proposed method can handle multiple signage detection. Furthermore, in order to provide more information for blind users to access the area associated with the detected signage, we develop a robust method to detect doors based on a geometric door frame model which is independent to door appearances. Experimental results on our collected datasets of indoor signage and doors demonstrate the effectiveness and efficiency of our proposed method. PMID:23914345

Detecting Signage and Doors for Blind Navigation and Wayfinding.

PubMed

Wang, Shuihua; Yang, Xiaodong; Tian, Yingli

2013-07-01

Signage plays a very important role to find destinations in applications of navigation and wayfinding. In this paper, we propose a novel framework to detect doors and signage to help blind people accessing unfamiliar indoor environments. In order to eliminate the interference information and improve the accuracy of signage detection, we first extract the attended areas by using a saliency map. Then the signage is detected in the attended areas by using a bipartite graph matching. The proposed method can handle multiple signage detection. Furthermore, in order to provide more information for blind users to access the area associated with the detected signage, we develop a robust method to detect doors based on a geometric door frame model which is independent to door appearances. Experimental results on our collected datasets of indoor signage and doors demonstrate the effectiveness and efficiency of our proposed method.

Using State Estimation Residuals to Detect Abnormal SCADA Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Jian; Chen, Yousu; Huang, Zhenyu

2010-04-30

Detection of abnormal supervisory control and data acquisition (SCADA) data is critically important for safe and secure operation of modern power systems. In this paper, a methodology of abnormal SCADA data detection based on state estimation residuals is presented. Preceded with a brief overview of outlier detection methods and bad SCADA data detection for state estimation, the framework of the proposed methodology is described. Instead of using original SCADA measurements as the bad data sources, the residuals calculated based on the results of the state estimator are used as the input for the outlier detection algorithm. The BACON algorithm ismore » applied to the outlier detection task. The IEEE 118-bus system is used as a test base to evaluate the effectiveness of the proposed methodology. The accuracy of the BACON method is compared with that of the 3-σ method for the simulated SCADA measurements and residuals.« less

Development of Rapid Detection and Genetic Characterization of Salmonella in Poultry Breeder Feeds

PubMed Central

Jarquin, Robin; Hanning, Irene; Ahn, Soohyoun; Ricke, Steven C.

2009-01-01

Salmonella is a leading cause of foodborne illness in the United States, with poultry and poultry products being a primary source of infection to humans. Poultry may carry some Salmonella serovars without any signs or symptoms of disease and without causing any adverse effects to the health of the bird. Salmonella may be introduced to a flock by multiple environmental sources, but poultry feed is suspected to be a leading source. Detecting Salmonella in feed can be challenging because low levels of the bacteria may not be recovered using traditional culturing techniques. Numerous detection methodologies have been examined over the years for quantifying Salmonella in feeds and many have proven to be effective for Salmonella isolation and detection in a variety of feeds. However, given the potential need for increased detection sensitivity, molecular detection technologies may the best candidate for developing rapid sensitive methods for identifying small numbers of Salmonella in the background of large volumes of feed. Several studies have been done using polymerase chain reaction (PCR) assays and commercial kits to detect Salmonella spp. in a wide variety of feed sources. In addition, DNA array technology has recently been utilized to track the dissemination of a specific Salmonella serotype in feed mills. This review will discuss the processing of feeds and potential points in the process that may introduce Salmonella contamination to the feed. Detection methods currently used and the need for advances in these methods also will be discussed. Finally, implementation of rapid detection for optimizing control methods to prevent and remove any Salmonella contamination of feeds will be considered. PMID:22346699

Using a higher criticism statistic to detect modest effects in a genome-wide study of rheumatoid arthritis

PubMed Central

2009-01-01

In high-dimensional studies such as genome-wide association studies, the correction for multiple testing in order to control total type I error results in decreased power to detect modest effects. We present a new analytical approach based on the higher criticism statistic that allows identification of the presence of modest effects. We apply our method to the genome-wide study of rheumatoid arthritis provided in the Genetic Analysis Workshop 16 Problem 1 data set. There is evidence for unknown bias in this study that could be explained by the presence of undetected modest effects. We compared the asymptotic and empirical thresholds for the higher criticism statistic. Using the asymptotic threshold we detected the presence of modest effects genome-wide. We also detected modest effects using 90th percentile of the empirical null distribution as a threshold; however, there is no such evidence when the 95th and 99th percentiles were used. While the higher criticism method suggests that there is some evidence for modest effects, interpreting individual single-nucleotide polymorphisms with significant higher criticism statistics is of undermined value. The goal of higher criticism is to alert the researcher that genetic effects remain to be discovered and to promote the use of more targeted and powerful studies to detect the remaining effects. PMID:20018032

Research on the method of improving the accuracy of CMM (coordinate measuring machine) testing aspheric surface

NASA Astrophysics Data System (ADS)

Cong, Wang; Xu, Lingdi; Li, Ang

2017-10-01

Large aspheric surface which have the deviation with spherical surface are being used widely in various of optical systems. Compared with spherical surface, Large aspheric surfaces have lots of advantages, such as improving image quality, correcting aberration, expanding field of view, increasing the effective distance and make the optical system compact, lightweight. Especially, with the rapid development of space optics, space sensor resolution is required higher and viewing angle is requred larger. Aspheric surface will become one of the essential components in the optical system. After finishing Aspheric coarse Grinding surface profile error is about Tens of microns[1].In order to achieve the final requirement of surface accuracy,the aspheric surface must be quickly modified, high precision testing is the basement of rapid convergence of the surface error . There many methods on aspheric surface detection[2], Geometric ray detection, hartmann detection, ronchi text, knifeedge method, direct profile test, interferometry, while all of them have their disadvantage[6]. In recent years the measure of the aspheric surface become one of the import factors which are restricting the aspheric surface processing development. A two meter caliber industrial CMM coordinate measuring machine is avaiable, but it has many drawbacks such as large detection error and low repeatability precision in the measurement of aspheric surface coarse grinding , which seriously affects the convergence efficiency during the aspherical mirror processing. To solve those problems, this paper presents an effective error control, calibration and removal method by calibration mirror position of the real-time monitoring and other effective means of error control, calibration and removal by probe correction and the measurement mode selection method to measure the point distribution program development. This method verified by real engineer examples, this method increases the original industrial-grade coordinate system nominal measurement accuracy PV value of 7 microns to 4microns, Which effectively improves the grinding efficiency of aspheric mirrors and verifies the correctness of the method. This paper also investigates the error detection and operation control method, the error calibration of the CMM and the random error calibration of the CMM .

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

NASA Astrophysics Data System (ADS)

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

2016-05-01

A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

A Comparison of Methods for Detecting Differential Distractor Functioning

ERIC Educational Resources Information Center

Koon, Sharon

2010-01-01

This study examined the effectiveness of the odds-ratio method (Penfield, 2008) and the multinomial logistic regression method (Kato, Moen, & Thurlow, 2009) for measuring differential distractor functioning (DDF) effects in comparison to the standardized distractor analysis approach (Schmitt & Bleistein, 1987). Students classified as participating…

Bird Radar Validation in the Field by Time-Referencing Line-Transect Surveys

PubMed Central

Dokter, Adriaan M.; Baptist, Martin J.; Ens, Bruno J.; Krijgsveld, Karen L.; van Loon, E. Emiel

2013-01-01

Track-while-scan bird radars are widely used in ornithological studies, but often the precise detection capabilities of these systems are unknown. Quantification of radar performance is essential to avoid observational biases, which requires practical methods for validating a radar’s detection capability in specific field settings. In this study a method to quantify the detection capability of a bird radar is presented, as well a demonstration of this method in a case study. By time-referencing line-transect surveys, visually identified birds were automatically linked to individual tracks using their transect crossing time. Detection probabilities were determined as the fraction of the total set of visual observations that could be linked to radar tracks. To avoid ambiguities in assigning radar tracks to visual observations, the observer’s accuracy in determining a bird’s transect crossing time was taken into account. The accuracy was determined by examining the effect of a time lag applied to the visual observations on the number of matches found with radar tracks. Effects of flight altitude, distance, surface substrate and species size on the detection probability by the radar were quantified in a marine intertidal study area. Detection probability varied strongly with all these factors, as well as species-specific flight behaviour. The effective detection range for single birds flying at low altitude for an X-band marine radar based system was estimated at ∼1.5 km. Within this range the fraction of individual flying birds that were detected by the radar was 0.50±0.06 with a detection bias towards higher flight altitudes, larger birds and high tide situations. Besides radar validation, which we consider essential when quantification of bird numbers is important, our method of linking radar tracks to ground-truthed field observations can facilitate species-specific studies using surveillance radars. The methodology may prove equally useful for optimising tracking algorithms. PMID:24066103

Bird radar validation in the field by time-referencing line-transect surveys.

PubMed

Dokter, Adriaan M; Baptist, Martin J; Ens, Bruno J; Krijgsveld, Karen L; van Loon, E Emiel

2013-01-01

Track-while-scan bird radars are widely used in ornithological studies, but often the precise detection capabilities of these systems are unknown. Quantification of radar performance is essential to avoid observational biases, which requires practical methods for validating a radar's detection capability in specific field settings. In this study a method to quantify the detection capability of a bird radar is presented, as well a demonstration of this method in a case study. By time-referencing line-transect surveys, visually identified birds were automatically linked to individual tracks using their transect crossing time. Detection probabilities were determined as the fraction of the total set of visual observations that could be linked to radar tracks. To avoid ambiguities in assigning radar tracks to visual observations, the observer's accuracy in determining a bird's transect crossing time was taken into account. The accuracy was determined by examining the effect of a time lag applied to the visual observations on the number of matches found with radar tracks. Effects of flight altitude, distance, surface substrate and species size on the detection probability by the radar were quantified in a marine intertidal study area. Detection probability varied strongly with all these factors, as well as species-specific flight behaviour. The effective detection range for single birds flying at low altitude for an X-band marine radar based system was estimated at ~1.5 km. Within this range the fraction of individual flying birds that were detected by the radar was 0.50 ± 0.06 with a detection bias towards higher flight altitudes, larger birds and high tide situations. Besides radar validation, which we consider essential when quantification of bird numbers is important, our method of linking radar tracks to ground-truthed field observations can facilitate species-specific studies using surveillance radars. The methodology may prove equally useful for optimising tracking algorithms.

Concentration of enteric viruses from tap water using an anion exchange resin-based method.

PubMed

Pérez-Méndez, A; Chandler, J C; Bisha, B; Goodridge, L D

2014-09-01

Detecting low concentrations of enteric viruses in water is needed for public health-related monitoring and control purposes. Thus, there is a need for sensitive, rapid and cost effective enteric viral concentration methods compatible with downstream molecular detection. Here, a virus concentration method based on adsorption of the virus to an anion exchange resin and direct isolation of nucleic acids is presented. Ten liter samples of tap water spiked with different concentrations (10-10,000 TCID50/10 L) of human adenovirus 40 (HAdV-40), hepatitis A virus (HAV) or rotavirus (RV) were concentrated and detected by real time PCR or real time RT-PCR. This method improved viral detection compared to direct testing of spiked water samples where the ΔCt was 12.1 for AdV-40 and 4.3 for HAV. Direct detection of RV in water was only possible for one of the three replicates tested (Ct of 37), but RV detection was improved using the resin method (all replicates tested positive with an average Ct of 30, n=3). The limit of detection of the method was 10 TCID50/10 L for HAdV-40 and HAV, and 100 TCID50/10 L of water for RV. These results compare favorably with detection limits reported for more expensive and laborious methods. Copyright © 2014 Elsevier B.V. All rights reserved.

Review of methodology and technology available for the detection of extrasolar planetary systems

NASA Technical Reports Server (NTRS)

Tarter, J. C.; Black, D. C.; Billingham, J.

1985-01-01

Four approaches exist for the detection of extrasolar planets. According to the only direct method, the planet is imaged at some wavelength in a manner which makes it possible to differentiate its own feeble luminosity (internal energy source plus reflected starlight) from that of the nearby host star. The three indirect methods involve the detection of a planetary mass companion on the basis of the observable effects it has on the host star. A search is conducted regarding the occurrence of regular, periodic changes in the stellar spatial motion (astrometric method) or the velocity of stellar emission line spectra (spectroscopic method) or in the apparent total stellar luminosity (photometric method). Details regarding the approaches employed for implementing the considered methods are discussed.

Detecting sulphate aerosol geoengineering with different methods

DOE PAGES

Lo, Y. T. Eunice; Charlton-Perez, Andrew J.; Lott, Fraser C.; ...

2016-12-15

Sulphate aerosol injection has been widely discussed as a possible way to engineer future climate. Monitoring it would require detecting its effects amidst internal variability and in the presence of other external forcings. Here, we investigate how the use of different detection methods and filtering techniques affects the detectability of sulphate aerosol geoengineering in annual-mean global-mean near-surface air temperature. This is done by assuming a future scenario that injects 5 Tg yr -1 of sulphur dioxide into the stratosphere and cross-comparing simulations from 5 climate models. 64% of the studied comparisons would require 25 years or more for detection whenmore » no filter and the multi-variate method that has been extensively used for attributing climate change are used, while 66% of the same comparisons would require fewer than 10 years for detection using a trend-based filter. This then highlights the high sensitivity of sulphate aerosol geoengineering detectability to the choice of filter. With the same trend-based filter but a non-stationary method, 80% of the comparisons would require fewer than 10 years for detection. This does not imply sulphate aerosol geoengineering should be deployed, but suggests that both detection methods could be used for monitoring geoengineering in global, annual mean temperature should it be needed.« less

Quench Detection and Protection for High Temperature Superconducting Transformers by Using the Active Power Method

NASA Astrophysics Data System (ADS)

Nanato, N.; Kobayashi, Y.

AC high temperature superconducting (HTS) coils have been developed for transformers, motors and so on. Quench detection and protection system are essential for safety operations of the AC HTS facilities. The balance voltage method is universally used for the quench detection and protection, however especially for AC operations, the method has risks in terms of high voltage sparks. Because the method needs a voltage tap soldered to a midpoint of the coil winding and the AC HTS facilities generally operate at high voltages and therefore high voltage sparks may occur at the midpoint with no insulation. We have proposed the active power method for the quench detection and protection. The method requires no voltage tap on the midpoint of the coil winding and therefore it has in-built effectiveness for the AC HTS facilities. In this paper, we show that the method can detect the quench in an HTS transformer and moreover our proposed quench protection circuits which consist of thyristors are simple and useful for the AC HTS facilities.

Effect of Treatment and Mammography Detection on Breast Cancer Survival Overtime: 1990–2011

PubMed Central

Kaplan, Henry G; Malmgren, Judith A; Atwood, Mary K; Calip, Gregory S.

2017-01-01

Background It is not known to what extent improvement over time in breast cancer survival is related to earlier detection by mammography or to more effective treatments. Methods At our comprehensive cancer care center we conducted a retrospective cohort study of women ages 50–69 years diagnosed with invasive stage I–III breast cancer and followed over three time periods: 1990–1994, 1995–1999 and 2000–2007. Data was chart abstracted on detection method, diagnosis, treatment, and follow up for vital status in our breast cancer registry (n=2998). Method of detection was categorized as patient or physician (Pt/PhysD) or mammography detected (MamD). Cox proportional hazards models to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI) for five year disease specific survival (DSS) in relation to detection method and treatment factors, testing for differences in survival using the Kaplan-Meier method. Results 58% of cases were MamD and 42% were Pt/PhysD with 56% stage I, 31% stage II and 13% stage III. Average length of follow up was 10.71 years. Combined five year DSS was 89% 1990–94, 94% 1995–99, and 96% in 2000–2007 (p<.001). In an adjusted model, mammography detection (HR=0.43, 95% CI 0.27–0.70), hormone therapy (HR=0.47, 95% CI 0.30–0.75), and taxane-containing chemotherapy (HR=0.61, 95% CI 0.37–0.99) were significantly associated with a decreased risk of disease-specific mortality Conclusions Better breast cancer survival over time is related to mammography detection, hormonal therapy and taxane-containing chemotherapy treatment. Treatment improvements alone are not sufficient to explain the observed survival improvements over time. PMID:25872471

Visual detection of nucleic acids based on Mie scattering and the magnetophoretic effect.

PubMed

Zhao, Zichen; Chen, Shan; Ho, John Kin Lim; Chieng, Ching-Chang; Chen, Ting-Hsuan

2015-12-07

Visual detection of nucleic acid biomarkers is a simple and convenient approach to point-of-care applications. However, issues of sensitivity and the handling of complex bio-fluids have posed challenges. Here we report on a visual method detecting nucleic acids using Mie scattering of polystyrene microparticles and the magnetophoretic effect. Magnetic microparticles (MMPs) and polystyrene microparticles (PMPs) were surface-functionalised with oligonucleotide probes, which can hybridise with target oligonucleotides in juxtaposition and lead to the formation of MMPs-targets-PMPs sandwich structures. Using an externally applied magnetic field, the magnetophoretic effect attracts the sandwich structure to the sidewall, which reduces the suspended PMPs and leads to a change in the light transmission via the Mie scattering. Based on the high extinction coefficient of the Mie scattering (∼3 orders of magnitude greater than that of the commonly used gold nanoparticles), our results showed the limit of detection to be 4 pM using a UV-Vis spectrometer or 10 pM by direct visual inspection. Meanwhile, we also demonstrated that this method is compatible with multiplex assays and detection in complex bio-fluids, such as whole blood or a pool of nucleic acids, without purification in advance. With a simplified operation procedure, low instrumentation requirement, high sensitivity and compatibility with complex bio-fluids, this method provides an ideal solution for visual detection of nucleic acids in resource-limited settings.

Microfluidic biosensor for β-Hydroxybutyrate (βHBA) determination of subclinical ketosis diagnosis.

PubMed

Weng, Xuan; Zhao, Wenting; Neethirajan, Suresh; Duffield, Todd

2015-02-12

Determination of β-hydroxybutyrate (βHBA) is a gold standard for diagnosis of Subclinical Ketosis (SCK), a common disease in dairy cows that causes significant economic loss. Early detection of SCK can help reduce the risk of the disease progressing into clinical stage, thus minimizing economic losses on dairy cattle. Conventional laboratory methods are time consuming and labor-intensive, requiring expensive and bulky equipment. Development of portable and robust devices for rapid on-site SCK diagnosis is an effective way to prevent and control ketosis and can significantly aid in the management of dairy animal health. Microfluidic technology provides a rapid, cost-effective way to develop handheld devices for on-farm detection of sub-clinical ketosis. In this study, a highly sensitive microfluidics-based biosensor for on-site SCK diagnosis has been developed. A rapid, low-cost microfluidic biosensor with high sensitivity and specificity was developed for SCK diagnosis. Determination of βHBA was employed as the indicator in the diagnosis of SCK. On-chip detection using miniaturized and cost-effective optical sensor can be finished in 1 minute with a detection limit of 0.05 mM concentration. Developed microfluidic biosensor was successfully tested with the serum samples from dairy cows affected by SCK. The results of the developed biosensor agreed well with two other laboratory methods. The biosensor was characterized by high sensitivity and specificity towards βHBA with a detection limit of 0.05 mM. The developed microfluidic biosensor provides a promising prototype for a cost-effective handheld meter for on-site SCK diagnosis. By using microfluidic method, the detection time is significantly decreased compared to other laboratory methods. Here, we demonstrate a field-deployable device to precisely identify and measure subclinical ketosis by specific labeling and quantification of β-hydroxybutyate in cow blood samples. A real-time on-site detection system will maximize convenience for the farmers.

Human papillomavirus detection and typing using a nested-PCR-RFLP assay.

PubMed

Coser, Janaina; Boeira, Thaís da Rocha; Fonseca, André Salvador Kazantzi; Ikuta, Nilo; Lunge, Vagner Ricardo

2011-01-01

It is clinically important to detect and type human papillomavirus (HPV) in a sensitive and specific manner. Development of a nested-polymerase chain reaction-restriction fragment length polymorphism (nested-PCR-RFLP) assay to detect and type HPV based on the analysis of L1 gene. Analysis of published DNA sequence of mucosal HPV types to select sequences of new primers. Design of an original nested-PCR assay using the new primers pair selected and classical MY09/11 primers. HPV detection and typing in cervical samples using the nested-PCR-RFLP assay. The nested-PCR-RFLP assay detected and typed HPV in cervical samples. Of the total of 128 clinical samples submitted to simple PCR and nested-PCR for detection of HPV, 37 (28.9%) were positive for the virus by both methods and 25 samples were positive only by nested-PCR (67.5% increase in detection rate compared with single PCR). All HPV positive samples were effectively typed by RFLP assay. The method of nested-PCR proved to be an effective diagnostic tool for HPV detection and typing.

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

PubMed Central

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-01-01

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies. PMID:29695113

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor.

PubMed

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-04-24

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies.

The effect of pre-enrichment media on the recovery and detection of Salmonella in feed

USDA-ARS?s Scientific Manuscript database

Current methodology for detecting Salmonella in feeds and feed ingredients are adapted from food safety methods. These methods do not take into account the stressed state of Salmonella in feed, presence of competing microorganisms nor the sample matrix. The objective was to evaluate four pre-enrichm...

EFFECTS OF SEEDING PROCEDURES AND WATER QUALITY ON RECOVERY OF CRYPTOSPORIDIUM OOCYSTS FROM STREAM WATER BY USING U.S. ENVIRONMENTAL PROTECTION AGENCY METHOD 1623

EPA Science Inventory

U.S.EPA Methods 1622 and 1623 are used to detect and quantify Cryptosporidium oocysts in water. The protocol consists of filtration, immunomagnetic separation (IMS), staining with a fluorescent antibody, and microscopic analysis. Microscopic analysis includes detection by fluor...

Capillary electrophoresis method for the analysis of organic acids and amino acids in the presence of strongly alternating concentrations of aqueous lactic acid.

PubMed

Laube, Hendrik; Boden, Jana; Schneider, Roland

2017-07-01

During the production of bio-based bulk chemicals, such as lactic acid (LA), organic impurities have to be removed to produce a ready-to-market product. A capillary electrophoresis method for the simultaneous detection of LA and organic impurities in less than 10 min was developed. LA and organic impurities were detected using a direct UV detection method with micellar background electrolyte, which consisted of borate and sodium dodecyl sulfate. We investigated the effects of electrolyte composition and temperature on the speed, sensitivity, and robustness of the separation. A few validation parameters, such as linearity, limit of detection, and internal and external standards, were evaluated under optimized conditions. The method was applied for the detection of LA and organic impurities, including tyrosine, phenylalanine, and pyroglutamic acid, in samples from a continuous LA fermentation process from post-extraction tapioca starch and yeast extract.

Dual Low-Rank Pursuit: Learning Salient Features for Saliency Detection.

PubMed

Lang, Congyan; Feng, Jiashi; Feng, Songhe; Wang, Jingdong; Yan, Shuicheng

2016-06-01

Saliency detection is an important procedure for machines to understand visual world as humans do. In this paper, we consider a specific saliency detection problem of predicting human eye fixations when they freely view natural images, and propose a novel dual low-rank pursuit (DLRP) method. DLRP learns saliency-aware feature transformations by utilizing available supervision information and constructs discriminative bases for effectively detecting human fixation points under the popular low-rank and sparsity-pursuit framework. Benefiting from the embedded high-level information in the supervised learning process, DLRP is able to predict fixations accurately without performing the expensive object segmentation as in the previous works. Comprehensive experiments clearly show the superiority of the proposed DLRP method over the established state-of-the-art methods. We also empirically demonstrate that DLRP provides stronger generalization performance across different data sets and inherits the advantages of both the bottom-up- and top-down-based saliency detection methods.

A target detection multi-layer matched filter for color and hyperspectral cameras

NASA Astrophysics Data System (ADS)

Miyanishi, Tomoya; Preece, Bradley L.; Reynolds, Joseph P.

2018-05-01

In this article, a method for applying matched filters to a 3-dimentional hyperspectral data cube is discussed. In many applications, color visible cameras or hyperspectral cameras are used for target detection where the color or spectral optical properties of the imaged materials are partially known in advance. Therefore, the use of matched filtering with spectral data along with shape data is an effective method for detecting certain targets. Since many methods for 2D image filtering have been researched, we propose a multi-layer filter where ordinary spatially matched filters are used before the spectral filters. We discuss a way to layer the spectral filters for a 3D hyperspectral data cube, accompanied by a detectability metric for calculating the SNR of the filter. This method is appropriate for visible color cameras and hyperspectral cameras. We also demonstrate an analysis using the Night Vision Integrated Performance Model (NV-IPM) and a Monte Carlo simulation in order to confirm the effectiveness of the filtering in providing a higher output SNR and a lower false alarm rate.

Label-free detection of salmonella typhimurium with ssDNA aptamers

USDA-ARS?s Scientific Manuscript database

Foodborne pathogen Salmonella enterica is one of the major causes of gastrointestinal infections in human and animals. Conventional detection methods are time consuming and not effective enough under emergency circumstances to control outbreaks immediately. Therefore, biosensors that can detect Salm...

Data fusion of multi-scale representations for structural damage detection

NASA Astrophysics Data System (ADS)

Guo, Tian; Xu, Zili

2018-01-01

Despite extensive researches into structural health monitoring (SHM) in the past decades, there are few methods that can detect multiple slight damage in noisy environments. Here, we introduce a new hybrid method that utilizes multi-scale space theory and data fusion approach for multiple damage detection in beams and plates. A cascade filtering approach provides multi-scale space for noisy mode shapes and filters the fluctuations caused by measurement noise. In multi-scale space, a series of amplification and data fusion algorithms are utilized to search the damage features across all possible scales. We verify the effectiveness of the method by numerical simulation using damaged beams and plates with various types of boundary conditions. Monte Carlo simulations are conducted to illustrate the effectiveness and noise immunity of the proposed method. The applicability is further validated via laboratory cases studies focusing on different damage scenarios. Both results demonstrate that the proposed method has a superior noise tolerant ability, as well as damage sensitivity, without knowing material properties or boundary conditions.

Near-Infrared Spectrum Detection of Wheat Gluten Protein Content Based on a Combined Filtering Method.

PubMed

Cai, Jian-Hua

2017-09-01

To eliminate the random error of the derivative near-IR (NIR) spectrum and to improve model stability and the prediction accuracy of the gluten protein content, a combined method is proposed for pretreatment of the NIR spectrum based on both empirical mode decomposition and the wavelet soft-threshold method. The principle and the steps of the method are introduced and the denoising effect is evaluated. The wheat gluten protein content is calculated based on the denoised spectrum, and the results are compared with those of the nine-point smoothing method and the wavelet soft-threshold method. Experimental results show that the proposed combined method is effective in completing pretreatment of the NIR spectrum, and the proposed method improves the accuracy of detection of wheat gluten protein content from the NIR spectrum.

A novel polydopamine-based chemiluminescence resonance energy transfer method for microRNA detection coupling duplex-specific nuclease-aided target recycling strategy.

PubMed

Wang, Qian; Yin, Bin-Cheng; Ye, Bang-Ce

2016-06-15

MicroRNAs (miRNAs), functioning as oncogenes or tumor suppressors, play significant regulatory roles in regulating gene expression and become as biomarkers for disease diagnostics and therapeutics. In this work, we have coupled a polydopamine (PDA) nanosphere-assisted chemiluminescence resonance energy transfer (CRET) platform and a duplex-specific nuclease (DSN)-assisted signal amplification strategy to develop a novel method for specific miRNA detection. With the assistance of hemin, luminol, and H2O2, the horseradish peroxidase (HRP)-mimicking G-rich sequence in the sensing probe produces chemiluminescence, which is quickly quenched by the CRET effect between PDA as energy acceptor and excited luminol as energy donor. The target miRNA triggers DSN to partially degrade the sensing probe in the DNA-miRNA heteroduplex to repeatedly release G-quadruplex formed by G-rich sequence from PDA for the production of chemiluminescence. The method allows quantitative detection of target miRNA in the range of 80 pM-50 nM with a detection limit of 49.6 pM. The method also shows excellent specificity to discriminate single-base differences, and can accurately quantify miRNA in biological samples, with good agreement with the result from a commercial miRNA detection kit. The procedure requires no organic dyes or labels, and is a simple and cost-effective method for miRNA detection for early clinical diagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.

A review on detection methods used for foodborne pathogens

PubMed Central

Priyanka, B.; Patil, Rajashekhar K.; Dwarakanath, Sulatha

2016-01-01

Foodborne pathogens have been a cause of a large number of diseases worldwide and more so in developing countries. This has a major economic impact. It is important to contain them, and to do so, early detection is very crucial. Detection and diagnostics relied on culture-based methods to begin with and have developed in the recent past parallel to the developments towards immunological methods such as enzyme-linked immunosorbent assays (ELISA) and molecular biology-based methods such as polymerase chain reaction (PCR). The aim has always been to find a rapid, sensitive, specific and cost-effective method. Ranging from culturing of microbes to the futuristic biosensor technology, the methods have had this common goal. This review summarizes the recent trends and brings together methods that have been developed over the years. PMID:28139531

Atmospheric Blocking and Intercomparison of Objective Detection Methods: Flow Field Characteristics

NASA Astrophysics Data System (ADS)

Pinheiro, M. C.; Ullrich, P. A.; Grotjahn, R.

2017-12-01

A number of objective methods for identifying and quantifying atmospheric blocking have been developed over the last couple of decades, but there is variable consensus on the resultant blocking climatology. This project examines blocking climatologies as produced by three different methods: two anomaly-based methods, and the geopotential height gradient method of Tibaldi and Molteni (1990). The results highlight the differences in blocking that arise from the choice of detection method, with emphasis on the physical characteristics of the flow field and the subsequent effects on the blocking patterns that emerge.

Field-Effect Biosensors for On-Site Detection: Recent Advances and Promising Targets.

PubMed

Choi, Jaebin; Seong, Tae Wha; Jeun, Minhong; Lee, Kwan Hyi

2017-10-01

There is an explosive interest in the immediate and cost-effective analysis of field-collected biological samples, as many advanced biodetection tools are highly sensitive, yet immobile. On-site biosensors are portable and convenient sensors that provide detection results at the point of care. They are designed to secure precision in highly ionic and heterogeneous solutions with minimal hardware. Among various methods that are capable of such analysis, field-effect biosensors are promising candidates due to their unique sensitivity, manufacturing scalability, and integrability with computational circuitry. Recent developments in nanotechnological surface modification show promising results in sensing from blood, serum, and urine. This report gives a particular emphasis on the on-site efficacy of recently published field-effect biosensors, specifically, detection limits in physiological solutions, response times, and scalability. The survey of the properties and existing detection methods of four promising biotargets, exosomes, bacteria, viruses, and metabolites, aims at providing a roadmap for future field-effect and other on-site biosensors. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Abnormality detection of mammograms by discriminative dictionary learning on DSIFT descriptors.

PubMed

Tavakoli, Nasrin; Karimi, Maryam; Nejati, Mansour; Karimi, Nader; Reza Soroushmehr, S M; Samavi, Shadrokh; Najarian, Kayvan

2017-07-01

Detection and classification of breast lesions using mammographic images are one of the most difficult studies in medical image processing. A number of learning and non-learning methods have been proposed for detecting and classifying these lesions. However, the accuracy of the detection/classification still needs improvement. In this paper we propose a powerful classification method based on sparse learning to diagnose breast cancer in mammograms. For this purpose, a supervised discriminative dictionary learning approach is applied on dense scale invariant feature transform (DSIFT) features. A linear classifier is also simultaneously learned with the dictionary which can effectively classify the sparse representations. Our experimental results show the superior performance of our method compared to existing approaches.

