Towards an Automated Acoustic Detection System for Free Ranging Elephants.

PubMed

Zeppelzauer, Matthias; Hensman, Sean; Stoeger, Angela S

The human-elephant conflict is one of the most serious conservation problems in Asia and Africa today. The involuntary confrontation of humans and elephants claims the lives of many animals and humans every year. A promising approach to alleviate this conflict is the development of an acoustic early warning system. Such a system requires the robust automated detection of elephant vocalizations under unconstrained field conditions. Today, no system exists that fulfills these requirements. In this paper, we present a method for the automated detection of elephant vocalizations that is robust to the diverse noise sources present in the field. We evaluate the method on a dataset recorded under natural field conditions to simulate a real-world scenario. The proposed method outperformed existing approaches and robustly and accurately detected elephants. It thus can form the basis for a future automated early warning system for elephants. Furthermore, the method may be a useful tool for scientists in bioacoustics for the study of wildlife recordings.

Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery.

PubMed

Sandino, Juan; Wooler, Adam; Gonzalez, Felipe

2017-09-24

The increased technological developments in Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence and Machine Learning (ML) approaches have opened the possibility of remote sensing of extensive areas of arid lands. In this paper, a novel approach towards the detection of termite mounds with the use of a UAV, hyperspectral imagery, ML and digital image processing is intended. A new pipeline process is proposed to detect termite mounds automatically and to reduce, consequently, detection times. For the classification stage, several ML classification algorithms' outcomes were studied, selecting support vector machines as the best approach for their role in image classification of pre-existing termite mounds. Various test conditions were applied to the proposed algorithm, obtaining an overall accuracy of 68%. Images with satisfactory mound detection proved that the method is "resolution-dependent". These mounds were detected regardless of their rotation and position in the aerial image. However, image distortion reduced the number of detected mounds due to the inclusion of a shape analysis method in the object detection phase, and image resolution is still determinant to obtain accurate results. Hyperspectral imagery demonstrated better capabilities to classify a huge set of materials than implementing traditional segmentation methods on RGB images only.

Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation.

PubMed

Szatkiewicz, Jin P; Wang, WeiBo; Sullivan, Patrick F; Wang, Wei; Sun, Wei

2013-02-01

Structural variation is an important class of genetic variation in mammals. High-throughput sequencing (HTS) technologies promise to revolutionize copy-number variation (CNV) detection but present substantial analytic challenges. Converging evidence suggests that multiple types of CNV-informative data (e.g. read-depth, read-pair, split-read) need be considered, and that sophisticated methods are needed for more accurate CNV detection. We observed that various sources of experimental biases in HTS confound read-depth estimation, and note that bias correction has not been adequately addressed by existing methods. We present a novel read-depth-based method, GENSENG, which uses a hidden Markov model and negative binomial regression framework to identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. Based on extensive calibration using multiple HTS data sets, we conclude that our method outperforms existing read-depth-based CNV detection algorithms. The concept of simultaneous bias correction and CNV detection can serve as a basis for combining read-depth with other types of information such as read-pair or split-read in a single analysis. A user-friendly and computationally efficient implementation of our method is freely available.

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram

PubMed Central

Chu, Catherine. J.; Chan, Arthur; Song, Dan; Staley, Kevin J.; Stufflebeam, Steven M.; Kramer, Mark A.

2017-01-01

Summary Background High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. New Method The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. Results We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. Comparison with Existing Method The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Conclusions Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. PMID:27988323

A General Purpose Feature Extractor for Light Detection and Ranging Data

DTIC Science & Technology

2010-11-17

datasets, and the 3D MIT DARPA Urban Challenge dataset. Keywords: SLAM ; LIDARs ; feature detection; uncertainty estimates; descriptors 1. Introduction The...November 2010 Abstract: Feature extraction is a central step of processing Light Detection and Ranging ( LIDAR ) data. Existing detectors tend to exploit...detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance

USGS Publications Warehouse

Clare, John; McKinney, Shawn T.; DePue, John E.; Loftin, Cynthia S.

2017-01-01

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture–recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters.

Comparison of brown sugar, hot water, and salt methods for detecting western cherry fruit fly (Diptera: Tephritidae) larvae in sweet cherry

USDA-ARS?s Scientific Manuscript database

Brown sugar or hot water methods have been developed to detect larvae of tephritid fruit flies in post-harvest fruit in order to maintain quarantine security. It would be useful to determine if variations of these methods can yield better results and if less expensive alternatives exist. This stud...

Identifying hidden common causes from bivariate time series: a method using recurrence plots.

PubMed

Hirata, Yoshito; Aihara, Kazuyuki

2010-01-01

We propose a method for inferring the existence of hidden common causes from observations of bivariate time series. We detect related time series by excessive simultaneous recurrences in the corresponding recurrence plots. We also use a noncoverage property of a recurrence plot by the other to deny the existence of a directional coupling. We apply the proposed method to real wind data.

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.

COSMOS: accurate detection of somatic structural variations through asymmetric comparison between tumor and normal samples.

PubMed

Yamagata, Koichi; Yamanishi, Ayako; Kokubu, Chikara; Takeda, Junji; Sese, Jun

2016-05-05

An important challenge in cancer genomics is precise detection of structural variations (SVs) by high-throughput short-read sequencing, which is hampered by the high false discovery rates of existing analysis tools. Here, we propose an accurate SV detection method named COSMOS, which compares the statistics of the mapped read pairs in tumor samples with isogenic normal control samples in a distinct asymmetric manner. COSMOS also prioritizes the candidate SVs using strand-specific read-depth information. Performance tests on modeled tumor genomes revealed that COSMOS outperformed existing methods in terms of F-measure. We also applied COSMOS to an experimental mouse cell-based model, in which SVs were induced by genome engineering and gamma-ray irradiation, followed by polymerase chain reaction-based confirmation. The precision of COSMOS was 84.5%, while the next best existing method was 70.4%. Moreover, the sensitivity of COSMOS was the highest, indicating that COSMOS has great potential for cancer genome analysis. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Application of Bayesian Methods for Detecting Fraudulent Behavior on Tests

ERIC Educational Resources Information Center

Sinharay, Sandip

2018-01-01

Producers and consumers of test scores are increasingly concerned about fraudulent behavior before and during the test. There exist several statistical or psychometric methods for detecting fraudulent behavior on tests. This paper provides a review of the Bayesian approaches among them. Four hitherto-unpublished real data examples are provided to…

FIND: difFerential chromatin INteractions Detection using a spatial Poisson process

PubMed Central

Chen, Yang; Zhang, Michael Q.

2018-01-01

Polymer-based simulations and experimental studies indicate the existence of a spatial dependency between the adjacent DNA fibers involved in the formation of chromatin loops. However, the existing strategies for detecting differential chromatin interactions assume that the interacting segments are spatially independent from the other segments nearby. To resolve this issue, we developed a new computational method, FIND, which considers the local spatial dependency between interacting loci. FIND uses a spatial Poisson process to detect differential chromatin interactions that show a significant difference in their interaction frequency and the interaction frequency of their neighbors. Simulation and biological data analysis show that FIND outperforms the widely used count-based methods and has a better signal-to-noise ratio. PMID:29440282

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.

Measurement of HO2 chemical kinetics with a new detection method

NASA Technical Reports Server (NTRS)

Lee, Long C.; Suto, Masako

1986-01-01

Reaction rate constants of HO2+O3 were measured at various temperatures using a newly developed HO2 detection method. HO2 was detected by the OH(A-X) emission produced from photodissociative excitation of HO2 at 147 nm. In order to examine the possible interference of other emitting species with the HO2 detection, the photoexcitation processes of all the chemical species existing in the discharge flow tube were also investigated. The results are summarized.

Power System Transient Diagnostics Based on Novel Traveling Wave Detection

NASA Astrophysics Data System (ADS)

Hamidi, Reza Jalilzadeh

Modern electrical power systems demand novel diagnostic approaches to enhancing the system resiliency by improving the state-of-the-art algorithms. The proliferation of high-voltage optical transducers and high time-resolution measurements provide opportunities to develop novel diagnostic methods of very fast transients in power systems. At the same time, emerging complex configuration, such as multi-terminal hybrid transmission systems, limits the applications of the traditional diagnostic methods, especially in fault location and health monitoring. The impedance-based fault-location methods are inefficient for cross-bounded cables, which are widely used for connection of offshore wind farms to the main grid. Thus, this dissertation first presents a novel traveling wave-based fault-location method for hybrid multi-terminal transmission systems. The proposed method utilizes time-synchronized high-sampling voltage measurements. The traveling wave arrival times (ATs) are detected by observation of the squares of wavelet transformation coefficients. Using the ATs, an over-determined set of linear equations are developed for noise reduction, and consequently, the faulty segment is determined based on the characteristics of the provided equation set. Then, the fault location is estimated. The accuracy and capabilities of the proposed fault location method are evaluated and also compared to the existing traveling-wave-based method for a wide range of fault parameters. In order to improve power systems stability, auto-reclosing (AR), single-phase auto-reclosing (SPAR), and adaptive single-phase auto-reclosing (ASPAR) methods have been developed with the final objectives of distinguishing between the transient and permanent faults to clear the transient faults without de-energization of the solid phases. However, the features of the electrical arcs (transient faults) are severely influenced by a number of random parameters, including the convection of the air and plasma, wind speed, air pressure, and humidity. Therefore, the dead-time (the de-energization duration of the faulty phase) is unpredictable. Accordingly, conservatively long dead-times are usually considered by protection engineers. However, if the exact arc distinction time is determined, the power system stability and quality will enhance. Therefore, a new method for detection of arc extinction times leading to a new ASPAR method utilizing power line carrier (PLC) signals is presented. The efficiency of the proposed ASPAR method is verified through simulations and compared with the existing ASPAR methods. High-sampling measurements are prone to be skewed by the environmental noises and analog-to-digital (A/D) converters quantization errors. Therefore noise-contaminated measurements are the major source of uncertainties and errors in the outcomes of traveling wave-based diagnostic applications. The existing AT-detection methods do not provide enough sensitivity and selectivity at the same time. Therefore, a new AT-detection method based on short-time matrix pencil (STMPM) is developed to accurately detect ATs of the traveling waves with low signal-to-noise (SNR) ratios. As STMPM is based on matrix algebra, it is a challenging to implement this new technique in microprocessor-based fault locators. Hence, a fully recursive and computationally efficient method based on adaptive discrete Kalman filter (ADKF) is introduced for AT-detection, which is proper for microprocessors and able to accomplish accurate AT-detection for online applications such as ultra-high-speed protection. Both proposed AT-detection methods are evaluated based on extensive simulation studies, and the superior outcomes are compared to the existing methods.