Effect of Pesticide Inoculation, Duration of Composting, and Degradation Time on the Content of Compost Fatty Acids, Quantified Using Two Methods ▿ †

PubMed Central

Cardinali, Alessandra; Otto, Stefan; Vischetti, Costantino; Brown, Colin; Zanin, Giuseppe

2010-01-01

Compost biobeds can promote biodegradation of pesticides. The microbial community structure changes during the composting process, and simple methods can potentially be used to follow these changes. In this study the microbial identification (MIDI) and ester-linked (EL) procedures were used to determine the composition of fatty acid methyl esters (FAMEs) in composts aged 3 and 12 months, inoculated with 3 recalcitrant pesticides (azoxystrobin, chlorotoluron, and epoxyconazole and a coapplication of all three) after 0, 56, and 125 days of degradation. Pesticide persistence was high, and after 125 days the residue was 22 to 70% of the applied amount depending mostly on the composting age. Seventy-one FAMEs belonging to nine groups were detected. The EL method provided three times as many detections as did the MIDI method and was more sensitive for all FAME groups except alcohol. Thirty-six and five FAMEs were unique to the EL and MIDI methods, respectively. The extraction method was of importance. The EL method provided a higher number of detections for 57 FAMEs, and the MIDI method provided a higher number for 9 FAMEs, while the two methods were equal for 5 FAMEs; thus, the EL method provided a more uniform overall FAME profile. Effects of the other factors were not always clear. Inoculation with pesticide did not influence the FAME profile with the MIDI method, while it influenced cyclopropane and monounsaturated content with the EL method. Composting age and degradation time had an effect on some groups of FAMEs, and this effect was greater with the EL method. The use of some FAMEs as biomarkers to follow microbial community succession was likely influenced by the type of compost and other factors. PMID:20693445

Detection of urinary creatinine using gold nanoparticles after solid phase extraction

NASA Astrophysics Data System (ADS)

Sittiwong, Jarinya; Unob, Fuangfa

2015-03-01

Label-free gold nanoparticles (AuNPs) were utilized in the detection of creatinine in human urine after a sample preparation by extraction of creatinine on sulfonic acid functionalized silica gel. With the proposed sample preparation method, the interfering effects of the urine matrix on creatinine detection by AuNPs were eliminated. Parameters affecting creatinine extraction were investigated. The aggregation of AuNPs induced by creatinine resulted in a change in the surface plasmon resonance signal with a concomitant color change that could be observed by the naked eye and quantified spectrometrically. The effect of AuNP concentration and reaction time on AuNP aggregation was investigated. The method described herein provides a determination of creatinine in a range of 15-40 mg L-1 with a detection limit of 13.7 mg L-1 and it was successfully used in the detection of creatinine in human urine samples.

Depth-Based Detection of Standing-Pigs in Moving Noise Environments.

PubMed

Kim, Jinseong; Chung, Yeonwoo; Choi, Younchang; Sa, Jaewon; Kim, Heegon; Chung, Yongwha; Park, Daihee; Kim, Hakjae

2017-11-29

In a surveillance camera environment, the detection of standing-pigs in real-time is an important issue towards the final goal of 24-h tracking of individual pigs. In this study, we focus on depth-based detection of standing-pigs with "moving noises", which appear every night in a commercial pig farm, but have not been reported yet. We first apply a spatiotemporal interpolation technique to remove the moving noises occurring in the depth images. Then, we detect the standing-pigs by utilizing the undefined depth values around them. Our experimental results show that this method is effective for detecting standing-pigs at night, in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (i.e., 94.47%), even with severe moving noises occluding up to half of an input depth image. Furthermore, without any time-consuming technique, the proposed method can be executed in real-time.

Target detection method by airborne and spaceborne images fusion based on past images

NASA Astrophysics Data System (ADS)

Chen, Shanjing; Kang, Qing; Wang, Zhenggang; Shen, ZhiQiang; Pu, Huan; Han, Hao; Gu, Zhongzheng

2017-11-01

To solve the problem that remote sensing target detection method has low utilization rate of past remote sensing data on target area, and can not recognize camouflage target accurately, a target detection method by airborne and spaceborne images fusion based on past images is proposed in this paper. The target area's past of space remote sensing image is taken as background. The airborne and spaceborne remote sensing data is fused and target feature is extracted by the means of airborne and spaceborne images registration, target change feature extraction, background noise suppression and artificial target feature extraction based on real-time aerial optical remote sensing image. Finally, the support vector machine is used to detect and recognize the target on feature fusion data. The experimental results have established that the proposed method combines the target area change feature of airborne and spaceborne remote sensing images with target detection algorithm, and obtains fine detection and recognition effect on camouflage and non-camouflage targets.

Rapid and sensitive detection of human astrovirus in water samples by loop-mediated isothermal amplification with hydroxynaphthol blue dye.

PubMed

Yang, Bo-Yun; Liu, Xiao-Lu; Wei, Yu-Mei; Wang, Jing-Qi; He, Xiao-Qing; Jin, Yi; Wang, Zi-Jian

2014-02-14

The aim of this paper was to develop a reverse transcription loop-mediated isothermal amplification (RT-LAMP) method for rapid, sensitive and inexpensive detection of astrovirus. The detection limit of LAMP using in vitro RNA transcripts was 3.6 × 10 copies·μL⁻¹, which is as sensitive as the presently used PCR assays. However, the LAMP products could be identified as different colors with the naked eye following staining with hydroxynaphthol blue dye (HNB). No cross-reactivity with other gastroenteric viruses (rotavirus and norovirus) was observed, indicating the relatively high specificity of LAMP. The RT-LAMP method with HNB was used to effectively detect astrovirus in reclaimed water samples. The LAMP technique described in this study is a cheap, sensitive, specific and rapid method for the detection of astrovirus. The RT-LAMP method can be simply applied for the specific detection of astrovirus and has the potential to be utilized in the field as a screening test.

An improved algorithm of laser spot center detection in strong noise background

NASA Astrophysics Data System (ADS)

Zhang, Le; Wang, Qianqian; Cui, Xutai; Zhao, Yu; Peng, Zhong

2018-01-01

Laser spot center detection is demanded in many applications. The common algorithms for laser spot center detection such as centroid and Hough transform method have poor anti-interference ability and low detection accuracy in the condition of strong background noise. In this paper, firstly, the median filtering was used to remove the noise while preserving the edge details of the image. Secondly, the binarization of the laser facula image was carried out to extract target image from background. Then the morphological filtering was performed to eliminate the noise points inside and outside the spot. At last, the edge of pretreated facula image was extracted and the laser spot center was obtained by using the circle fitting method. In the foundation of the circle fitting algorithm, the improved algorithm added median filtering, morphological filtering and other processing methods. This method could effectively filter background noise through theoretical analysis and experimental verification, which enhanced the anti-interference ability of laser spot center detection and also improved the detection accuracy.

A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors

PubMed Central

Xu, Zhengyi; Wei, Jianming; Zhang, Bo; Yang, Weijun

2015-01-01

This paper proposes a robust zero velocity (ZV) detector algorithm to accurately calculate stationary periods in a gait cycle. The proposed algorithm adopts an effective gait cycle segmentation method and introduces a Bayesian network (BN) model based on the measurements of inertial sensors and kinesiology knowledge to infer the ZV period. During the detected ZV period, an Extended Kalman Filter (EKF) is used to estimate the error states and calibrate the position error. The experiments reveal that the removal rate of ZV false detections by the proposed method increases 80% compared with traditional method at high walking speed. Furthermore, based on the detected ZV, the Personal Inertial Navigation System (PINS) algorithm aided by EKF performs better, especially in the altitude aspect. PMID:25831086

An efficient cloud detection method for high resolution remote sensing panchromatic imagery

NASA Astrophysics Data System (ADS)

Li, Chaowei; Lin, Zaiping; Deng, Xinpu

2018-04-01

In order to increase the accuracy of cloud detection for remote sensing satellite imagery, we propose an efficient cloud detection method for remote sensing satellite panchromatic images. This method includes three main steps. First, an adaptive intensity threshold value combined with a median filter is adopted to extract the coarse cloud regions. Second, a guided filtering process is conducted to strengthen the textural features difference and then we conduct the detection process of texture via gray-level co-occurrence matrix based on the acquired texture detail image. Finally, the candidate cloud regions are extracted by the intersection of two coarse cloud regions above and we further adopt an adaptive morphological dilation to refine them for thin clouds in boundaries. The experimental results demonstrate the effectiveness of the proposed method.

Highlight on Bottlenecks in Food Allergen Analysis: Detection and Quantification by Mass Spectrometry.

PubMed

Planque, Mélanie; Arnould, Thierry; Renard, Patricia; Delahaut, Philippe; Dieu, Marc; Gillard, Nathlie

2017-07-01

Food laboratories have developed methods for testing allergens in foods. The efficiency of qualitative and quantitative methods is of prime importance in protecting allergic populations. Unfortunately, food laboratories encounter barriers to developing efficient methods. Bottlenecks include the lack of regulatory thresholds, delays in the emergence of reference materials and guidelines, and the need to detect processed allergens. In this study, ultra-HPLC coupled to tandem MS was used to illustrate difficulties encountered in determining method performances. We measured the major influences of both processing and matrix effects on the detection of egg, milk, soy, and peanut allergens in foodstuffs. The main goals of this work were to identify difficulties that food laboratories still encounter in detecting and quantifying allergens and to sensitize researchers to them.

Micro-vibration detection with heterodyne holography based on time-averaged method

NASA Astrophysics Data System (ADS)

Qin, XiaoDong; Pan, Feng; Chen, ZongHui; Hou, XueQin; Xiao, Wen

2017-02-01

We propose a micro-vibration detection method by introducing heterodyne interferometry to time-averaged holography. This method compensates for the deficiency of time-average holography in quantitative measurements and widens its range of application effectively. Acousto-optic modulators are used to modulate the frequencies of the reference beam and the object beam. Accurate detection of the maximum amplitude of each point in the vibration plane is performed by altering the frequency difference of both beams. The range of amplitude detection of plane vibration is extended. In the stable vibration mode, the distribution of the maximum amplitude of each point is measured and the fitted curves are plotted. Hence the plane vibration mode of the object is demonstrated intuitively and detected quantitatively. We analyzed the method in theory and built an experimental system with a sine signal as the excitation source and a typical piezoelectric ceramic plate as the target. The experimental results indicate that, within a certain error range, the detected vibration mode agrees with the intrinsic vibration characteristics of the object, thus proving the validity of this method.

Live face detection based on the analysis of Fourier spectra

NASA Astrophysics Data System (ADS)

Li, Jiangwei; Wang, Yunhong; Tan, Tieniu; Jain, Anil K.

2004-08-01

Biometrics is a rapidly developing technology that is to identify a person based on his or her physiological or behavioral characteristics. To ensure the correction of authentication, the biometric system must be able to detect and reject the use of a copy of a biometric instead of the live biometric. This function is usually termed "liveness detection". This paper describes a new method for live face detection. Using structure and movement information of live face, an effective live face detection algorithm is presented. Compared to existing approaches, which concentrate on the measurement of 3D depth information, this method is based on the analysis of Fourier spectra of a single face image or face image sequences. Experimental results show that the proposed method has an encouraging performance.

Research on Daily Objects Detection Based on Deep Neural Network

NASA Astrophysics Data System (ADS)

Ding, Sheng; Zhao, Kun

2018-03-01

With the rapid development of deep learning, great breakthroughs have been made in the field of object detection. In this article, the deep learning algorithm is applied to the detection of daily objects, and some progress has been made in this direction. Compared with traditional object detection methods, the daily objects detection method based on deep learning is faster and more accurate. The main research work of this article: 1. collect a small data set of daily objects; 2. in the TensorFlow framework to build different models of object detection, and use this data set training model; 3. the training process and effect of the model are improved by fine-tuning the model parameters.

Detection of the Vibration Signal from Human Vocal Folds Using a 94-GHz Millimeter-Wave Radar

PubMed Central

Chen, Fuming; Li, Sheng; Zhang, Yang; Wang, Jianqi

2017-01-01

The detection of the vibration signal from human vocal folds provides essential information for studying human phonation and diagnosing voice disorders. Doppler radar technology has enabled the noncontact measurement of the human-vocal-fold vibration. However, existing systems must be placed in close proximity to the human throat and detailed information may be lost because of the low operating frequency. In this paper, a long-distance detection method, involving the use of a 94-GHz millimeter-wave radar sensor, is proposed for detecting the vibration signals from human vocal folds. An algorithm that combines empirical mode decomposition (EMD) and the auto-correlation function (ACF) method is proposed for detecting the signal. First, the EMD method is employed to suppress the noise of the radar-detected signal. Further, the ratio of the energy and entropy is used to detect voice activity in the radar-detected signal, following which, a short-time ACF is employed to extract the vibration signal of the human vocal folds from the processed signal. For validating the method and assessing the performance of the radar system, a vibration measurement sensor and microphone system are additionally employed for comparison. The experimental results obtained from the spectrograms, the vibration frequency of the vocal folds, and coherence analysis demonstrate that the proposed method can effectively detect the vibration of human vocal folds from a long detection distance. PMID:28282892

Evaluation of Anomaly Detection Method Based on Pattern Recognition

NASA Astrophysics Data System (ADS)

Fontugne, Romain; Himura, Yosuke; Fukuda, Kensuke

The number of threats on the Internet is rapidly increasing, and anomaly detection has become of increasing importance. High-speed backbone traffic is particularly degraded, but their analysis is a complicated task due to the amount of data, the lack of payload data, the asymmetric routing and the use of sampling techniques. Most anomaly detection schemes focus on the statistical properties of network traffic and highlight anomalous traffic through their singularities. In this paper, we concentrate on unusual traffic distributions, which are easily identifiable in temporal-spatial space (e.g., time/address or port). We present an anomaly detection method that uses a pattern recognition technique to identify anomalies in pictures representing traffic. The main advantage of this method is its ability to detect attacks involving mice flows. We evaluate the parameter set and the effectiveness of this approach by analyzing six years of Internet traffic collected from a trans-Pacific link. We show several examples of detected anomalies and compare our results with those of two other methods. The comparison indicates that the only anomalies detected by the pattern-recognition-based method are mainly malicious traffic with a few packets.

A new method for detecting small and dim targets in starry background

NASA Astrophysics Data System (ADS)

Yao, Rui; Zhang, Yanning; Jiang, Lei

2011-08-01

Small visible optical space targets detection is one of the key issues in the research of long-range early warning and space debris surveillance. The SNR(Signal to Noise Ratio) of the target is very low because of the self influence of image device. Random noise and background movement also increase the difficulty of target detection. In order to detect small visible optical space targets effectively and rapidly, we bring up a novel detecting method based on statistic theory. Firstly, we get a reasonable statistical model of visible optical space image. Secondly, we extract SIFT(Scale-Invariant Feature Transform) feature of the image frames, and calculate the transform relationship, then use the transform relationship to compensate whole visual field's movement. Thirdly, the influence of star was wiped off by using interframe difference method. We find segmentation threshold to differentiate candidate targets and noise by using OTSU method. Finally, we calculate statistical quantity to judge whether there is the target for every pixel position in the image. Theory analysis shows the relationship of false alarm probability and detection probability at different SNR. The experiment result shows that this method could detect target efficiently, even the target passing through stars.

Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

PubMed

Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

2017-03-01

Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effective screen of CRISPR/Cas9-induced mutants in rice by single-strand conformation polymorphism.

PubMed

Zheng, Xuelian; Yang, Shixin; Zhang, Dengwei; Zhong, Zhaohui; Tang, Xu; Deng, Kejun; Zhou, Jianping; Qi, Yiping; Zhang, Yong

2016-07-01

A method based on DNA single-strand conformation polymorphism is demonstrated for effective genotyping of CRISPR/Cas9-induced mutants in rice. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated 9 (Cas9) has been widely adopted for genome editing in many organisms. A large proportion of mutations generated by CRISPR/Cas9 are very small insertions and deletions (indels), presumably because Cas9 generates blunt-ended double-strand breaks which are subsequently repaired without extensive end-processing. CRISPR/Cas9 is highly effective for targeted mutagenesis in the important crop, rice. For example, homozygous mutant seedlings are commonly recovered from CRISPR/Cas9-treated calli. However, many current mutation detection methods are not very suitable for screening homozygous mutants that typically carry small indels. In this study, we tested a mutation detection method based on single-strand conformational polymorphism (SSCP). We found it can effectively detect small indels in pilot experiments. By applying the SSCP method for CRISRP-Cas9-mediated targeted mutagenesis in rice, we successfully identified multiple mutants of OsROC5 and OsDEP1. In conclusion, the SSCP analysis will be a useful genotyping method for rapid identification of CRISPR/Cas9-induced mutants, including the most desirable homozygous mutants. The method also has high potential for similar applications in other plant species.

A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors

PubMed Central

Tian, Xiaochun; Chen, Jiabin; Han, Yongqiang; Shang, Jianyu; Li, Nan

2016-01-01

Zero velocity update (ZUPT) plays an important role in pedestrian navigation algorithms with the premise that the zero velocity interval (ZVI) should be detected accurately and effectively. A novel adaptive ZVI detection algorithm based on a smoothed pseudo Wigner–Ville distribution to remove multiple frequencies intelligently (SPWVD-RMFI) is proposed in this paper. The novel algorithm adopts the SPWVD-RMFI method to extract the pedestrian gait frequency and to calculate the optimal ZVI detection threshold in real time by establishing the function relationships between the thresholds and the gait frequency; then, the adaptive adjustment of thresholds with gait frequency is realized and improves the ZVI detection precision. To put it into practice, a ZVI detection experiment is carried out; the result shows that compared with the traditional fixed threshold ZVI detection method, the adaptive ZVI detection algorithm can effectively reduce the false and missed detection rate of ZVI; this indicates that the novel algorithm has high detection precision and good robustness. Furthermore, pedestrian trajectory positioning experiments at different walking speeds are carried out to evaluate the influence of the novel algorithm on positioning precision. The results show that the ZVI detected by the adaptive ZVI detection algorithm for pedestrian trajectory calculation can achieve better performance. PMID:27669266

An Acoustic-Based Method to Detect and Quantify the Effect of Exhalation into a Dry Powder Inhaler.

PubMed

Holmes, Martin S; Seheult, Jansen N; O'Connell, Peter; D'Arcy, Shona; Ehrhardt, Carsten; Healy, Anne Marie; Costello, Richard W; Reilly, Richard B

2015-08-01

Dry powder inhaler (DPI) users frequently exhale into their inhaler mouthpiece before the inhalation step. This error in technique compromises the integrity of the drug and results in poor bronchodilation. This study investigated the effect of four exhalation factors (exhalation flow rate, distance from mouth to inhaler, exhalation duration, and relative air humidity) on dry powder dose delivery. Given that acoustic energy can be related to the factors associated with exhalation sounds, we then aimed to develop a method of identifying and quantifying this critical inhaler technique error using acoustic based methods. An in vitro test rig was developed to simulate this critical error. The effect of the four factors on subsequent drug delivery were investigated using multivariate regression models. In a further study we then used an acoustic monitoring device to unobtrusively record the sounds 22 asthmatic patients made whilst using a Diskus(™) DPI. Acoustic energy was employed to automatically detect and analyze exhalation events in the audio files. All exhalation factors had a statistically significant effect on drug delivery (p<0.05); distance from the inhaler mouthpiece had the largest effect size. Humid air exhalations were found to reduce the fine particle fraction (FPF) compared to dry air. In a dataset of 110 audio files from 22 asthmatic patients, the acoustic method detected exhalations with an accuracy of 89.1%. We were able to classify exhalations occurring 5 cm or less in the direction of the inhaler mouthpiece or recording device with a sensitivity of 72.2% and specificity of 85.7%. Exhaling into a DPI has a significant detrimental effect. Acoustic based methods can be employed to objectively detect and analyze exhalations during inhaler use, thus providing a method of remotely monitoring inhaler technique and providing personalized inhaler technique feedback.

LC/DAD/ESI/MS method for the determination of imidacloprid, thiacloprid, and spinosad in olives and olive oil after field treatment.

PubMed

Angioni, Alberto; Porcu, Luciano; Pirisi, Filippo

2011-10-26

The behavior in the field and the transfer from olives to olive oil during the technological process of imidacloprid, thiacloprid, and spinosad were studied. The extraction method used was effective in extracting the analytes of interest, and no interfering peaks were detected in the chromatogram. The residue levels found in olives after treatment were 0.14, 0.04, and 0.30 mg/kg for imidacloprid, thiacloprid, and spinosad, respectively, far below the maximum residue levels (MRLs) set for these insecticides in EU. At the preharvest interval (PHI), no residue was detected for imidacloprid and thiacloprid, while spinosad showed a residue level of 0.04 mg/kg. The study of the effect of the technological process on pesticide transfer in olive oil showed that these insecticides tend to remain in the olive cake. The LC/DAD/ESI/MS method showed good performance with adequate recoveries ranging from 80 to 119% and good method limits of quantitation (LOQs) and of determination (LODs). No matrix effect was detected.

An image overall complexity evaluation method based on LSD line detection

NASA Astrophysics Data System (ADS)

Li, Jianan; Duan, Jin; Yang, Xu; Xiao, Bo

2017-04-01

In the artificial world, whether it is the city's traffic roads or engineering buildings contain a lot of linear features. Therefore, the research on the image complexity of linear information has become an important research direction in digital image processing field. This paper, by detecting the straight line information in the image and using the straight line as the parameter index, establishing the quantitative and accurate mathematics relationship. In this paper, we use LSD line detection algorithm which has good straight-line detection effect to detect the straight line, and divide the detected line by the expert consultation strategy. Then we use the neural network to carry on the weight training and get the weight coefficient of the index. The image complexity is calculated by the complexity calculation model. The experimental results show that the proposed method is effective. The number of straight lines in the image, the degree of dispersion, uniformity and so on will affect the complexity of the image.

Detection of Multiple Stationary Humans Using UWB MIMO Radar.

PubMed

Liang, Fulai; Qi, Fugui; An, Qiang; Lv, Hao; Chen, Fuming; Li, Zhao; Wang, Jianqi

2016-11-16

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls.

Detection of Multiple Stationary Humans Using UWB MIMO Radar

PubMed Central

Liang, Fulai; Qi, Fugui; An, Qiang; Lv, Hao; Chen, Fuming; Li, Zhao; Wang, Jianqi

2016-01-01

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls. PMID:27854356

Damage Detection Based on Static Strain Responses Using FBG in a Wind Turbine Blade

PubMed Central

Tian, Shaohua; Yang, Zhibo; Chen, Xuefeng; Xie, Yong

2015-01-01

The damage detection of a wind turbine blade enables better operation of the turbines, and provides an early alert to the destroyed events of the blade in order to avoid catastrophic losses. A new non-baseline damage detection method based on the Fiber Bragg grating (FBG) in a wind turbine blade is developed in this paper. Firstly, the Chi-square distribution is proven to be an effective damage-sensitive feature which is adopted as the individual information source for the local decision. In order to obtain the global and optimal decision for the damage detection, the feature information fusion (FIF) method is proposed to fuse and optimize information in above individual information sources, and the damage is detected accurately through of the global decision. Then a 13.2 m wind turbine blade with the distributed strain sensor system is adopted to describe the feasibility of the proposed method, and the strain energy method (SEM) is used to describe the advantage of the proposed method. Finally results show that the proposed method can deliver encouraging results of the damage detection in the wind turbine blade. PMID:26287200

Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

USGS Publications Warehouse

Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.

2011-01-01

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.

Detection of shigella in lettuce by the use of a rapid molecular assay with increased sensitivity

PubMed Central

Jiménez, Kenia Barrantes; McCoy², Clyde B.; Achí, Rosario

2010-01-01

A Multiplex Polymerase Chain Reaction (PCR) assay to be used as an alternative to the conventional culture method in detecting Shigella and enteroinvasive Escherichia coli (EIEC) virulence genes ipaH and ial in lettuce was developed. Efficacy and rapidity of the molecular method were determined as compared to the conventional culture. Lettuce samples were inoculated with different Shigella flexneri concentrations (from 10 CFU/ml to 107 CFU/ml). DNA was extracted directly from lettuce after inoculation (direct-PCR) and after an enrichment step (enrichment PCR). Multiplex PCR detection limit was 104CFU/ml, diagnostic sensitivity and specificity were 100% accurate. An internal amplification control (IAC) of 100 bp was used in order to avoid false negative results. This method produced results in 1 to 2 days while the conventional culture method required 5 to 6 days. Also, the culture method detection limit was 106 CFU/ml, diagnostic sensitivity was 53% and diagnostic specificity was 100%. In this study a Multiplex PCR method for detection of virulence genes in Shigella and EIEC was shown to be effective in terms of diagnostic sensitivity, detection limit and amount of time as compared to Shigella conventional culture. PMID:24031579

Ship Detection from Ocean SAR Image Based on Local Contrast Variance Weighted Information Entropy

PubMed Central

Huang, Yulin; Pei, Jifang; Zhang, Qian; Gu, Qin; Yang, Jianyu

2018-01-01

Ship detection from synthetic aperture radar (SAR) images is one of the crucial issues in maritime surveillance. However, due to the varying ocean waves and the strong echo of the sea surface, it is very difficult to detect ships from heterogeneous and strong clutter backgrounds. In this paper, an innovative ship detection method is proposed to effectively distinguish the vessels from complex backgrounds from a SAR image. First, the input SAR image is pre-screened by the maximally-stable extremal region (MSER) method, which can obtain the ship candidate regions with low computational complexity. Then, the proposed local contrast variance weighted information entropy (LCVWIE) is adopted to evaluate the complexity of those candidate regions and the dissimilarity between the candidate regions with their neighborhoods. Finally, the LCVWIE values of the candidate regions are compared with an adaptive threshold to obtain the final detection result. Experimental results based on measured ocean SAR images have shown that the proposed method can obtain stable detection performance both in strong clutter and heterogeneous backgrounds. Meanwhile, it has a low computational complexity compared with some existing detection methods. PMID:29652863

Effect of specimen storage, antibiotics, and feminine hygiene products on the detection of group B Streptococcus by culture and the STREP B OIA test.

PubMed

Ostroff, R M; Steaffens, J W

1995-07-01

Agar culture from vaginal swabs is the routine method for diagnosis of maternal Group B Streptococcus (GBS) colonization. Swab specimens are often transported to a clinical laboratory for processing. In these studies, specimen transport was simulated by inoculating swabs with GBS and storing them at selected temperatures and with or without transport medium. The recovery of viable GBS was assessed by agar culture. GBS antigen was detected immunologically with an Optical ImmunoAssay (OIA) method. Swabs that were stored with transport medium harbored viable but rapidly declining numbers of GBS. In contrast, a strong OIA signal was maintained. Recovery of viable GBS organisms declined more quickly when swabs were stored in the absence of transport medium, whereas detection of GBS antigen remained consistent. Both methods were tested for interference from either antibiotics or feminine hygiene products. These compounds inhibited the detection of GBS by culture but had no detrimental effect on the OIA result.

Fatigue crack detection and identification by the elastic wave propagation method

NASA Astrophysics Data System (ADS)

Stawiarski, Adam; Barski, Marek; Pająk, Piotr

2017-05-01

In this paper the elastic wave propagation phenomenon was used to detect the initiation of the fatigue damage in isotropic plate with a circular hole. The safety and reliability of structures mostly depend on the effectiveness of the monitoring methods. The Structural Health Monitoring (SHM) system based on the active pitch-catch measurement technique was proposed. The piezoelectric (PZT) elements was used as an actuators and sensors in the multipoint measuring system. The comparison of the intact and defected structures has been used by damage detection algorithm. One part of the SHM system has been responsible for detection of the fatigue crack initiation. The second part observed the evolution of the damage growth and assess the size of the defect. The numerical results of the wave propagation phenomenon has been used to present the effectiveness and accuracy of the proposed method. The preliminary experimental analysis has been carried out during the tension test of the aluminum plate with a circular hole to determine the efficiency of the measurement technique.

Partial Discharge Monitoring on Metal-Enclosed Switchgear with Distributed Non-Contact Sensors.

PubMed

Zhang, Chongxing; Dong, Ming; Ren, Ming; Huang, Wenguang; Zhou, Jierui; Gao, Xuze; Albarracín, Ricardo

2018-02-11

Metal-enclosed switchgear, which are widely used in the distribution of electrical energy, play an important role in power distribution networks. Their safe operation is directly related to the reliability of power system as well as the power quality on the consumer side. Partial discharge detection is an effective way to identify potential faults and can be utilized for insulation diagnosis of metal-enclosed switchgear. The transient earth voltage method, an effective non-intrusive method, has substantial engineering application value for estimating the insulation condition of switchgear. However, the practical application effectiveness of TEV detection is not satisfactory because of the lack of a TEV detection application method, i.e., a method with sufficient technical cognition and analysis. This paper proposes an innovative online PD detection system and a corresponding application strategy based on an intelligent feedback distributed TEV wireless sensor network, consisting of sensing, communication, and diagnosis layers. In the proposed system, the TEV signal or status data are wirelessly transmitted to the terminal following low-energy signal preprocessing and acquisition by TEV sensors. Then, a central server analyzes the correlation of the uploaded data and gives a fault warning level according to the quantity, trend, parallel analysis, and phase resolved partial discharge pattern recognition. In this way, a TEV detection system and strategy with distributed acquisition, unitized fault warning, and centralized diagnosis is realized. The proposed system has positive significance for reducing the fault rate of medium voltage switchgear and improving its operation and maintenance level.

Defect analysis and detection of micro nano structured optical thin film

NASA Astrophysics Data System (ADS)

Xu, Chang; Shi, Nuo; Zhou, Lang; Shi, Qinfeng; Yang, Yang; Li, Zhuo

2017-10-01

This paper focuses on developing an automated method for detecting defects on our wavelength conversion thin film. We analyzes the operating principle of our wavelength conversion Micro/Nano thin film which absorbing visible light and emitting infrared radiation, indicates the relationship between the pixel's pattern and the radiation of the thin film, and issues the principle of defining blind pixels and their categories due to the calculated and experimental results. An effective method is issued for the automated detection based on wavelet transform and template matching. The results reveal that this method has desired accuracy and processing speed.

Slip Ratio Estimation and Regenerative Brake Control for Decelerating Electric Vehicles without Detection of Vehicle Velocity and Acceleration

NASA Astrophysics Data System (ADS)

Suzuki, Toru; Fujimoto, Hiroshi

In slip ratio control systems, it is necessary to detect the vehicle velocity in order to obtain the slip ratio. However, it is very difficult to measure this velocity directly. We have proposed slip ratio estimation and control methods that do not require the vehicle velocity with acceleration. In this paper, the slip ratio estimation and control methods are proposed without detecting the vehicle velocity and acceleration when it is decelerating. We carried out simulations and experiments by using an electric vehicle to verify the effectiveness of the proposed method.

Unsupervised change detection of multispectral images based on spatial constraint chi-squared transform and Markov random field model

NASA Astrophysics Data System (ADS)

Shi, Aiye; Wang, Chao; Shen, Shaohong; Huang, Fengchen; Ma, Zhenli

2016-10-01

Chi-squared transform (CST), as a statistical method, can describe the difference degree between vectors. The CST-based methods operate directly on information stored in the difference image and are simple and effective methods for detecting changes in remotely sensed images that have been registered and aligned. However, the technique does not take spatial information into consideration, which leads to much noise in the result of change detection. An improved unsupervised change detection method is proposed based on spatial constraint CST (SCCST) in combination with a Markov random field (MRF) model. First, the mean and variance matrix of the difference image of bitemporal images are estimated by an iterative trimming method. In each iteration, spatial information is injected to reduce scattered changed points (also known as "salt and pepper" noise). To determine the key parameter confidence level in the SCCST method, a pseudotraining dataset is constructed to estimate the optimal value. Then, the result of SCCST, as an initial solution of change detection, is further improved by the MRF model. The experiments on simulated and real multitemporal and multispectral images indicate that the proposed method performs well in comprehensive indices compared with other methods.

An operant-based detection method for inferring tinnitus in mice.

PubMed

Zuo, Hongyan; Lei, Debin; Sivaramakrishnan, Shobhana; Howie, Benjamin; Mulvany, Jessica; Bao, Jianxin

2017-11-01

Subjective tinnitus is a hearing disorder in which a person perceives sound when no external sound is present. It can be acute or chronic. Because our current understanding of its pathology is incomplete, no effective cures have yet been established. Mouse models are useful for studying the pathophysiology of tinnitus as well as for developing therapeutic treatments. We have developed a new method for determining acute and chronic tinnitus in mice, called sound-based avoidance detection (SBAD). The SBAD method utilizes one paradigm to detect tinnitus and another paradigm to monitor possible confounding factors, such as motor impairment, loss of motivation, and deficits in learning and memory. The SBAD method has succeeded in monitoring both acute and chronic tinnitus in mice. Its detection ability is further validated by functional studies demonstrating an abnormal increase in neuronal activity in the inferior colliculus of mice that had previously been identified as having tinnitus by the SBAD method. The SBAD method provides a new means by which investigators can detect tinnitus in a single mouse accurately and with more control over potential confounding factors than existing methods. This work establishes a new behavioral method for detecting tinnitus in mice. The detection outcome is consistent with functional validation. One key advantage of mouse models is they provide researchers the opportunity to utilize an extensive array of genetic tools. This new method could lead to a deeper understanding of the molecular pathways underlying tinnitus pathology. Copyright © 2017 Elsevier B.V. All rights reserved.

Stepwise and stagewise approaches for spatial cluster detection

PubMed Central

Xu, Jiale

2016-01-01

Spatial cluster detection is an important tool in many areas such as sociology, botany and public health. Previous work has mostly taken either hypothesis testing framework or Bayesian framework. In this paper, we propose a few approaches under a frequentist variable selection framework for spatial cluster detection. The forward stepwise methods search for multiple clusters by iteratively adding currently most likely cluster while adjusting for the effects of previously identified clusters. The stagewise methods also consist of a series of steps, but with tiny step size in each iteration. We study the features and performances of our proposed methods using simulations on idealized grids or real geographic area. From the simulations, we compare the performance of the proposed methods in terms of estimation accuracy and power of detections. These methods are applied to the the well-known New York leukemia data as well as Indiana poverty data. PMID:27246273

Evaluation of shrinkage and cracking in concrete of ring test by acoustic emission method

NASA Astrophysics Data System (ADS)

Watanabe, Takeshi; Hashimoto, Chikanori

2015-03-01

Drying shrinkage of concrete is one of the typical problems related to reduce durability and defilation of concrete structures. Lime stone, expansive additive and low-heat Portland cement are used to reduce drying shrinkage in Japan. Drying shrinkage is commonly evaluated by methods of measurement for length change of mortar and concrete. In these methods, there is detected strain due to drying shrinkage of free body, although visible cracking does not occur. In this study, the ring test was employed to detect strain and age cracking of concrete. The acoustic emission (AE) method was adopted to detect micro cracking due to shrinkage. It was recognized that in concrete using lime stone, expansive additive and low-heat Portland cement are effective to decrease drying shrinkage and visible cracking. Micro cracking due to shrinkage of this concrete was detected and evaluated by the AE method.

A model-guided symbolic execution approach for network protocol implementations and vulnerability detection.

PubMed

Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

2017-01-01

Formal techniques have been devoted to analyzing whether network protocol specifications violate security policies; however, these methods cannot detect vulnerabilities in the implementations of the network protocols themselves. Symbolic execution can be used to analyze the paths of the network protocol implementations, but for stateful network protocols, it is difficult to reach the deep states of the protocol. This paper proposes a novel model-guided approach to detect vulnerabilities in network protocol implementations. Our method first abstracts a finite state machine (FSM) model, then utilizes the model to guide the symbolic execution. This approach achieves high coverage of both the code and the protocol states. The proposed method is implemented and applied to test numerous real-world network protocol implementations. The experimental results indicate that the proposed method is more effective than traditional fuzzing methods such as SPIKE at detecting vulnerabilities in the deep states of network protocol implementations.

Detection of bovine mastitis pathogens by loop-mediated isothermal amplification and an electrochemical DNA chip.

PubMed

Kawai, Kazuhiro; Inada, Mika; Ito, Keiko; Hashimoto, Koji; Nikaido, Masaru; Hata, Eiji; Katsuda, Ken; Kiku, Yoshio; Tagawa, Yuichi; Hayashi, Tomohito

2017-12-22

Bovine mastitis causes significant economic losses in the dairy industry. Effective prevention of bovine mastitis requires an understanding of the infection status of a pathogenic microorganism in a herd that has not yet shown clinical signs of mastitis and appropriate treatment specific for the pathogenic microorganism. However, bacterial identification by culture has drawbacks in that the sensitivity may be low and the procedure can be complex. In this study, we developed a genetic detection method to identify mastitis pathogens using a simple and highly sensitive electrochemical DNA chip which can specifically detect bacterial DNA in milk specimens. First, we selected microorganisms belonging to 12 families and/or genera associated with mastitis for which testing should be performed. Next, we optimized the conditions for amplifying microorganism DNA by loop-mediated isothermal amplification (LAMP) using 32 primers and the use of a DNA chip capable of measuring all pathogens simultaneously. Sample detection could be completed in just a few hours using this method. Comparison of the results obtained with our DNA chip method and those obtained by bacterial culture verified that when the culture method was set to 100%, the total positive concordance rate of the DNA chip was 85.0% and the total negative concordance rate was 86.9%. Furthermore, the proposed method allows both rapid and highly sensitive detection of mastitis pathogens. We believe that this method will contribute to the development of an effective mastitis control program.

Detection of bovine mastitis pathogens by loop-mediated isothermal amplification and an electrochemical DNA chip

PubMed Central

KAWAI, Kazuhiro; INADA, Mika; ITO, Keiko; HASHIMOTO, Koji; NIKAIDO, Masaru; HATA, Eiji; KATSUDA, Ken; KIKU, Yoshio; TAGAWA, Yuichi; HAYASHI, Tomohito

2017-01-01

Bovine mastitis causes significant economic losses in the dairy industry. Effective prevention of bovine mastitis requires an understanding of the infection status of a pathogenic microorganism in a herd that has not yet shown clinical signs of mastitis and appropriate treatment specific for the pathogenic microorganism. However, bacterial identification by culture has drawbacks in that the sensitivity may be low and the procedure can be complex. In this study, we developed a genetic detection method to identify mastitis pathogens using a simple and highly sensitive electrochemical DNA chip which can specifically detect bacterial DNA in milk specimens. First, we selected microorganisms belonging to 12 families and/or genera associated with mastitis for which testing should be performed. Next, we optimized the conditions for amplifying microorganism DNA by loop-mediated isothermal amplification (LAMP) using 32 primers and the use of a DNA chip capable of measuring all pathogens simultaneously. Sample detection could be completed in just a few hours using this method. Comparison of the results obtained with our DNA chip method and those obtained by bacterial culture verified that when the culture method was set to 100%, the total positive concordance rate of the DNA chip was 85.0% and the total negative concordance rate was 86.9%. Furthermore, the proposed method allows both rapid and highly sensitive detection of mastitis pathogens. We believe that this method will contribute to the development of an effective mastitis control program. PMID:29093278

Fault detection in reciprocating compressor valves under varying load conditions

NASA Astrophysics Data System (ADS)

Pichler, Kurt; Lughofer, Edwin; Pichler, Markus; Buchegger, Thomas; Klement, Erich Peter; Huschenbett, Matthias

2016-03-01

This paper presents a novel approach for detecting cracked or broken reciprocating compressor valves under varying load conditions. The main idea is that the time frequency representation of vibration measurement data will show typical patterns depending on the fault state. The problem is to detect these patterns reliably. For the detection task, we make a detour via the two dimensional autocorrelation. The autocorrelation emphasizes the patterns and reduces noise effects. This makes it easier to define appropriate features. After feature extraction, classification is done using logistic regression and support vector machines. The method's performance is validated by analyzing real world measurement data. The results will show a very high detection accuracy while keeping the false alarm rates at a very low level for different compressor loads, thus achieving a load-independent method. The proposed approach is, to our best knowledge, the first automated method for reciprocating compressor valve fault detection that can handle varying load conditions.

Automatic small target detection in synthetic infrared images

NASA Astrophysics Data System (ADS)

Yardımcı, Ozan; Ulusoy, Ä.°lkay

2017-05-01

Automatic detection of targets from far distances is a very challenging problem. Background clutter and small target size are the main difficulties which should be solved while reaching a high detection performance as well as a low computational load. The pre-processing, detection and post-processing approaches are very effective on the final results. In this study, first of all, various methods in the literature were evaluated separately for each of these stages using the simulated test scenarios. Then, a full system of detection was constructed among available solutions which resulted in the best performance in terms of detection. However, although a precision rate as 100% was reached, the recall values stayed low around 25-45%. Finally, a post-processing method was proposed which increased the recall value while keeping the precision at 100%. The proposed post-processing method, which is based on local operations, increased the recall value to 65-95% in all test scenarios.

Detection and differentiation of wild-type and vaccine strains of canine distemper virus by a duplex reverse transcription polymerase chain reaction

PubMed Central

Dong, X. Y.; Li, W. H.; Zhu, J. L.; Liu, W. J.; Zhao, M. Q.; Luo, Y. W.; Chen, J. D.

2015-01-01

Canine distemper virus (CDV) is the cause of canine distemper (CD) which is a severe and highly contagious disease in dogs. In the present study, a duplex reverse transcription polymerase chain reaction (RT-PCR) method was developed for the detection and differentiation of wild-type and vaccine strains of CDV. Four primers were designed to detect and discriminate the two viruses by generating 638- and 781-bp cDNA products, respectively. Furthermore, the duplex RT-PCR method was used to detect 67 field samples suspected of CD from Guangdong province in China. Results showed that, 33 samples were to be wild-type-like. The duplex RT-PCR method exhibited high specificity and sensitivity which could be used to effectively detect and differentiate wild-type and vaccine CDV, indicating its use for clinical detection and epidemiological surveillance. PMID:27175171

A New Approach to Detect Mover Position in Linear Motors Using Magnetic Sensors

PubMed Central

Paul, Sarbajit; Chang, Junghwan

2015-01-01

A new method to detect the mover position of a linear motor is proposed in this paper. This method employs a simple cheap Hall Effect sensor-based magnetic sensor unit to detect the mover position of the linear motor. With the movement of the linear motor, Hall Effect sensor modules electrically separated 120° along with the idea of three phase balanced condition (va + vb + vc = 0) are used to produce three phase signals. The amplitude of the sensor output voltage signals are adjusted to unit amplitude to minimize the amplitude errors. With the unit amplitude signals three to two phase transformation is done to reduce the three multiples of harmonic components. The final output thus obtained is converted to position data by the use of arctangent function. The measurement accuracy of the new method is analyzed by experiments and compared with the conventional two phase method. Using the same number of sensor modules as the conventional two phase method, the proposed method gives more accurate position information compared to the conventional system where sensors are separated by 90° electrical angles. PMID:26506348

A Kernel Machine Method for Detecting Effects of Interaction Between Multidimensional Variable Sets: An Imaging Genetics Application

PubMed Central

Ge, Tian; Nichols, Thomas E.; Ghosh, Debashis; Mormino, Elizabeth C.

2015-01-01

Measurements derived from neuroimaging data can serve as markers of disease and/or healthy development, are largely heritable, and have been increasingly utilized as (intermediate) phenotypes in genetic association studies. To date, imaging genetic studies have mostly focused on discovering isolated genetic effects, typically ignoring potential interactions with non-genetic variables such as disease risk factors, environmental exposures, and epigenetic markers. However, identifying significant interaction effects is critical for revealing the true relationship between genetic and phenotypic variables, and shedding light on disease mechanisms. In this paper, we present a general kernel machine based method for detecting effects of interaction between multidimensional variable sets. This method can model the joint and epistatic effect of a collection of single nucleotide polymorphisms (SNPs), accommodate multiple factors that potentially moderate genetic influences, and test for nonlinear interactions between sets of variables in a flexible framework. As a demonstration of application, we applied the method to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to detect the effects of the interactions between candidate Alzheimer's disease (AD) risk genes and a collection of cardiovascular disease (CVD) risk factors, on hippocampal volume measurements derived from structural brain magnetic resonance imaging (MRI) scans. Our method identified that two genes, CR1 and EPHA1, demonstrate significant interactions with CVD risk factors on hippocampal volume, suggesting that CR1 and EPHA1 may play a role in influencing AD-related neurodegeneration in the presence of CVD risks. PMID:25600633

Change detection for synthetic aperture radar images based on pattern and intensity distinctiveness analysis

NASA Astrophysics Data System (ADS)

Wang, Xiao; Gao, Feng; Dong, Junyu; Qi, Qiang

2018-04-01

Synthetic aperture radar (SAR) image is independent on atmospheric conditions, and it is the ideal image source for change detection. Existing methods directly analysis all the regions in the speckle noise contaminated difference image. The performance of these methods is easily affected by small noisy regions. In this paper, we proposed a novel change detection framework for saliency-guided change detection based on pattern and intensity distinctiveness analysis. The saliency analysis step can remove small noisy regions, and therefore makes the proposed method more robust to the speckle noise. In the proposed method, the log-ratio operator is first utilized to obtain a difference image (DI). Then, the saliency detection method based on pattern and intensity distinctiveness analysis is utilized to obtain the changed region candidates. Finally, principal component analysis and k-means clustering are employed to analysis pixels in the changed region candidates. Thus, the final change map can be obtained by classifying these pixels into changed or unchanged class. The experiment results on two real SAR images datasets have demonstrated the effectiveness of the proposed method.

Multi-Frequency Signal Detection Based on Frequency Exchange and Re-Scaling Stochastic Resonance and Its Application to Weak Fault Diagnosis.

PubMed

Liu, Jinjun; Leng, Yonggang; Lai, Zhihui; Fan, Shengbo

2018-04-25

Mechanical fault diagnosis usually requires not only identification of the fault characteristic frequency, but also detection of its second and/or higher harmonics. However, it is difficult to detect a multi-frequency fault signal through the existing Stochastic Resonance (SR) methods, because the characteristic frequency of the fault signal as well as its second and higher harmonics frequencies tend to be large parameters. To solve the problem, this paper proposes a multi-frequency signal detection method based on Frequency Exchange and Re-scaling Stochastic Resonance (FERSR). In the method, frequency exchange is implemented using filtering technique and Single SideBand (SSB) modulation. This new method can overcome the limitation of "sampling ratio" which is the ratio of the sampling frequency to the frequency of target signal. It also ensures that the multi-frequency target signals can be processed to meet the small-parameter conditions. Simulation results demonstrate that the method shows good performance for detecting a multi-frequency signal with low sampling ratio. Two practical cases are employed to further validate the effectiveness and applicability of this method.

A Note on Testing Mediated Effects in Structural Equation Models: Reconciling Past and Current Research on the Performance of the Test of Joint Significance

ERIC Educational Resources Information Center

Valente, Matthew J.; Gonzalez, Oscar; Miocevic, Milica; MacKinnon, David P.

2016-01-01

Methods to assess the significance of mediated effects in education and the social sciences are well studied and fall into two categories: single sample methods and computer-intensive methods. A popular single sample method to detect the significance of the mediated effect is the test of joint significance, and a popular computer-intensive method…

Cell tracking for cell image analysis

NASA Astrophysics Data System (ADS)

Bise, Ryoma; Sato, Yoichi

2017-04-01

Cell image analysis is important for research and discovery in biology and medicine. In this paper, we present our cell tracking methods, which is capable of obtaining fine-grain cell behavior metrics. In order to address difficulties under dense culture conditions, where cell detection cannot be done reliably since cell often touch with blurry intercellular boundaries, we proposed two methods which are global data association and jointly solving cell detection and association. We also show the effectiveness of the proposed methods by applying the method to the biological researches.

Detection and estimation of defects in a circular plate using operational deflection shapes

NASA Astrophysics Data System (ADS)

Pai, Perngjin F.; Oh, Yunje; Kim, Byeong-Seok

2002-06-01

This paper investigates dynamic characteristics (mode shapes and natural frequencies) and defect detection of circular plates using a scanning laser vibrometer. Exact dynamic characteristics of a circular aluminum plate having a clamped inner rim and a free outer rim are obtained using two methods; one uses Bessel functions and the other uses a multiple shooting method. An in-house finite element code GESA is also used to analyze the circular plate using the DKT plate element. Numerical results show that some reports in the literature are incorrect and that high-frequency Operational Deflection Shapes (ODSs) are needed in order to locate small defects. Detection of two defects in the circular aluminum plate is experimentally studied using the distributions of RMS velocities under broadband periodic chirp excitations. RMS velocities of ODSs, symmetry breaking of ODSs, splitting of natural frequencies and ODSs, and a Boundary Effect Detection (BED) method. The BED method is non-destructive and model-independent; it processes experimental ODSs to reveal extra local boundary effects caused by defects to reveal locations of defects. Experimental results show that small defects in circular plates can be pinpointed by these approaches. Moreover, a new concept of using the balance of elastic and kinetic energies within a mode cell for detecting defects in two- dimensional structures of irregular shapes is proposed.

Detection of abrupt changes in dynamic systems

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1984-01-01

Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed.

How Travel Demand Affects Detection of Non-Recurrent Traffic Congestion on Urban Road Networks

NASA Astrophysics Data System (ADS)

Anbaroglu, B.; Heydecker, B.; Cheng, T.

2016-06-01

Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London's urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.

[Application of optical flow dynamic texture in land use/cover change detection].

PubMed

Yan, Li; Gong, Yi-Long; Zhang, Yi; Duan, Wei

2014-11-01

In the present study, a novel change detection approach for high resolution remote sensing images is proposed based on the optical flow dynamic texture (OFDT), which could achieve the land use & land cover change information automatically with a dynamic description of ground-object changes. This paper describes the ground-object gradual change process from the principle using optical flow theory, which breaks the ground-object sudden change hypothesis in remote sensing change detection methods in the past. As the steps of this method are simple, it could be integrated in the systems and software such as Land Resource Management and Urban Planning software that needs to find ground-object changes. This method takes into account the temporal dimension feature between remote sensing images, which provides a richer set of information for remote sensing change detection, thereby improving the status that most of the change detection methods are mainly dependent on the spatial dimension information. In this article, optical flow dynamic texture is the basic reflection of changes, and it is used in high resolution remote sensing image support vector machine post-classification change detection, combined with spectral information. The texture in the temporal dimension which is considered in this article has a smaller amount of data than most of the textures in the spatial dimensions. The highly automated texture computing has only one parameter to set, which could relax the onerous manual evaluation present status. The effectiveness of the proposed approach is evaluated with the 2011 and 2012 QuickBird datasets covering Duerbert Mongolian Autonomous County of Daqing City, China. Then, the effects of different optical flow smooth coefficient and the impact on the description of the ground-object changes in the method are deeply analyzed: The experiment result is satisfactory, with an 87.29% overall accuracy and an 0.850 7 Kappa index, and the method achieves better performance than the post-classification change detection methods using spectral information only.

DEVELOPMENT OF DECISION MAKING ALGORITHM FOR CONTROL OF SEA CARGO CONTAINERS BY 'TAGGED' NEUTRON METHOD

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anan'ev, A. A.; Belichenko, S. G.; Bogolyubov, E. P.

Nowadays in Russia and abroad there are several groups of scientists, engaged in development of systems based on 'tagged' neutron method (API method) and intended for detection of dangerous materials, including high explosives (HE). Particular attention is paid to possibility of detection of dangerous objects inside a sea cargo container. Energy gamma-spectrum, registered from object under inspection is used for determination of oxygen/carbon and nitrogen/carbon chemical ratios, according to which dangerous object is distinguished from not dangerous one. Material of filled container, however, gives rise to additional effects of rescattering and moderation of 14 MeV primary neutrons of generator, attenuationmore » of secondary gamma-radiation from reactions of inelastic neutron scattering on objects under inspection. These effects lead to distortion of energy gamma-response from examined object and therefore prevent correct recognition of chemical ratios. These difficulties are taken into account in analytical method, presented in the paper. Method has been validated against experimental data, obtained by the system for HE detection in sea cargo, based on API method and developed in VNIIA. Influence of shielding materials on results of HE detection and identification is considered. Wood and iron were used as shielding materials. Results of method application for analysis of experimental data on HE simulator measurement (tetryl, trotyl, hexogen) are presented.« less

Effect of saliva stabilisers on detection of porcine reproductive and respiratory syndrome virus in oral fluid by quantitative reverse transcriptase real-time PCR.

PubMed

Decorte, Inge; Van der Stede, Yves; Nauwynck, Hans; De Regge, Nick; Cay, Ann Brigitte

2013-08-01

This study evaluated the effect of extraction-amplification methods, storage temperature and saliva stabilisers on detection of porcine reproductive and respiratory syndrome virus (PRRSV) RNA by quantitative reverse transcriptase real-time PCR (qRT-PCR) in porcine oral fluid. The diagnostic performance of different extraction-amplification methods was examined using a dilution series of oral fluid spiked with PRRSV. To determine RNA stability, porcine oral fluid, with or without commercially available saliva stabilisers, was spiked with PRRSV, stored at 4°C or room temperature and tested for the presence of PRRSV RNA by qRT-PCR. PRRSV RNA could be detected in oral fluid using all extraction-amplification combinations, but the limit of detection varied amongst different combinations. Storage temperature and saliva stabilisers had an effect on the stability of PRRSV RNA, which could only be detected for 7 days when PRRSV spiked oral fluid was kept at 4°C or stabilised at room temperature with a commercial mRNA stabiliser. Copyright © 2013 Elsevier Ltd. All rights reserved.

Detection of l-Cysteine in wheat flour by Raman microspectroscopy combined chemometrics of HCA and PCA.

PubMed

Cebi, Nur; Dogan, Canan Ekinci; Develioglu, Ayşen; Yayla, Mediha Esra Altuntop; Sagdic, Osman

2017-08-01

l-Cysteine is deliberately added to various flour types since l-Cysteine has enabled favorable baking conditions such as low viscosity, increased elasticity and rise during baking. In Turkey, usage of l-Cysteine as a food additive isn't allowed in wheat flour according to the Turkish Food Codex Regulation on food additives. There is an urgent need for effective methods to detect l-Cysteine in wheat flour. In this study, for the first time, a new, rapid, effective, non-destructive and cost-effective method was developed for detection of l-Cysteine in wheat flour using Raman microscopy. Detection of l-Cysteine in wheat flour was accomplished successfully using Raman microscopy combined chemometrics of PCA (Principal Component Analysis) and HCA (Hierarchical Cluster Analysis). In this work, 500-2000cm -1 spectral range (fingerprint region) was determined to perform PCA and HCA analysis. l-Cysteine and l-Cystine were determined with detection limit of 0.125% (w/w) in different wheat flour samples. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization

NASA Astrophysics Data System (ADS)

Zhang, Chuan-Xin; Yuan, Yuan; Zhang, Hao-Wei; Shuai, Yong; Tan, He-Ping

2016-09-01

Considering features of stellar spectral radiation and sky surveys, we established a computational model for stellar effective temperatures, detected angular parameters and gray rates. Using known stellar flux data in some bands, we estimated stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization (SPSO). We first verified the reliability of SPSO, and then determined reasonable parameters that produced highly accurate estimates under certain gray deviation levels. Finally, we calculated 177 860 stellar effective temperatures and detected angular parameters using data from the Midcourse Space Experiment (MSX) catalog. These derived stellar effective temperatures were accurate when we compared them to known values from literatures. This research makes full use of catalog data and presents an original technique for studying stellar characteristics. It proposes a novel method for calculating stellar effective temperatures and detecting angular parameters, and provides theoretical and practical data for finding information about radiation in any band.

Effectiveness of a flow-based device using riboflavin photochemistry in damaging blood-borne viral nucleic acids.

PubMed

Zhu, Liguo; Tong, Hongli; Wang, Shufang; Yu, Yang; Liu, Zhong; Li, Changqing; Wang, Deqing

2018-05-03

Effectiveness of a flow-based treatment device using riboflavin photochemistry was demonstrated by cytopathic effect method using indicator viruses. However, inactivation efficacy against real blood-borne viruses needs to be evaluated, especially at nucleic acid level. Special plasma samples with varying concentrations of blood-borne virus were selected using a strict blood selection procedure and were treated with device treatment (DT). Nucleic acid test (NAT) using polymerase chain reaction fluorescence method was used to detect virus copies. The NAT value of 4325 in plasma with high Hepatitis B Virus (HBV) concentrations decreased to 1330 with DT. After 100-fold dilution, the NAT value was below the NAT detection limits with DT compared with 23.0 that without DT. The NAT value of 61.9 in plasma with medium HBV concentrations decreased to 37.8 with DT, and after 10-fold dilution, the NAT value was below the NAT detection limits with DT compared with below 20 that without DT. The Ct values of plasma with low concentrations of blood-borne viruses were below the NAT detection limits with DT. There was a dose effect with DT which was effective in blood-borne viruses damaging nucleic acids to a level below the NAT detection limits. Copyright © 2018 Elsevier B.V. All rights reserved.

Recent developments in detection and enumeration of waterborne bacteria: a retrospective minireview.

PubMed

Deshmukh, Rehan A; Joshi, Kopal; Bhand, Sunil; Roy, Utpal

2016-12-01

Waterborne diseases have emerged as global health problems and their rapid and sensitive detection in environmental water samples is of great importance. Bacterial identification and enumeration in water samples is significant as it helps to maintain safe drinking water for public consumption. Culture-based methods are laborious, time-consuming, and yield false-positive results, whereas viable but nonculturable (VBNCs) microorganisms cannot be recovered. Hence, numerous methods have been developed for rapid detection and quantification of waterborne pathogenic bacteria in water. These rapid methods can be classified into nucleic acid-based, immunology-based, and biosensor-based detection methods. This review summarizes the principle and current state of rapid methods for the monitoring and detection of waterborne bacterial pathogens. Rapid methods outlined are polymerase chain reaction (PCR), digital droplet PCR, real-time PCR, multiplex PCR, DNA microarray, Next-generation sequencing (pyrosequencing, Illumina technology and genomics), and fluorescence in situ hybridization that are categorized as nucleic acid-based methods. Enzyme-linked immunosorbent assay (ELISA) and immunofluorescence are classified into immunology-based methods. Optical, electrochemical, and mass-based biosensors are grouped into biosensor-based methods. Overall, these methods are sensitive, specific, time-effective, and important in prevention and diagnosis of waterborne bacterial diseases. © 2016 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.

Surveying drainage culvert use by carnivores: sampling design and cost-benefit analyzes of track-pads vs. video-surveillance methods.

PubMed

Mateus, Ana Rita A; Grilo, Clara; Santos-Reis, Margarida

2011-10-01

Environmental assessment studies often evaluate the effectiveness of drainage culverts as habitat linkages for species, however, the efficiency of the sampling designs and the survey methods are not known. Our main goal was to estimate the most cost-effective monitoring method for sampling carnivore culvert using track-pads and video-surveillance. We estimated the most efficient (lower costs and high detection success) interval between visits (days) when using track-pads and also determined the advantages of using each method. In 2006, we selected two highways in southern Portugal and sampled 15 culverts over two 10-day sampling periods (spring and summer). Using the track-pad method, 90% of the animal tracks were detected using a 2-day interval between visits. We recorded a higher number of crossings for most species using video-surveillance (n = 129) when compared with the track-pad technique (n = 102); however, the detection ability using the video-surveillance method varied with type of structure and species. More crossings were detected in circular culverts (1 m and 1.5 m diameter) than in box culverts (2 m to 4 m width), likely because video cameras had a reduced vision coverage area. On the other hand, carnivore species with small feet such as the common genet Genetta genetta were detected less often using the track-pad surveying method. The cost-benefit analyzes shows that the track-pad technique is the most appropriate technique, but video-surveillance allows year-round surveys as well as the behavior response analyzes of species using crossing structures.

Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.

PubMed

Mansouri, Majdi; Nounou, Mohamed N; Nounou, Hazem N

2017-09-01

In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to combine the advantages brought forward by the proposed EWMA-GLRT fault detection chart with the KPCA model. Thus, it is used to enhance fault detection of the Cad System in E. coli model through monitoring some of the key variables involved in this model such as enzymes, transport proteins, regulatory proteins, lysine, and cadaverine. The results demonstrate the effectiveness of the proposed KPCA-based EWMA-GLRT method over Q , GLRT, EWMA, Shewhart, and moving window-GLRT methods. The detection performance is assessed and evaluated in terms of FAR, missed detection rates, and average run length (ARL 1 ) values.

Colorimetric detection of cholesterol based on enzyme modified gold nanoparticles

NASA Astrophysics Data System (ADS)

Nirala, Narsingh R.; Saxena, Preeti S.; Srivastava, Anchal

2018-02-01

We develop a simple colorimetric method for determination of free cholesterol in aqueous solution based on functionalized gold nanoparticles with cholesterol oxidase. Functionalized gold nanoparticles interact with free cholesterol to produce H2O2 in proportion to the level of cholesterol visually is being detected. The quenching in optical properties and agglomeration of functionalized gold nanoparticles play a key role in cholesterol sensing due to the electron accepting property of H2O2. While the lower ranges of cholesterol (lower detection limit i.e. 0.2 mg/dL) can be effectively detected using fluorescence study, the absorption study attests evident visual color change which becomes effective for detection of higher ranges of cholesterol (lower detection limit i.e. 19 mg/dL). The shades of red gradually change to blue/purple as the level of cholesterol detected (as evident at 100 mg/dL) using unaided eye without the use of expensive instruments. The potential of the proposed method to be applied in the field is shown by the proposed cholesterol measuring color wheel.

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope

PubMed Central

2017-01-01

Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs. PMID:29065613

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope.

PubMed

Park, Jeong-Seon; Lee, Sang-Woong; Park, Unsang

2017-01-01

Rapid automatic detection of the fiducial points-namely, the P wave, QRS complex, and T wave-is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

Enhanced data validation strategy of air quality monitoring network.

PubMed

Harkat, Mohamed-Faouzi; Mansouri, Majdi; Nounou, Mohamed; Nounou, Hazem

2018-01-01

Quick validation and detection of faults in measured air quality data is a crucial step towards achieving the objectives of air quality networks. Therefore, the objectives of this paper are threefold: (i) to develop a modeling technique that can be used to predict the normal behavior of air quality variables and help provide accurate reference for monitoring purposes; (ii) to develop fault detection method that can effectively and quickly detect any anomalies in measured air quality data. For this purpose, a new fault detection method that is based on the combination of generalized likelihood ratio test (GLRT) and exponentially weighted moving average (EWMA) will be developed. GLRT is a well-known statistical fault detection method that relies on maximizing the detection probability for a given false alarm rate. In this paper, we propose to develop GLRT-based EWMA fault detection method that will be able to detect the changes in the values of certain air quality variables; (iii) to develop fault isolation and identification method that allows defining the fault source(s) in order to properly apply appropriate corrective actions. In this paper, reconstruction approach that is based on Midpoint-Radii Principal Component Analysis (MRPCA) model will be developed to handle the types of data and models associated with air quality monitoring networks. All air quality modeling, fault detection, fault isolation and reconstruction methods developed in this paper will be validated using real air quality data (such as particulate matter, ozone, nitrogen and carbon oxides measurement). Copyright © 2017 Elsevier Inc. All rights reserved.

Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity.

PubMed

Napoletano, Paolo; Piccoli, Flavio; Schettini, Raimondo

2018-01-12

Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art.

Frequency domain phase-shifted confocal microscopy (FDPCM) with array detection

NASA Astrophysics Data System (ADS)

Ge, Baoliang; Huang, Yujia; Fang, Yue; Kuang, Cuifang; Xiu, Peng; Liu, Xu

2017-09-01

We proposed a novel method to reconstruct images taken by array detected confocal microscopy without prior knowledge about its detector distribution. The proposed frequency domain phase-shifted confocal microscopy (FDPCM) shifts the image from each detection channel to its corresponding place by substituting the phase information in Fourier domain. Theoretical analysis shows that our method could approach the resolution nearly twofold of wide-field microscopy. Simulation and experiment results are also shown to verify the applicability and effectiveness of our method. Compared to Airyscan, our method holds the advantage of simplicity and convenience to be applied to array detectors with different structure, which makes FDPCM have great potential in the application of biomedical observation in the future.

A comparative study on methods of improving SCR for ship detection in SAR image

NASA Astrophysics Data System (ADS)

Lang, Haitao; Shi, Hongji; Tao, Yunhong; Ma, Li

2017-10-01

Knowledge about ship positions plays a critical role in a wide range of maritime applications. To improve the performance of ship detector in SAR image, an effective strategy is improving the signal-to-clutter ratio (SCR) before conducting detection. In this paper, we present a comparative study on methods of improving SCR, including power-law scaling (PLS), max-mean and max-median filter (MMF1 and MMF2), method of wavelet transform (TWT), traditional SPAN detector, reflection symmetric metric (RSM), scattering mechanism metric (SMM). The ability of SCR improvement to SAR image and ship detection performance associated with cell- averaging CFAR (CA-CFAR) of different methods are evaluated on two real SAR data.

Estimation of effective refractive index of birefringent particles using a combination of the immersion liquid method and light scattering.

PubMed

Niskanen, Ilpo; Räty, Jukka; Peiponen, Kai-Erik

2008-04-01

A method to detect the effective refractive index and concentration of birefringent pigments is suggested. The method is based on the utilization of the immersion liquid method and a multifunction spectrophotometer for the measurement of back scattered light. The method has applications in the measurement of the effective refractive index of pigments that are used, e.g., in the paper industry to improve the opacity of paper products.