Leak detection using structure-borne noise

NASA Technical Reports Server (NTRS)

Holland, Stephen D. (Inventor); Roberts, Ronald A. (Inventor); Chimenti, Dale E. (Inventor)

2010-01-01

A method for detection and location of air leaks in a pressure vessel, such as a spacecraft, includes sensing structure-borne ultrasound waveforms associated with turbulence caused by a leak from a plurality of sensors and cross correlating the waveforms to determine existence and location of the leak. Different configurations of sensors and corresponding methods can be used. An apparatus for performing the methods is also provided.

Quantitative PCR detection of Batrachochytrium dendrobatidis DNA from sediments and water

USGS Publications Warehouse

Kirshtein, Julie D.; Anderson, Chauncey W.; Wood, J.S.; Longcore, Joyce E.; Voytek, Mary A.

2007-01-01

The fungal pathogen Batrachochytrium dendrobatidis (Bd) causes chytridiomycosis, a disease implicated in amphibian declines on 5 continents. Polymerase chain reaction (PCR) primer sets exist with which amphibians can be tested for this disease, and advances in sampling techniques allow non-invasive testing of animals. We developed filtering and PCR based quantitative methods by modifying existing PCR assays to detect Bd DNA in water and sediments, without the need for testing amphibians; we tested the methods at 4 field sites. The SYBR based assay using Boyle primers (SYBR/Boyle assay) and the Taqman based assay using Wood primers performed similarly with samples generated in the laboratory (Bd spiked filters), but the SYBR/Boyle assay detected Bd DNA in more field samples. We detected Bd DNA in water from 3 of 4 sites tested, including one pond historically negative for chytridiomycosis. Zoospore equivalents in sampled water ranged from 19 to 454 l-1 (nominal detection limit is 10 DNA copies, or about 0.06 zoospore). We did not detect DNA of Bd from sediments collected at any sites. Our filtering and amplification methods provide a new tool to investigate critical aspects of Bd in the environment. ?? Inter-Research 2007.

Surface defect detection in tiling Industries using digital image processing methods: analysis and evaluation.

PubMed

Karimi, Mohammad H; Asemani, Davud

2014-05-01

Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Salient object detection method based on multiple semantic features

NASA Astrophysics Data System (ADS)

Wang, Chunyang; Yu, Chunyan; Song, Meiping; Wang, Yulei

2018-04-01

The existing salient object detection model can only detect the approximate location of salient object, or highlight the background, to resolve the above problem, a salient object detection method was proposed based on image semantic features. First of all, three novel salient features were presented in this paper, including object edge density feature (EF), object semantic feature based on the convex hull (CF) and object lightness contrast feature (LF). Secondly, the multiple salient features were trained with random detection windows. Thirdly, Naive Bayesian model was used for combine these features for salient detection. The results on public datasets showed that our method performed well, the location of salient object can be fixed and the salient object can be accurately detected and marked by the specific window.

Determination of Network Attributes from a High Resolution Terrain Data Base

DTIC Science & Technology

1987-09-01

and existing models is in the method used to make decisions. All of ,he models- reviewed when developing the ALARM strategy depended either on threshold...problems with the methods currently accepted and used to *model the decision process. These methods are recognized because they have their uses...observation, detection, and lines of sight along a narrow strip of terrain relative to the overall size of the sectors of the two forces. Existing methods of

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.

A Type-2 fuzzy data fusion approach for building reliable weighted protein interaction networks with application in protein complex detection.

PubMed

Mehranfar, Adele; Ghadiri, Nasser; Kouhsar, Morteza; Golshani, Ashkan

2017-09-01

Detecting the protein complexes is an important task in analyzing the protein interaction networks. Although many algorithms predict protein complexes in different ways, surveys on the interaction networks indicate that about 50% of detected interactions are false positives. Consequently, the accuracy of existing methods needs to be improved. In this paper we propose a novel algorithm to detect the protein complexes in 'noisy' protein interaction data. First, we integrate several biological data sources to determine the reliability of each interaction and determine more accurate weights for the interactions. A data fusion component is used for this step, based on the interval type-2 fuzzy voter that provides an efficient combination of the information sources. This fusion component detects the errors and diminishes their effect on the detection protein complexes. So in the first step, the reliability scores have been assigned for every interaction in the network. In the second step, we have proposed a general protein complex detection algorithm by exploiting and adopting the strong points of other algorithms and existing hypotheses regarding real complexes. Finally, the proposed method has been applied for the yeast interaction datasets for predicting the interactions. The results show that our framework has a better performance regarding precision and F-measure than the existing approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Anomaly Detection in Power Quality at Data Centers

NASA Technical Reports Server (NTRS)

Grichine, Art; Solano, Wanda M.

2015-01-01

The goal during my internship at the National Center for Critical Information Processing and Storage (NCCIPS) is to implement an anomaly detection method through the StruxureWare SCADA Power Monitoring system. The benefit of the anomaly detection mechanism is to provide the capability to detect and anticipate equipment degradation by monitoring power quality prior to equipment failure. First, a study is conducted that examines the existing techniques of power quality management. Based on these findings, and the capabilities of the existing SCADA resources, recommendations are presented for implementing effective anomaly detection. Since voltage, current, and total harmonic distortion demonstrate Gaussian distributions, effective set-points are computed using this model, while maintaining a low false positive count.

Immunochemical Detection Methods for Gluten in Food Products: Where Do We Go from Here?

PubMed

Slot, I D Bruins; van der Fels-Klerx, H J; Bremer, M G E G; Hamer, R J

2016-11-17

Accurate and reliable quantification methods for gluten in food are necessary to ensure proper product labeling and thus safeguard the gluten sensitive consumer against exposure. Immunochemical detection is the method of choice, as it is sensitive, rapid and relatively easy to use. Although a wide range of detection kits are commercially available, there are still many difficulties in gluten detection that have not yet been overcome. This review gives an overview of the currently commercially available immunochemical detection methods, and discusses the problems that still exist in gluten detection in food. The largest problems are encountered in the extraction of gluten from food matrices, the choice of epitopes targeted by the detection method, and the use of a standardized reference material. By comparing the available techniques with the unmet needs in gluten detection, the possible benefit of a new multiplex immunoassay is investigated. This detection method would allow for the detection and quantification of multiple harmful gluten peptides at once and would, therefore, be a logical advancement in gluten detection in food.

Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique

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

Teramoto, Atsushi, E-mail: teramoto@fujita-hu.ac.jp; Fujita, Hiroshi; Yamamuro, Osamu

Purpose: Automated detection of solitary pulmonary nodules using positron emission tomography (PET) and computed tomography (CT) images shows good sensitivity; however, it is difficult to detect nodules in contact with normal organs, and additional efforts are needed so that the number of false positives (FPs) can be further reduced. In this paper, the authors propose an improved FP-reduction method for the detection of pulmonary nodules in PET/CT images by means of convolutional neural networks (CNNs). Methods: The overall scheme detects pulmonary nodules using both CT and PET images. In the CT images, a massive region is first detected using anmore » active contour filter, which is a type of contrast enhancement filter that has a deformable kernel shape. Subsequently, high-uptake regions detected by the PET images are merged with the regions detected by the CT images. FP candidates are eliminated using an ensemble method; it consists of two feature extractions, one by shape/metabolic feature analysis and the other by a CNN, followed by a two-step classifier, one step being rule based and the other being based on support vector machines. Results: The authors evaluated the detection performance using 104 PET/CT images collected by a cancer-screening program. The sensitivity in detecting candidates at an initial stage was 97.2%, with 72.8 FPs/case. After performing the proposed FP-reduction method, the sensitivity of detection was 90.1%, with 4.9 FPs/case; the proposed method eliminated approximately half the FPs existing in the previous study. Conclusions: An improved FP-reduction scheme using CNN technique has been developed for the detection of pulmonary nodules in PET/CT images. The authors’ ensemble FP-reduction method eliminated 93% of the FPs; their proposed method using CNN technique eliminates approximately half the FPs existing in the previous study. These results indicate that their method may be useful in the computer-aided detection of pulmonary nodules using PET/CT images.« less

Detecting a liquid and solid H2O layer by geophysical methods

NASA Astrophysics Data System (ADS)

Yoshikawa, K.; Romanovsky, V.; Tsapin, A.; Brown, J.

2002-12-01

The objective is to detect the hydrological and cryological structure of the cold continuous permafrost subsurface using geophysical methods. We believe that a lot of water potentially exists as solid and liquid phases underground on Mars. It is likely that the liquid fluid would be high in saline concentration (brine). The ground freezing process involves many hydrological processes including enrichment of the brine layer. The brine layer is an important environment for ancient and/or current life to exist on terrestrial permafrost regions. The existence of a Martian brine layer would increase the possibility of the existence of life, as on Earth. In situ electric resistivity measurement will be the most efficient method to determine brine layer as well as massive H2O ice in the permafrost. However, the wiring configuration is unlikely to operate on the remote planetary surface. Satellite-born Radar and/or EM methods will be the most accessible methods for detecting the hydrological and cryological structure. We are testing several geophysical methods at the brine layer site in Barrow and massive pingo ice site in Fairbanks, Alaska. The radar system is affected by the dielectric properties of subsurface materials, which allows for evidence of liquid phase in the frozen ground. The dielectric constant varies greatly between liquid water and frozen ground. The depth of the terrestrial (and probably Martian) brine layer is frequently located deeper than the maximum detecting depth of the impulse type of the ground penetrating radar system. Once we develop a radar system with a deeper penetrating capability (Lower frequency), the dispersion of the ground ice will be the key function for interpretation of these signals. We will improve and use radar signals to understand the hydrological and cryological structure in the permafrost. The core samples and borehole temperature data validate these radar signals.

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance.

PubMed

Clare, John; McKinney, Shawn T; DePue, John E; Loftin, Cynthia S

2017-10-01

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture-recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters. © 2017 by the Ecological Society of America.

Enzyme-linked immunosorbent assay detection and bioactivity of Cry1Ab protein fragments

USDA-ARS?s Scientific Manuscript database

Enzyme-linked immunosorbent assay (ELISA) has emerged as the preferred detection method for Cry proteins in environmental matrices. Concerns exist that ELISAs are capable of detecting fragments of Cry proteins, which may lead to an over-estimation of the concentration of these proteins in the enviro...

Fast microcalcification detection in ultrasound images using image enhancement and threshold adjacency statistics

NASA Astrophysics Data System (ADS)

Cho, Baek Hwan; Chang, Chuho; Lee, Jong-Ha; Ko, Eun Young; Seong, Yeong Kyeong; Woo, Kyoung-Gu

2013-02-01

The existence of microcalcifications (MCs) is an important marker of malignancy in breast cancer. In spite of the benefits in mass detection for dense breasts, ultrasonography is believed that it might not reliably detect MCs. For computer aided diagnosis systems, however, accurate detection of MCs has the possibility of improving the performance in both Breast Imaging-Reporting and Data System (BI-RADS) lexicon description for calcifications and malignancy classification. We propose a new efficient and effective method for MC detection using image enhancement and threshold adjacency statistics (TAS). The main idea of TAS is to threshold an image and to count the number of white pixels with a given number of adjacent white pixels. Our contribution is to adopt TAS features and apply image enhancement to facilitate MC detection in ultrasound images. We employed fuzzy logic, tophat filter, and texture filter to enhance images for MCs. Using a total of 591 images, the classification accuracy of the proposed method in MC detection showed 82.75%, which is comparable to that of Haralick texture features (81.38%). When combined, the performance was as high as 85.11%. In addition, our method also showed the ability in mass classification when combined with existing features. In conclusion, the proposed method exploiting image enhancement and TAS features has the potential to deal with MC detection in ultrasound images efficiently and extend to the real-time localization and visualization of MCs.

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors.

PubMed

Kim, Jong Hyun; Hong, Hyung Gil; Park, Kang Ryoung

2017-05-08

Because intelligent surveillance systems have recently undergone rapid growth, research on accurately detecting humans in videos captured at a long distance is growing in importance. The existing research using visible light cameras has mainly focused on methods of human detection for daytime hours when there is outside light, but human detection during nighttime hours when there is no outside light is difficult. Thus, methods that employ additional near-infrared (NIR) illuminators and NIR cameras or thermal cameras have been used. However, in the case of NIR illuminators, there are limitations in terms of the illumination angle and distance. There are also difficulties because the illuminator power must be adaptively adjusted depending on whether the object is close or far away. In the case of thermal cameras, their cost is still high, which makes it difficult to install and use them in a variety of places. Because of this, research has been conducted on nighttime human detection using visible light cameras, but this has focused on objects at a short distance in an indoor environment or the use of video-based methods to capture multiple images and process them, which causes problems related to the increase in the processing time. To resolve these problems, this paper presents a method that uses a single image captured at night on a visible light camera to detect humans in a variety of environments based on a convolutional neural network. Experimental results using a self-constructed Dongguk night-time human detection database (DNHD-DB1) and two open databases (Korea advanced institute of science and technology (KAIST) and computer vision center (CVC) databases), as well as high-accuracy human detection in a variety of environments, show that the method has excellent performance compared to existing methods.

Predicting protein complexes using a supervised learning method combined with local structural information.

PubMed

Dong, Yadong; Sun, Yongqi; Qin, Chao

2018-01-01

The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

Systems and methods of monitoring acoustic pressure to detect a flame condition in a gas turbine

DOEpatents

Ziminsky, Willy Steve [Simpsonville, SC; Krull, Anthony Wayne [Anderson, SC; Healy, Timothy Andrew , Yilmaz, Ertan

2011-05-17

A method may detect a flashback condition in a fuel nozzle of a combustor. The method may include obtaining a current acoustic pressure signal from the combustor, analyzing the current acoustic pressure signal to determine current operating frequency information for the combustor, and indicating that the flashback condition exists based at least in part on the current operating frequency information.

Mapping Diffuse Seismicity Using Empirical Matched Field Processing Techniques

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

Wang, J; Templeton, D C; Harris, D B

The objective of this project is to detect and locate more microearthquakes using the empirical matched field processing (MFP) method than can be detected using only conventional earthquake detection techniques. We propose that empirical MFP can complement existing catalogs and techniques. We test our method on continuous seismic data collected at the Salton Sea Geothermal Field during November 2009 and January 2010. In the Southern California Earthquake Data Center (SCEDC) earthquake catalog, 619 events were identified in our study area during this time frame and our MFP technique identified 1094 events. Therefore, we believe that the empirical MFP method combinedmore » with conventional methods significantly improves the network detection ability in an efficient matter.« less

FIND: difFerential chromatin INteractions Detection using a spatial Poisson process.

PubMed

Djekidel, Mohamed Nadhir; Chen, Yang; Zhang, Michael Q

2018-02-12

Polymer-based simulations and experimental studies indicate the existence of a spatial dependency between the adjacent DNA fibers involved in the formation of chromatin loops. However, the existing strategies for detecting differential chromatin interactions assume that the interacting segments are spatially independent from the other segments nearby. To resolve this issue, we developed a new computational method, FIND, which considers the local spatial dependency between interacting loci. FIND uses a spatial Poisson process to detect differential chromatin interactions that show a significant difference in their interaction frequency and the interaction frequency of their neighbors. Simulation and biological data analysis show that FIND outperforms the widely used count-based methods and has a better signal-to-noise ratio. © 2018 Djekidel et al.; Published by Cold Spring Harbor Laboratory Press.

Feasibility study for detection and quantification of corrosion in bridge barrier rails.

DOT National Transportation Integrated Search

2013-04-01

Technical challenges exist with infrastructure that can be addressed by nondestructive evaluation (NDE) methods, such as detecting corrosion damage to reinforcing steel that anchor concrete bridge railings to bridge road decks. Moisture and chloride ...

Instrumentation development for drug detection on the breath

DOT National Transportation Integrated Search

1972-09-01

Based on a survey of candidate analytical methods, mass spectrometry was identified as a promising technique for drug detection on the breath. To demonstrate its capabilities, an existing laboratory mass spectrometer was modified by the addition of a...

[Review of driver fatigue/drowsiness detection methods].

PubMed

Wang, Lei; Wu, Xiaojuan; Yu, Mengsun

2007-02-01

Driver fatigue/drowsiness is one of the important causes of serious traffic accidents and results in so many people deaths or injuries, but also substantial directly and indirectly economic expenses. Therefore, many countries make great effort on how to detect drowsiness during driving. In this paper, we introduce the recent developments of driver fatigue/drowsiness detection technology of world wide and try to classify the existing methods into several kinds according to different features measured, and analyzed. Finally, the challenges faced to fatigue/drowsiness detection technology and the development trend are presented.

A simple and sensitive high-throughput GFP screening in woody and herbaceous plants.

PubMed

Hily, Jean-Michel; Liu, Zongrang

2009-03-01

Green fluorescent protein (GFP) has been used widely as a powerful bioluminescent reporter, but its visualization by existing methods in tissues or whole plants and its utilization for high-throughput screening remains challenging in many species. Here, we report a fluorescence image analyzer-based method for GFP detection and its utility for high-throughput screening of transformed plants. Of three detection methods tested, the Typhoon fluorescence scanner was able to detect GFP fluorescence in all Arabidopsis thaliana tissues and apple leaves, while regular fluorescence microscopy detected it only in Arabidopsis flowers and siliques but barely in the leaves of either Arabidopsis or apple. The hand-held UV illumination method failed in all tissues of both species. Additionally, the Typhoon imager was able to detect GFP fluorescence in both green and non-green tissues of Arabidopsis seedlings as well as in imbibed seeds, qualifying it as a high-throughput screening tool, which was further demonstrated by screening the seedlings of primary transformed T(0) seeds. Of the 30,000 germinating Arabidopsis seedlings screened, at least 69 GFP-positive lines were identified, accounting for an approximately 0.23% transformation efficiency. About 14,000 seedlings grown in 16 Petri plates could be screened within an hour, making the screening process significantly more efficient and robust than any other existing high-throughput screening method for transgenic plants.

Rapid Isolation of Phenol Degrading Bacteria by Fourier Transform Infrared (FTIR) Spectroscopy.

PubMed

Li, Fei; Song, Wen-jun; Wei, Ji-ping; Wang, Su-ying; Liu, Chong-ji

2015-05-01

Phenol is an important chemical engineering material and ubiquitous in industry wastewater, its existence has become a thorny issue in many developed and developing country. More and more stringent standards for effluent all over the world with human realizing the toxicity of phenol have been announced. Many advanced biological methods are applied to industrial wastewater treatment with low cost, high efficiency and no secondary pollution, but the screening of function microorganisms is certain cumbersome process. In our study a rapid procedure devised for screening bacteria on solid medium can degrade phenol coupled with attenuated total reflection fourier transform infrared (ATR-FTIR) which is a detection method has the characteristics of efficient, fast, high fingerprint were used. Principal component analysis (PCA) is a method in common use to extract fingerprint peaks effectively, it couples with partial least squares (PLS) statistical method could establish a credible model. The model we created using PCA-PLS can reach 99. 5% of coefficient determination and validation data get 99. 4%, which shows the promising fitness and forecasting of the model. The high fitting model is used for predicting the concentration of phenol at solid medium where the bacteria were grown. The highly consistent result of two screening methods, solid cultural with ATR-FTIR detected and traditional liquid cultural detected by GC methods, suggests the former can rapid isolate the bacteria which can degrade substrates as well as traditional cumbersome liquid cultural method. Many hazardous substrates widely existed in industry wastewater, most of them has specialize fingerprint peaks detected by ATR-FTIR, thereby this detected method could be used as a rapid detection for isolation of functional microorganisms those can degrade many other toxic substrates.

Single kernel method for detection of 2-acetyl-1-pyrroline in aromatic rice germplasm using SPME-GC/MS

USDA-ARS?s Scientific Manuscript database

INTRODUCTION Aromatic rice or fragrant rice, (Oryza sativa L.), has a strong popcorn-like aroma due to the presence of a five-membered N-heterocyclic ring compound known as 2-acetyl-1-pyrroline (2-AP). To date, existing methods for detecting this compound in rice require the use of several kernels. ...

Efficient method of image edge detection based on FSVM

NASA Astrophysics Data System (ADS)

Cai, Aiping; Xiong, Xiaomei

2013-07-01

For efficient object cover edge detection in digital images, this paper studied traditional methods and algorithm based on SVM. It analyzed Canny edge detection algorithm existed some pseudo-edge and poor anti-noise capability. In order to provide a reliable edge extraction method, propose a new detection algorithm based on FSVM. Which contains several steps: first, trains classify sample and gives the different membership function to different samples. Then, a new training sample is formed by increase the punishment some wrong sub-sample, and use the new FSVM classification model for train and test them. Finally the edges are extracted of the object image by using the model. Experimental result shows that good edge detection image will be obtained and adding noise experiments results show that this method has good anti-noise.