Detecting wood surface defects with fusion algorithm of visual saliency and local threshold segmentation

NASA Astrophysics Data System (ADS)

Wang, Xuejuan; Wu, Shuhang; Liu, Yunpeng

2018-04-01

This paper presents a new method for wood defect detection. It can solve the over-segmentation problem existing in local threshold segmentation methods. This method effectively takes advantages of visual saliency and local threshold segmentation. Firstly, defect areas are coarsely located by using spectral residual method to calculate global visual saliency of them. Then, the threshold segmentation of maximum inter-class variance method is adopted for positioning and segmenting the wood surface defects precisely around the coarse located areas. Lastly, we use mathematical morphology to process the binary images after segmentation, which reduces the noise and small false objects. Experiments on test images of insect hole, dead knot and sound knot show that the method we proposed obtains ideal segmentation results and is superior to the existing segmentation methods based on edge detection, OSTU and threshold segmentation.

A New Reassigned Spectrogram Method in Interference Detection for GNSS Receivers.

PubMed

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-09-02

Interference detection is very important for Global Navigation Satellite System (GNSS) receivers. Current work on interference detection in GNSS receivers has mainly focused on time-frequency (TF) analysis techniques, such as spectrogram and Wigner-Ville distribution (WVD), where the spectrogram approach presents the TF resolution trade-off problem, since the analysis window is used, and the WVD method suffers from the very serious cross-term problem, due to its quadratic TF distribution nature. In order to solve the cross-term problem and to preserve good TF resolution in the TF plane at the same time, in this paper, a new TF distribution by using a reassigned spectrogram has been proposed in interference detection for GNSS receivers. This proposed reassigned spectrogram method efficiently combines the elimination of the cross-term provided by the spectrogram itself according to its inherent nature and the improvement of the TF aggregation property achieved by the reassignment method. Moreover, a notch filter has been adopted in interference mitigation for GNSS receivers, where receiver operating characteristics (ROCs) are used as metrics for the characterization of interference mitigation performance. The proposed interference detection method by using a reassigned spectrogram is evaluated by experiments on GPS L1 signals in the disturbing scenarios in comparison to the state-of-the-art TF analysis approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-term problem and also keeps good TF localization properties, which has been proven to be valid and effective to enhance the interference Sensors 2015, 15 22168 detection performance; in addition, the adoption of the notch filter in interference mitigation has shown a significant acquisition performance improvement in terms of ROC curves for GNSS receivers in jamming environments.

A New Reassigned Spectrogram Method in Interference Detection for GNSS Receivers

PubMed Central

Sun, Kewen; Jin, Tian; Yang, Dongkai

2015-01-01

Interference detection is very important for Global Navigation Satellite System (GNSS) receivers. Current work on interference detection in GNSS receivers has mainly focused on time-frequency (TF) analysis techniques, such as spectrogram and Wigner–Ville distribution (WVD), where the spectrogram approach presents the TF resolution trade-off problem, since the analysis window is used, and the WVD method suffers from the very serious cross-term problem, due to its quadratic TF distribution nature. In order to solve the cross-term problem and to preserve good TF resolution in the TF plane at the same time, in this paper, a new TF distribution by using a reassigned spectrogram has been proposed in interference detection for GNSS receivers. This proposed reassigned spectrogram method efficiently combines the elimination of the cross-term provided by the spectrogram itself according to its inherent nature and the improvement of the TF aggregation property achieved by the reassignment method. Moreover, a notch filter has been adopted in interference mitigation for GNSS receivers, where receiver operating characteristics (ROCs) are used as metrics for the characterization of interference mitigation performance. The proposed interference detection method by using a reassigned spectrogram is evaluated by experiments on GPS L1 signals in the disturbing scenarios in comparison to the state-of-the-art TF analysis approaches. The analysis results show that the proposed interference detection technique effectively overcomes the cross-term problem and also keeps good TF localization properties, which has been proven to be valid and effective to enhance the interference detection performance; in addition, the adoption of the notch filter in interference mitigation has shown a significant acquisition performance improvement in terms of ROC curves for GNSS receivers in jamming environments. PMID:26364637

A novel method of multiple nucleic acid detection: Real-time RT-PCR coupled with probe-melting curve analysis.

PubMed

Han, Yang; Hou, Shao-Yang; Ji, Shang-Zhi; Cheng, Juan; Zhang, Meng-Yue; He, Li-Juan; Ye, Xiang-Zhong; Li, Yi-Min; Zhang, Yi-Xuan

2017-11-15

A novel method, real-time reverse transcription PCR (real-time RT-PCR) coupled with probe-melting curve analysis, has been established to detect two kinds of samples within one fluorescence channel. Besides a conventional TaqMan probe, this method employs another specially designed melting-probe with a 5' terminus modification which meets the same label with the same fluorescent group. By using an asymmetric PCR method, the melting-probe is able to detect an extra sample in the melting stage effectively while it almost has little influence on the amplification detection. Thus, this method allows the availability of united employment of both amplification stage and melting stage for detecting samples in one reaction. The further demonstration by simultaneous detection of human immunodeficiency virus (HIV) and hepatitis C virus (HCV) in one channel as a model system is presented in this essay. The sensitivity of detection by real-time RT-PCR coupled with probe-melting analysis was proved to be equal to that detected by conventional real-time RT-PCR. Because real-time RT-PCR coupled with probe-melting analysis can double the detection throughputs within one fluorescence channel, it is expected to be a good solution for the problem of low-throughput in current real-time PCR. Copyright © 2017 Elsevier Inc. All rights reserved.

Bacteriophage Amplification-Coupled Detection and Identification of Bacterial Pathogens

NASA Astrophysics Data System (ADS)

Cox, Christopher R.; Voorhees, Kent J.

Current methods of species-specific bacterial detection and identification are complex, time-consuming, and often require expensive specialized equipment and highly trained personnel. Numerous biochemical and genotypic identification methods have been applied to bacterial characterization, but all rely on tedious microbiological culturing practices and/or costly sequencing protocols which render them impractical for deployment as rapid, cost-effective point-of-care or field detection and identification methods. With a view towards addressing these shortcomings, we have exploited the evolutionarily conserved interactions between a bacteriophage (phage) and its bacterial host to develop species-specific detection methods. Phage amplification-coupled matrix assisted laser desorption time-of-flight mass spectrometry (MALDI-TOF-MS) was utilized to rapidly detect phage propagation resulting from species-specific in vitro bacterial infection. This novel signal amplification method allowed for bacterial detection and identification in as little as 2 h, and when combined with disulfide bond reduction methods developed in our laboratory to enhance MALDI-TOF-MS resolution, was observed to lower the limit of detection by several orders of magnitude over conventional spectroscopy and phage typing methods. Phage amplification has been combined with lateral flow immunochromatography (LFI) to develop rapid, easy-to-operate, portable, species-specific point-of-care (POC) detection devices. Prototype LFI detectors have been developed and characterized for Yersinia pestis and Bacillus anthracis, the etiologic agents of plague and anthrax, respectively. Comparable sensitivity and rapidity was observed when phage amplification was adapted to a species-specific handheld LFI detector, thus allowing for rapid, simple, POC bacterial detection and identification while eliminating the need for bacterial culturing or DNA isolation and amplification techniques.

Exploiting vibrational resonance in weak-signal detection

NASA Astrophysics Data System (ADS)

Ren, Yuhao; Pan, Yan; Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek

2017-08-01

In this paper, we investigate the first exploitation of the vibrational resonance (VR) effect to detect weak signals in the presence of strong background noise. By injecting a series of sinusoidal interference signals of the same amplitude but with different frequencies into a generalized correlation detector, we show that the detection probability can be maximized at an appropriate interference amplitude. Based on a dual-Dirac probability density model, we compare the VR method with the stochastic resonance approach via adding dichotomous noise. The compared results indicate that the VR method can achieve a higher detection probability for a wider variety of noise distributions.

Exploiting vibrational resonance in weak-signal detection.

PubMed

Ren, Yuhao; Pan, Yan; Duan, Fabing; Chapeau-Blondeau, François; Abbott, Derek

2017-08-01

In this paper, we investigate the first exploitation of the vibrational resonance (VR) effect to detect weak signals in the presence of strong background noise. By injecting a series of sinusoidal interference signals of the same amplitude but with different frequencies into a generalized correlation detector, we show that the detection probability can be maximized at an appropriate interference amplitude. Based on a dual-Dirac probability density model, we compare the VR method with the stochastic resonance approach via adding dichotomous noise. The compared results indicate that the VR method can achieve a higher detection probability for a wider variety of noise distributions.

Conjugated polymer nanoparticles-based fluorescent biosensor for ultrasensitive detection of hydroquinone.

PubMed

Liu, Yuan; Wang, Yu-Min; Zhu, Wu-Yang; Zhang, Chong-Hua; Tang, Hao; Jiang, Jian-Hui

2018-07-05

This work describes a simple and sensitive fluorescent method for detection of hydroquinone utilizing conjugated polymer nanoparticles (CPNs). The CPNs serve both as a catalyst to accelerate the conversion of hydroquinone to benzoquinone and a fluorescent probe. In the presence of hydroquinone, the fluorescence of CPNs can be effectively quenched by benzoquinone. The detection limit of hydroquinone was down to 5 nM and excellent selectivity toward possible interferences was obtained. This method was successfully applied for hydroquinone detection in lake water and satisfactory results were achieved. Copyright © 2018 Elsevier B.V. All rights reserved.

Conclusion of LOD-score analysis for family data generated under two-locus models.

PubMed

Dizier, M H; Babron, M C; Clerget-Darpoux, F

1996-06-01

The power to detect linkage by the LOD-score method is investigated here for diseases that depend on the effects of two genes. The classical strategy is, first, to detect a major-gene (MG) effect by segregation analysis and, second, to seek for linkage with genetic markers by the LOD-score method using the MG parameters. We already showed that segregation analysis can lead to evidence for a MG effect for many two-locus models, with the estimates of the MG parameters being very different from those of the two genes involved in the disease. We show here that use of these MG parameter estimates in the LOD-score analysis may lead to a failure to detect linkage for some two-locus models. For these models, use of the sib-pair method gives a non-negligible increase of power to detect linkage. The linkage-homogeneity test among subsamples differing for the familial disease distribution provides evidence of parameter misspecification, when the MG parameters are used. Moreover, for most of the models, use of the MG parameters in LOD-score analysis leads to a large bias in estimation of the recombination fraction and sometimes also to a rejection of linkage for the true recombination fraction. A final important point is that a strong evidence of an MG effect, obtained by segregation analysis, does not necessarily imply that linkage will be detected for at least one of the two genes, even with the true parameters and with a close informative marker.

Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

PubMed

Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

2017-01-01

Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

Edge detection, cosmic strings and the south pole telescope

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stewart, Andrew; Brandenberger, Robert, E-mail: stewarta@physics.mcgill.ca, E-mail: rhb@physics.mcgill.ca

2009-02-15

We develop a method of constraining the cosmic string tension G{mu} which uses the Canny edge detection algorithm as a means of searching CMB temperature maps for the signature of the Kaiser-Stebbins effect. We test the potential of this method using high resolution, simulated CMB temperature maps. By modeling the future output from the South Pole Telescope project (including anticipated instrumental noise), we find that cosmic strings with G{mu} > 5.5 Multiplication-Sign 10{sup -8} could be detected.

Regional Principal Color Based Saliency Detection

PubMed Central

Lou, Jing; Ren, Mingwu; Wang, Huan

2014-01-01

Saliency detection is widely used in many visual applications like image segmentation, object recognition and classification. In this paper, we will introduce a new method to detect salient objects in natural images. The approach is based on a regional principal color contrast modal, which incorporates low-level and medium-level visual cues. The method allows a simple computation of color features and two categories of spatial relationships to a saliency map, achieving higher F-measure rates. At the same time, we present an interpolation approach to evaluate resulting curves, and analyze parameters selection. Our method enables the effective computation of arbitrary resolution images. Experimental results on a saliency database show that our approach produces high quality saliency maps and performs favorably against ten saliency detection algorithms. PMID:25379960

A method of real-time detection for distant moving obstacles by monocular vision

NASA Astrophysics Data System (ADS)

Jia, Bao-zhi; Zhu, Ming

2013-12-01

In this paper, we propose an approach for detection of distant moving obstacles like cars and bicycles by a monocular camera to cooperate with ultrasonic sensors in low-cost condition. We are aiming at detecting distant obstacles that move toward our autonomous navigation car in order to give alarm and keep away from them. Method of frame differencing is applied to find obstacles after compensation of camera's ego-motion. Meanwhile, each obstacle is separated from others in an independent area and given a confidence level to indicate whether it is coming closer. The results on an open dataset and our own autonomous navigation car have proved that the method is effective for detection of distant moving obstacles in real-time.

Method for detecting an image of an object

DOEpatents

Chapman, Leroy Dean; Thomlinson, William C.; Zhong, Zhong

1999-11-16

A method for detecting an absorption, refraction and scatter image of an object by independently analyzing, detecting, digitizing, and combining images acquired on a high and a low angle side of a rocking curve of a crystal analyzer. An x-ray beam which is generated by any suitable conventional apparatus can be irradiated upon either a Bragg type crystal analyzer or a Laue type crystal analyzer. Images of the absorption, refraction and scattering effects are detected, such as on an image plate, and then digitized. The digitized images are simultaneously solved, preferably on a pixel-by-pixel basis, to derive a combined visual image which has dramatically improved contrast and spatial resolution over an image acquired through conventional radiology methods.

A novel approach to describing and detecting performance anti-patterns

NASA Astrophysics Data System (ADS)

Sheng, Jinfang; Wang, Yihan; Hu, Peipei; Wang, Bin

2017-08-01

Anti-pattern, as an extension to pattern, describes a widely used poor solution which can bring negative influence to application systems. Aiming at the shortcomings of the existing anti-pattern descriptions, an anti-pattern description method based on first order predicate is proposed. This method synthesizes anti-pattern forms and symptoms, which makes the description more accurate and has good scalability and versatility as well. In order to improve the accuracy of anti-pattern detection, a Bayesian classification method is applied in validation for detection results, which can reduce false negatives and false positives of anti-pattern detection. Finally, the proposed approach in this paper is applied to a small e-commerce system, the feasibility and effectiveness of the approach is demonstrated further through experiments.

Face liveness detection for face recognition based on cardiac features of skin color image

NASA Astrophysics Data System (ADS)

Suh, Kun Ha; Lee, Eui Chul

2016-07-01

With the growth of biometric technology, spoofing attacks have been emerged a threat to the security of the system. Main spoofing scenarios in the face recognition system include the printing attack, replay attack, and 3D mask attack. To prevent such attacks, techniques that evaluating liveness of the biometric data can be considered as a solution. In this paper, a novel face liveness detection method based on cardiac signal extracted from face is presented. The key point of proposed method is that the cardiac characteristic is detected in live faces but not detected in non-live faces. Experimental results showed that the proposed method can be effective way for determining printing attack or 3D mask attack.

Salient man-made structure detection in infrared images

NASA Astrophysics Data System (ADS)

Li, Dong-jie; Zhou, Fu-gen; Jin, Ting

2013-09-01

Target detection, segmentation and recognition is a hot research topic in the field of image processing and pattern recognition nowadays, among which salient area or object detection is one of core technologies of precision guided weapon. Many theories have been raised in this paper; we detect salient objects in a series of input infrared images by using the classical feature integration theory and Itti's visual attention system. In order to find the salient object in an image accurately, we present a new method to solve the edge blur problem by calculating and using the edge mask. We also greatly improve the computing speed by improving the center-surround differences method. Unlike the traditional algorithm, we calculate the center-surround differences through rows and columns separately. Experimental results show that our method is effective in detecting salient object accurately and rapidly.

The ship edge feature detection based on high and low threshold for remote sensing image

NASA Astrophysics Data System (ADS)

Li, Xuan; Li, Shengyang

2018-05-01

In this paper, a method based on high and low threshold is proposed to detect the ship edge feature due to the low accuracy rate caused by the noise. Analyze the relationship between human vision system and the target features, and to determine the ship target by detecting the edge feature. Firstly, using the second-order differential method to enhance the quality of image; Secondly, to improvement the edge operator, we introduction of high and low threshold contrast to enhancement image edge and non-edge points, and the edge as the foreground image, non-edge as a background image using image segmentation to achieve edge detection, and remove the false edges; Finally, the edge features are described based on the result of edge features detection, and determine the ship target. The experimental results show that the proposed method can effectively reduce the number of false edges in edge detection, and has the high accuracy of remote sensing ship edge detection.

A method for detecting small targets based on cumulative weighted value of target properties

NASA Astrophysics Data System (ADS)

Jin, Xing; Sun, Gang; Wang, Wei-hua; Liu, Fang; Chen, Zeng-ping

2015-03-01

Laser detection based on the "cat's eye effect" has become the hot research project for its initiative compared to the passivity of sound detection and infrared detection. And the target detection is one of the core technologies in this system. The paper puts forward a method for detecting small targets based on cumulative weighted value of target properties using given data. Firstly, we make a frame difference to the images, then make image processing based on Morphology Principles. Secondly, we segment images, and screen the targets; then find some interesting locations. Finally, comparing to a quantity of frames, we locate the target. We did an exam to 394 true frames, the experimental result shows that the mathod can detect small targets efficiently.

Real-time obstructive sleep apnea detection from frequency analysis of EDR and HRV using Lomb Periodogram.

PubMed

Fan, Shu-Han; Chou, Chia-Ching; Chen, Wei-Chen; Fang, Wai-Chi

2015-01-01

In this study, an effective real-time obstructive sleep apnea (OSA) detection method from frequency analysis of ECG-derived respiratory (EDR) and heart rate variability (HRV) is proposed. Compared to traditional Polysomnography (PSG) which needs several physiological signals measured from patients, the proposed OSA detection method just only use ECG signals to determine the time interval of OSA. In order to be feasible to be implemented in hardware to achieve the real-time detection and portable application, the simplified Lomb Periodogram is utilized to perform the frequency analysis of EDR and HRV in this study. The experimental results of this work indicate that the overall accuracy can be effectively increased with values of Specificity (Sp) of 91%, Sensitivity (Se) of 95.7%, and Accuracy of 93.2% by integrating the EDR and HRV indexes.

Improved imaging algorithm for bridge crack detection

NASA Astrophysics Data System (ADS)

Lu, Jingxiao; Song, Pingli; Han, Kaihong

2012-04-01

This paper present an improved imaging algorithm for bridge crack detection, through optimizing the eight-direction Sobel edge detection operator, making the positioning of edge points more accurate than without the optimization, and effectively reducing the false edges information, so as to facilitate follow-up treatment. In calculating the crack geometry characteristics, we use the method of extracting skeleton on single crack length. In order to calculate crack area, we construct the template of area by making logical bitwise AND operation of the crack image. After experiment, the results show errors of the crack detection method and actual manual measurement are within an acceptable range, meet the needs of engineering applications. This algorithm is high-speed and effective for automated crack measurement, it can provide more valid data for proper planning and appropriate performance of the maintenance and rehabilitation processes of bridge.

Diffuse prior monotonic likelihood ratio test for evaluation of fused image quality measures.

PubMed

Wei, Chuanming; Kaplan, Lance M; Burks, Stephen D; Blum, Rick S

2011-02-01

This paper introduces a novel method to score how well proposed fused image quality measures (FIQMs) indicate the effectiveness of humans to detect targets in fused imagery. The human detection performance is measured via human perception experiments. A good FIQM should relate to perception results in a monotonic fashion. The method computes a new diffuse prior monotonic likelihood ratio (DPMLR) to facilitate the comparison of the H(1) hypothesis that the intrinsic human detection performance is related to the FIQM via a monotonic function against the null hypothesis that the detection and image quality relationship is random. The paper discusses many interesting properties of the DPMLR and demonstrates the effectiveness of the DPMLR test via Monte Carlo simulations. Finally, the DPMLR is used to score FIQMs with test cases considering over 35 scenes and various image fusion algorithms.

Caries Detection around Restorations Using ICDAS and Optical Devices.

PubMed

Diniz, Michele Baffi; Eckert, George Joseph; González-Cabezas, Carlos; Cordeiro, Rita de Cássia Loiola; Ferreira-Zandona, Andrea Gonçalves

2016-01-01

Secondary caries is the major reason for replacement of restorations in operative dentistry. New detection methods and technology have the potential to improve the accuracy for diagnosis of secondary carious lesions. This in vitro study evaluated the performance of the ICDAS (International Caries Detection and Assessment System) visual criteria and optical devices for detecting secondary caries around amalgam and composite resin restorations in permanent teeth. A total of 180 extracted teeth with Class I amalgam (N = 90) and resin composite (N = 90) restorations were selected. Two examiners analyzed the teeth twice using the visual criteria (ICDAS), laser fluorescence (LF), light-emitting diode device (MID), quantitative light-induced fluorescence system (QLF), and a prototype system based on the Fluorescence Enamel Imaging technique (Professional Caries Detection System, PCDS). The gold standard was determined by means of confocal laser scanning microscopy. High-reproducibility values were shown for all methods, except for MID in the amalgam group. For both groups the QLF and PCDS were the most sensitive methods, whereas the other methods presented better specificity (p < 0.05). All methods, except the MID device appeared to be potential methods for detecting secondary caries only around resin composite restorations, whereas around amalgam restorations all methods seemed to be questionable. Using Internal Caries Detection and Assessment System (ICDAS), an LF device, quantitative light-induced fluorescence and a novel method based on Fluorescence Enamel Imaging technique may be effective for evaluating secondary caries around composite resin restorations. © 2016 Wiley Periodicals, Inc.

A pdf-Free Change Detection Test Based on Density Difference Estimation.

PubMed

Bu, Li; Alippi, Cesare; Zhao, Dongbin

2018-02-01

The ability to detect online changes in stationarity or time variance in a data stream is a hot research topic with striking implications. In this paper, we propose a novel probability density function-free change detection test, which is based on the least squares density-difference estimation method and operates online on multidimensional inputs. The test does not require any assumption about the underlying data distribution, and is able to operate immediately after having been configured by adopting a reservoir sampling mechanism. Thresholds requested to detect a change are automatically derived once a false positive rate is set by the application designer. Comprehensive experiments validate the effectiveness in detection of the proposed method both in terms of detection promptness and accuracy.

Probability of Detection (POD) as a statistical model for the validation of qualitative methods.

PubMed

Wehling, Paul; LaBudde, Robert A; Brunelle, Sharon L; Nelson, Maria T

2011-01-01

A statistical model is presented for use in validation of qualitative methods. This model, termed Probability of Detection (POD), harmonizes the statistical concepts and parameters between quantitative and qualitative method validation. POD characterizes method response with respect to concentration as a continuous variable. The POD model provides a tool for graphical representation of response curves for qualitative methods. In addition, the model allows comparisons between candidate and reference methods, and provides calculations of repeatability, reproducibility, and laboratory effects from collaborative study data. Single laboratory study and collaborative study examples are given.

Detection of circulating tumor cells using oHSV1-hTERT-GFP in lung cancer.

PubMed

Gao, Hongjun; Liu, Wenjing; Yang, Shaoxing; Zhang, Wen; Li, Xiaoyan; Qin, Haifeng; Wang, Weixia; Zhao, Changyun

2018-01-01

This study was conducted to evaluate the clinical utility of the oHSV1-hTERT-GFP circulating tumor cell (CTC) detection method in the peripheral blood of patients with lung cancer by comparing its sensitivity to the CellSearch CTC detection method. The oHSV1-hTERT-GFP and CellSearch CTC detection methods were compared using peripheral blood samples of patients pathologically diagnosed with lung cancer. A total of 240 patients with lung cancer were recruited, including 89 patients who were newly diagnosed and 151 patients who had previously received treatment. Sixty-six newly diagnosed patients were evaluated using both methods. The CTC detection rates were 71.2% and 33.3% using the oHSV1-hTERT-GFP and CellSearch methods, respectively; this difference was statistically significant (P = 0.000). Among the entire cohort (n = 240), the CTC detection rate using the oHSV1-hTERT-GFP method was 76.3%, with a CTC count of 0-81. The CTC detection rates were 76.7%, 68.9%, and 76.3% in patients with squamous cell carcinoma, adenocarcinoma, and small cell lung cancer, respectively. There was no statistically significant difference in the CTC detection rates between these different pathological subtypes (P = 0.738). The CTC detection rates of 79.8% and 74.4% in patients with stage I-III and IV lung cancer, respectively, were not significantly different (P = 0.427). The oHSV1-hTERT-GFP method is highly effective for detecting CTCs in patients with lung cancer, independent of pathological type and disease stage, and is ideal for large-scale clinical applications. © 2017 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.

A Hyperspherical Adaptive Sparse-Grid Method for High-Dimensional Discontinuity Detection

DOE PAGES

Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max D.; ...

2015-06-24

This study proposes and analyzes a hyperspherical adaptive hierarchical sparse-grid method for detecting jump discontinuities of functions in high-dimensional spaces. The method is motivated by the theoretical and computational inefficiencies of well-known adaptive sparse-grid methods for discontinuity detection. Our novel approach constructs a function representation of the discontinuity hypersurface of an N-dimensional discontinuous quantity of interest, by virtue of a hyperspherical transformation. Then, a sparse-grid approximation of the transformed function is built in the hyperspherical coordinate system, whose value at each point is estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of the hypersurface, the newmore » technique can identify jump discontinuities with significantly reduced computational cost, compared to existing methods. In addition, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. Rigorous complexity analyses of the new method are provided as are several numerical examples that illustrate the effectiveness of the approach.« less

A hyper-spherical adaptive sparse-grid method for high-dimensional discontinuity detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max D.

This work proposes and analyzes a hyper-spherical adaptive hierarchical sparse-grid method for detecting jump discontinuities of functions in high-dimensional spaces is proposed. The method is motivated by the theoretical and computational inefficiencies of well-known adaptive sparse-grid methods for discontinuity detection. Our novel approach constructs a function representation of the discontinuity hyper-surface of an N-dimensional dis- continuous quantity of interest, by virtue of a hyper-spherical transformation. Then, a sparse-grid approximation of the transformed function is built in the hyper-spherical coordinate system, whose value at each point is estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of themore » hyper-surface, the new technique can identify jump discontinuities with significantly reduced computational cost, compared to existing methods. Moreover, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. Rigorous error estimates and complexity analyses of the new method are provided as are several numerical examples that illustrate the effectiveness of the approach.« less

Damage detection and locating using tone burst and continuous excitation modulation method

NASA Astrophysics Data System (ADS)

Li, Zheng; Wang, Zhi; Xiao, Li; Qu, Wenzhong

2014-03-01

Among structural health monitoring techniques, nonlinear ultrasonic spectroscopy methods are found to be effective diagnostic approach to detecting nonlinear damage such as fatigue crack, due to their sensitivity to incipient structural changes. In this paper, a nonlinear ultrasonic modulation method was developed to detect and locate a fatigue crack on an aluminum plate. The method is different with nonlinear wave modulation method which recognizes the modulation of low-frequency vibration and high-frequency ultrasonic wave; it recognizes the modulation of tone burst and high-frequency ultrasonic wave. In the experiment, a Hanning window modulated sinusoidal tone burst and a continuous sinusoidal excitation were simultaneously imposed on the PZT array which was bonded on the surface of an aluminum plate. The modulations of tone burst and continuous sinusoidal excitation was observed in different actuator-sensor paths, indicating the presence and location of fatigue crack. The results of experiments show that the proposed method is capable of detecting and locating the fatigue crack successfully.

Near Infrared Imaging as a Diagnostic Tool for Detecting Enamel Demineralization: An in vivo Study

NASA Astrophysics Data System (ADS)

Lucas, Seth Adam

Background and Objectives: For decades there has been an effort to develop alternative optical methods of imaging dental decay utilizing non-ionizing radiation methods. The purpose of this in-vivo study was to demonstrate whether NIR can be used as a diagnostic tool to evaluate dental caries and to compare the sensitivity and specificity of this method with that of conventional methods, including bitewing x-rays and visual inspection. Materials and Methods: 31 test subjects (n=31) from the UCSF orthodontic clinic undergoing orthodontic treatment with planned premolar extractions were recruited. Calibrated examiners performed caries detection examinations using conventional methods: bitewing radiographs and visual inspection. These findings were compared with the results from NIR examinations: transillumination and reflectance. To confirm the results found in the two different detection methods, a gold standard was used. After teeth were extracted, polarized light microscopy and transverse microradiography were performed. Results: A total of 87 premolars were used in the study. NIR identified the occlusal lesions with a sensitivity of 71% and a specificity of 77%, whereas, the visual examination had a sensitivity of only 40% and a specifity of 39%. For interproximal lesions halfway to DEJ, specificity remained constant, but sensitivity improved to 100% for NIR and 75% for x-rays. Conclusions: The results of this preliminary study demonstrate that NIR is just as effective at detecting enamel interproximal lesions as standard dental x-rays. NIR was more effective at detecting occlusal lesions than visual examination alone. NIR shows promise as an alternative diagnostic tool to the conventional methods of x-rays and visual examination and provides a non-ionizing radiation technique.

Evaluation of camouflage effectiveness using hyperspectral images

NASA Astrophysics Data System (ADS)

Zavvartorbati, Ahmad; Dehghani, Hamid; Rashidi, Ali Jabar

2017-10-01

Recent advances in camouflage engineering have made it more difficult to detect targets. Assessing the effectiveness of camouflage against different target detection methods leads to identifying the strengths and weaknesses of camouflage designs. One of the target detection methods is to analyze the content of the scene using remote sensing hyperspectral images. In the process of evaluating camouflage designs, there must be comprehensive and efficient evaluation criteria. Three parameters were considered as the main factors affecting the target detection and based on these factors, camouflage effectiveness assessment criteria were proposed. To combine the criteria in the form of a single equation, the equation used in target visual search models was employed and for determining the criteria, a model was presented based on the structure of the computational visual attention systems. Also, in software implementations on the HyMap hyperspectral image, a variety of camouflage levels were created for the real targets in the image. Assessing the camouflage levels using the proposed criteria, comparing and analyzing the results can show that the provided criteria and model are effective for the evaluation of camouflage designs using hyperspectral images.

The analysis of tetracyclines, quinolones, macrolides, lincosamides, pleuromutilins, and sulfonamides in chicken feathers using UHPLC-MS/MS in order to monitor antibiotic use in the poultry sector.

PubMed

Jansen, Larissa J M; Bolck, Yvette J C; Rademaker, Janneau; Zuidema, Tina; Berendsen, Bjorn J A

2017-08-01

In The Netherlands, all antibiotic treatments should be registered at the farm and in a central database. To enforce correct antibiotic use and registration, and to enforce prudent use of antibiotics, there is a need for methods that are able to detect antibiotic treatments. Ideally, such a method is able to detect antibiotic applications during the entire lifespan of an animal, including treatments administered during the first days of the animals' lives. Monitoring tissue, as is common practice, only provides a limited window of opportunity, as residue levels in tissue soon drop below measurable quantities. The analysis of feathers proves to be a promising tool in this respect. Furthermore, a qualitative confirmatory method was developed for the analyses of six major groups of antibiotics in ground chicken feathers, aiming for a detection limit as low as reasonably possible. The method was validated according to Commission Decision 2002/657/EC. All compounds comply with the criteria and, as a matter of fact, 58% of the compounds could also be quantified according to regulations. Additionally, we demonstrated that a less laborious method, in which whole feathers were analyzed, proved successful in the detection of applied antibiotics. Most compounds could be detected at levels of 2 μg kg -1 or below with the exception of sulfachloropyridazine, tylosin, and tylvalosin. This demonstrates the effectiveness of feather analysis to detect antibiotic use to allow effective enforcement of antibiotic use and prevent the illegal, off-label, and nonregistered use of antibiotics.

LEAKAGE CHARACTERISTICS OF BASE OF RIVERBANK BY SELF POTENTIAL METHOD AND EXAMINATION OF EFFECTIVENESS OF SELF POTENTIAL METHOD TO HEALTH MONITORING OF BASE OF RIVERBANK

NASA Astrophysics Data System (ADS)

Matsumoto, Kensaku; Okada, Takashi; Takeuchi, Atsuo; Yazawa, Masato; Uchibori, Sumio; Shimizu, Yoshihiko

Field Measurement of Self Potential Method using Copper Sulfate Electrode was performed in base of riverbank in WATARASE River, where has leakage problem to examine leakage characteristics. Measurement results showed typical S-shape what indicates existence of flow groundwater. The results agreed with measurement results by Ministry of Land, Infrastructure and Transport with good accuracy. Results of 1m depth ground temperature detection and Chain-Array detection showed good agreement with results of the Self Potential Method. Correlation between Self Potential value and groundwater velocity was examined model experiment. The result showed apparent correlation. These results indicate that the Self Potential Method was effective method to examine the characteristics of ground water of base of riverbank in leakage problem.

Development of Multiplex Reverse Transcription-Polymerase Chain Reaction for Simultaneous Detection of Influenza A, B and Adenoviruses

PubMed Central

Nakhaie, Mohsen; Soleimanjahi, Hoorieh; Mollaie, Hamid Reza; Arabzadeh, Seyed Mohamad Ali

2018-01-01

Background and objective: Millions of people in developing countries lose their lives due to acute respiratory infections, such as Influenza A & B and Adeno viruses. Given the importance of rapid identification of the virus, in this study the researchers attempted to design a method that enables detection of influenza A, B, and adenoviruses, quickly and simultaneously. The Multiplex RT PCR method was the preferred method for the detection of influenza A, B, and adenoviruses in clinical specimens because it is rapid, sensitive, specific, and more cost-effective than alternative methods Methods: After collecting samples from patients with respiratory disease, virus genome was extracted, then Monoplex PCR was used on positive samples and Multiplex RT-PCR on clinical specimens. Finally, by comparing the bands of these samples, the type of virus in the clinical samples was determined. Results: Performing Multiplex RT-PCR on 50 samples of respiratory tract led to following results; flu A: 12.5%, fluB: 50%, adeno: 27.5%, negative: 7.5%, and 2.5% contamination. Conclusion: Reverse transcription-multiplex Polymerase Chain Reaction (PCR) technique, a rapid diagnostic tool, has potential for high-throughput testing. This method has a significant advantage, which provides simultaneous amplification of numerous viruses in a single reaction. This study concentrates on multiplex molecular technologies and their clinical application for the detection and quantification of respiratory pathogens. The improvement in diagnostic testing for viral respiratory pathogens effects patient management, and leads to more cost-effective delivery of care. It limits unnecessary antibiotic use and improves clinical management by use of suitable treatment. PMID:29731796

Nanobarcoding for improved nanoparticle detection in nanomedical biodistribution studies

NASA Astrophysics Data System (ADS)

Eustaquio, Trisha

Determination of the fate of nanoparticles (NPs) in a biological system, or NP biodistribution, is critical in evaluating a NP formulation for nanomedicine. Unlike small-molecule drugs, NPs impose unique challenges in the design of appropriate biodistribution studies due to their small size and subsequent detection signal. Current methods to determine NP biodistribution are greatly inadequate due to their limited detection thresholds. There is an overwhelming need for a sensitive and efficient imaging-based method that can (1) detect and measure small numbers of NPs of various types, ideally single NPs, (2) associate preferential NP uptake with histological cell type by preserving spatial information in samples, and (3) allow for relatively quick and accurate NP detection in in vitro (and possibly ex vivo) samples for comprehensive NP biodistribution studies. Herein, a novel method for improved NP detection is proposed, coined "nanobarcoding." Nanobarcoding utilizes a non-endogenous oligonucleotide, or "nanobarcode" (NB), conjugated to the NP surface to amplify the detection signal from a single NP via in situ polymerase chain reaction (ISPCR), and this signal amplification will facilitate rapid and precise detection of single NPs inside cells over large areas of sample such that more sophisticated studies can be performed on the NP-positive subpopulation. Moreover, nanobarcoding has the potential to be applied to the detection of more than one NP type to study the effects of physicochemical properties, targeting mechanisms, and route of entry on NP biodistribution. The nanobarcoding method was validated in vitro using NB-functionalized superparamagnetic iron oxide NPs (NB-SPIONs) as the model NP type for improved NP detection inside HeLa human cervical cancer cells, a cell line commonly used for ISPCR-mediated detection of human papilloma virus (HPV). Nanotoxicity effects of NB-SPIONs were also evaluated at the single-cell level using LEAP (Laser-Enabled Analysis and Processing, Intrexon, San Diego, CA), and NB-SPIONs were found to be less toxic than its precursor, carboxylated SPIONs (COOH-SPIONs).

Application of seismic interferometric migration for shallow seismic high precision data processing: A case study in the Shenhu area

NASA Astrophysics Data System (ADS)

Wei, Jia; Liu, Huaishan; Xing, Lei; Du, Dong

2018-02-01

The stability of submarine geological structures has a crucial influence on the construction of offshore engineering projects and the exploitation of seabed resources. Marine geologists should possess a detailed understanding of common submarine geological hazards. Current marine seismic exploration methods are based on the most effective detection technologies. Therefore, current research focuses on improving the resolution and precision of shallow stratum structure detection methods. In this article, the feasibility of shallow seismic structure imaging is assessed by building a complex model, and differences between the seismic interferometry imaging method and the traditional imaging method are discussed. The imaging effect of the model is better for shallow layers than for deep layers because coherent noise produced by this method can result in an unsatisfactory imaging effect for deep layers. The seismic interference method has certain advantages for geological structural imaging of shallow submarine strata, which indicates continuous horizontal events, a high resolution, a clear fault, and an obvious structure boundary. The effects of the actual data applied to the Shenhu area can fully illustrate the advantages of the method. Thus, this method has the potential to provide new insights for shallow submarine strata imaging in the area.

Elaborately designed diblock nanoprobes for simultaneous multicolor detection of microRNAs

NASA Astrophysics Data System (ADS)

Wang, Chenguang; Zhang, Huan; Zeng, Dongdong; Sun, Wenliang; Zhang, Honglu; Aldalbahi, Ali; Wang, Yunsheng; San, Lili; Fan, Chunhai; Zuo, Xiaolei; Mi, Xianqiang

2015-09-01

Simultaneous detection of multiple biomarkers has important prospects in the biomedical field. In this work, we demonstrated a novel strategy for the detection of multiple microRNAs (miRNAs) based on gold nanoparticles (Au NPs) and polyadenine (polyA) mediated nanoscale molecular beacon (MB) probes (denoted p-nanoMBs). Novel fluorescent labeled p-nanoMBs bearing consecutive adenines were designed, of which polyA served as an effective anchoring block binding to the surface of Au NPs, and the appended hairpin block formed an upright conformation that favored the hybridization with targets. Using the co-assembling method and the improved hybridization conformation of the hairpin probes, we achieved high selectivity for specifically distinguishing DNA targets from single-base mismatched DNA targets. We also realized multicolor detection of three different synthetic miRNAs in a wide dynamic range from 0.01 nM to 200 nM with a detection limit of 10 pM. What's more, we even detected miRNAs in a simulated serum environment, which indicated that our method could be used in complex media. Compared with the traditional method, our strategy provides a promising alternative method for the qualitative and quantitative detection of miRNAs.Simultaneous detection of multiple biomarkers has important prospects in the biomedical field. In this work, we demonstrated a novel strategy for the detection of multiple microRNAs (miRNAs) based on gold nanoparticles (Au NPs) and polyadenine (polyA) mediated nanoscale molecular beacon (MB) probes (denoted p-nanoMBs). Novel fluorescent labeled p-nanoMBs bearing consecutive adenines were designed, of which polyA served as an effective anchoring block binding to the surface of Au NPs, and the appended hairpin block formed an upright conformation that favored the hybridization with targets. Using the co-assembling method and the improved hybridization conformation of the hairpin probes, we achieved high selectivity for specifically distinguishing DNA targets from single-base mismatched DNA targets. We also realized multicolor detection of three different synthetic miRNAs in a wide dynamic range from 0.01 nM to 200 nM with a detection limit of 10 pM. What's more, we even detected miRNAs in a simulated serum environment, which indicated that our method could be used in complex media. Compared with the traditional method, our strategy provides a promising alternative method for the qualitative and quantitative detection of miRNAs. Electronic supplementary information (ESI) available: Sequences for oligonucleotides used for this work, dynamic light scattering (DLS) measurements, fluorescent signal intensity with different ratios between p-MBs and A5 oligonucleotides, quantification of the fluorescent p-MB, and UV-Vis spectra for naked AuNPs and the p-nanoMB. See DOI: 10.1039/c5nr04618a

Gap Detection and Temporal Modulation Transfer Function as Behavioral Estimates of Auditory Temporal Acuity Using Band-Limited Stimuli in Young and Older Adults

PubMed Central

Shen, Yi

2015-01-01

Purpose Gap detection and the temporal modulation transfer function (TMTF) are 2 common methods to obtain behavioral estimates of auditory temporal acuity. However, the agreement between the 2 measures is not clear. This study compares results from these 2 methods and their dependencies on listener age and hearing status. Method Gap detection thresholds and the parameters that describe the TMTF (sensitivity and cutoff frequency) were estimated for young and older listeners who were naive to the experimental tasks. Stimuli were 800-Hz-wide noises with upper frequency limits of 2400 Hz, presented at 85 dB SPL. A 2-track procedure (Shen & Richards, 2013) was used for the efficient estimation of the TMTF. Results No significant correlation was found between gap detection threshold and the sensitivity or the cutoff frequency of the TMTF. No significant effect of age and hearing loss on either the gap detection threshold or the TMTF cutoff frequency was found, while the TMTF sensitivity improved with increasing hearing threshold and worsened with increasing age. Conclusion Estimates of temporal acuity using gap detection and TMTF paradigms do not seem to provide a consistent description of the effects of listener age and hearing status on temporal envelope processing. PMID:25087722

[Detecting fire smoke based on the multispectral image].

PubMed

Wei, Ying-Zhuo; Zhang, Shao-Wu; Liu, Yan-Wei

2010-04-01

Smoke detection is very important for preventing forest-fire in the fire early process. Because the traditional technologies based on video and image processing are easily affected by the background dynamic information, three limitations exist in these technologies, i. e. lower anti-interference ability, higher false detection rate and the fire smoke and water fog being not easily distinguished. A novel detection method for detecting smoke based on the multispectral image was proposed in the present paper. Using the multispectral digital imaging technique, the multispectral image series of fire smoke and water fog were obtained in the band scope of 400 to 720 nm, and the images were divided into bins. The Euclidian distance among the bins was taken as a measurement for showing the difference of spectrogram. After obtaining the spectral feature vectors of dynamic region, the regions of fire smoke and water fog were extracted according to the spectrogram feature difference between target and background. The indoor and outdoor experiments show that the smoke detection method based on multispectral image can be applied to the smoke detection, which can effectively distinguish the fire smoke and water fog. Combined with video image processing method, the multispectral image detection method can also be applied to the forest fire surveillance, reducing the false alarm rate in forest fire detection.

Hypersensitive Detection and Quantitation of BoNT/A by IgY Antibody against Substrate Linear-Peptide

PubMed Central

Li, Tao; Liu, Hao; Cai, Kun; Tian, Maoren; Wang, Qin; Shi, Jing; Gao, Xiang; Wang, Hui

2013-01-01

Botulinum neurotoxin A (BoNT/A), the most acutely poisonous substance to humans known, cleave its SNAP-25 substrate with high specificity. Based on the endopeptidase activity, different methods have been developed to detect BoNT/A, but most lack ideal reproducibility or sensitivity, or suffer from long-term or unwanted interferences. In this study, we developed a simple method to detect and quantitate trace amounts of botulinum neurotoxin A using the IgY antibody against a linear-peptide substrate. The effects of reaction buffer, time, and temperature were analyzed and optimized. When the optimized assay was used to detect BoNT/A, the limit of detection of the assay was 0.01 mouse LD50 (0.04 pg), and the limit of quantitation was 0.12 mouse LD50/ml (0.48 pg). The findings also showed favorable specificity of detecting BoNT/A. When used to detect BoNT/A in milk or human serum, the proposed assay exhibited good quantitative accuracy (88% < recovery < 111%; inter- and intra-assay CVs < 18%). This method of detection took less than 3 h to complete, indicating that it can be a valuable method of detecting BoNT/A in food or clinical diagnosis. PMID:23555605

Hypersensitive detection and quantitation of BoNT/A by IgY antibody against substrate linear-peptide.

PubMed

Li, Tao; Liu, Hao; Cai, Kun; Tian, Maoren; Wang, Qin; Shi, Jing; Gao, Xiang; Wang, Hui

2013-01-01

Botulinum neurotoxin A (BoNT/A), the most acutely poisonous substance to humans known, cleave its SNAP-25 substrate with high specificity. Based on the endopeptidase activity, different methods have been developed to detect BoNT/A, but most lack ideal reproducibility or sensitivity, or suffer from long-term or unwanted interferences. In this study, we developed a simple method to detect and quantitate trace amounts of botulinum neurotoxin A using the IgY antibody against a linear-peptide substrate. The effects of reaction buffer, time, and temperature were analyzed and optimized. When the optimized assay was used to detect BoNT/A, the limit of detection of the assay was 0.01 mouse LD50 (0.04 pg), and the limit of quantitation was 0.12 mouse LD50/ml (0.48 pg). The findings also showed favorable specificity of detecting BoNT/A. When used to detect BoNT/A in milk or human serum, the proposed assay exhibited good quantitative accuracy (88% < recovery < 111%; inter- and intra-assay CVs < 18%). This method of detection took less than 3 h to complete, indicating that it can be a valuable method of detecting BoNT/A in food or clinical diagnosis.

Highly selective colorimetric bacteria sensing based on protein-capped nanoparticles.

PubMed

Qiu, Suyan; Lin, Zhenyu; Zhou, Yaomin; Wang, Donggen; Yuan, Lijuan; Wei, Yihua; Dai, Tingcan; Luo, Linguang; Chen, Guonan

2015-02-21

A rapid and cost-effective colorimetric sensor has been developed for the detection of bacteria (Bacillus subtilis was selected as an example). The sensor was designed to rely on lysozyme-capped AuNPs with the advantages of effective amplification and high specificity. In the sensing system, lysozyme was able to bind strongly to Bacillus subtilis, which effectively induced a color change of the solution from light purple to purplish red. The lowest concentration of Bacillus subtilis detectable by the naked eye was 4.5 × 10(3) colony-forming units (CFU) mL(-1). Similar results were discernable from UV-Vis absorption measurements. A good specificity was observed through a statistical analysis method using the SPSS software (version 17.0). This simple colorimetric sensor may therefore be a rapid and specific method for a bacterial detection assay in complex samples.

Identifying reprioritization response shift in a stroke caregiver population: a comparison of missing data methods.

PubMed

Sajobi, Tolulope T; Lix, Lisa M; Singh, Gurbakhshash; Lowerison, Mark; Engbers, Jordan; Mayo, Nancy E

2015-03-01

Response shift (RS) is an important phenomenon that influences the assessment of longitudinal changes in health-related quality of life (HRQOL) studies. Given that RS effects are often small, missing data due to attrition or item non-response can contribute to failure to detect RS effects. Since missing data are often encountered in longitudinal HRQOL data, effective strategies to deal with missing data are important to consider. This study aims to compare different imputation methods on the detection of reprioritization RS in the HRQOL of caregivers of stroke survivors. Data were from a Canadian multi-center longitudinal study of caregivers of stroke survivors over a one-year period. The Stroke Impact Scale physical function score at baseline, with a cutoff of 75, was used to measure patient stroke severity for the reprioritization RS analysis. Mean imputation, likelihood-based expectation-maximization imputation, and multiple imputation methods were compared in test procedures based on changes in relative importance weights to detect RS in SF-36 domains over a 6-month period. Monte Carlo simulation methods were used to compare the statistical powers of relative importance test procedures for detecting RS in incomplete longitudinal data under different missing data mechanisms and imputation methods. Of the 409 caregivers, 15.9 and 31.3 % of them had missing data at baseline and 6 months, respectively. There were no statistically significant changes in relative importance weights on any of the domains when complete-case analysis was adopted. But statistical significant changes were detected on physical functioning and/or vitality domains when mean imputation or EM imputation was adopted. There were also statistically significant changes in relative importance weights for physical functioning, mental health, and vitality domains when multiple imputation method was adopted. Our simulations revealed that relative importance test procedures were least powerful under complete-case analysis method and most powerful when a mean imputation or multiple imputation method was adopted for missing data, regardless of the missing data mechanism and proportion of missing data. Test procedures based on relative importance measures are sensitive to the type and amount of missing data and imputation method. Relative importance test procedures based on mean imputation and multiple imputation are recommended for detecting RS in incomplete data.

Mass Spectrometry for Paper-Based Immunoassays: Toward On-Demand Diagnosis.

PubMed

Chen, Suming; Wan, Qiongqiong; Badu-Tawiah, Abraham K

2016-05-25

Current analytical methods, either point-of-care or centralized detection, are not able to meet recent demands of patient-friendly testing and increased reliability of results. Here, we describe a two-point separation on-demand diagnostic strategy based on a paper-based mass spectrometry immunoassay platform that adopts stable and cleavable ionic probes as mass reporter; these probes make possible sensitive, interruptible, storable, and restorable on-demand detection. In addition, a new touch paper spray method was developed for on-chip, sensitive, and cost-effective analyte detection. This concept is successfully demonstrated via (i) the detection of Plasmodium falciparum histidine-rich protein 2 antigen and (ii) multiplexed and simultaneous detection of cancer antigen 125 and carcinoembryonic antigen.

Comparison of Real-Time PCR, Reverse Transcriptase Real-Time PCR, Loop-Mediated Isothermal Amplification, and the FDA Conventional Microbiological Method for the Detection of Salmonella spp. in Produce ▿ †

PubMed Central

Zhang, Guodong; Brown, Eric W.; González-Escalona, Narjol

2011-01-01

Contamination of foods, especially produce, with Salmonella spp. is a major concern for public health. Several methods are available for the detection of Salmonella in produce, but their relative efficiency for detecting Salmonella in commonly consumed vegetables, often associated with outbreaks of food poisoning, needs to be confirmed. In this study, the effectiveness of three molecular methods for detection of Salmonella in six produce matrices was evaluated and compared to the FDA microbiological detection method. Samples of cilantro (coriander leaves), lettuce, parsley, spinach, tomato, and jalapeno pepper were inoculated with Salmonella serovars at two different levels (105 and <101 CFU/25 g of produce). The inoculated produce was assayed by the FDA Salmonella culture method (Bacteriological Analytical Manual) and by three molecular methods: quantitative real-time PCR (qPCR), quantitative reverse transcriptase real-time PCR (RT-qPCR), and loop-mediated isothermal amplification (LAMP). Comparable results were obtained by these four methods, which all detected as little as 2 CFU of Salmonella cells/25 g of produce. All control samples (not inoculated) were negative by the four methods. RT-qPCR detects only live Salmonella cells, obviating the danger of false-positive results from nonviable cells. False negatives (inhibition of either qPCR or RT-qPCR) were avoided by the use of either a DNA or an RNA amplification internal control (IAC). Compared to the conventional culture method, the qPCR, RT-qPCR, and LAMP assays allowed faster and equally accurate detection of Salmonella spp. in six high-risk produce commodities. PMID:21803916

A new effective assay to detect antimicrobial activity of filamentous fungi.

PubMed

Pereira, Eric; Santos, Ana; Reis, Francisca; Tavares, Rui M; Baptista, Paula; Lino-Neto, Teresa; Almeida-Aguiar, Cristina

2013-01-15

The search for new antimicrobial compounds and the optimization of production methods turn the use of antimicrobial susceptibility tests a routine. The most frequently used methods are based on agar diffusion assays or on dilution in agar or broth. For filamentous fungi, the most common antimicrobial activity detection methods comprise the co-culture of two filamentous fungal strains or the use of fungal extracts to test against single-cell microorganisms. Here we report a rapid, effective and reproducible assay to detect fungal antimicrobial activity against single-cell microorganisms. This method allows an easy way of performing a fast antimicrobial screening of actively growing fungi directly against yeast. Because it makes use of an actively growing mycelium, this bioassay also provides a way for studying the production dynamics of antimicrobial compounds by filamentous fungi. The proposed assay is less time consuming and introduces the innovation of allowing the direct detection of fungal antimicrobial properties against single cell microorganisms without the prior isolation of the active substance(s). This is particularly useful when performing large screenings for fungal antimicrobial activity. With this bioassay, antimicrobial activity of Hypholoma fasciculare against yeast species was observed for the first time. Copyright © 2012 Elsevier GmbH. All rights reserved.

A comparative analysis of the effectiveness of cytogenetic and molecular genetic methods in the detection of Down syndrome.

PubMed

Mačkić-Đurović, Mirela; Projić, Petar; Ibrulj, Slavka; Cakar, Jasmina; Marjanović, Damir

2014-05-01

The goal of this study was to examine the effectiveness of 6 STR markers application (D21S1435, D21S11, D21S1270, D21S1411, D21S226 and IFNAR) in molecular genetic diagnostics of Down syndrome (DS) and to compare it with cytogenetic method. Testing was performed on 73 children, with the previously cytogenetically confirmed Down syndrome. DNA isolated from the buccal swab was used. Previously mentioned loci located on chromosome 21 were simultaneously amplified using quantitative fluorescence PCR (QF PCR). Using this method, 60 previously cytogenetically diagnosed DS with standard type of trisomy 21 were confirmed. Furthermore, six of eight children with mosaic type of DS were detected. Two false negative results for mosaic type of DS were obtained. Finally, five children with the translocation type of Down syndrome were also confirmed with this molecular test. In conclusion, molecular genetic analysis of STR loci is fast, cheap and simple method that could be used in detection of DS. Regarding possible false results detected for certain number of mosaic types, cytogenetic analysis should be used as a confirmatory test.

Vital sign sensing method based on EMD in terahertz band

NASA Astrophysics Data System (ADS)

Xu, Zhengwu; Liu, Tong

2014-12-01

Non-contact respiration and heartbeat rates detection could be applied to find survivors trapped in the disaster or the remote monitoring of the respiration and heartbeat of a patient. This study presents an improved algorithm that extracts the respiration and heartbeat rates of humans by utilizing the terahertz radar, which further lessens the effects of noise, suppresses the cross-term, and enhances the detection accuracy. A human target echo model for the terahertz radar is first presented. Combining the over-sampling method, low-pass filter, and Empirical Mode Decomposition improves the signal-to-noise ratio. The smoothed pseudo Wigner-Ville distribution time-frequency technique and the centroid of the spectrogram are used to estimate the instantaneous velocity of the target's cardiopulmonary motion. The down-sampling method is adopted to prevent serious distortion. Finally, a second time-frequency analysis is applied to the centroid curve to extract the respiration and heartbeat rates of the individual. Simulation results show that compared with the previously presented vital sign sensing method, the improved algorithm enhances the signal-to-noise ratio to 1 dB with a detection accuracy of 80%. The improved algorithm is an effective approach for the detection of respiration and heartbeat signal in a complicated environment.

Geologic Carbon Sequestration Leakage Detection: A Physics-Guided Machine Learning Approach

NASA Astrophysics Data System (ADS)

Lin, Y.; Harp, D. R.; Chen, B.; Pawar, R.

2017-12-01

One of the risks of large-scale geologic carbon sequestration is the potential migration of fluids out of the storage formations. Accurate and fast detection of this fluids migration is not only important but also challenging, due to the large subsurface uncertainty and complex governing physics. Traditional leakage detection and monitoring techniques rely on geophysical observations including pressure. However, the resulting accuracy of these methods is limited because of indirect information they provide requiring expert interpretation, therefore yielding in-accurate estimates of leakage rates and locations. In this work, we develop a novel machine-learning technique based on support vector regression to effectively and efficiently predict the leakage locations and leakage rates based on limited number of pressure observations. Compared to the conventional data-driven approaches, which can be usually seem as a "black box" procedure, we develop a physics-guided machine learning method to incorporate the governing physics into the learning procedure. To validate the performance of our proposed leakage detection method, we employ our method to both 2D and 3D synthetic subsurface models. Our novel CO2 leakage detection method has shown high detection accuracy in the example problems.

A method of multiplex PCR for detection of field released Beauveria bassiana, a fungal entomopathogen applied for pest management in jute (Corchorus olitorius).

PubMed

Biswas, Chinmay; Dey, Piyali; Gotyal, B S; Satpathy, Subrata

2015-04-01

The fungal entomopathogen Beauveria bassiana is a promising biocontrol agent for many pests. Some B. bassiana strains have been found effective against jute pests. To monitor the survival of field released B. bassiana a rapid and efficient detection technique is essential. Conventional methods such as plating method or direct culture method which are based on cultivation on selective media followed by microscopy are time consuming and not so sensitive. PCR based methods are rapid, sensitive and reliable. A single primer PCR may fail to amplify some of the strains. However, multiplex PCR increases the possibility of detection as it uses multiple primers. Therefore, in the present investigation a multiplex PCR protocol was developed by multiplexing three primers SCA 14, SCA 15 and SCB 9 to detect field released B. bassiana strains from soil as well as foliage of jute field. Using our multiplex PCR protocol all the five B. bassiana strains could be detected from soil and three strains viz., ITCC 6063, ITCC 4563 and ITCC 4796 could be detected even from the crop foliage after 45 days of spray.

Oil Spill AISA+ Hyperspectral Data Detection Based on Different Sea Surface Glint Suppression Methods

NASA Astrophysics Data System (ADS)

Yang, J.; Ren, G.; Ma, Y.; Dong, L.; Wan, J.

2018-04-01

The marine oil spill is a sudden event, and the airborne hyperspectral means to detect the oil spill is an important part of the rapid response. Sun glint, the specular reflection of sun light from water surface to sensor, is inevitable due to the limitation of observation geometry, which makes so much bright glint in image that it is difficult to extract oil spill feature information from the remote sensing data. This paper takes AISA+ airborne hyperspectral oil spill image as data source, using multi-scale wavelet transform, enhanced Lee filter, enhanced Frost filter and mean filter method for sea surface glint suppression of images. And then the classical SVM method is used for the oil spill information detection, and oil spill information distribution map obtained by human-computer interactive interpretation is used to verify the accuracy of oil spill detection. The results show that the above methods can effectively suppress the sea surface glints and improve the accuracy of oil spill detection. The enhanced Lee filter method has the highest detection accuracy of 88.28 %, which is 12.2 % higher than that of the original image.

Assisted inhibition effect of acetylcholinesterase with n-octylphosphonic acid and application in high sensitive detection of organophosphorous pesticides by matrix-assisted laser desorption/ionization Fourier transform mass spectrometry.

PubMed

Cai, Tingting; Zhang, Li; Wang, Haoyang; Zhang, Jing; Guo, Yinlong

2011-11-14

A simple and practical approach to improve the sensitivity of acetylcholinesterase (AChE)-inhibited method has been developed for monitoring organophosphorous (OP) pesticide residues. In this work, matrix-assisted laser desorption/ionization Fourier transform mass spectrometry (MALDI-FTMS) was used to detect AChE activity. Due to its good salt-tolerance and low sample consumption, MALDI-FTMS facilitates rapid and high-throughput screening of OP pesticides. Here we describe a new method to obtain low detection limits via employing external reagents. Among candidate compounds, n-octylphosphonic acid (n-Octyl-PA) displays assistant effect to enhance AChE inhibition by OP pesticides. In presence of n-Octyl-PA, the percentages of AChE inhibition still kept correlation with OP pesticide concentrations. The detection limits were improved significantly even by 10(2)-10(3) folds in comparison with conventional enzyme-inhibited methods. Different detection limits of OP pesticides with different toxicities were as low as 0.005 μg L(-1) for high toxic pesticides and 0.05 μg L(-1) for low toxic pesticides. Besides, the reliability of results from this method to analyze cowpea samples had been demonstrated by liquid-chromatography tandem mass spectrometry (LC-MS/MS). The application of this commercial available assistant agent shows great promise to detect OP compounds in complicated biological matrix and broadens the mind for high sensitivity detection of OP pesticide residues in agricultural products. Copyright © 2011 Elsevier B.V. All rights reserved.

Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode

PubMed Central

Liu, Lei; Qiu, Xiaolan; Lei, Bin

2017-01-01

This study aims to detect vessels with lengths ranging from about 70 to 300 m, in Gaofen-3 (GF-3) SAR images with ultrafine strip-map (UFS) mode as fast as possible. Based on the analysis of the characteristics of vessels in GF-3 SAR imagery, an effective vessel detection method is proposed in this paper. Firstly, the iterative constant false alarm rate (CFAR) method is employed to detect the potential ship pixels. Secondly, the mean-shift operation is applied on each potential ship pixel to identify the candidate target region. During the mean-shift process, we maintain a selection matrix recording which pixels can be taken, and these pixels are called as the valid points of the candidate target. The l1 norm regression is used to extract the principal axis and detect the valid points. Finally, two kinds of false alarms, the bright line and the azimuth ambiguity, are removed by comparing the valid area of the candidate target with a pre-defined value and computing the displacement between the true target and the corresponding replicas respectively. Experimental results on three GF-3 SAR images with UFS mode demonstrate the effectiveness and efficiency of the proposed method. PMID:28678197

Optimization of a sensitive method for the determination of nitro musk fragrances in waters by solid-phase microextraction and gas chromatography with micro electron capture detection using factorial experimental design.

PubMed

Polo, Maria; Garcia-Jares, Carmen; Llompart, Maria; Cela, Rafael

2007-08-01

A solid-phase microextraction method (SPME) followed by gas chromatography with micro electron capture detection for determining trace levels of nitro musk fragrances in residual waters was optimized. Four nitro musks, musk xylene, musk moskene, musk tibetene and musk ketone, were selected for the optimization of the method. Factors affecting the extraction process were studied using a multivariate approach. Two extraction modes (direct SPME and headspace SPME) were tried at different extraction temperatures using two fiber coatings [Carboxen-polydimethylsiloxane (CAR/PDMS) and polydimethylsiloxane-divinylbenzene (PDMS/DVB)] selected among five commercial tested fibers. Sample agitation and the salting-out effect were also factors studied. The main effects and interactions between the factors were studied for all the target compounds. An extraction temperature of 100 degrees C and sampling the headspace over the sample, using either CAR/PDMS or PDMS/DVB as fiber coatings, were found to be the experimental conditions that led to a more effective extraction. High sensitivity, with detection limits in the low nanogram per liter range, and good linearity and repeatability were achieved for all nitro musks. Since the method proposed performed well for real samples, it was applied to different water samples, including wastewater and sewage, in which some of the target compounds (musk xylene and musk ketone) were detected and quantified.

Optimized signal detection and analysis methods for in vivo photoacoustic flow cytometry

NASA Astrophysics Data System (ADS)

Wang, Qiyan; Zhou, Quanyu; Yang, Ping; Wang, Xiaoling; Niu, Zhenyu; Suo, Yuanzhen; He, Hao; Gao, Wenyuan; Tang, Shuo; Wei, Xunbin

2017-02-01

Melanoma is known as a malignant tumor of melanocytes, which usually appear in the blood circulation at the metastasis stage of cancer. Thus the detection of circulating melanoma cells is useful for early diagnosis and therapy of cancer. Here we have developed an in vivo photoacoustic flow cytometry (PAFC) based on the photoacoustic effect to detect melanoma cells. However, the raw signals we obtain from the target cells contain noises such as environmental sonic noises and electronic noises. Therefore we apply correlation comparison and feature separation methods to the detection and verification of the in vivo signals. Due to similar shape and structure of cells, the photoacoustic signals usually have similar vibration mode. By analyzing the correlations and the signal features in time domain and frequency domain, we are able to provide a method for separating photoacoustic signals generated by target cells from background noises. The method introduced here has proved to optimize the signal acquisition and signal processing, which can improve the detection accuracy in PAFC.

Portable and sensitive quantitative detection of DNA based on personal glucose meters and isothermal circular strand-displacement polymerization reaction.

PubMed

Xu, Xue-tao; Liang, Kai-yi; Zeng, Jia-ying

2015-02-15

A portable and sensitive quantitative DNA detection method based on personal glucose meters and isothermal circular strand-displacement polymerization reaction was developed. The target DNA triggered target recycling process, which opened capture DNA. The released target then found another capture DNA to trigger another polymerization cycle, which was repeated for many rounds, resulting in the multiplication of the DNA-invertase conjugation on the surface of Streptavidin-MNBs. The DNA-invertase was used to catalyze the hydrolysis of sucrose into glucose for PGM readout. There was a liner relationship between the signal of PGM and the concentration of target DNA in the range of 5.0 to 1000 fM, which is lower than some DNA detection method. In addition, the method exhibited excellent sequence selectivity and there was almost no effect of biological complex to the detection performance, which suggested our method can be successfully applied to DNA detection in real biological samples. Copyright © 2014 Elsevier B.V. All rights reserved.