Novel Method For Low-Rate Ddos Attack Detection

NASA Astrophysics Data System (ADS)

Chistokhodova, A. A.; Sidorov, I. D.

2018-05-01

The relevance of the work is associated with an increasing number of advanced types of DDoS attacks, in particular, low-rate HTTP-flood. Last year, the power and complexity of such attacks increased significantly. The article is devoted to the analysis of DDoS attacks detecting methods and their modifications with the purpose of increasing the accuracy of DDoS attack detection. The article details low-rate attacks features in comparison with conventional DDoS attacks. During the analysis, significant shortcomings of the available method for detecting low-rate DDoS attacks were found. Thus, the result of the study is an informal description of a new method for detecting low-rate denial-of-service attacks. The architecture of the stand for approbation of the method is developed. At the current stage of the study, it is possible to improve the efficiency of an already existing method by using a classifier with memory, as well as additional information.

Detection of artifacts from high energy bursts in neonatal EEG.

PubMed

Bhattacharyya, Sourya; Biswas, Arunava; Mukherjee, Jayanta; Majumdar, Arun Kumar; Majumdar, Bandana; Mukherjee, Suchandra; Singh, Arun Kumar

2013-11-01

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well. © 2013 Elsevier Ltd. All rights reserved.

Hybrid Clustering And Boundary Value Refinement for Tumor Segmentation using Brain MRI

NASA Astrophysics Data System (ADS)

Gupta, Anjali; Pahuja, Gunjan

2017-08-01

The method of brain tumor segmentation is the separation of tumor area from Brain Magnetic Resonance (MR) images. There are number of methods already exist for segmentation of brain tumor efficiently. However it’s tedious task to identify the brain tumor from MR images. The segmentation process is extraction of different tumor tissues such as active, tumor, necrosis, and edema from the normal brain tissues such as gray matter (GM), white matter (WM), as well as cerebrospinal fluid (CSF). As per the survey study, most of time the brain tumors are detected easily from brain MR image using region based approach but required level of accuracy, abnormalities classification is not predictable. The segmentation of brain tumor consists of many stages. Manually segmenting the tumor from brain MR images is very time consuming hence there exist many challenges in manual segmentation. In this research paper, our main goal is to present the hybrid clustering which consists of Fuzzy C-Means Clustering (for accurate tumor detection) and level set method(for handling complex shapes) for the detection of exact shape of tumor in minimal computational time. using this approach we observe that for a certain set of images 0.9412 sec of time is taken to detect tumor which is very less in comparison to recent existing algorithm i.e. Hybrid clustering (Fuzzy C-Means and K Means clustering).

Methods for artifact detection and removal from scalp EEG: A review.

PubMed

Islam, Md Kafiul; Rastegarnia, Amir; Yang, Zhi

2016-11-01

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Detection of oral HPV infection - Comparison of two different specimen collection methods and two HPV detection methods.

PubMed

de Souza, Marjorie M A; Hartel, Gunter; Whiteman, David C; Antonsson, Annika

2018-04-01

Very little is known about the natural history of oral HPV infection. Several different methods exist to collect oral specimens and detect HPV, but their respective performance characteristics are unknown. We compared two different methods for oral specimen collection (oral saline rinse and commercial saliva kit) from 96 individuals and then analyzed the samples for HPV by two different PCR detection methods (single GP5+/6+ PCR and nested MY09/11 and GP5+/6+ PCR). For the oral rinse samples, the oral HPV prevalence was 10.4% (GP+ PCR; 10% repeatability) vs 11.5% (nested PCR method; 100% repeatability). For the commercial saliva kit samples, the prevalences were 3.1% vs 16.7% with the GP+ PCR vs the nested PCR method (repeatability 100% for both detection methods). Overall the agreement was fair or poor between samples and methods (kappa 0.06-0.36). Standardizing methods of oral sample collection and HPV detection would ensure comparability between future oral HPV studies. Copyright © 2017 Elsevier Inc. All rights reserved.

A comparison of moving object detection methods for real-time moving object detection

NASA Astrophysics Data System (ADS)

Roshan, Aditya; Zhang, Yun

2014-06-01

Moving object detection has a wide variety of applications from traffic monitoring, site monitoring, automatic theft identification, face detection to military surveillance. Many methods have been developed across the globe for moving object detection, but it is very difficult to find one which can work globally in all situations and with different types of videos. The purpose of this paper is to evaluate existing moving object detection methods which can be implemented in software on a desktop or laptop, for real time object detection. There are several moving object detection methods noted in the literature, but few of them are suitable for real time moving object detection. Most of the methods which provide for real time movement are further limited by the number of objects and the scene complexity. This paper evaluates the four most commonly used moving object detection methods as background subtraction technique, Gaussian mixture model, wavelet based and optical flow based methods. The work is based on evaluation of these four moving object detection methods using two (2) different sets of cameras and two (2) different scenes. The moving object detection methods have been implemented using MatLab and results are compared based on completeness of detected objects, noise, light change sensitivity, processing time etc. After comparison, it is observed that optical flow based method took least processing time and successfully detected boundary of moving objects which also implies that it can be implemented for real-time moving object detection.

Novel method for edge detection of retinal vessels based on the model of the retinal vascular network and mathematical morphology

NASA Astrophysics Data System (ADS)

Xu, Lei; Zheng, Xiaoxiang; Zhang, Hengyi; Yu, Yajun

1998-09-01

Accurate edge detection of retinal vessels is a prerequisite for quantitative analysis of subtle morphological changes of retinal vessels under different pathological conditions. A novel method for edge detection of retinal vessels is presented in this paper. Methods: (1) Wavelet-based image preprocessing. (2) The signed edge detection algorithm and mathematical morphological operation are applied to get the approximate regions that contain retinal vessels. (3) By convolving the preprocessed image with a LoG operator only on the detected approximate regions of retinal vessels, followed by edges refining, clear edge maps of the retinal vessels are fast obtained. Results: A detailed performance evaluation together with the existing techniques is given to demonstrate the strong features of our method. Conclusions: True edge locations of retinal vessels can be fast detected with continuous structures of retinal vessels, less non- vessel segments left and insensitivity to noise. The method is also suitable for other application fields such as road edge detection.

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.

Real time algorithms for sharp wave ripple detection.

PubMed

Sethi, Ankit; Kemere, Caleb

2014-01-01

Neural activity during sharp wave ripples (SWR), short bursts of co-ordinated oscillatory activity in the CA1 region of the rodent hippocampus, is implicated in a variety of memory functions from consolidation to recall. Detection of these events in an algorithmic framework, has thus far relied on simple thresholding techniques with heuristically derived parameters. This study is an investigation into testing and improving the current methods for detection of SWR events in neural recordings. We propose and profile methods to reduce latency in ripple detection. Proposed algorithms are tested on simulated ripple data. The findings show that simple realtime algorithms can improve upon existing power thresholding methods and can detect ripple activity with latencies in the range of 10-20 ms.

Methods and apparatus for rotor blade ice detection

DOEpatents

LeMieux, David Lawrence

2006-08-08

A method for detecting ice on a wind turbine having a rotor and one or more rotor blades each having blade roots includes monitoring meteorological conditions relating to icing conditions and monitoring one or more physical characteristics of the wind turbine in operation that vary in accordance with at least one of the mass of the one or more rotor blades or a mass imbalance between the rotor blades. The method also includes using the one or more monitored physical characteristics to determine whether a blade mass anomaly exists, determining whether the monitored meteorological conditions are consistent with blade icing; and signaling an icing-related blade mass anomaly when a blade mass anomaly is determined to exist and the monitored meteorological conditions are determined to be consistent with icing.

Smart accelerometer. [vibration damage detection

NASA Technical Reports Server (NTRS)

Bozeman, Richard J., Jr. (Inventor)

1994-01-01

The invention discloses methods and apparatus for detecting vibrations from machines which indicate an impending malfunction for the purpose of preventing additional damage and allowing for an orderly shutdown or a change in mode of operation. The method and apparatus is especially suited for reliable operation in providing thruster control data concerning unstable vibration in an electrical environment which is typically noisy and in which unrecognized ground loops may exist.

An improved parameter estimation scheme for image modification detection based on DCT coefficient analysis.

PubMed

Yu, Liyang; Han, Qi; Niu, Xiamu; Yiu, S M; Fang, Junbin; Zhang, Ye

2016-02-01

Most of the existing image modification detection methods which are based on DCT coefficient analysis model the distribution of DCT coefficients as a mixture of a modified and an unchanged component. To separate the two components, two parameters, which are the primary quantization step, Q1, and the portion of the modified region, α, have to be estimated, and more accurate estimations of α and Q1 lead to better detection and localization results. Existing methods estimate α and Q1 in a completely blind manner, without considering the characteristics of the mixture model and the constraints to which α should conform. In this paper, we propose a more effective scheme for estimating α and Q1, based on the observations that, the curves on the surface of the likelihood function corresponding to the mixture model is largely smooth, and α can take values only in a discrete set. We conduct extensive experiments to evaluate the proposed method, and the experimental results confirm the efficacy of our method. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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.

Spatial Lattice Modulation for MIMO Systems

NASA Astrophysics Data System (ADS)

Choi, Jiwook; Nam, Yunseo; Lee, Namyoon

2018-06-01

This paper proposes spatial lattice modulation (SLM), a spatial modulation method for multipleinput-multiple-output (MIMO) systems. The key idea of SLM is to jointly exploit spatial, in-phase, and quadrature dimensions to modulate information bits into a multi-dimensional signal set that consists oflattice points. One major finding is that SLM achieves a higher spectral efficiency than the existing spatial modulation and spatial multiplexing methods for the MIMO channel under the constraint ofM-ary pulseamplitude-modulation (PAM) input signaling per dimension. In particular, it is shown that when the SLM signal set is constructed by using dense lattices, a significant signal-to-noise-ratio (SNR) gain, i.e., a nominal coding gain, is attainable compared to the existing methods. In addition, closed-form expressions for both the average mutual information and average symbol-vector-error-probability (ASVEP) of generic SLM are derived under Rayleigh-fading environments. To reduce detection complexity, a low-complexity detection method for SLM, which is referred to as lattice sphere decoding, is developed by exploiting lattice theory. Simulation results verify the accuracy of the conducted analysis and demonstrate that the proposed SLM techniques achieve higher average mutual information and lower ASVEP than do existing methods.

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.

Probabilistic resident space object detection using archival THEMIS fluxgate magnetometer data

NASA Astrophysics Data System (ADS)

Brew, Julian; Holzinger, Marcus J.

2018-05-01

Recent progress in the detection of small space objects, at geosynchronous altitudes, through ground-based optical and radar measurements is demonstrated as a viable method. However, in general, these methods are limited to detection of objects greater than 10 cm. This paper examines the use of magnetometers to detect plausible flyby encounters with charged space objects using a matched filter signal existence binary hypothesis test approach. Relevant data-set processing and reduction of archival fluxgate magnetometer data from the NASA THEMIS mission is discussed in detail. Using the proposed methodology and a false alarm rate of 10%, 285 plausible detections with probability of detection greater than 80% are claimed and several are reviewed in detail.

RAPTR-SV: a hybrid method for the detection of structural variants

USDA-ARS?s Scientific Manuscript database

Motivation: Identification of Structural Variants (SV) in sequence data results in a large number of false positive calls using existing software, which overburdens subsequent validation. Results: Simulations using RAPTR-SV and another software package that uses a similar algorithm for SV detection...

Microwave imaging of spinning object using orbital angular momentum

NASA Astrophysics Data System (ADS)

Liu, Kang; Li, Xiang; Gao, Yue; Wang, Hongqiang; Cheng, Yongqiang

2017-09-01

The linear Doppler shift used for the detection of a spinning object becomes significantly weakened when the line of sight (LOS) is perpendicular to the object, which will result in the failure of detection. In this paper, a new detection and imaging technique for spinning objects is developed. The rotational Doppler phenomenon is observed by using the microwave carrying orbital angular momentum (OAM). To converge the radiation energy on the area where objects might exist, the generation method of OAM beams is proposed based on the frequency diversity principle, and the imaging model is derived accordingly. The detection method of the rotational Doppler shift and the imaging approach of the azimuthal profiles are proposed, which are verified by proof-of-concept experiments. Simulation and experimental results demonstrate that OAM beams can still be used to obtain the azimuthal profiles of spinning objects even when the LOS is perpendicular to the object. This work remedies the insufficiency in existing microwave sensing technology and offers a new solution to the object identification problem.

Automatic Detection and Vulnerability Analysis of Areas Endangered by Heavy Rain

NASA Astrophysics Data System (ADS)

Krauß, Thomas; Fischer, Peter

2016-08-01

In this paper we present a new method for fully automatic detection and derivation of areas endangered by heavy rainfall based only on digital elevation models. Tracking news show that the majority of occuring natural hazards are flood events. So already many flood prediction systems were developed. But most of these existing systems for deriving areas endangered by flooding events are based only on horizontal and vertical distances to existing rivers and lakes. Typically such systems take not into account dangers arising directly from heavy rain events. In a study conducted by us together with a german insurance company a new approach for detection of areas endangered by heavy rain was proven to give a high correlation of the derived endangered areas and the losses claimed at the insurance company. Here we describe three methods for classification of digital terrain models and analyze their usability for automatic detection and vulnerability analysis for areas endangered by heavy rainfall and analyze the results using the available insurance data.

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.

Detection of Different β-Lactamases and their Co-existence by Using Various Discs Combination Methods in Clinical Isolates of Enterobacteriaceae and Pseudomonas spp.

PubMed Central

Rawat, Vinita; Singhai, Monil; Verma, Pankaj Kumar

2013-01-01

Background: Resistance to broad spectrum beta-lactams mediated by extended spectrum β-lactamase (ESBL), AmpC, and metallobetalactamase (MBLs) enzymes are an increasing problem worldwide. The study was aimed to detect occurrence rate and to evaluate different substrates and inhibitors by disc combination method for detecting varying degree of β-lactamase enzymes and their co-production. Materials and Methods: A disc panel containing imipenem (IMP), IMP/EDTA, ceftazidime (CA), ceftazidime-tazobactum (CAT), CAT/cloxacillin (CLOX), ceftazidime-clavulanic acid (CAC), CAC/CLOX, cefoxitin (CN), and CN/CLOX in a single plate was used to detect presence of ESBLs, AmpC, and MBLs and/or their co-existence in 184 consecutive, nonrepetitive, clinical isolates of Enterobacteriace (n = 96) and Pseudomonas spp. (n = 88) from pus samples of hospitalized patients, resistant to 3rd generation cephalosporins. Results: Out of a total of 96 clinical isolates of Enterobacteriaceae, 18.7, 20.8, and 27% were pure ESBL, AmpC, and MBL producers, respectively. ESBL and AmpC were co-produced by 25% isolates. Among 88 Pseudomonas spp. 38.6, 13, and 6% were pure MBL, ESBL, and AmpC producers, respectively. ESBL/AmpC and MBL/AmpC co-production was seen in 20% and 18% isolates, respectively. Among ESBL and AmpC co-producers, CA/CAC/CLOX disc combination (DC) missed 7 of the 24 ESBL producers in Enterobacteriace and 4 of the 18 ESBL in Pseudomonas spp., which were detected by CA/CAT/CLOX DC. No mechanism was detected among 8.3% Enterobacteriaceae and 2.3% Pseudomonas isolates. Conclusion: Diagnostic problems posed by co-existence of different classes of β-lactamases in a single isolate could be solved by disc combination method by using simple panel of discs containing CA, CAT, CAT/CLOX, IMP, and IMP/EDTA. PMID:24014963

Existence of the sugar-bisulfite adducts and its inhibiting effect on degradation of monosaccharide in acid system.

PubMed

Shi, Yan

2014-02-01

Degradation of fermentable monosaccharides is one of the primary concerns for acid prehydrolysis of lignocellulosic biomass. Recently, in our research on degradation of pure monosaccharides in aqueous SO₂ solution by gas chromatography (GC) analysis, we found that detected yield was not actual yield of each monosaccharide due to the existence of sugar-bisulfite adducts, and a new method was developed by ourselves which led to accurate detection of recovery yield of each monosaccharide in aqueous SO₂ solution by GC analysis. By the use of this method, degradation of each monosaccharide in aqueous SO₂ was investigated and results showed that sugar-bisulfite adducts have different inhibiting effect on degradation of each monosaccharide in aqueous SO₂ because of their different stability. In addition, NMR testing also demonstrated possible existence of reaction between conjugated based HSO₃(-) and aldehyde group of sugars in acid system.

Event Detection Using Mobile Phone Mass GPS Data and Their Reliavility Verification by Dmsp/ols Night Light Image

NASA Astrophysics Data System (ADS)

Yuki, Akiyama; Satoshi, Ueyama; Ryosuke, Shibasaki; Adachi, Ryuichiro

2016-06-01

In this study, we developed a method to detect sudden population concentration on a certain day and area, that is, an "Event," all over Japan in 2012 using mass GPS data provided from mobile phone users. First, stay locations of all phone users were detected using existing methods. Second, areas and days where Events occurred were detected by aggregation of mass stay locations into 1-km-square grid polygons. Finally, the proposed method could detect Events with an especially large number of visitors in the year by removing the influences of Events that occurred continuously throughout the year. In addition, we demonstrated reasonable reliability of the proposed Event detection method by comparing the results of Event detection with light intensities obtained from the night light images from the DMSP/OLS night light images. Our method can detect not only positive events such as festivals but also negative events such as natural disasters and road accidents. These results are expected to support policy development of urban planning, disaster prevention, and transportation management.

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.

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.

Joint Facial Action Unit Detection and Feature Fusion: A Multi-conditional Learning Approach.

PubMed

Eleftheriadis, Stefanos; Rudovic, Ognjen; Pantic, Maja

2016-10-05

Automated analysis of facial expressions can benefit many domains, from marketing to clinical diagnosis of neurodevelopmental disorders. Facial expressions are typically encoded as a combination of facial muscle activations, i.e., action units. Depending on context, these action units co-occur in specific patterns, and rarely in isolation. Yet, most existing methods for automatic action unit detection fail to exploit dependencies among them, and the corresponding facial features. To address this, we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection. Specifically, the proposed model performs feature fusion in a generative fashion via a low-dimensional shared subspace, while simultaneously performing action unit detection using a discriminative classification approach. We show that by combining the merits of both approaches, the proposed methodology outperforms existing purely discriminative/generative methods for the target task. To reduce the number of parameters, and avoid overfitting, a novel Bayesian learning approach based on Monte Carlo sampling is proposed, to integrate out the shared subspace. We validate the proposed method on posed and spontaneous data from three publicly available datasets (CK+, DISFA and Shoulder-pain), and show that both feature fusion and joint learning of action units leads to improved performance compared to the state-of-the-art methods for the task.

Artificial Neural Network for Probabilistic Feature Recognition in Liquid Chromatography Coupled to High-Resolution Mass Spectrometry.

PubMed

Woldegebriel, Michael; Derks, Eduard

2017-01-17

In this work, a novel probabilistic untargeted feature detection algorithm for liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) using artificial neural network (ANN) is presented. The feature detection process is approached as a pattern recognition problem, and thus, ANN was utilized as an efficient feature recognition tool. Unlike most existing feature detection algorithms, with this approach, any suspected chromatographic profile (i.e., shape of a peak) can easily be incorporated by training the network, avoiding the need to perform computationally expensive regression methods with specific mathematical models. In addition, with this method, we have shown that the high-resolution raw data can be fully utilized without applying any arbitrary thresholds or data reduction, therefore improving the sensitivity of the method for compound identification purposes. Furthermore, opposed to existing deterministic (binary) approaches, this method rather estimates the probability of a feature being present/absent at a given point of interest, thus giving chance for all data points to be propagated down the data analysis pipeline, weighed with their probability. The algorithm was tested with data sets generated from spiked samples in forensic and food safety context and has shown promising results by detecting features for all compounds in a computationally reasonable time.

Incremental Principal Component Analysis Based Outlier Detection Methods for Spatiotemporal Data Streams

NASA Astrophysics Data System (ADS)

Bhushan, A.; Sharker, M. H.; Karimi, H. A.

2015-07-01

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

Search for extraterrestrial intelligence (SETI)

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

Morrison, P.; Billingham, J.; Wolfe, J.