Targets Mask U-Net for Wind Turbines Detection in Remote Sensing Images

NASA Astrophysics Data System (ADS)

Han, M.; Wang, H.; Wang, G.; Liu, Y.

2018-04-01

To detect wind turbines precisely and quickly in very high resolution remote sensing images (VHRRSI) we propose target mask U-Net. This convolution neural network (CNN), which is carefully designed to be a wide-field detector, models the pixel class assignment to wind turbines and their context information. The shadow, which is the context information of the target in this study, has been regarded as part of a wind turbine instance. We have trained the target mask U-Net on training dataset, which is composed of down sampled image blocks and instance mask blocks. Some post-processes have been integrated to eliminate wrong spots and produce bounding boxes of wind turbine instances. The evaluation metrics prove the reliability and effectiveness of our method for the average F1-score of our detection method is up to 0.97. The comparison of detection accuracy and time consuming with the weakly supervised targets detection method based on CNN illustrates the superiority of our method.

Laboratory evaluation of sample collection methods (organs vs swabs) for Tasmanian salmon reovirus detection in farmed Atlantic salmon, Salmo salar L.

PubMed

Zainathan, S C; Carson, J; Crane, M St J; Nowak, B F

2013-04-01

The use of swabs relative to organs as a sample collection method for the detection of Tasmanian salmon reovirus (TSRV) in farmed Tasmanian Atlantic salmon, Salmo salar L., was evaluated by RT-qPCR. Evaluation of individual and pooled sample collection (organs vs swabs) was carried out to determine the sensitivity of the collection methods and the effect of pooling of samples for the detection of TSRV. Detection of TSRV in individual samples was as sensitive when organs were sampled compared to swabs, and in pooled samples, organs demonstrated a sensitivity of one 10-fold dilution higher than sampling of pooled swabs. Storage of swabs at 4 °C for t = 24 h demonstrated results similar to those at t = 0. Advantages of using swabs as a preferred sample collection method for the detection of TSRV compared to organ samples are evident from these experimental trials. © 2012 Blackwell Publishing Ltd.

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations

DOE PAGES

Azim, Riyasat; Li, Fangxing; Xue, Yaosuo; ...

2017-07-14

Distributed generations (DGs) for grid-connected applications require an accurate and reliable islanding detection methodology (IDM) for secure system operation. This paper presents an IDM for grid-connected inverter-based DGs. The proposed method is a combination of passive and active islanding detection techniques for aggregation of their advantages and elimination/minimisation of the drawbacks. In the proposed IDM, the passive method utilises critical system attributes extracted from local voltage measurements at target DG locations as well as employs decision tree-based classifiers for characterisation and detection of islanding events. The active method is based on Sandia frequency shift technique and is initiated only whenmore » the passive method is unable to differentiate islanding events from other system events. Thus, the power quality degradation introduced into the system by active islanding detection techniques can be minimised. Furthermore, a combination of active and passive techniques allows detection of islanding events under low power mismatch scenarios eliminating the disadvantage associated with the use of passive techniques alone. Finally, detailed case study results demonstrate the effectiveness of the proposed method in detection of islanding events under various power mismatch scenarios, load quality factors and in the presence of single or multiple grid-connected inverter-based DG units.« less

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Azim, Riyasat; Li, Fangxing; Xue, Yaosuo

Distributed generations (DGs) for grid-connected applications require an accurate and reliable islanding detection methodology (IDM) for secure system operation. This paper presents an IDM for grid-connected inverter-based DGs. The proposed method is a combination of passive and active islanding detection techniques for aggregation of their advantages and elimination/minimisation of the drawbacks. In the proposed IDM, the passive method utilises critical system attributes extracted from local voltage measurements at target DG locations as well as employs decision tree-based classifiers for characterisation and detection of islanding events. The active method is based on Sandia frequency shift technique and is initiated only whenmore » the passive method is unable to differentiate islanding events from other system events. Thus, the power quality degradation introduced into the system by active islanding detection techniques can be minimised. Furthermore, a combination of active and passive techniques allows detection of islanding events under low power mismatch scenarios eliminating the disadvantage associated with the use of passive techniques alone. Finally, detailed case study results demonstrate the effectiveness of the proposed method in detection of islanding events under various power mismatch scenarios, load quality factors and in the presence of single or multiple grid-connected inverter-based DG units.« less

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

PubMed Central

Sivaraks, Haemwaan

2015-01-01

Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods. PMID:25688284

Automatically Detect and Track Multiple Fish Swimming in Shallow Water with Frequent Occlusion

PubMed Central

Qian, Zhi-Ming; Cheng, Xi En; Chen, Yan Qiu

2014-01-01

Due to its universality, swarm behavior in nature attracts much attention of scientists from many fields. Fish schools are examples of biological communities that demonstrate swarm behavior. The detection and tracking of fish in a school are of important significance for the quantitative research on swarm behavior. However, different from other biological communities, there are three problems in the detection and tracking of fish school, that is, variable appearances, complex motion and frequent occlusion. To solve these problems, we propose an effective method of fish detection and tracking. In this method, first, the fish head region is positioned through extremum detection and ellipse fitting; second, The Kalman filtering and feature matching are used to track the target in complex motion; finally, according to the feature information obtained by the detection and tracking, the tracking problems caused by frequent occlusion are processed through trajectory linking. We apply this method to track swimming fish school of different densities. The experimental results show that the proposed method is both accurate and reliable. PMID:25207811

Applying the Multiple Signal Classification Method to Silent Object Detection Using Ambient Noise

NASA Astrophysics Data System (ADS)

Mori, Kazuyoshi; Yokoyama, Tomoki; Hasegawa, Akio; Matsuda, Minoru

2004-05-01

The revolutionary concept of using ocean ambient noise positively to detect objects, called acoustic daylight imaging, has attracted much attention. The authors attempted the detection of a silent target object using ambient noise and a wide-band beam former consisting of an array of receivers. In experimental results obtained in air, using the wide-band beam former, we successfully applied the delay-sum array (DSA) method to detect a silent target object in an acoustic noise field generated by a large number of transducers. This paper reports some experimental results obtained by applying the multiple signal classification (MUSIC) method to a wide-band beam former to detect silent targets. The ocean ambient noise was simulated by transducers decentralized to many points in air. Both MUSIC and DSA detected a spherical target object in the noise field. The relative power levels near the target obtained with MUSIC were compared with those obtained by DSA. Then the effectiveness of the MUSIC method was evaluated according to the rate of increase in the maximum and minimum relative power levels.

Detecting and Estimating Contamination of Human DNA Samples in Sequencing and Array-Based Genotype Data

PubMed Central

Jun, Goo; Flickinger, Matthew; Hetrick, Kurt N.; Romm, Jane M.; Doheny, Kimberly F.; Abecasis, Gonçalo R.; Boehnke, Michael; Kang, Hyun Min

2012-01-01

DNA sample contamination is a serious problem in DNA sequencing studies and may result in systematic genotype misclassification and false positive associations. Although methods exist to detect and filter out cross-species contamination, few methods to detect within-species sample contamination are available. In this paper, we describe methods to identify within-species DNA sample contamination based on (1) a combination of sequencing reads and array-based genotype data, (2) sequence reads alone, and (3) array-based genotype data alone. Analysis of sequencing reads allows contamination detection after sequence data is generated but prior to variant calling; analysis of array-based genotype data allows contamination detection prior to generation of costly sequence data. Through a combination of analysis of in silico and experimentally contaminated samples, we show that our methods can reliably detect and estimate levels of contamination as low as 1%. We evaluate the impact of DNA contamination on genotype accuracy and propose effective strategies to screen for and prevent DNA contamination in sequencing studies. PMID:23103226

Rapid and sensitive detection of human astrovirus in water samples by loop-mediated isothermal amplification with hydroxynaphthol blue dye

PubMed Central

2014-01-01

Background The aim of this paper was to develop a reverse transcription loop-mediated isothermal amplification (RT-LAMP) method for rapid, sensitive and inexpensive detection of astrovirus. Results The detection limit of LAMP using in vitro RNA transcripts was 3.6×10 copies·μL-1, which is as sensitive as the presently used PCR assays. However, the LAMP products could be identified as different colors with the naked eye following staining with hydroxynaphthol blue dye (HNB). No cross-reactivity with other gastroenteric viruses (rotavirus and norovirus) was observed, indicating the relatively high specificity of LAMP. The RT-LAMP method with HNB was used to effectively detect astrovirus in reclaimed water samples. Conclusions The LAMP technique described in this study is a cheap, sensitive, specific and rapid method for the detection of astrovirus. The RT-LAMP method can be simply applied for the specific detection of astrovirus and has the potential to be utilized in the field as a screening test. PMID:24524254

Detection method based on Kalman filter for high speed rail defect AE signal on wheel-rail rolling rig

NASA Astrophysics Data System (ADS)

Hao, Qiushi; Shen, Yi; Wang, Yan; Zhang, Xin

2018-01-01

Nondestructive test (NDT) of rails has been carried out intermittently in traditional approaches, which highly restricts the detection efficiency under rapid development of high speed railway nowadays. It is necessary to put forward a dynamic rail defect detection method for rail health monitoring. Acoustic emission (AE) as a practical real-time detection technology takes advantage of dynamic AE signal emitted from plastic deformation of material. Detection capacities of AE on rail defects have been verified due to its sensitivity and dynamic merits. Whereas the application under normal train service circumstance has been impeded by synchronous background noises, which are directly linked to the wheel speed. In this paper, surveys on a wheel-rail rolling rig are performed to investigate defect AE signals with varying speed. A dynamic denoising method based on Kalman filter is proposed and its detection effectiveness and flexibility are demonstrated by theory and computational results. Moreover, after comparative analysis of modelling precision at different speeds, it is predicted that the method is also applicable for high speed condition beyond experiments.

Evaluation of nutria (Myocastor coypus) detection methods in Maryland, USA

USGS Publications Warehouse

Pepper, Margaret A.; Herrmann, Valentine; Hines, James; Nichols, James D.; Kendrot, Stephen R

2017-01-01

Nutria (Myocaster coypus), invasive, semi-aquatic rodents native to South America, were introduced into Maryland near Blackwater National Wildlife Refuge (BNWR) in 1943. Irruptive population growth, expansion, and destructive feeding habits resulted in the destruction of thousands of acres of emergent marshes at and surrounding BNWR. In 2002, a partnership of federal, state and private entities initiated an eradication campaign to protect remaining wetlands from further damage and facilitate the restoration of coastal wetlands throughout the Chesapeake Bay region. Program staff removed nearly 14,000 nutria from five infested watersheds in a systematic trapping and hunting program between 2002 and 2014. As part of ongoing surveillance activities, the Chesapeake Bay Nutria Eradication Project uses a variety of tools to detect and remove nutria. Project staff developed a floating raft, or monitoring platform, to determine site occupancy. These platforms are placed along waterways and checked periodically for evidence of nutria visitation. We evaluated the effectiveness of monitoring platforms and three associated detection methods: hair snares, presence of scat, and trail cameras. Our objectives were to (1) determine if platform placement on land or water influenced nutria visitation rates, (2) determine if the presence of hair snares influenced visitation rates, and (3) determine method-specific detection probabilities. Our analyses indicated that platforms placed on land were 1.5–3.0 times more likely to be visited than those placed in water and that platforms without snares were an estimated 1.7–3.7 times more likely to be visited than those with snares. Although the presence of snares appears to have discouraged visitation, seasonal variation may confound interpretation of these results. Scat was the least effective method of determining nutria visitation, while hair snares were as effective as cameras. Estimated detection probabilities provided by occupancy modeling were 0.73 for hair snares, 0.71 for cameras and 0.40 for scat. We recommend the use of hair snares on monitoring platforms as they are the most cost-effective and reliable detection method available at this time. Future research should focus on determining the cause for the observed decrease in nutria visits after snares were applied.

Optimizing detection and analysis of slow waves in sleep EEG.

PubMed

Mensen, Armand; Riedner, Brady; Tononi, Giulio

2016-12-01

Analysis of individual slow waves in EEG recording during sleep provides both greater sensitivity and specificity compared to spectral power measures. However, parameters for detection and analysis have not been widely explored and validated. We present a new, open-source, Matlab based, toolbox for the automatic detection and analysis of slow waves; with adjustable parameter settings, as well as manual correction and exploration of the results using a multi-faceted visualization tool. We explore a large search space of parameter settings for slow wave detection and measure their effects on a selection of outcome parameters. Every choice of parameter setting had some effect on at least one outcome parameter. In general, the largest effect sizes were found when choosing the EEG reference, type of canonical waveform, and amplitude thresholding. Previously published methods accurately detect large, global waves but are conservative and miss the detection of smaller amplitude, local slow waves. The toolbox has additional benefits in terms of speed, user-interface, and visualization options to compare and contrast slow waves. The exploration of parameter settings in the toolbox highlights the importance of careful selection of detection METHODS: The sensitivity and specificity of the automated detection can be improved by manually adding or deleting entire waves and or specific channels using the toolbox visualization functions. The toolbox standardizes the detection procedure, sets the stage for reliable results and comparisons and is easy to use without previous programming experience. Copyright © 2016 Elsevier B.V. All rights reserved.

SNP discovery and genotyping using Genotyping-by-Sequencing in Pekin ducks.

PubMed

Zhu, Feng; Cui, Qian-Qian; Hou, Zhuo-Cheng

2016-11-15

Genomic selection and genome-wide association studies need thousands to millions of SNPs. However, many non-model species do not have reference chips for detecting variation. Our goal was to develop and validate an inexpensive but effective method for detecting SNP variation. Genotyping by sequencing (GBS) can be a highly efficient strategy for genome-wide SNP detection, as an alternative to microarray chips. Here, we developed a GBS protocol for ducks and tested it to genotype 49 Pekin ducks. A total of 169,209 SNPs were identified from all animals, with a mean of 55,920 SNPs per individual. The average SNP density reached 1156 SNPs/MB. In this study, the first application of GBS to ducks, we demonstrate the power and simplicity of this method. GBS can be used for genetic studies in to provide an effective method for genome-wide SNP discovery.

Detection of no-model input-output pairs in closed-loop systems.

PubMed

Potts, Alain Segundo; Alvarado, Christiam Segundo Morales; Garcia, Claudio

2017-11-01

The detection of no-model input-output (IO) pairs is important because it can speed up the multivariable system identification process, since all the pairs with null transfer functions are previously discarded and it can also improve the identified model quality, thus improving the performance of model based controllers. In the available literature, the methods focus just on the open-loop case, since in this case there is not the effect of the controller forcing the main diagonal in the transfer matrix to one and all the other terms to zero. In this paper, a modification of a previous method able to detect no-model IO pairs in open-loop systems is presented, but adapted to perform this duty in closed-loop systems. Tests are performed by using the traditional methods and the proposed one to show its effectiveness. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Shallow Reflection Method for Water-Filled Void Detection and Characterization

NASA Astrophysics Data System (ADS)

Zahari, M. N. H.; Madun, A.; Dahlan, S. H.; Joret, A.; Hazreek, Z. A. M.; Mohammad, A. H.; Izzaty, R. A.

2018-04-01

Shallow investigation is crucial in enhancing the characteristics of subsurface void commonly encountered in civil engineering, and one such technique commonly used is seismic-reflection technique. An assessment of the effectiveness of such an approach is critical to determine whether the quality of the works meets the prescribed requirements. Conventional quality testing suffers limitations including: limited coverage (both area and depth) and problems with resolution quality. Traditionally quality assurance measurements use laboratory and in-situ invasive and destructive tests. However geophysical approaches, which are typically non-invasive and non-destructive, offer a method by which improvement of detection can be measured in a cost-effective way. Of this seismic reflection have proved useful to assess void characteristic, this paper evaluates the application of shallow seismic-reflection method in characterizing the water-filled void properties at 0.34 m depth, specifically for detection and characterization of void measurement using 2-dimensional tomography.

How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation.

PubMed

Scholkmann, F; Spichtig, S; Muehlemann, T; Wolf, M

2010-05-01

Near-infrared imaging (NIRI) is a neuroimaging technique which enables us to non-invasively measure hemodynamic changes in the human brain. Since the technique is very sensitive, the movement of a subject can cause movement artifacts (MAs), which affect the signal quality and results to a high degree. No general method is yet available to reduce these MAs effectively. The aim was to develop a new MA reduction method. A method based on moving standard deviation and spline interpolation was developed. It enables the semi-automatic detection and reduction of MAs in the data. It was validated using simulated and real NIRI signals. The results show that a significant reduction of MAs and an increase in signal quality are achieved. The effectiveness and usability of the method is demonstrated by the improved detection of evoked hemodynamic responses. The present method can not only be used in the postprocessing of NIRI signals but also for other kinds of data containing artifacts, for example ECG or EEG signals.

Modified Method for Detection of Benzoylecgonine in Human Urine by GC-MS: Derivatization Using Pentafluoropropanol/Acetic Anhydride.

PubMed

Serafin, Michelle C; Paulemon, Kasandra M; Fuller, Zachary J; Bronner, William E

2017-05-01

An existing GC-MS method for detecting benzoylecgonine (BZE) in urine was modified by changing derivatizing reagents. This method modification presents a cost-effective alternative derivatization procedure for the detection of BZE in urine by GC-MS. The combination of pentafluoropropanol and acetic anhydride was found to produce the same reaction product for BZE as pentafluoropropanol with pentafluoropropionic anhydride, while reducing reagent cost. With no anhydride present, derivatization of BZE by pentafluoropropanol did not occur. Published by Oxford University Press 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Correlation Study Of Diffenrential Skin Temperatures (DST) For Ovulation Detection Using Infra-Red Thermography

NASA Astrophysics Data System (ADS)

Rao, K. H. S.; Shah, A. v.; Ruedi, B.

1982-11-01

The importance of ovulation time detection in the Practice of Natural Birth Control (NBC) as a contraceptive tool, and for natural/artificial insemination among women having the problem of in-fertility, is well known. The simple Basal Body Temperature (BBT) method of ovulation detection is so far unreliable. A newly proposed Differential Skin Temperature (DST) method may help minimize disturbing physiological effects and improve reliability. This paper explains preliminary results of a detailed correlative study on the DST method, using Infra-Red Thermography (IRT) imaging, and computer analysis techniques. Results obtained with five healthy, normally menstruating women volunteers will be given.

Highly sensitive and selective detection of dopamine using one-pot synthesized highly photoluminescent silicon nanoparticles.

PubMed

Zhang, Xiaodong; Chen, Xiaokai; Kai, Siqi; Wang, Hong-Yin; Yang, Jingjing; Wu, Fu-Gen; Chen, Zhan

2015-03-17

A simple and highly efficient method for dopamine (DA) detection using water-soluble silicon nanoparticles (SiNPs) was reported. The SiNPs with a high quantum yield of 23.6% were synthesized by using a one-pot microwave-assisted method. The fluorescence quenching capability of a variety of molecules on the synthesized SiNPs has been tested; only DA molecules were found to be able to quench the fluorescence of these SiNPs effectively. Therefore, such a quenching effect can be used to selectively detect DA. All other molecules tested have little interference with the dopamine detection, including ascorbic acid, which commonly exists in cells and can possibly affect the dopamine detection. The ratio of the fluorescence intensity difference between the quenched and unquenched cases versus the fluorescence intensity without quenching (ΔI/I) was observed to be linearly proportional to the DA analyte concentration in the range from 0.005 to 10.0 μM, with a detection limit of 0.3 nM (S/N = 3). To the best of our knowledge, this is the lowest limit for DA detection reported so far. The mechanism of fluorescence quenching is attributed to the energy transfer from the SiNPs to the oxidized dopamine molecules through Förster resonance energy transfer. The reported method of SiNP synthesis is very simple and cheap, making the above sensitive and selective DA detection approach using SiNPs practical for many applications.

A novel rail defect detection method based on undecimated lifting wavelet packet transform and Shannon entropy-improved adaptive line enhancer

NASA Astrophysics Data System (ADS)

Hao, Qiushi; Zhang, Xin; Wang, Yan; Shen, Yi; Makis, Viliam

2018-07-01

Acoustic emission (AE) technology is sensitive to subliminal rail defects, however strong wheel-rail contact rolling noise under high-speed condition has gravely impeded detecting of rail defects using traditional denoising methods. In this context, the paper develops an adaptive detection method for rail cracks, which combines multiresolution analysis with an improved adaptive line enhancer (ALE). To obtain elaborate multiresolution information of transient crack signals with low computational cost, lifting scheme-based undecimated wavelet packet transform is adopted. In order to feature the impulsive property of crack signals, a Shannon entropy-improved ALE is proposed as a signal enhancing approach, where Shannon entropy is introduced to improve the cost function. Then a rail defect detection plan based on the proposed method for high-speed condition is put forward. From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in enhancing the rail defect AE signal and reducing the strong background noise, offering an effective multiresolution approach for rail defect detection under high-speed and strong-noise condition.

A novel IgY-Aptamer hybrid system for cost-effective detection of SEB and its evaluation on food and clinical samples

PubMed Central

Mudili, Venkataramana; Makam, Shivakiran S.; Sundararaj, Naveen; Siddaiah, Chandranayaka; Gupta, Vijai Kumar; Rao, Putcha V. Lakshmana

2015-01-01

In the present study, we introduce a novel hybrid sandwich-ALISA employing chicken IgY and ssDNA aptamers for the detection of staphylococcal enterotoxin B (SEB). Cloning, expression and purification of the full length recombinant SEB was carried out. Anti-SEB IgY antibodies generated by immunizing white leg-horn chickens with purified recombinant SEB protein and were purified from the immunized egg yolk. Simultaneously, ssDNA aptamers specific to the toxin were prepared by SELEX method on microtiter well plates. The sensitivity levels of both probe molecules i.e., IgY and ssDNA aptamers were evaluated. We observed that the aptamer at 250 ngmL−1 concentration could detect the target antigen at 50 ngmL−1 and the IgY antibodies at 250 ngmL−1, could able to detect 100 ngmL−1 antigen. We further combined both the probes to prepare a hybrid sandwich aptamer linked immune sorbent assay (ALISA) wherein the IgY as capturing molecule and biotinylated aptamer as revealing probe. Limit of detection (LOD) for the developed method was determined as 50 ngmL−1. Further, developed method was evaluated with artificially SEB spiked milk and natural samples and obtained results were validated with PCR. In conclusion, developed ALISA method may provide cost-effective and robust detection of SEB from food and environmental samples. PMID:26477645

A novel cloning template designing method by using an artificial bee colony algorithm for edge detection of CNN based imaging sensors.

PubMed

Parmaksızoğlu, Selami; Alçı, Mustafa

2011-01-01

Cellular Neural Networks (CNNs) have been widely used recently in applications such as edge detection, noise reduction and object detection, which are among the main computer imaging processes. They can also be realized as hardware based imaging sensors. The fact that hardware CNN models produce robust and effective results has attracted the attention of researchers using these structures within image sensors. Realization of desired CNN behavior such as edge detection can be achieved by correctly setting a cloning template without changing the structure of the CNN. To achieve different behaviors effectively, designing a cloning template is one of the most important research topics in this field. In this study, the edge detecting process that is used as a preliminary process for segmentation, identification and coding applications is conducted by using CNN structures. In order to design the cloning template of goal-oriented CNN architecture, an Artificial Bee Colony (ABC) algorithm which is inspired from the foraging behavior of honeybees is used and the performance analysis of ABC for this application is examined with multiple runs. The CNN template generated by the ABC algorithm is tested by using artificial and real test images. The results are subjectively and quantitatively compared with well-known classical edge detection methods, and other CNN based edge detector cloning templates available in the imaging literature. The results show that the proposed method is more successful than other methods.

A Novel Cloning Template Designing Method by Using an Artificial Bee Colony Algorithm for Edge Detection of CNN Based Imaging Sensors

PubMed Central

Parmaksızoğlu, Selami; Alçı, Mustafa

2011-01-01

Cellular Neural Networks (CNNs) have been widely used recently in applications such as edge detection, noise reduction and object detection, which are among the main computer imaging processes. They can also be realized as hardware based imaging sensors. The fact that hardware CNN models produce robust and effective results has attracted the attention of researchers using these structures within image sensors. Realization of desired CNN behavior such as edge detection can be achieved by correctly setting a cloning template without changing the structure of the CNN. To achieve different behaviors effectively, designing a cloning template is one of the most important research topics in this field. In this study, the edge detecting process that is used as a preliminary process for segmentation, identification and coding applications is conducted by using CNN structures. In order to design the cloning template of goal-oriented CNN architecture, an Artificial Bee Colony (ABC) algorithm which is inspired from the foraging behavior of honeybees is used and the performance analysis of ABC for this application is examined with multiple runs. The CNN template generated by the ABC algorithm is tested by using artificial and real test images. The results are subjectively and quantitatively compared with well-known classical edge detection methods, and other CNN based edge detector cloning templates available in the imaging literature. The results show that the proposed method is more successful than other methods. PMID:22163903

a Landsat Time-Series Stacks Model for Detection of Cropland Change

NASA Astrophysics Data System (ADS)

Chen, J.; Chen, J.; Zhang, J.

2017-09-01

Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

Visual and highly sensitive detection of cancer cells by a colorimetric aptasensor based on cell-triggered cyclic enzymatic signal amplification.

PubMed

Zhang, Xianxia; Xiao, Kunyi; Cheng, Liwei; Chen, Hui; Liu, Baohong; Zhang, Song; Kong, Jilie

2014-06-03

Rapid and efficient detection of cancer cells at their earliest stages is one of the central challenges in cancer diagnostics. We developed a simple, cost-effective, and highly sensitive colorimetric method for visually detecting rare cancer cells based on cell-triggered cyclic enzymatic signal amplification (CTCESA). In the absence of target cells, hairpin aptamer probes (HAPs) and linker DNAs stably coexist in solution, and the linker DNA assembles DNA-AuNPs, producing a purple solution. In the presence of target cells, the specific binding of HAPs to the target cells triggers a conformational switch that results in linker DNA hybridization and cleavage by nicking endonuclease-strand scission cycles. Consequently, the cleaved fragments of linker DNA can no longer assemble into DNA-AuNPs, resulting in a red color. UV-vis spectrometry and photograph analyses demonstrated that this CTCESA-based method exhibited selective and sensitive colorimetric responses to the presence of target CCRF-CEM cells, which could be detected by the naked eye. The linear response for CCRF-CEM cells in a concentration range from 10(2) to 10(4) cells was obtained with a detection limit of 40 cells, which is approximately 20 times lower than the detection limit of normal AuNP-based methods without amplification. Given the high specificity and sensitivity of CTCESA, this colorimetric method provides a sensitive, label-free, and cost-effective approach for early cancer diagnosis and point-to-care applications.

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

Sentinel lymph node detection in breast cancer patients using surgical navigation system based on fluorescence molecular imaging technology

NASA Astrophysics Data System (ADS)

Chi, Chongwei; Kou, Deqiang; Ye, Jinzuo; Mao, Yamin; Qiu, Jingdan; Wang, Jiandong; Yang, Xin; Tian, Jie

2015-03-01

Introduction: Precision and personalization treatments are expected to be effective methods for early stage cancer studies. Breast cancer is a major threat to women's health and sentinel lymph node biopsy (SLNB) is an effective method to realize precision and personalized treatment for axillary lymph node (ALN) negative patients. In this study, we developed a surgical navigation system (SNS) based on optical molecular imaging technology for the precise detection of the sentinel lymph node (SLN) in breast cancer patients. This approach helps surgeons in precise positioning during surgery. Methods: The SNS was mainly based on the technology of optical molecular imaging. A novel optical path has been designed in our hardware system and a feature-matching algorithm has been devised to achieve rapid fluorescence and color image registration fusion. Ten in vivo studies of SLN detection in rabbits using indocyanine green (ICG) and blue dye were executed for system evaluation and 8 breast cancer patients accepted the combination method for therapy. Results: The detection rate of the combination method was 100% and an average of 2.6 SLNs was found in all patients. Our results showed that the method of using SNS to detect SLN has the potential to promote its application. Conclusion: The advantage of this system is the real-time tracing of lymph flow in a one-step procedure. The results demonstrated the feasibility of the system for providing accurate location and reliable treatment for surgeons. Our approach delivers valuable information and facilitates more detailed exploration for image-guided surgery research.

Detection Progress of Selected Drugs in TLC

PubMed Central

Pyka, Alina

2014-01-01

This entry describes applications of known indicators and dyes as new visualizing reagents and various visualizing systems as well as photocatalytic reactions and bioautography method for the detection of bioactive compounds including drugs and compounds isolated from herbal extracts. Broadening index, detection index, characteristics of densitometric band, modified contrast index, limit of detection, densitometric visualizing index, and linearity range of detected compounds were used for the evaluation of visualizing effects of applied visualizing reagents. It was shown that visualizing effect depends on the chemical structure of the visualizing reagent, the structure of the substance detected, and the chromatographic adsorbent applied. The usefulness of densitometry to direct detection of some drugs was also shown. Quoted papers indicate the detection progress of selected drugs investigated by thin-layer chromatography (TLC). PMID:24551853

Crack image segmentation based on improved DBC method

NASA Astrophysics Data System (ADS)

Cao, Ting; Yang, Nan; Wang, Fengping; Gao, Ting; Wang, Weixing

2017-11-01

With the development of computer vision technology, crack detection based on digital image segmentation method arouses global attentions among researchers and transportation ministries. Since the crack always exhibits the random shape and complex texture, it is still a challenge to accomplish reliable crack detection results. Therefore, a novel crack image segmentation method based on fractal DBC (differential box counting) is introduced in this paper. The proposed method can estimate every pixel fractal feature based on neighborhood information which can consider the contribution from all possible direction in the related block. The block moves just one pixel every time so that it could cover all the pixels in the crack image. Unlike the classic DBC method which only describes fractal feature for the related region, this novel method can effectively achieve crack image segmentation according to the fractal feature of every pixel. The experiment proves the proposed method can achieve satisfactory results in crack detection.

Microbial and Bioconversion Production of D-xylitol and Its Detection and Application

PubMed Central

Chen, Xi; Jiang, Zi-Hua; Chen, Sanfeng; Qin, Wensheng

2010-01-01

D-Xylitol is found in low content as a natural constituent of many fruits and vegetables. It is a five-carbon sugar polyol and has been used as a food additive and sweetening agent to replace sucrose, especially for non-insulin dependent diabetics. It has multiple beneficial health effects, such as the prevention of dental caries, and acute otitis media. In industry, it has been produced by chemical reduction of D-xylose mainly from photosynthetic biomass hydrolysates. As an alternative method of chemical reduction, biosynthesis of D-xylitol has been focused on the metabolically engineered Saccharomyces cerevisiae and Candida strains. In order to detect D-xylitol in the production processes, several detection methods have been established, such as gas chromatography (GC)-based methods, high performance liquid chromatography (HPLC)-based methods, LC-MS methods, and capillary electrophoresis methods (CE). The advantages and disadvantages of these methods are compared in this review. PMID:21179590

On-line focusing of flavin derivatives using Dynamic pH junction-sweeping capillary electrophoresis with laser-induced fluorescence detection.

PubMed

Britz-McKibbin, Philip; Otsuka, Koji; Terabe, Shigeru

2002-08-01

Simple yet effective methods to enhance concentration sensitivity is needed for capillary electrophoresis (CE) to become a practical method to analyze trace levels of analytes in real samples. In this report, the development of a novel on-line preconcentration technique combining dynamic pH junction and sweeping modes of focusing is applied to the sensitive and selective analysis of three flavin derivatives: riboflavin, flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD). Picomolar (pM) detectability of flavins by CE with laser-induced fluorescence (LIF) detection is demonstrated through effective focusing of large sample volumes (up to 22% capillary length) using a dual pH junction-sweeping focusing mode. This results in greater than a 1,200-fold improvement in sensitivity relative to conventional injection methods, giving a limit of detection (S/N = 3) of approximately 4.0 pM for FAD and FMN. Flavin focusing is examined in terms of analyte mobility dependence on buffer pH, borate complexation and SDS interaction. Dynamic pH junction-sweeping extends on-line focusing to both neutral (hydrophobic) and weakly acidic (hydrophilic) species and is considered useful in cases when either conventional sweeping or dynamic pH junction techniques used alone are less effective for certain classes of analytes. Enhanced focusing performance by this hyphenated method was demonstrated by greater than a 4-fold reduction in flavin bandwidth, as compared to either sweeping or dynamic pH junction, reflected by analyte detector bandwidths <0.20 cm. Novel on-line focusing strategies are required to improve sensitivity in CE, which may be applied toward more effective biochemical analysis methods for diverse types of analytes.