1977-01-01

Findings are presented of a series of workshops on the existence of extraterrestrial intelligent life and ways in which extraterrestrial intelligence might be detected. The coverage includes the cosmic and cultural evolutions, search strategies, detection of other planetary systems, alternate methods of communication, and radio frequency interference. 17 references. (JFP)

Circulating Mycobacterium bovis peptides and host response proteins as biomarkers for unambiguous detection of subclinical infection

USDA-ARS?s Scientific Manuscript database

Background: Bovine tuberculosis remains one of the most damaging zoonotic diseases. A critical need exists for rapid and inexpensive diagnostics capable of detecting and differentiating M. bovis infection from other pathogenic and environmental mycobacteria at multiple surveillance levels. 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.

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.

Statistical approaches to the analysis of point count data: A little extra information can go a long way

USGS Publications Warehouse

Farnsworth, G.L.; Nichols, J.D.; Sauer, J.R.; Fancy, S.G.; Pollock, K.H.; Shriner, S.A.; Simons, T.R.; Ralph, C. John; Rich, Terrell D.

2005-01-01

Point counts are a standard sampling procedure for many bird species, but lingering concerns still exist about the quality of information produced from the method. It is well known that variation in observer ability and environmental conditions can influence the detection probability of birds in point counts, but many biologists have been reluctant to abandon point counts in favor of more intensive approaches to counting. However, over the past few years a variety of statistical and methodological developments have begun to provide practical ways of overcoming some of the problems with point counts. We describe some of these approaches, and show how they can be integrated into standard point count protocols to greatly enhance the quality of the information. Several tools now exist for estimation of detection probability of birds during counts, including distance sampling, double observer methods, time-depletion (removal) methods, and hybrid methods that combine these approaches. Many counts are conducted in habitats that make auditory detection of birds much more likely than visual detection. As a framework for understanding detection probability during such counts, we propose separating two components of the probability a bird is detected during a count into (1) the probability a bird vocalizes during the count and (2) the probability this vocalization is detected by an observer. In addition, we propose that some measure of the area sampled during a count is necessary for valid inferences about bird populations. This can be done by employing fixed-radius counts or more sophisticated distance-sampling models. We recommend any studies employing point counts be designed to estimate detection probability and to include a measure of the area sampled.

Application of Coamplification at Lower Denaturation Temperature-PCR Sequencing for Early Detection of Antiviral Drug Resistance Mutations of Hepatitis B Virus

PubMed Central

Wong, Danny Ka-Ho; Tsoi, Ottilia; Huang, Fung-Yu; Seto, Wai-Kay; Fung, James; Lai, Ching-Lung

2014-01-01

Nucleoside/nucleotide analogue for the treatment of chronic hepatitis B virus (HBV) infection is hampered by the emergence of drug resistance mutations. Conventional PCR sequencing cannot detect minor variants of <20%. We developed a modified co-amplification at lower denaturation temperature-PCR (COLD-PCR) method for the detection of HBV minority drug resistance mutations. The critical denaturation temperature for COLD-PCR was determined to be 78°C. Sensitivity of COLD-PCR sequencing was determined using serially diluted plasmids containing mixed proportions of HBV reverse transcriptase (rt) wild-type and mutant sequences. Conventional PCR sequencing detected mutations only if they existed in ≥25%, whereas COLD-PCR sequencing detected mutations when they existed in 5 to 10% of the viral population. The performance of COLD-PCR was compared to conventional PCR sequencing and a line probe assay (LiPA) using 215 samples obtained from 136 lamivudine- or telbivudine-treated patients with virological breakthrough. Among these 215 samples, drug resistance mutations were detected in 155 (72%), 148 (69%), and 113 samples (53%) by LiPA, COLD-PCR, and conventional PCR sequencing, respectively. Nineteen (9%) samples had mutations detectable by COLD-PCR but not LiPA, while 26 (12%) samples had mutations detectable by LiPA but not COLD-PCR, indicating both methods were comparable (P = 0.371). COLD-PCR was more sensitive than conventional PCR sequencing. Thirty-five (16%) samples had mutations detectable by COLD-PCR but not conventional PCR sequencing, while none had mutations detected by conventional PCR sequencing but not COLD-PCR (P < 0.0001). COLD-PCR sequencing is a simple method which is comparable to LiPA and superior to conventional PCR sequencing in detecting minor lamivudine/telbivudine resistance mutations. PMID:24951803

Signal existence verification (SEV) for GPS low received power signal detection using the time-frequency approach.

PubMed

Jan, Shau-Shiun; Sun, Chih-Cheng

2010-01-01

The detection of low received power of global positioning system (GPS) signals in the signal acquisition process is an important issue for GPS applications. Improving the miss-detection problem of low received power signal is crucial, especially for urban or indoor environments. This paper proposes a signal existence verification (SEV) process to detect and subsequently verify low received power GPS signals. The SEV process is based on the time-frequency representation of GPS signal, and it can capture the characteristic of GPS signal in the time-frequency plane to enhance the GPS signal acquisition performance. Several simulations and experiments are conducted to show the effectiveness of the proposed method for low received power signal detection. The contribution of this work is that the SEV process is an additional scheme to assist the GPS signal acquisition process in low received power signal detection, without changing the original signal acquisition or tracking algorithms.

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.

A Simple Method to Simultaneously Detect and Identify Spikes from Raw Extracellular Recordings.

PubMed

Petrantonakis, Panagiotis C; Poirazi, Panayiota

2015-01-01

The ability to track when and which neurons fire in the vicinity of an electrode, in an efficient and reliable manner can revolutionize the neuroscience field. The current bottleneck lies in spike sorting algorithms; existing methods for detecting and discriminating the activity of multiple neurons rely on inefficient, multi-step processing of extracellular recordings. In this work, we show that a single-step processing of raw (unfiltered) extracellular signals is sufficient for both the detection and identification of active neurons, thus greatly simplifying and optimizing the spike sorting approach. The efficiency and reliability of our method is demonstrated in both real and simulated data.

A sensitive method for detecting and genotyping Cryptosporidium parvum oocysts

USDA-ARS?s Scientific Manuscript database

Cryptosporidium parvum oocysts represent a considerable health risk to humans and animals because the parasite has a low infectious dose and usually exists in low numbers in environmental samples, which makes detection problematic. The purpose of this study was to evaluate Cryspovirus as a target f...

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.

First experiences with an accelerated CMV antigenemia test: CMV Brite Turbo assay.

PubMed

Visser, C E; van Zeijl, C J; de Klerk, E P; Schillizi, B M; Beersma, M F; Kroes, A C

2000-06-01

Cytomegalovirus disease is still a major problem in immunocompromised patients, such as bone marrow or kidney transplantation patients. The detection of viral antigen in leukocytes (antigenemia) has proven to be a clinically relevant marker of CMV activity and has found widespread application. Because most existing assays are rather time-consuming and laborious, an accelerated version (Brite Turbo) of an existing method (Brite) has been developed. The major modification is in the direct lysis of erythrocytes instead of separation by sedimentation. In this study the Brite Turbo method has been compared with the conventional Brite method to detect CMV antigen pp65 in peripheral blood leukocytes of 107 consecutive immunocompromised patients. Both tests produced similar results. Discrepancies were limited to the lowest positive range and sensitivity and specificity were comparable for both tests. Two major advantages of the Brite Turbo method could be observed in comparison to the original method: assay-time was reduced by more than 50% and only 2 ml of blood was required. An additional advantage was the higher number of positive nuclei in the Brite Turbo method attributable to the increased number of granulocytes in the assay. Early detection of CMV infection or reactivation has become faster and easier with this modified assay.

A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery.

PubMed

Siddiqui, Fasahat Ullah; Teng, Shyh Wei; Awrangjeb, Mohammad; Lu, Guojun

2016-07-19

Existing automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics.

A Robust Gradient Based Method for Building Extraction from LiDAR and Photogrammetric Imagery

PubMed Central

Siddiqui, Fasahat Ullah; Teng, Shyh Wei; Awrangjeb, Mohammad; Lu, Guojun

2016-01-01

Existing automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics. PMID:27447631

Fabricating data: How substituting values for nondetects can ruin results, and what can be done about it

USGS Publications Warehouse

Helsel, D.R.

2006-01-01

The most commonly used method in environmental chemistry to deal with values below detection limits is to substitute a fraction of the detection limit for each nondetect. Two decades of research has shown that this fabrication of values produces poor estimates of statistics, and commonly obscures patterns and trends in the data. Papers using substitution may conclude that significant differences, correlations, and regression relationships do not exist, when in fact they do. The reverse may also be true. Fortunately, good alternative methods for dealing with nondetects already exist, and are summarized here with references to original sources. Substituting values for nondetects should be used rarely, and should generally be considered unacceptable in scientific research. There are better ways.

Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis.

PubMed

Yang, Chao; He, Zengyou; Yu, Weichuan

2009-01-06

In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis. Recently, there has been significant progress in the development of various peak detection algorithms. However, neither a comprehensive survey nor an experimental comparison of these algorithms is yet available. The main objective of this paper is to provide such a survey and to compare the performance of single spectrum based peak detection methods. In general, we can decompose a peak detection procedure into three consequent parts: smoothing, baseline correction and peak finding. We first categorize existing peak detection algorithms according to the techniques used in different phases. Such a categorization reveals the differences and similarities among existing peak detection algorithms. Then, we choose five typical peak detection algorithms to conduct a comprehensive experimental study using both simulation data and real MALDI MS data. The results of comparison show that the continuous wavelet-based algorithm provides the best average performance.

A novel infrared small moving target detection method based on tracking interest points under complicated background

NASA Astrophysics Data System (ADS)

Dong, Xiabin; Huang, Xinsheng; Zheng, Yongbin; Bai, Shengjian; Xu, Wanying

2014-07-01

Infrared moving target detection is an important part of infrared technology. We introduce a novel infrared small moving target detection method based on tracking interest points under complicated background. Firstly, Difference of Gaussians (DOG) filters are used to detect a group of interest points (including the moving targets). Secondly, a sort of small targets tracking method inspired by Human Visual System (HVS) is used to track these interest points for several frames, and then the correlations between interest points in the first frame and the last frame are obtained. Last, a new clustering method named as R-means is proposed to divide these interest points into two groups according to the correlations, one is target points and another is background points. In experimental results, the target-to-clutter ratio (TCR) and the receiver operating characteristics (ROC) curves are computed experimentally to compare the performances of the proposed method and other five sophisticated methods. From the results, the proposed method shows a better discrimination of targets and clutters and has a lower false alarm rate than the existing moving target detection methods.