A new framework for analysing automated acoustic species-detection data: occupancy estimation and optimization of recordings post-processing

USGS Publications Warehouse

Chambert, Thierry A.; Waddle, J. Hardin; Miller, David A.W.; Walls, Susan; Nichols, James D.

2018-01-01

The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors.We developed a hierarchical modelling framework for estimating species occupancy from automated species-detection data. We explore design and optimization of data post-processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false positive errors and utilizes information about both occurrence and abundance of detections to improve estimation.Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders.When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post-validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post-validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated.Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species-detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programs.

Comparison between nasopharyngeal swab and nasal wash, using culture and PCR, in the detection of potential respiratory pathogens.

PubMed

Gritzfeld, Jenna F; Roberts, Paul; Roche, Lorna; El Batrawy, Sherouk; Gordon, Stephen B

2011-04-13

Nasopharyngeal carriage of potential pathogens is important as it is both the major source of transmission and the prerequisite of invasive disease. New methods for detecting carriage could improve comfort, accuracy and laboratory utility. The aims of this study were to compare the sensitivities of a nasopharyngeal swab (NPS) and a nasal wash (NW) in detecting potential respiratory pathogens in healthy adults using microbiological culture and PCR. Healthy volunteers attended for nasal washing and brushing of the posterior nasopharynx. Conventional and real-time PCR were used to detect pneumococcus and meningococcus. Statistical differences between the two nasal sampling methods were determined using a nonparametric Mann-Whitney U test; differences between culture and PCR methods were determined using the McNemar test.Nasal washing was more comfortable for volunteers than swabbing (n = 24). In detection by culture, the NW was significantly more likely to detect pathogens than the NPS (p < 0.00001). Overall, there was a low carriage rate of pathogens in this sample; no significant difference was seen in the detection of bacteria between culture and PCR methods. Nasal washing and PCR may provide effective alternatives to nasopharyngeal swabbing and classical microbiology, respectively.

Polarization-dependent optical reflection ultrasonic detection

NASA Astrophysics Data System (ADS)

Zhu, Xiaoyi; Huang, Zhiyu; Wang, Guohe; Li, Wenzhao; Li, Changhui

2017-03-01

Although ultrasound transducers based on commercial piezoelectric-material have been widely used, they generally have limited bandwidth centered at the resonant frequency. Currently, several pure-optical ultrasonic detection methods have gained increasing interest due to their wide bandwidth and high sensitivity. However, most of them require customized components (such as micro-ring, SPR, Fabry-Perot film, etc), which limit their broad implementations. In this study, we presented a simple pure-optical ultrasound detection method, called "Polarization-dependent Reflection Ultrasonic Detection" (PRUD). It detects the intensity difference between two polarization components of the probe beam that is modulated by ultrasound waves. PRUD detect the two components by using a balanced detector, which effectively suppressed much of the unwanted noise. We have achieved the sensitivity (noise equivalent pressure) to be 1.7kPa, and this can be further improved. In addition, like many other pure-optical ultrasonic detection methods, PRUD also has a flat and broad bandwidth from almost zero to over 100MHz. Besides theoretical analysis, we did a phantom study by imaging a tungsten filament to demonstrate the performance of PRUD. We believe this simple and economic method will attract both researchers and engineers in optical and ultrasound fields.

Model-based fault detection and isolation for intermittently active faults with application to motion-based thruster fault detection and isolation for spacecraft

NASA Technical Reports Server (NTRS)

Wilson, Edward (Inventor)

2008-01-01

The present invention is a method for detecting and isolating fault modes in a system having a model describing its behavior and regularly sampled measurements. The models are used to calculate past and present deviations from measurements that would result with no faults present, as well as with one or more potential fault modes present. Algorithms that calculate and store these deviations, along with memory of when said faults, if present, would have an effect on the said actual measurements, are used to detect when a fault is present. Related algorithms are used to exonerate false fault modes and finally to isolate the true fault mode. This invention is presented with application to detection and isolation of thruster faults for a thruster-controlled spacecraft. As a supporting aspect of the invention, a novel, effective, and efficient filtering method for estimating the derivative of a noisy signal is presented.

A kernel machine method for detecting effects of interaction between multidimensional variable sets: an imaging genetics application.

PubMed

Ge, Tian; Nichols, Thomas E; Ghosh, Debashis; Mormino, Elizabeth C; Smoller, Jordan W; Sabuncu, Mert R

2015-04-01

Measurements derived from neuroimaging data can serve as markers of disease and/or healthy development, are largely heritable, and have been increasingly utilized as (intermediate) phenotypes in genetic association studies. To date, imaging genetic studies have mostly focused on discovering isolated genetic effects, typically ignoring potential interactions with non-genetic variables such as disease risk factors, environmental exposures, and epigenetic markers. However, identifying significant interaction effects is critical for revealing the true relationship between genetic and phenotypic variables, and shedding light on disease mechanisms. In this paper, we present a general kernel machine based method for detecting effects of the interaction between multidimensional variable sets. This method can model the joint and epistatic effect of a collection of single nucleotide polymorphisms (SNPs), accommodate multiple factors that potentially moderate genetic influences, and test for nonlinear interactions between sets of variables in a flexible framework. As a demonstration of application, we applied the method to the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to detect the effects of the interactions between candidate Alzheimer's disease (AD) risk genes and a collection of cardiovascular disease (CVD) risk factors, on hippocampal volume measurements derived from structural brain magnetic resonance imaging (MRI) scans. Our method identified that two genes, CR1 and EPHA1, demonstrate significant interactions with CVD risk factors on hippocampal volume, suggesting that CR1 and EPHA1 may play a role in influencing AD-related neurodegeneration in the presence of CVD risks. Copyright © 2015 Elsevier Inc. All rights reserved.

Monitoring Colony-level Effects of Sublethal Pesticide Exposure on Honey Bees

PubMed Central

Meikle, William G.; Weiss, Milagra

2017-01-01

The effects of sublethal pesticide exposure to honey bee colonies may be significant but difficult to detect in the field using standard visual assessment methods. Here we describe methods to measure the quantities of adult bees, brood, and food resources by weighing hives and hive parts, by photographing frames, and by installing hives on scales and with internal sensors. Data from these periodic evaluations are then combined with running average and daily detrended data on hive weight and internal hive temperature. The resulting datasets have been used to detect colony-level effects of imidacloprid applied in a sugar syrup as low as 5 parts per billion. The methods are objective, require little training, and provide permanent records in the form of sensor output and photographs. PMID:29286367

I Environmental DNA sampling is more sensitive than a traditional survey technique for detecting an aquatic invader.

PubMed

Smart, Adam S; Tingley, Reid; Weeks, Andrew R; van Rooyen, Anthony R; McCarthy, Michael A

2015-10-01

Effective management of alien species requires detecting populations in the early stages of invasion. Environmental DNA (eDNA) sampling can detect aquatic species at relatively low densities, but few studies have directly compared detection probabilities of eDNA sampling with those of traditional sampling methods. We compare the ability of a traditional sampling technique (bottle trapping) and eDNA to detect a recently established invader, the smooth newt Lissotriton vulgaris vulgaris, at seven field sites in Melbourne, Australia. Over a four-month period, per-trap detection probabilities ranged from 0.01 to 0.26 among sites where L. v. vulgaris was detected, whereas per-sample eDNA estimates were much higher (0.29-1.0). Detection probabilities of both methods varied temporally (across days and months), but temporal variation appeared to be uncorrelated between methods. Only estimates of spatial variation were strongly correlated across the two sampling techniques. Environmental variables (water depth, rainfall, ambient temperature) were not clearly correlated with detection probabilities estimated via trapping, whereas eDNA detection probabilities were negatively correlated with water depth, possibly reflecting higher eDNA concentrations at lower water levels. Our findings demonstrate that eDNA sampling can be an order of magnitude more sensitive than traditional methods, and illustrate that traditional- and eDNA-based surveys can provide independent information on species distributions when occupancy surveys are conducted over short timescales.

Mover Position Detection for PMTLM Based on Linear Hall Sensors through EKF Processing

PubMed Central

Yan, Leyang; Zhang, Hui; Ye, Peiqing

2017-01-01

Accurate mover position is vital for a permanent magnet tubular linear motor (PMTLM) control system. In this paper, two linear Hall sensors are utilized to detect the mover position. However, Hall sensor signals contain third-order harmonics, creating errors in mover position detection. To filter out the third-order harmonics, a signal processing method based on the extended Kalman filter (EKF) is presented. The limitation of conventional processing method is first analyzed, and then EKF is adopted to detect the mover position. In the EKF model, the amplitude of the fundamental component and the percentage of the harmonic component are taken as state variables, and they can be estimated based solely on the measured sensor signals. Then, the harmonic component can be calculated and eliminated. The proposed method has the advantages of faster convergence, better stability and higher accuracy. Finally, experimental results validate the effectiveness and superiority of the proposed method. PMID:28383505

Gamma/neutron time-correlation for special nuclear material detection – Active stimulation of highly enriched uranium

DOE PAGES

Paff, Marc G.; Monterial, Mateusz; Marleau, Peter; ...

2014-06-21

A series of simulations and experiments were undertaken to explore and evaluate the potential for a novel new technique for fissile material detection and characterization, the timecorrelated pulse-height (TCPH) method, to be used concurrent with active stimulation of potential nuclear materials. In previous work TCPH has been established as a highly sensitive method for the detection and characterization of configurations of fissile material containing Plutonium in passive measurements. By actively stimulating fission with the introduction of an external radiation source, we have shown that TCPH is also an effective method of detecting and characterizing configurations of fissile material containing Highlymore » Enriched Uranium (HEU). The TCPH method is shown to be robust in the presence of the proper choice of external radiation source. An evaluation of potential interrogation sources is presented.« less

PCR-Based Method for Detecting Viral Penetration of Medical Exam Gloves

PubMed Central

Broyles, John M.; O'Connell, Kevin P.; Korniewicz, Denise M.

2002-01-01

The test approved by the U.S. Food and Drug Administration for assessment of the barrier quality of medical exam gloves includes visual inspection and a water leak test. Neither method tests directly the ability of gloves to prevent penetration by microorganisms. Methods that use microorganisms (viruses and bacteria) to test gloves have been developed but require classical culturing of the organism to detect it. We have developed a PCR assay for bacteriophage φX174 that allows the rapid detection of penetration of gloves by this virus. The method is suitable for use with both latex and synthetic gloves. The presence of glove powder on either latex or synthetic gloves had no effect on the ability of the PCR assay to detect bacteriophage DNA. The assay is rapid, sensitive, and inexpensive; requires only small sample volumes; and can be automated. PMID:12149320

Seamless image stitching by homography refinement and structure deformation using optimal seam pair detection

NASA Astrophysics Data System (ADS)

Lee, Daeho; Lee, Seohyung

2017-11-01

We propose an image stitching method that can remove ghost effects and realign the structure misalignments that occur in common image stitching methods. To reduce the artifacts caused by different parallaxes, an optimal seam pair is selected by comparing the cross correlations from multiple seams detected by variable cost weights. Along the optimal seam pair, a histogram of oriented gradients is calculated, and feature points for matching are detected. The homography is refined using the matching points, and the remaining misalignment is eliminated using the propagation of deformation vectors calculated from matching points. In multiband blending, the overlapping regions are determined from a distance between the matching points to remove overlapping artifacts. The experimental results show that the proposed method more robustly eliminates misalignments and overlapping artifacts than the existing method that uses single seam detection and gradient features.

Testing local anisotropy using the method of smoothed residuals I — methodology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Appleby, Stephen; Shafieloo, Arman, E-mail: stephen.appleby@apctp.org, E-mail: arman@apctp.org

2014-03-01

We discuss some details regarding the method of smoothed residuals, which has recently been used to search for anisotropic signals in low-redshift distance measurements (Supernovae). In this short note we focus on some details regarding the implementation of the method, particularly the issue of effectively detecting signals in data that are inhomogeneously distributed on the sky. Using simulated data, we argue that the original method proposed in Colin et al. [1] will not detect spurious signals due to incomplete sky coverage, and that introducing additional Gaussian weighting to the statistic as in [2] can hinder its ability to detect amore » signal. Issues related to the width of the Gaussian smoothing are also discussed.« less

Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity

PubMed Central

Schettini, Raimondo

2018-01-01

Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art. PMID:29329268

Structural Damage Detection with Piezoelectric Wafer Active Sensors

NASA Astrophysics Data System (ADS)

Giurgiutiu, Victor

2011-07-01

Piezoelectric wafer active sensors (PWAS) are lightweight and inexpensive enablers for a large class of damage detection and structural health monitoring (SHM) applications. This paper starts with a brief review of PWAS physical principles and basic modelling and continues by considering the various ways in which PWAS can be used for damage detection: (a) embedded guided-wave ultrasonics, i.e., pitch-catch, pulse-echo, phased arrays, thickness mode; (b) high-frequency modal sensing, i.e., the electro-mechanical (E/M) impedance method; (c) passive detection, i.e., acoustic emission and impact detection. An example of crack-like damage detection and localization with PWAS phased arrays on a small metallic plate is given. The modelling of PWAS detection of disbond damage in adhesive joints is achieved with the analytical transfer matrix method (TMM). The analytical methods offer the advantage of fast computation which enables parameter studies and carpet plots. A parametric study of the effect of crack size and PWAS location on disbond detection is presented. The power and energy transduction between PWAS and structure is studied analytically with a wave propagation method. Special attention is given to the mechatronics modeling of the complete transduction cycle from electrical excitation into ultrasonic acoustic waves by the piezoelectric effect, the transfer through the structure, and finally reverse piezoelectric transduction to generate the received electric signal. It is found that the combination of PWAS size and wave frequency/wavelength play an important role in identifying transduction maxima and minima that could be exploited to achieve an optimum power-efficient design. The multi-physics finite element method (MP-FEM), which permits fine discretization of damaged regions and complicated structural geometries, is used to study the generation of guided waves in a plate from an electrically excited transmitter PWAS and the capture of these waves as electric signals at a receiver PWAS. Wave diffraction from a hole damage is illustrated through time-frame snapshots. The paper ends with conclusions and suggestions for further work.

A method based on multi-sensor data fusion for fault detection of planetary gearboxes.

PubMed

Lei, Yaguo; Lin, Jing; He, Zhengjia; Kong, Detong

2012-01-01

Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.

Detection of Legionella species in environmental water by the quantitative PCR method in combination with ethidium monoazide treatment.

PubMed

Inoue, Hiroaki; Takama, Tomoko; Yoshizaki, Miwa; Agata, Kunio

2015-01-01

We detected Legionella species in 111 bath water samples and 95 cooling tower water samples by using a combination of conventional plate culture, quantitative polymerase chain reaction (qPCR) and qPCR combined with ethidium monoazide treatment (EMA-qPCR) methods. In the case of bath water samples, Legionella spp. were detected in 30 samples by plate culture, in 85 samples by qPCR, and in 49 samples by EMA-qPCR. Of 81 samples determined to be Legionella-negative by plate culture, 56 and 23 samples were positive by qPCR and EMA-qPCR, respectively. Therefore, EMA treatment decreased the number of Legionella-positive bath water samples detected by qPCR. In contrast, EMA treatment had no effect on cooling tower water samples. We therefore expect that EMA-qPCR is a useful method for the rapid detection of viable Legionella spp. from bath water samples.

DNAzyme based gap-LCR detection of single-nucleotide polymorphism.

PubMed

Zhou, Li; Du, Feng; Zhao, Yongyun; Yameen, Afshan; Chen, Haodong; Tang, Zhuo

2013-07-15

Fast and accurate detection of single-nucleotide polymorphism (SNP) is thought more and more important for understanding of human physiology and elucidating the molecular based diseases. A great deal of effort has been devoted to developing accurate, rapid, and cost-effective technologies for SNP analysis. However most of those methods developed to date incorporate complicated probe labeling and depend on advanced equipment. The DNAzyme based Gap-LCR detection method averts any chemical modification on probes and circumvents those problems by incorporating a short functional DNA sequence into one of LCR primers. Two kinds of exonuclease are utilized in our strategy to digest all the unreacted probes and release the DNAzymes embedded in the LCR product. The DNAzyme applied in our method is a versatile tool to report the result of SNP detection in colorimetric or fluorometric ways for different detection purposes. Copyright © 2013 Elsevier B.V. All rights reserved.

A parameter selection for Raman spectroscopy-based detection of chemical contaminants in food powders

USDA-ARS?s Scientific Manuscript database

Raman spectroscopy technique has proven to be a reliable method for detection of chemical contaminants in food ingredients and products. To detect each contaminant particle in a food sample, it is important to determine the effective depth of penetration of laser through the food sample and the corr...

Detecting double compressed MPEG videos with the same quantization matrix and synchronized group of pictures structure

NASA Astrophysics Data System (ADS)

Aghamaleki, Javad Abbasi; Behrad, Alireza

2018-01-01

Double compression detection is a crucial stage in digital image and video forensics. However, the detection of double compressed videos is challenging when the video forger uses the same quantization matrix and synchronized group of pictures (GOP) structure during the recompression history to conceal tampering effects. A passive approach is proposed for detecting double compressed MPEG videos with the same quantization matrix and synchronized GOP structure. To devise the proposed algorithm, the effects of recompression on P frames are mathematically studied. Then, based on the obtained guidelines, a feature vector is proposed to detect double compressed frames on the GOP level. Subsequently, sparse representations of the feature vectors are used for dimensionality reduction and enrich the traces of recompression. Finally, a support vector machine classifier is employed to detect and localize double compression in temporal domain. The experimental results show that the proposed algorithm achieves the accuracy of more than 95%. In addition, the comparisons of the results of the proposed method with those of other methods reveal the efficiency of the proposed algorithm.

Explosive detection using high-volume vapor sampling and analysis by trained canines and ultra-trace detection equipment

NASA Astrophysics Data System (ADS)

Fisher, Mark; Sikes, John; Prather, Mark

2004-09-01

The dog's nose is an effective, highly-mobile sampling system, while the canine olfactory organs are an extremely sensitive detector. Having been trained to detect a wide variety of substances with exceptional results, canines are widely regarded as the 'gold standard' in chemical vapor detection. Historically, attempts to mimic the ability of dogs to detect vapors of explosives using electronic 'dogs noses' has proven difficult. However, recent advances in technology have resulted in development of detection (i.e., sampling and sensor) systems with performance that is rapidly approaching that of trained canines. The Nomadics Fido was the first sensor to demonstrate under field conditions the detection of landmines with performance approaching that of canines. More recently, comparative testing of Fido against canines has revealed that electronic vapor detection, when coupled with effective sampling methods, can produce results comparable to that of highly-trained canines. The results of these comparative tests will be presented, as will recent test results in which explosives hidden in cargo were detected using Fido with a high-volume sampling technique. Finally, the use of canines along with electronic sensors will be discussed as a means of improving the performance and expanding the capabilities of both methods.

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

NASA Astrophysics Data System (ADS)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

Rapid detection of Listeria monocytogenes in raw milk and soft cheese by a redox potential measurement based method combined with real-time PCR.

PubMed

Erdősi, Orsolya; Szakmár, Katalin; Reichart, Olivér; Szili, Zsuzsanna; László, Noémi; Székely Körmöczy, Péter; Laczay, Péter

2014-09-01

The incidence of outbreaks of foodborne listeriosis has indicated the need for a reliable and rapid detection of the microbe in different foodstuffs. A method combining redox potential measurement and real-time polymerase chain reaction (PCR) was developed to detect Listeria monocytogenes in artificially contaminated raw milk and soft cheese. Food samples of 25 g or 25 ml were homogenised in 225 ml of Listeria Enrichment Broth (LEB) with Oxford supplement, and the redox potential measurement technique was applied. For Listeria species the measuring time was maximum 34 h. The absence of L. monocytogenes could reliably be proven by the redox potential measurement method, but Listeria innocua and Bacillus subtilis could not be differentiated from L. monocytogenes on the basis of the redox curves. The presence of L. monocytogenes had to be confirmed by real-time PCR. The combination of these two methods proved to detect < 10 cfu/g of L. monocytogenes in a cost- and time-effective manner. This method can potentially be used as an alternative to the standard nutrient method for the rapid detection of L. monocytogenes in food.

Multi-Frequency Signal Detection Based on Frequency Exchange and Re-Scaling Stochastic Resonance and Its Application to Weak Fault Diagnosis

PubMed Central

Leng, Yonggang; Fan, Shengbo

2018-01-01

Mechanical fault diagnosis usually requires not only identification of the fault characteristic frequency, but also detection of its second and/or higher harmonics. However, it is difficult to detect a multi-frequency fault signal through the existing Stochastic Resonance (SR) methods, because the characteristic frequency of the fault signal as well as its second and higher harmonics frequencies tend to be large parameters. To solve the problem, this paper proposes a multi-frequency signal detection method based on Frequency Exchange and Re-scaling Stochastic Resonance (FERSR). In the method, frequency exchange is implemented using filtering technique and Single SideBand (SSB) modulation. This new method can overcome the limitation of "sampling ratio" which is the ratio of the sampling frequency to the frequency of target signal. It also ensures that the multi-frequency target signals can be processed to meet the small-parameter conditions. Simulation results demonstrate that the method shows good performance for detecting a multi-frequency signal with low sampling ratio. Two practical cases are employed to further validate the effectiveness and applicability of this method. PMID:29693577

High-resolution metabolomics assessment of military personnel: Evaluating analytical strategies for chemical detection

PubMed Central

Liu, Ken H.; Walker, Douglas I.; Uppal, Karan; Tran, ViLinh; Rohrbeck, Patricia; Mallon, Timothy M.; Jones, Dean P.

2016-01-01

Objective To maximize detection of serum metabolites with high-resolution metabolomics (HRM). Methods Department of Defense Serum Repository (DoDSR) samples were analyzed using ultra-high resolution mass spectrometry with three complementary chromatographic phases and four ionization modes. Chemical coverage was evaluated by number of ions detected and accurate mass matches to a human metabolomics database. Results Individual HRM platforms provided accurate mass matches for up to 58% of the KEGG metabolite database. Combining two analytical methods increased matches to 72%, and included metabolites in most major human metabolic pathways and chemical classes. Detection and feature quality varied by analytical configuration. Conclusions Dual chromatography HRM with positive and negative electrospray ionization provides an effective generalized method for metabolic assessment of military personnel. PMID:27501105

[Monolithic column-gold composite substrate preparation and application to SERS detection of pigment].

PubMed

Xie, Yun-Fei; Li, Yan; Yu, Hui; Qian, He; Yao, Wei-Rong

2014-03-01

In the present study, we developed a novel SERS substrate with the porous monolith material combined with classic gold nanoparticles, and erythrosine as the research object, by adjusting the different experimental conditions for optimal SERS enhancements, including system pH and mixing time, and ultimately selected the optimum pH value 5.06 and mixing time 25 min. Compared with the traditional gold plastic substrate enhancement effect, the experimental conditions were applied to the monolith substrate SERS detection of dye erythrosine, different concentrations of samples were used for erythrosine SERS detection, and the detection limit reached 0.1 g x mL(-1). The method uses the payload of gold nanoparticles in mesoporous materials to effectively enhance the SERS signal. And this method has the advantages of simpleness and good stability, which provides a favorable theoretical basis for the rapid prohibited colorings screening.

Thermal detection thresholds in 5-year-old preterm born children; IQ does matter.

PubMed

de Graaf, Joke; Valkenburg, Abraham J; Tibboel, Dick; van Dijk, Monique

2012-07-01

Experiencing pain at newborn age may have consequences on one's somatosensory perception later in life. Children's perception for cold and warm stimuli may be determined with the Thermal Sensory Analyzer (TSA) device by two different methods. This pilot study in 5-year-old children born preterm aimed at establishing whether the TSA method of limits, which is dependent of reaction time, and the method of levels, which is independent of reaction time, would yield different cold and warm detection thresholds. The second aim was to establish possible associations between intellectual ability and the detection thresholds obtained with either method. A convenience sample was drawn from the participants in an ongoing 5-year follow-up study of a randomized controlled trial on effects of morphine during mechanical ventilation. Thresholds were assessed using both methods and statistically compared. Possible associations between the child's intelligence quotient (IQ) and threshold levels were analyzed. The method of levels yielded more sensitive thresholds than did the method of limits, i.e. mean (SD) cold detection thresholds: 30.3 (1.4) versus 28.4 (1.7) (Cohen'sd=1.2, P=0.001) and warm detection thresholds; 33.9 (1.9) versus 35.6 (2.1) (Cohen's d=0.8, P=0.04). IQ was statistically significantly associated only with the detection thresholds obtained with the method of limits (cold: r=0.64, warm: r=-0.52). The TSA method of levels, is to be preferred over the method of limits in 5-year-old preterm born children, as it establishes more sensitive detection thresholds and is independent of IQ. Copyright © 2011 Elsevier Ltd. All rights reserved.

A Hybrid Approach for CpG Island Detection in the Human Genome.

PubMed

Yang, Cheng-Hong; Lin, Yu-Da; Chiang, Yi-Cheng; Chuang, Li-Yeh

2016-01-01

CpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging. A novel procedure is proposed to detect CpG islands by combining clustering technology with the sliding-window method (PSO-based). Clustering technology is used to detect the locations of all possible CpG islands and process the data, thus effectively obviating the need for the extensive and unnecessary processing of DNA fragments, and thus improving the efficiency of sliding-window based particle swarm optimization (PSO) search. This proposed approach, named ClusterPSO, provides versatile and highly-sensitive detection of CpG islands in the human genome. In addition, the detection efficiency of ClusterPSO is compared with eight CpG island detection methods in the human genome. Comparison of the detection efficiency for the CpG islands in human genome, including sensitivity, specificity, accuracy, performance coefficient (PC), and correlation coefficient (CC), ClusterPSO revealed superior detection ability among all of the test methods. Moreover, the combination of clustering technology and PSO method can successfully overcome their respective drawbacks while maintaining their advantages. Thus, clustering technology could be hybridized with the optimization algorithm method to optimize CpG island detection. The prediction accuracy of ClusterPSO was quite high, indicating the combination of CpGcluster and PSO has several advantages over CpGcluster and PSO alone. In addition, ClusterPSO significantly reduced implementation time.

Detection of anomaly in human retina using Laplacian Eigenmaps and vectorized matched filtering

NASA Astrophysics Data System (ADS)

Yacoubou Djima, Karamatou A.; Simonelli, Lucia D.; Cunningham, Denise; Czaja, Wojciech

2015-03-01

We present a novel method for automated anomaly detection on auto fluorescent data provided by the National Institute of Health (NIH). This is motivated by the need for new tools to improve the capability of diagnosing macular degeneration in its early stages, track the progression over time, and test the effectiveness of new treatment methods. In previous work, macular anomalies have been detected automatically through multiscale analysis procedures such as wavelet analysis or dimensionality reduction algorithms followed by a classification algorithm, e.g., Support Vector Machine. The method that we propose is a Vectorized Matched Filtering (VMF) algorithm combined with Laplacian Eigenmaps (LE), a nonlinear dimensionality reduction algorithm with locality preserving properties. By applying LE, we are able to represent the data in the form of eigenimages, some of which accentuate the visibility of anomalies. We pick significant eigenimages and proceed with the VMF algorithm that classifies anomalies across all of these eigenimages simultaneously. To evaluate our performance, we compare our method to two other schemes: a matched filtering algorithm based on anomaly detection on single images and a combination of PCA and VMF. LE combined with VMF algorithm performs best, yielding a high rate of accurate anomaly detection. This shows the advantage of using a nonlinear approach to represent the data and the effectiveness of VMF, which operates on the images as a data cube rather than individual images.

Analysis of shape memory alloy sensory particles for damage detection via substructure and continuum damage modeling

NASA Astrophysics Data System (ADS)

Bielefeldt, Brent R.; Benzerga, A. Amine; Hartl, Darren J.

2016-04-01

The ability to monitor and predict the structural health of an aircraft is of growing importance to the aerospace industry. Currently, structural inspections and maintenance are based upon experiences with similar aircraft operating in similar conditions. While effective, these methods are time-intensive and unnecessary if the aircraft is not in danger of structural failure. It is imagined that future aircraft will utilize non-destructive evaluation methods, allowing for the near real-time monitoring of structural health. A particularly interesting method involves utilizing the unique transformation response of shape memory alloy (SMA) particles embedded in an aircraft structure. By detecting changes in the mechanical and/or electromagnetic responses of embedded particles, operators could detect the formation or propagation of fatigue cracks in the vicinity of these particles. This work focuses on a finite element model of SMA particles embedded in an aircraft wing using a substructure modeling approach in which degrees of freedom are retained only at specified points of connection to other parts or the application of boundary conditions, greatly reducing computational cost. Previous work evaluated isolated particle response to a static crack to numerically demonstrate and validate this damage detection method. This paper presents the implementation of a damage model to account for crack propagation and examine for the first time the effect of particle configuration and/or relative placement with respect to the ability to detect damage.

Bearing failure detection of micro wind turbine via power spectral density analysis for stator current signals spectrum

NASA Astrophysics Data System (ADS)

Mahmood, Faleh H.; Kadhim, Hussein T.; Resen, Ali K.; Shaban, Auday H.

2018-05-01

The failure such as air gap weirdness, rubbing, and scrapping between stator and rotor generator arise unavoidably and may cause extremely terrible results for a wind turbine. Therefore, we should pay more attention to detect and identify its cause-bearing failure in wind turbine to improve the operational reliability. The current paper tends to use of power spectral density analysis method of detecting internal race and external race bearing failure in micro wind turbine by estimation stator current signal of the generator. The failure detector method shows that it is well suited and effective for bearing failure detection.

Near real time vapor detection and enhancement using aerosol adsorption

DOEpatents

Novick, Vincent J.; Johnson, Stanley A.

1999-01-01

A vapor sample detection method where the vapor sample contains vapor and ambient air and surrounding natural background particles. The vapor sample detection method includes the steps of generating a supply of aerosol that have a particular effective median particle size, mixing the aerosol with the vapor sample forming aerosol and adsorbed vapor suspended in an air stream, impacting the suspended aerosol and adsorbed vapor upon a reflecting element, alternatively directing infrared light to the impacted aerosol and adsorbed vapor, detecting and analyzing the alternatively directed infrared light in essentially real time using a spectrometer and a microcomputer and identifying the vapor sample.

Near real time vapor detection and enhancement using aerosol adsorption

DOEpatents

Novick, V.J.; Johnson, S.A.

1999-08-03

A vapor sample detection method is described where the vapor sample contains vapor and ambient air and surrounding natural background particles. The vapor sample detection method includes the steps of generating a supply of aerosol that have a particular effective median particle size, mixing the aerosol with the vapor sample forming aerosol and adsorbed vapor suspended in an air stream, impacting the suspended aerosol and adsorbed vapor upon a reflecting element, alternatively directing infrared light to the impacted aerosol and adsorbed vapor, detecting and analyzing the alternatively directed infrared light in essentially real time using a spectrometer and a microcomputer and identifying the vapor sample. 13 figs.

Effect of sample stratification on dairy GWAS results

PubMed Central

2012-01-01

Background Artificial insemination and genetic selection are major factors contributing to population stratification in dairy cattle. In this study, we analyzed the effect of sample stratification and the effect of stratification correction on results of a dairy genome-wide association study (GWAS). Three methods for stratification correction were used: the efficient mixed-model association expedited (EMMAX) method accounting for correlation among all individuals, a generalized least squares (GLS) method based on half-sib intraclass correlation, and a principal component analysis (PCA) approach. Results Historical pedigree data revealed that the 1,654 contemporary cows in the GWAS were all related when traced through approximately 10–15 generations of ancestors. Genome and phenotype stratifications had a striking overlap with the half-sib structure. A large elite half-sib family of cows contributed to the detection of favorable alleles that had low frequencies in the general population and high frequencies in the elite cows and contributed to the detection of X chromosome effects. All three methods for stratification correction reduced the number of significant effects. EMMAX method had the most severe reduction in the number of significant effects, and the PCA method using 20 principal components and GLS had similar significance levels. Removal of the elite cows from the analysis without using stratification correction removed many effects that were also removed by the three methods for stratification correction, indicating that stratification correction could have removed some true effects due to the elite cows. SNP effects with good consensus between different methods and effect size distributions from USDA’s Holstein genomic evaluation included the DGAT1-NIBP region of BTA14 for production traits, a SNP 45kb upstream from PIGY on BTA6 and two SNPs in NIBP on BTA14 for protein percentage. However, most of these consensus effects had similar frequencies in the elite and average cows. Conclusions Genetic selection and extensive use of artificial insemination contributed to overlapped genome, pedigree and phenotype stratifications. The presence of an elite cluster of cows was related to the detection of rare favorable alleles that had high frequencies in the elite cluster and low frequencies in the remaining cows. Methods for stratification correction could have removed some true effects associated with genetic selection. PMID:23039970

Effect of sample storage time on detection of hybridization signals in Checkerboard DNA-DNA hybridization.

PubMed

do Nascimento, Cássio; Muller, Katia; Sato, Sandra; Albuquerque Junior, Rubens Ferreira

2012-04-01

Long-term sample storage can affect the intensity of the hybridization signals provided by molecular diagnostic methods that use chemiluminescent detection. The aim of this study was to evaluate the effect of different storage times on the hybridization signals of 13 bacterial species detected by the Checkerboard DNA-DNA hybridization method using whole-genomic DNA probes. Ninety-six subgingival biofilm samples were collected from 36 healthy subjects, and the intensity of hybridization signals was evaluated at 4 different time periods: (1) immediately after collecting (n = 24) and (2) after storage at -20 °C for 6 months (n = 24), (3) for 12 months (n = 24), and (4) for 24 months (n = 24). The intensity of hybridization signals obtained from groups 1 and 2 were significantly higher than in the other groups (p < 0.001). No differences were found between groups 1 and 2 (p > 0.05). The Checkerboard DNA-DNA hybridization method was suitable to detect hybridization signals from all groups evaluated, and the intensity of signals decreased significantly after long periods of sample storage.

Effect of processing on recovery and variability associated with immunochemical analytical methods for multiple allergens in a single matrix: sugar cookies.

PubMed

Khuda, Sefat; Slate, Andrew; Pereira, Marion; Al-Taher, Fadwa; Jackson, Lauren; Diaz-Amigo, Carmen; Bigley, Elmer C; Whitaker, Thomas; Williams, Kristina M

2012-05-02

Among the major food allergies, peanut, egg, and milk are the most common. The immunochemical detection of food allergens depends on various factors, such as the food matrix and processing method, which can affect allergen conformation and extractability. This study aimed to (1) develop matrix-specific incurred reference materials for allergen testing, (2) determine whether multiple allergens in the same model food can be simultaneously detected, and (3) establish the effect of processing on reference material stability and allergen detection. Defatted peanut flour, whole egg powder, and spray-dried milk were added to cookie dough at seven incurred levels before baking. Allergens were measured using five commercial enzyme-linked immunosorbent assay (ELISA) kits. All kits showed decreased recovery of all allergens after baking. Analytical coefficients of variation for most kits increased with baking time, but decreased with incurred allergen level. Thus, food processing negatively affects the recovery and variability of peanut, egg, and milk detection in a sugar cookie matrix when using immunochemical methods.

Crop Row Detection in Maize Fields Inspired on the Human Visual Perception

PubMed Central

Romeo, J.; Pajares, G.; Montalvo, M.; Guerrero, J. M.; Guijarro, M.; Ribeiro, A.

2012-01-01

This paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields in the images. The vision system is designed to be installed onboard a mobile agricultural vehicle, that is, submitted to gyros, vibrations, and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of two main processes: image segmentation and crop row detection. The first one applies a threshold to separate green plants or pixels (crops and weeds) from the rest (soil, stones, and others). It is based on a fuzzy clustering process, which allows obtaining the threshold to be applied during the normal operation process. The crop row detection applies a method based on image perspective projection that searches for maximum accumulation of segmented green pixels along straight alignments. They determine the expected crop lines in the images. The method is robust enough to work under the above-mentioned undesired effects. It is favorably compared against the well-tested Hough transformation for line detection. PMID:22623899

Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system.

PubMed

Min, Jianliang; Wang, Ping; Hu, Jianfeng

2017-01-01

Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1-2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver.

Glue detection based on teaching points constraint and tracking model of pixel convolution

NASA Astrophysics Data System (ADS)

Geng, Lei; Ma, Xiao; Xiao, Zhitao; Wang, Wen

2018-01-01

On-line glue detection based on machine version is significant for rust protection and strengthening in car production. Shadow stripes caused by reflect light and unevenness of inside front cover of car reduce the accuracy of glue detection. In this paper, we propose an effective algorithm to distinguish the edges of the glue and shadow stripes. Teaching points are utilized to calculate slope between the two adjacent points. Then a tracking model based on pixel convolution along motion direction is designed to segment several local rectangular regions using distance. The distance is the height of rectangular region. The pixel convolution along the motion direction is proposed to extract edges of gules in local rectangular region. A dataset with different illumination and complexity shape stripes are used to evaluate proposed method, which include 500 thousand images captured from the camera of glue gun machine. Experimental results demonstrate that the proposed method can detect the edges of glue accurately. The shadow stripes are distinguished and removed effectively. Our method achieves the 99.9% accuracies for the image dataset.

Intelligent Method for Diagnosing Structural Faults of Rotating Machinery Using Ant Colony Optimization

PubMed Central

Li, Ke; Chen, Peng

2011-01-01

Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called “relative ratio symptom parameters” are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks. PMID:22163833

Intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization.

PubMed

Li, Ke; Chen, Peng

2011-01-01

Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called "relative ratio symptom parameters" are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks.

Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering.

PubMed

Xia, Yong; Han, Junze; Wang, Kuanquan

2015-01-01

Based on the idea of telemedicine, 24-hour uninterrupted monitoring on electrocardiograms (ECG) has started to be implemented. To create an intelligent ECG monitoring system, an efficient and quick detection algorithm for the characteristic waveforms is needed. This paper aims to give a quick and effective method for detecting QRS-complexes and R-waves in ECGs. The real ECG signal from the MIT-BIH Arrhythmia Database is used for the performance evaluation. The method proposed combined a wavelet transform and the K-means clustering algorithm. A wavelet transform is adopted in the data analysis and preprocessing. Then, based on the slope information of the filtered data, a segmented K-means clustering method is adopted to detect the QRS region. Detection of the R-peak is based on comparing the local amplitudes in each QRS region, which is different from other approaches, and the time cost of R-wave detection is reduced. Of the tested 8 records (total 18201 beats) from the MIT-BIH Arrhythmia Database, an average R-peak detection sensitivity of 99.72 and a positive predictive value of 99.80% are gained; the average time consumed detecting a 30-min original signal is 5.78s, which is competitive with other methods.

State Recognition of High Voltage Isolation Switch Based on Background Difference and Iterative Search

NASA Astrophysics Data System (ADS)

Xu, Jiayuan; Yu, Chengtao; Bo, Bin; Xue, Yu; Xu, Changfu; Chaminda, P. R. Dushantha; Hu, Chengbo; Peng, Kai

2018-03-01

The automatic recognition of the high voltage isolation switch by remote video monitoring is an effective means to ensure the safety of the personnel and the equipment. The existing methods mainly include two ways: improving monitoring accuracy and adopting target detection technology through equipment transformation. Such a method is often applied to specific scenarios, with limited application scope and high cost. To solve this problem, a high voltage isolation switch state recognition method based on background difference and iterative search is proposed in this paper. The initial position of the switch is detected in real time through the background difference method. When the switch starts to open and close, the target tracking algorithm is used to track the motion trajectory of the switch. The opening and closing state of the switch is determined according to the angle variation of the switch tracking point and the center line. The effectiveness of the method is verified by experiments on different switched video frames of switching states. Compared with the traditional methods, this method is more robust and effective.

Ionospheric earthquake effects detection based on Total Electron Content (TEC) GPS Correlation

NASA Astrophysics Data System (ADS)

Sunardi, Bambang; Muslim, Buldan; Eka Sakya, Andi; Rohadi, Supriyanto; Sulastri; Murjaya, Jaya

2018-03-01

Advances in science and technology showed that ground-based GPS receiver was able to detect ionospheric Total Electron Content (TEC) disturbances caused by various natural phenomena such as earthquakes. One study of Tohoku (Japan) earthquake, March 11, 2011, magnitude M 9.0 showed TEC fluctuations observed from GPS observation network spread around the disaster area. This paper discussed the ionospheric earthquake effects detection using TEC GPS data. The case studies taken were Kebumen earthquake, January 25, 2014, magnitude M 6.2, Sumba earthquake, February 12, 2016, M 6.2 and Halmahera earthquake, February 17, 2016, M 6.1. TEC-GIM (Global Ionosphere Map) correlation methods for 31 days were used to monitor TEC anomaly in ionosphere. To ensure the geomagnetic disturbances due to solar activity, we also compare with Dst index in the same time window. The results showed anomalous ratio of correlation coefficient deviation to its standard deviation upon occurrences of Kebumen and Sumba earthquake, but not detected a similar anomaly for the Halmahera earthquake. It was needed a continous monitoring of TEC GPS data to detect the earthquake effects in ionosphere. This study giving hope in strengthening the earthquake effect early warning system using TEC GPS data. The method development of continuous TEC GPS observation derived from GPS observation network that already exists in Indonesia is needed to support earthquake effects early warning systems.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

PubMed

Oh, Sang-Il; Kang, Hang-Bong

2017-01-22

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742

Detection of Coronal Mass Ejections Using Multiple Features and Space-Time Continuity

NASA Astrophysics Data System (ADS)

Zhang, Ling; Yin, Jian-qin; Lin, Jia-ben; Feng, Zhi-quan; Zhou, Jin

2017-07-01

Coronal Mass Ejections (CMEs) release tremendous amounts of energy in the solar system, which has an impact on satellites, power facilities and wireless transmission. To effectively detect a CME in Large Angle Spectrometric Coronagraph (LASCO) C2 images, we propose a novel algorithm to locate the suspected CME regions, using the Extreme Learning Machine (ELM) method and taking into account the features of the grayscale and the texture. Furthermore, space-time continuity is used in the detection algorithm to exclude the false CME regions. The algorithm includes three steps: i) define the feature vector which contains textural and grayscale features of a running difference image; ii) design the detection algorithm based on the ELM method according to the feature vector; iii) improve the detection accuracy rate by using the decision rule of the space-time continuum. Experimental results show the efficiency and the superiority of the proposed algorithm in the detection of CMEs compared with other traditional methods. In addition, our algorithm is insensitive to most noise.

New coherent laser communication detection scheme based on channel-switching method.

PubMed

Liu, Fuchuan; Sun, Jianfeng; Ma, Xiaoping; Hou, Peipei; Cai, Guangyu; Sun, Zhiwei; Lu, Zhiyong; Liu, Liren

2015-04-01

A new coherent laser communication detection scheme based on the channel-switching method is proposed. The detection front end of this scheme comprises a 90° optical hybrid and two balanced photodetectors which outputs the in-phase (I) channel and quadrature-phase (Q) channel signal current, respectively. With this method, the ultrahigh speed analog/digital transform of the signal of the I or Q channel is not required. The phase error between the signal and local lasers is obtained by simple analog circuit. Using the phase error signal, the signals of the I/Q channel are switched alternately. The principle of this detection scheme is presented. Moreover, the comparison of the sensitivity of this scheme with that of homodyne detection with an optical phase-locked loop is discussed. An experimental setup was constructed to verify the proposed detection scheme. The offline processing procedure and results are presented. This scheme could be realized through simple structure and has potential applications in cost-effective high-speed laser communication.

Detection of neovascularization based on fractal and texture analysis with interaction effects in diabetic retinopathy.

PubMed

Lee, Jack; Zee, Benny Chung Ying; Li, Qing

2013-01-01

Diabetic retinopathy is a major cause of blindness. Proliferative diabetic retinopathy is a result of severe vascular complication and is visible as neovascularization of the retina. Automatic detection of such new vessels would be useful for the severity grading of diabetic retinopathy, and it is an important part of screening process to identify those who may require immediate treatment for their diabetic retinopathy. We proposed a novel new vessels detection method including statistical texture analysis (STA), high order spectrum analysis (HOS), fractal analysis (FA), and most importantly we have shown that by incorporating their associated interactions the accuracy of new vessels detection can be greatly improved. To assess its performance, the sensitivity, specificity and accuracy (AUC) are obtained. They are 96.3%, 99.1% and 98.5% (99.3%), respectively. It is found that the proposed method can improve the accuracy of new vessels detection significantly over previous methods. The algorithm can be automated and is valuable to detect relatively severe cases of diabetic retinopathy among diabetes patients.

Comparison of real-time PCR methods for the detection of Naegleria fowleri in surface water and sediment.

PubMed

Streby, Ashleigh; Mull, Bonnie J; Levy, Karen; Hill, Vincent R

2015-05-01

Naegleria fowleri is a thermophilic free-living ameba found in freshwater environments worldwide. It is the cause of a rare but potentially fatal disease in humans known as primary amebic meningoencephalitis. Established N. fowleri detection methods rely on conventional culture techniques and morphological examination followed by molecular testing. Multiple alternative real-time PCR assays have been published for rapid detection of Naegleria spp. and N. fowleri. Foursuch assays were evaluated for the detection of N. fowleri from surface water and sediment. The assays were compared for thermodynamic stability, analytical sensitivity and specificity, detection limits, humic acid inhibition effects, and performance with seeded environmental matrices. Twenty-one ameba isolates were included in the DNA panel used for analytical sensitivity and specificity analyses. N. fowleri genotypes I and III were used for method performance testing. Two of the real-time PCR assays were determined to yield similar performance data for specificity and sensitivity for detecting N. fowleri in environmental matrices.

Comparison of real-time PCR methods for the detection of Naegleria fowleri in surface water and sediment

PubMed Central

Streby, Ashleigh; Mull, Bonnie J.; Levy, Karen

2015-01-01

Naegleria fowleri is a thermophilic free-living ameba found in freshwater environments worldwide. It is the cause of a rare but potentially fatal disease in humans known as primary amebic meningoencephalitis. Established N. fowleri detection methods rely on conventional culture techniques and morphological examination followed by molecular testing. Multiple alternative real-time PCR assays have been published for rapid detection of Naegleria spp. and N. fowleri. Four such assays were evaluated for the detection of N. fowleri from surface water and sediment. The assays were compared for thermodynamic stability, analytical sensitivity and specificity, detection limits, humic acid inhibition effects, and performance with seeded environmental matrices. Twenty-one ameba isolates were included in the DNA panel used for analytical sensitivity and specificity analyses. N. fowleri genotypes I and III were used for method performance testing. Two of the real-time PCR assays were determined to yield similar performance data for specificity and sensitivity for detecting N. fowleri in environmental matrices. PMID:25855343

Multiplex PCR detection of waterborne intestinal protozoa: microsporidia, Cyclospora, and Cryptosporidium.

PubMed

Lee, Seung-Hyun; Joung, Migyo; Yoon, Sejoung; Choi, Kyoungjin; Park, Woo-Yoon; Yu, Jae-Ran

2010-12-01

Recently, emerging waterborne protozoa, such as microsporidia, Cyclospora, and Cryptosporidium, have become a challenge to human health worldwide. Rapid, simple, and economical detection methods for these major waterborne protozoa in environmental and clinical samples are necessary to control infection and improve public health. In the present study, we developed a multiplex PCR test that is able to detect all these 3 major waterborne protozoa at the same time. Detection limits of the multiplex PCR method ranged from 10(1) to 10(2) oocysts or spores. The primers for microsporidia or Cryptosporidium used in this study can detect both Enterocytozoon bieneusi and Encephalitozoon intestinalis, or both Cryptosporidium hominis and Cryptosporidium parvum, respectively. Restriction enzyme digestion of PCR products with BsaBI or BsiEI makes it possible to distinguish the 2 species of microsporidia or Cryptosporidium, respectively. This simple, rapid, and cost-effective multiplex PCR method will be useful for detecting outbreaks or sporadic cases of waterborne protozoa infections.

Detection of medication-related problems in hospital practice: a review

PubMed Central

Manias, Elizabeth

2013-01-01

This review examines the effectiveness of detection methods in terms of their ability to identify and accurately determine medication-related problems in hospitals. A search was conducted of databases from inception to June 2012. The following keywords were used in combination: medication error or adverse drug event or adverse drug reaction, comparison, detection, hospital and method. Seven detection methods were considered: chart review, claims data review, computer monitoring, direct care observation, interviews, prospective data collection and incident reporting. Forty relevant studies were located. Detection methods that were better able to identify medication-related problems compared with other methods tested in the same study included chart review, computer monitoring, direct care observation and prospective data collection. However, only small numbers of studies were involved in comparisons with direct care observation (n = 5) and prospective data collection (n = 6). There was little focus on detecting medication-related problems during various stages of the medication process, and comparisons associated with the seriousness of medication-related problems were examined in 19 studies. Only 17 studies involved appropriate comparisons with a gold standard, which provided details about sensitivities and specificities. In view of the relatively low identification of medication-related problems with incident reporting, use of this method in tracking trends over time should be met with some scepticism. Greater attention should be placed on combining methods, such as chart review and computer monitoring in examining trends. More research is needed on the use of claims data, direct care observation, interviews and prospective data collection as detection methods. PMID:23194349

Sequential change detection and monitoring of temporal trends in random-effects meta-analysis.

PubMed

Dogo, Samson Henry; Clark, Allan; Kulinskaya, Elena

2017-06-01

Temporal changes in magnitude of effect sizes reported in many areas of research are a threat to the credibility of the results and conclusions of meta-analysis. Numerous sequential methods for meta-analysis have been proposed to detect changes and monitor trends in effect sizes so that meta-analysis can be updated when necessary and interpreted based on the time it was conducted. The difficulties of sequential meta-analysis under the random-effects model are caused by dependencies in increments introduced by the estimation of the heterogeneity parameter τ 2 . In this paper, we propose the use of a retrospective cumulative sum (CUSUM)-type test with bootstrap critical values. This method allows retrospective analysis of the past trajectory of cumulative effects in random-effects meta-analysis and its visualization on a chart similar to CUSUM chart. Simulation results show that the new method demonstrates good control of Type I error regardless of the number or size of the studies and the amount of heterogeneity. Application of the new method is illustrated on two examples of medical meta-analyses. © 2016 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd. © 2016 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.

Checklist and "Pollard Walk" butterfly survey methods on public lands

USGS Publications Warehouse

Royer, Ronald A.; Austin, Jane E.; Newton, Wesley E.

1998-01-01

Checklist and “Pollard Walk” butterfly survey methods were contemporaneously applied to seven public sites in North Dakota during the summer of 1995. Results were compared for effect of method and site on total number of butterflies and total number of species detected per hour. Checklist searching produced significantly more butterfly detections per hour than Pollard Walks at all sites. Number of species detected per hour did not differ significantly either among sites or between methods. Many species were detected by only one method, and at most sites generalist and invader species were more likely to be observed during checklist searches than during Pollard Walks. Results indicate that checklist surveys are a more efficient means for initial determination of a species list for a site, whereas for long-term monitoring the Pollard Walk is more practical and statistically manageable. Pollard Walk transects are thus recommended once a prairie butterfly fauna has been defined for a site by checklist surveys.

Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads.

PubMed

Sasagawa, Yohei; Danno, Hiroki; Takada, Hitomi; Ebisawa, Masashi; Tanaka, Kaori; Hayashi, Tetsutaro; Kurisaki, Akira; Nikaido, Itoshi

2018-03-09

High-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in the reaction steps make it possible to effectively convert initial reads to UMI counts, at a rate of 30-50%, and detect more genes. To demonstrate the power of Quartz-Seq2, we analyzed approximately 10,000 transcriptomes from in vitro embryonic stem cells and an in vivo stromal vascular fraction with a limited number of reads.

Geometric shapes inversion method of space targets by ISAR image segmentation

NASA Astrophysics Data System (ADS)

Huo, Chao-ying; Xing, Xiao-yu; Yin, Hong-cheng; Li, Chen-guang; Zeng, Xiang-yun; Xu, Gao-gui

2017-11-01

The geometric shape of target is an effective characteristic in the process of space targets recognition. This paper proposed a method of shape inversion of space target based on components segmentation from ISAR image. The Radon transformation, Hough transformation, K-means clustering, triangulation will be introduced into ISAR image processing. Firstly, we use Radon transformation and edge detection to extract space target's main body spindle and solar panel spindle from ISAR image. Then the targets' main body, solar panel, rectangular and circular antenna are segmented from ISAR image based on image detection theory. Finally, the sizes of every structural component are computed. The effectiveness of this method is verified using typical targets' simulation data.

A Videotape-Based Training Method for Improving the Detection of Depression in Residents of Long-Term Care Facilities

ERIC Educational Resources Information Center

Wood, Stacey; Cummings, Jeffrey L.; Schnelle, Betha; Stephens, Mary

2002-01-01

Purpose: This article reviews the effectiveness of a new training program for improving nursing staffs' detection of depression within long-term care facilities. The course was designed to increase recognition of the Minimal Data Set (MDS) Mood Trigger items, to be brief, and to rely on images rather than didactics. Design and Methods: This study…

Diagnosis of amphimeriasis by LAMPhimerus assay in human stool samples long-term storage onto filter paper

PubMed Central

Calvopiña, Manuel; Buendía-Sánchez, María; López-Abán, Julio; Vicente, Belén; Muro, Antonio

2018-01-01

Amphimeriasis, a fish-borne zoonotic disease caused by the liver fluke Amphimerus spp., has recently been reported as an emerging disease affecting an indigenous Ameridian group, the Chachi, living in Ecuador. The only method for diagnosing amphimeriasis was the microscopic detection of eggs from the parasite in patients' stool samples with very low sensitivity. Our group developed an ELISA technique for detection of anti-Amphimerus IgG in human sera and a molecular method based on LAMP technology (named LAMPhimerus) for specific and sensitive parasite DNA detection. The LAMPhimerus method showed to be much more sensitive than classical parasitological methods for amphimeriasis diagnosis using human stool samples for analysis. The objective of this work is to demonstrate the feasibility of using dried stool samples on filter paper as source of DNA in combination with the effectiveness of our previously designed LAMPhimerus assay for successfully Amphimerus sp. detection in clinical stool samples. A total of 102 untreated and undiluted stool samples collected from Chachi population were spread as thin layer onto common filter paper for easily transportation to our laboratory and stored at room temperature for one year until DNA extraction. When LAMPhimerus method was applied for Amphimerus sp. DNA detection, a higher number of positive results was detected (61/102; 59.80%) in comparison to parasitological methods (38/102; 37.25%), including 28/61 (45.90%) microscopy-confirmed Amphimerus sp. infections. The diagnostic parameters for the sensitivity and specificity werecalculated for our LAMPhimerus assay, which were 79.17% and 65.98%, respectively. We demonstrate, for the first time, that common filter paper is useful for easy collection and long-term storage of human stool samples for later DNA extraction and molecular analysis of human-parasitic trematode eggs. This simple, economic and easily handling method combined with the specific and sensible LAMPhimerus assay has the potential to beused as an effective molecular large-scale screening test for amphimeriasis-endemic areas. PMID:29444135

Conclusion of LOD-score analysis for family data generated under two-locus models.

PubMed Central

Dizier, M. H.; Babron, M. C.; Clerget-Darpoux, F.

1996-01-01

The power to detect linkage by the LOD-score method is investigated here for diseases that depend on the effects of two genes. The classical strategy is, first, to detect a major-gene (MG) effect by segregation analysis and, second, to seek for linkage with genetic markers by the LOD-score method using the MG parameters. We already showed that segregation analysis can lead to evidence for a MG effect for many two-locus models, with the estimates of the MG parameters being very different from those of the two genes involved in the disease. We show here that use of these MG parameter estimates in the LOD-score analysis may lead to a failure to detect linkage for some two-locus models. For these models, use of the sib-pair method gives a non-negligible increase of power to detect linkage. The linkage-homogeneity test among subsamples differing for the familial disease distribution provides evidence of parameter misspecification, when the MG parameters are used. Moreover, for most of the models, use of the MG parameters in LOD-score analysis leads to a large bias in estimation of the recombination fraction and sometimes also to a rejection of linkage for the true recombination fraction. A final important point is that a strong evidence of an MG effect, obtained by segregation analysis, does not necessarily imply that linkage will be detected for at least one of the two genes, even with the true parameters and with a close informative marker. PMID:8651311

Detection of linkage between a quantitative trait and a marker locus by the lod score method: sample size and sampling considerations.

PubMed

Demenais, F; Lathrop, G M; Lalouel, J M

1988-07-01

A simulation study is here conducted to measure the power of the lod score method to detect linkage between a quantitative trait and a marker locus in various situations. The number of families necessary to detect such linkage with 80% power is assessed for different sets of parameters at the trait locus and different values of the recombination fraction. The effects of varying the mode of sampling families and the sibship size are also evaluated.

Fundamentals of electrochemical detection techniques for CE and MCE.

PubMed

Kubán, Pavel; Hauser, Peter C

2009-10-01

The electroanalytical techniques of amperometry, conductometry and potentiometry match well with the instrumental simplicity of CE. Indeed, all three detection approaches have been reported for electrophoretic separations. However, the characteristics of the three methods are quite distinct and these are not related to the optical methods more commonly employed. A detailed discussion of the underlying principles of each is given. The issue of possible effects of the separation voltage on the electrochemical detection techniques is considered in depth, and approaches to the elimination of such interferences are also discussed for each case.

New Methods of Low-Field Magnetic Resonance Imaging for Application to Traumatic Brain Injury

DTIC Science & Technology

2013-02-01

magnet based ), the development of novel high-speed parallel imaging detection systems, and work on advanced adaptive reconstruction methods ...signal many times within the acquisition time . We present here a new method for 3D OMRI based on b-SSFP at a constant field of 6.5 mT that provides up...developing injury-sensitive MRI based on the detection of free radicals associat- ed with injury using the Overhauser effect and subsequently imaging that

On-line monitoring of H2 generation and the HTF degradation in parabolic trough solar thermal power plants: Development of an optical sensor based on an innovative approach

NASA Astrophysics Data System (ADS)

Pagola, Iñigo; Funcia, Ibai; Sánchez, Marcelino; Gil, Javier; González-Vallejo, Victoria; Bedoya, Maxi; Orellana, Guillermo

2017-06-01

The work presented in this paper offers a robust, effective and economically competitive method for online detection and monitoring of the presence of molecular hydrogen in the heat transfer fluids of parabolic trough collector plants. The novel method is based on a specific fluorescent sensor according to the ES2425002 patent ("Method for the detection and quantification of hydrogen in a heat transfer fluid").

A Simple and Computationally Efficient Sampling Approach to Covariate Adjustment for Multifactor Dimensionality Reduction Analysis of Epistasis

PubMed Central

Gui, Jiang; Andrew, Angeline S.; Andrews, Peter; Nelson, Heather M.; Kelsey, Karl T.; Karagas, Margaret R.; Moore, Jason H.

2010-01-01

Epistasis or gene-gene interaction is a fundamental component of the genetic architecture of complex traits such as disease susceptibility. Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free method to detect epistasis when there are no significant marginal genetic effects. However, in many studies of complex disease, other covariates like age of onset and smoking status could have a strong main effect and may potentially interfere with MDR's ability to achieve its goal. In this paper, we present a simple and computationally efficient sampling method to adjust for covariate effects in MDR. We use simulation to show that after adjustment, MDR has sufficient power to detect true gene-gene interactions. We also compare our method with the state-of-art technique in covariate adjustment. The results suggest that our proposed method performs similarly, but is more computationally efficient. We then apply this new method to an analysis of a population-based bladder cancer study in New Hampshire. PMID:20924193

A study on efficient detection of network-based IP spoofing DDoS and malware-infected Systems.

PubMed

Seo, Jung Woo; Lee, Sang Jin

2016-01-01

Large-scale network environments require effective detection and response methods against DDoS attacks. Depending on the advancement of IT infrastructure such as the server or network equipment, DDoS attack traffic arising from a few malware-infected systems capable of crippling the organization's internal network has become a significant threat. This study calculates the frequency of network-based packet attributes and analyzes the anomalies of the attributes in order to detect IP-spoofed DDoS attacks. Also, a method is proposed for the effective detection of malware infection systems triggering IP-spoofed DDoS attacks on an edge network. Detection accuracy and performance of the collected real-time traffic on a core network is analyzed thru the use of the proposed algorithm, and a prototype was developed to evaluate the performance of the algorithm. As a result, DDoS attacks on the internal network were detected in real-time and whether or not IP addresses were spoofed was confirmed. Detecting hosts infected by malware in real-time allowed the execution of intrusion responses before stoppage of the internal network caused by large-scale attack traffic.

Magnetoelastic Effect-Based Transmissive Stress Detection for Steel Strips: Theory and Experiment

PubMed Central

Zhang, Qingdong; Su, Yuanxiao; Zhang, Liyuan; Bi, Jia; Luo, Jiang

2016-01-01

For the deficiencies of traditional stress detection methods for steel strips in industrial production, this paper proposes a non-contact stress detection scheme based on the magnetoelastic effect. The theoretical model of the transmission-type stress detection is established, in which the output voltage and the tested stress obey a linear relation. Then, a stress detection device is built for the experiment, and Q235 steel under uniaxial tension is tested as an example. The result shows that the output voltage rises linearly with the increase of the tensile stress, consistent with the theoretical prediction. To ensure the accuracy of the stress detection method in actual application, the temperature compensation, magnetic shielding and some other key technologies are investigated to reduce the interference of the external factors, such as environment temperature and surrounding magnetic field. The present research develops the theoretical and experimental foundations for the magnetic stress detection system, which can be used for online non-contact monitoring of strip flatness-related stress (tension distribution or longitudinal residual stress) in the steel strip rolling process, the quality evaluation of strip flatness after rolling, the life and safety assessment of metal construction and other industrial production links. PMID:27589742

Detection of supernova neutrinos at spallation neutron sources

NASA Astrophysics Data System (ADS)

Huang, Ming-Yang; Guo, Xin-Heng; Young, Bing-Lin

2016-07-01

After considering supernova shock effects, Mikheyev-Smirnov-Wolfenstein effects, neutrino collective effects, and Earth matter effects, the detection of supernova neutrinos at the China Spallation Neutron Source is studied and the expected numbers of different flavor supernova neutrinos observed through various reaction channels are calculated with the neutrino energy spectra described by the Fermi-Dirac distribution and the “beta fit” distribution respectively. Furthermore, the numerical calculation method of supernova neutrino detection on Earth is applied to some other spallation neutron sources, and the total expected numbers of supernova neutrinos observed through different reactions channels are given. Supported by National Natural Science Foundation of China (11205185, 11175020, 11275025, 11575023)

Significance of parametric spectral ratio methods in detection and recognition of whispered speech

NASA Astrophysics Data System (ADS)

Mathur, Arpit; Reddy, Shankar M.; Hegde, Rajesh M.

2012-12-01

In this article the significance of a new parametric spectral ratio method that can be used to detect whispered speech segments within normally phonated speech is described. Adaptation methods based on the maximum likelihood linear regression (MLLR) are then used to realize a mismatched train-test style speech recognition system. This proposed parametric spectral ratio method computes a ratio spectrum of the linear prediction (LP) and the minimum variance distortion-less response (MVDR) methods. The smoothed ratio spectrum is then used to detect whispered segments of speech within neutral speech segments effectively. The proposed LP-MVDR ratio method exhibits robustness at different SNRs as indicated by the whisper diarization experiments conducted on the CHAINS and the cell phone whispered speech corpus. The proposed method also performs reasonably better than the conventional methods for whisper detection. In order to integrate the proposed whisper detection method into a conventional speech recognition engine with minimal changes, adaptation methods based on the MLLR are used herein. The hidden Markov models corresponding to neutral mode speech are adapted to the whispered mode speech data in the whispered regions as detected by the proposed ratio method. The performance of this method is first evaluated on whispered speech data from the CHAINS corpus. The second set of experiments are conducted on the cell phone corpus of whispered speech. This corpus is collected using a set up that is used commercially for handling public transactions. The proposed whisper speech recognition system exhibits reasonably better performance when compared to several conventional methods. The results shown indicate the possibility of a whispered speech recognition system for cell phone based transactions.

Detectability limit and uncertainty considerations for laser induced fluorescence spectroscopy in flames

NASA Technical Reports Server (NTRS)

Daily, J. W.

1978-01-01

Laser induced fluorescence spectroscopy of flames is discussed, and derived uncertainty relations are used to calculate detectability limits due to statistical errors. Interferences due to Rayleigh scattering from molecules as well as Mie scattering and incandescence from particles have been examined for their effect on detectability limits. Fluorescence trapping is studied, and some methods for reducing the effect are considered. Fluorescence trapping places an upper limit on the number density of the fluorescing species that can be measured without signal loss.