The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures

PubMed Central

Eltzner, Benjamin; Wollnik, Carina; Gottschlich, Carsten; Huckemann, Stephan; Rehfeldt, Florian

2015-01-01

A reliable extraction of filament data from microscopic images is of high interest in the analysis of acto-myosin structures as early morphological markers in mechanically guided differentiation of human mesenchymal stem cells and the understanding of the underlying fiber arrangement processes. In this paper, we propose the filament sensor (FS), a fast and robust processing sequence which detects and records location, orientation, length, and width for each single filament of an image, and thus allows for the above described analysis. The extraction of these features has previously not been possible with existing methods. We evaluate the performance of the proposed FS in terms of accuracy and speed in comparison to three existing methods with respect to their limited output. Further, we provide a benchmark dataset of real cell images along with filaments manually marked by a human expert as well as simulated benchmark images. The FS clearly outperforms existing methods in terms of computational runtime and filament extraction accuracy. The implementation of the FS and the benchmark database are available as open source. PMID:25996921

Analytical evaluation of current starch methods used in the international sugar industry: Part I.

PubMed

Cole, Marsha; Eggleston, Gillian; Triplett, Alexa

2017-08-01

Several analytical starch methods exist in the international sugar industry to mitigate starch-related processing challenges and assess the quality of traded end-products. These methods use iodometric chemistry, mostly potato starch standards, and utilize similar solubilization strategies, but had not been comprehensively compared. In this study, industrial starch methods were compared to the USDA Starch Research method using simulated raw sugars. Type of starch standard, solubilization approach, iodometric reagents, and wavelength detection affected total starch determination in simulated raw sugars. Simulated sugars containing potato starch were more accurately detected by the industrial methods, whereas those containing corn starch, a better model for sugarcane starch, were only accurately measured by the USDA Starch Research method. Use of a potato starch standard curve over-estimated starch concentrations. Among the variables studied, starch standard, solubilization approach, and wavelength detection affected the sensitivity, accuracy/precision, and limited the detection/quantification of the current industry starch methods the most. Published by Elsevier Ltd.

Data based abnormality detection

NASA Astrophysics Data System (ADS)

Purwar, Yashasvi

Data based abnormality detection is a growing research field focussed on extracting information from feature rich data. They are considered to be non-intrusive and non-destructive in nature which gives them a clear advantage over conventional methods. In this study, we explore different streams of data based anomalies detection. We propose extension and revisions to existing valve stiction detection algorithm supported with industrial case study. We also explored the area of image analysis and proposed a complete solution for Malaria diagnosis. The proposed method is tested over images provided by pathology laboratory at Alberta Health Service. We also address the robustness and practicality of the solution proposed.

Reference point detection for camera-based fingerprint image based on wavelet transformation.

PubMed

Khalil, Mohammed S

2015-04-30

Fingerprint recognition systems essentially require core-point detection prior to fingerprint matching. The core-point is used as a reference point to align the fingerprint with a template database. When processing a larger fingerprint database, it is necessary to consider the core-point during feature extraction. Numerous core-point detection methods are available and have been reported in the literature. However, these methods are generally applied to scanner-based images. Hence, this paper attempts to explore the feasibility of applying a core-point detection method to a fingerprint image obtained using a camera phone. The proposed method utilizes a discrete wavelet transform to extract the ridge information from a color image. The performance of proposed method is evaluated in terms of accuracy and consistency. These two indicators are calculated automatically by comparing the method's output with the defined core points. The proposed method is tested on two data sets, controlled and uncontrolled environment, collected from 13 different subjects. In the controlled environment, the proposed method achieved a detection rate 82.98%. In uncontrolled environment, the proposed method yield a detection rate of 78.21%. The proposed method yields promising results in a collected-image database. Moreover, the proposed method outperformed compare to existing method.

Influence analysis in quantitative trait loci detection.

PubMed

Dou, Xiaoling; Kuriki, Satoshi; Maeno, Akiteru; Takada, Toyoyuki; Shiroishi, Toshihiko

2014-07-01

This paper presents systematic methods for the detection of influential individuals that affect the log odds (LOD) score curve. We derive general formulas of influence functions for profile likelihoods and introduce them into two standard quantitative trait locus detection methods-the interval mapping method and single marker analysis. Besides influence analysis on specific LOD scores, we also develop influence analysis methods on the shape of the LOD score curves. A simulation-based method is proposed to assess the significance of the influence of the individuals. These methods are shown useful in the influence analysis of a real dataset of an experimental population from an F2 mouse cross. By receiver operating characteristic analysis, we confirm that the proposed methods show better performance than existing diagnostics. © 2014 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Using constrained information entropy to detect rare adverse drug reactions from medical forums.

PubMed

Yi Zheng; Chaowang Lan; Hui Peng; Jinyan Li

2016-08-01

Adverse drug reactions (ADRs) detection is critical to avoid malpractices yet challenging due to its uncertainty in pre-marketing review and the underreporting in post-marketing surveillance. To conquer this predicament, social media based ADRs detection methods have been proposed recently. However, existing researches are mostly co-occurrence based methods and face several issues, in particularly, leaving out the rare ADRs and unable to distinguish irrelevant ADRs. In this work, we introduce a constrained information entropy (CIE) method to solve these problems. CIE first recognizes the drug-related adverse reactions using a predefined keyword dictionary and then captures high- and low-frequency (rare) ADRs by information entropy. Extensive experiments on medical forums dataset demonstrate that CIE outperforms the state-of-the-art co-occurrence based methods, especially in rare ADRs detection.

Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging

PubMed Central

Patel, Tapan P.; Man, Karen; Firestein, Bonnie L.; Meaney, David F.

2017-01-01

Background Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s–1000 +neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking. New method Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge. Results We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau. Comparison with existing method(s) We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods. Conclusions We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease. PMID:25629800

Detecting fixation on a target using time-frequency distributions of a retinal birefringence scanning signal

PubMed Central

2013-01-01

Background The fovea, which is the most sensitive part of the retina, is known to have birefringent properties, i.e. it changes the polarization state of light upon reflection. Existing devices use this property to obtain information on the orientation of the fovea and the direction of gaze. Such devices employ specific frequency components that appear during moments of fixation on a target. To detect them, previous methods have used solely the power spectrum of the Fast Fourier Transform (FFT), which, unfortunately, is an integral method, and does not give information as to where exactly the events of interest occur. With very young patients who are not cooperative enough, this presents a problem, because central fixation may be present only during very short-lasting episodes, and can easily be missed by the FFT. Method This paper presents a method for detecting short-lasting moments of central fixation in existing devices for retinal birefringence scanning, with the goal of a reliable detection of eye alignment. Signal analysis is based on the Continuous Wavelet Transform (CWT), which reliably localizes such events in the time-frequency plane. Even though the characteristic frequencies are not always strongly expressed due to possible artifacts, simple topological analysis of the time-frequency distribution can detect fixation reliably. Results In all six subjects tested, the CWT allowed precise identification of both frequency components. Moreover, in four of these subjects, episodes of intermittent but definitely present central fixation were detectable, similar to those in Figure 4. A simple FFT is likely to treat them as borderline cases, or entirely miss them, depending on the thresholds used. Conclusion Joint time-frequency analysis is a powerful tool in the detection of eye alignment, even in a noisy environment. The method is applicable to similar situations, where short-lasting diagnostic events need to be detected in time series acquired by means of scanning some substrate along a specific path. PMID:23668264

RECOVERY ACT: MULTIMODAL IMAGING FOR SOLAR CELL MICROCRACK DETECTION

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

Janice Hudgings; Lawrence Domash

2012-02-08

Undetected microcracks in solar cells are a principal cause of failure in service due to subsequent weather exposure, mechanical flexing or diurnal temperature cycles. Existing methods have not been able to detect cracks early enough in the production cycle to prevent inadvertent shipment to customers. This program, sponsored under the DOE Photovoltaic Supply Chain and Cross-Cutting Technologies program, studied the feasibility of quantifying surface micro-discontinuities by use of a novel technique, thermoreflectance imaging, to detect surface temperature gradients with very high spatial resolution, in combination with a suite of conventional imaging methods such as electroluminescence. The project carried out laboratorymore » tests together with computational image analyses using sample solar cells with known defects supplied by industry sources or DOE National Labs. Quantitative comparisons between the effectiveness of the new technique and conventional methods were determined in terms of the smallest detectable crack. Also the robustness of the new technique for reliable microcrack detection was determined at various stages of processing such as before and after antireflectance treatments. An overall assessment is that the new technique compares favorably with existing methods such as lock-in thermography or ultrasonics. The project was 100% completed in Sept, 2010. A detailed report of key findings from this program was published as: Q.Zhou, X.Hu, K.Al-Hemyari, K.McCarthy, L.Domash and J.Hudgings, High spatial resolution characterization of silicon solar cells using thermoreflectance imaging, J. Appl. Phys, 110, 053108 (2011).« less

Pseudorange Measurement Method Based on AIS Signals.

PubMed

Zhang, Jingbo; Zhang, Shufang; Wang, Jinpeng

2017-05-22

In order to use the existing automatic identification system (AIS) to provide additional navigation and positioning services, a complete pseudorange measurements solution is presented in this paper. Through the mathematical analysis of the AIS signal, the bit-0-phases in the digital sequences were determined as the timestamps. Monte Carlo simulation was carried out to compare the accuracy of the zero-crossing and differential peak, which are two timestamp detection methods in the additive white Gaussian noise (AWGN) channel. Considering the low-speed and low-dynamic motion characteristics of ships, an optimal estimation method based on the minimum mean square error is proposed to improve detection accuracy. Furthermore, the α difference filter algorithm was used to achieve the fusion of the optimal estimation results of the two detection methods. The results show that the algorithm can greatly improve the accuracy of pseudorange estimation under low signal-to-noise ratio (SNR) conditions. In order to verify the effectiveness of the scheme, prototypes containing the measurement scheme were developed and field tests in Xinghai Bay of Dalian (China) were performed. The test results show that the pseudorange measurement accuracy was better than 28 m (σ) without any modification of the existing AIS system.

Pseudorange Measurement Method Based on AIS Signals

PubMed Central

Zhang, Jingbo; Zhang, Shufang; Wang, Jinpeng

2017-01-01

In order to use the existing automatic identification system (AIS) to provide additional navigation and positioning services, a complete pseudorange measurements solution is presented in this paper. Through the mathematical analysis of the AIS signal, the bit-0-phases in the digital sequences were determined as the timestamps. Monte Carlo simulation was carried out to compare the accuracy of the zero-crossing and differential peak, which are two timestamp detection methods in the additive white Gaussian noise (AWGN) channel. Considering the low-speed and low-dynamic motion characteristics of ships, an optimal estimation method based on the minimum mean square error is proposed to improve detection accuracy. Furthermore, the α difference filter algorithm was used to achieve the fusion of the optimal estimation results of the two detection methods. The results show that the algorithm can greatly improve the accuracy of pseudorange estimation under low signal-to-noise ratio (SNR) conditions. In order to verify the effectiveness of the scheme, prototypes containing the measurement scheme were developed and field tests in Xinghai Bay of Dalian (China) were performed. The test results show that the pseudorange measurement accuracy was better than 28 m (σ) without any modification of the existing AIS system. PMID:28531153

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.

Improved detection of soma location and morphology in fluorescence microscopy images of neurons.

PubMed

Kayasandik, Cihan Bilge; Labate, Demetrio

2016-12-01

Automated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures. In this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel. Our method relies on a new geometrical descriptor called Directional Ratio and a collection of multiscale orientable filters to quantify the level of local isotropy in an image. To optimize the application of this approach, we introduce a new construction of multiscale anisotropic filters that is implemented by separable convolution. Extensive numerical experiments using 2D and 3D confocal images show that our automated algorithm reliably detects somas, accurately segments them, and separates contiguous ones. We include a detailed comparison with state-of-the-art existing methods to demonstrate that our algorithm is extremely competitive in terms of accuracy, reliability and computational efficiency. Our algorithm will facilitate the development of automated platforms for high content neuron image processing. A Matlab code is released open-source and freely available to the scientific community. Copyright © 2016 Elsevier B.V. All rights reserved.

Peripleural lung disease detection based on multi-slice CT images

NASA Astrophysics Data System (ADS)

Matsuhiro, M.; Suzuki, H.; Kawata, Y.; Niki, N.; Nakano, Y.; Ohmatsu, H.; Kusumoto, M.; Tsuchida, T.; Eguchi, K.; Kaneko, M.

2015-03-01

With the development of multi-slice CT technology, obtaining accurate 3D images of lung field in a short time become possible. To support that, a lot of image processing methods need to be developed. Detection peripleural lung disease is difficult due to its existence out of lung region, because lung extraction is often performed based on threshold processing. The proposed method uses thoracic inner region extracted by inner cavity of bone as well as air region, covers peripleural lung diseased cases such as lung nodule, calcification, pleural effusion and pleural plaque. We applied this method to 50 cases including 39 peripleural lung diseased cases. This method was able to detect 39 peripleural lung disease with 2.9 false positive per case.

A source-attractor approach to network detection of radiation sources

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

Wu, Qishi; Barry, M. L..; Grieme, M.

Radiation source detection using a network of detectors is an active field of research for homeland security and defense applications. We propose Source-attractor Radiation Detection (SRD) method to aggregate measurements from a network of detectors for radiation source detection. SRD method models a potential radiation source as a magnet -like attractor that pulls in pre-computed virtual points from the detector locations. A detection decision is made if a sufficient level of attraction, quantified by the increase in the clustering of the shifted virtual points, is observed. Compared with traditional methods, SRD has the following advantages: i) it does not requiremore » an accurate estimate of the source location from limited and noise-corrupted sensor readings, unlike the localizationbased methods, and ii) its virtual point shifting and clustering calculation involve simple arithmetic operations based on the number of detectors, avoiding the high computational complexity of grid-based likelihood estimation methods. We evaluate its detection performance using canonical datasets from Domestic Nuclear Detection Office s (DNDO) Intelligence Radiation Sensors Systems (IRSS) tests. SRD achieves both lower false alarm rate and false negative rate compared to three existing algorithms for network source detection.« less

Community Detection in Complex Networks via Clique Conductance.

PubMed

Lu, Zhenqi; Wahlström, Johan; Nehorai, Arye

2018-04-13

Network science plays a central role in understanding and modeling complex systems in many areas including physics, sociology, biology, computer science, economics, politics, and neuroscience. One of the most important features of networks is community structure, i.e., clustering of nodes that are locally densely interconnected. Communities reveal the hierarchical organization of nodes, and detecting communities is of great importance in the study of complex systems. Most existing community-detection methods consider low-order connection patterns at the level of individual links. But high-order connection patterns, at the level of small subnetworks, are generally not considered. In this paper, we develop a novel community-detection method based on cliques, i.e., local complete subnetworks. The proposed method overcomes the deficiencies of previous similar community-detection methods by considering the mathematical properties of cliques. We apply the proposed method to computer-generated graphs and real-world network datasets. When applied to networks with known community structure, the proposed method detects the structure with high fidelity and sensitivity. When applied to networks with no a priori information regarding community structure, the proposed method yields insightful results revealing the organization of these complex networks. We also show that the proposed method is guaranteed to detect near-optimal clusters in the bipartition case.

Analysis of modal behavior at frequency cross-over

NASA Astrophysics Data System (ADS)

Costa, Robert N., Jr.

1994-11-01

The existence of the mode crossing condition is detected and analyzed in the Active Control of Space Structures Model 4 (ACOSS4). The condition is studied for its contribution to the inability of previous algorithms to successfully optimize the structure and converge to a feasible solution. A new algorithm is developed to detect and correct for mode crossings. The existence of the mode crossing condition is verified in ACOSS4 and found not to have appreciably affected the solution. The structure is then successfully optimized using new analytic methods based on modal expansion. An unrelated error in the optimization algorithm previously used is verified and corrected, thereby equipping the optimization algorithm with a second analytic method for eigenvector differentiation based on Nelson's Method. The second structure is the Control of Flexible Structures (COFS). The COFS structure is successfully reproduced and an initial eigenanalysis completed.

Characterization of circulating tumor cell aggregates identified in patients with epithelial tumors

NASA Astrophysics Data System (ADS)

Cho, Edward H.; Wendel, Marco; Luttgen, Madelyn; Yoshioka, Craig; Marrinucci, Dena; Lazar, Daniel; Schram, Ethan; Nieva, Jorge; Bazhenova, Lyudmila; Morgan, Alison; Ko, Andrew H.; Korn, W. Michael; Kolatkar, Anand; Bethel, Kelly; Kuhn, Peter

2012-02-01

Circulating tumor cells (CTCs) have been implicated as a population of cells that may seed metastasis and venous thromboembolism (VTE), two major causes of mortality in cancer patients. Thus far, existing CTC detection technologies have been unable to reproducibly detect CTC aggregates in order to address what contribution CTC aggregates may make to metastasis or VTE. We report here an enrichment-free immunofluorescence detection method that can reproducibly detect and enumerate homotypic CTC aggregates in patient samples. We identified CTC aggregates in 43% of 86 patient samples. The fraction of CTC aggregation was investigated in blood draws from 24 breast, 14 non-small cell lung, 18 pancreatic, 15 prostate stage IV cancer patients and 15 normal blood donors. Both single CTCs and CTC aggregates were measured to determine whether differences exist in the physical characteristics of these two populations. Cells contained in CTC aggregates had less area and length, on average, than single CTCs. Nuclear to cytoplasmic ratios between single CTCs and CTC aggregates were similar. This detection method may assist future studies in determining which population of cells is more physically likely to contribute to metastasis and VTE.

SCOUT: simultaneous time segmentation and community detection in dynamic networks

PubMed Central

Hulovatyy, Yuriy; Milenković, Tijana

2016-01-01

Many evolving complex real-world systems can be modeled via dynamic networks. An important problem in dynamic network research is community detection, which finds groups of topologically related nodes. Typically, this problem is approached by assuming either that each time point has a distinct community organization or that all time points share a single community organization. The reality likely lies between these two extremes. To find the compromise, we consider community detection in the context of the problem of segment detection, which identifies contiguous time periods with consistent network structure. Consequently, we formulate a combined problem of segment community detection (SCD), which simultaneously partitions the network into contiguous time segments with consistent community organization and finds this community organization for each segment. To solve SCD, we introduce SCOUT, an optimization framework that explicitly considers both segmentation quality and partition quality. SCOUT addresses limitations of existing methods that can be adapted to solve SCD, which consider only one of segmentation quality or partition quality. In a thorough evaluation, SCOUT outperforms the existing methods in terms of both accuracy and computational complexity. We apply SCOUT to biological network data to study human aging. PMID:27881879

BinQuasi: a peak detection method for ChIP-sequencing data with biological replicates.

PubMed

Goren, Emily; Liu, Peng; Wang, Chao; Wang, Chong

2018-04-19

ChIP-seq experiments that are aimed at detecting DNA-protein interactions require biological replication to draw inferential conclusions, however there is no current consensus on how to analyze ChIP-seq data with biological replicates. Very few methodologies exist for the joint analysis of replicated ChIP-seq data, with approaches ranging from combining the results of analyzing replicates individually to joint modeling of all replicates. Combining the results of individual replicates analyzed separately can lead to reduced peak classification performance compared to joint modeling. Currently available methods for joint analysis may fail to control the false discovery rate at the nominal level. We propose BinQuasi, a peak caller for replicated ChIP-seq data, that jointly models biological replicates using a generalized linear model framework and employs a one-sided quasi-likelihood ratio test to detect peaks. When applied to simulated data and real datasets, BinQuasi performs favorably compared to existing methods, including better control of false discovery rate than existing joint modeling approaches. BinQuasi offers a flexible approach to joint modeling of replicated ChIP-seq data which is preferable to combining the results of replicates analyzed individually. Source code is freely available for download at https://cran.r-project.org/package=BinQuasi, implemented in R. pliu@iastate.edu or egoren@iastate.edu. Supplementary material is available at Bioinformatics online.

Detection surveys for fishers and american martens in California, 1989-1994: summary and interpertations

Treesearch

William J. Zielinski; Richard L. Truex; Chester V. Ogan; Kelly Busse

1997-01-01

Commercial trapping of fishers (Martes pennanti) and American martens (M. americana) has been prohibited in California since the mid-1900's, yet concern continues to exist about the status of their populations. Recently developed methods for detecting the presence of forest carnivores have made it possible to estimate their...

Prevalence of purulent vaginal discharge in dairy herds depends on timing but not method of detection

USDA-ARS?s Scientific Manuscript database

A review of existing literature was conducted to determine the prevalence of purulent vaginal discharge (PVD) in dairy herds around the world and detection methodologies that influence prevalence estimates. Four databases (PubMed, Google Scholar, Web of Science, and Scopus) were queried with the sea...

Histogram-based automatic thresholding for bruise detection of apples by structured-illumination reflectance imaging

USDA-ARS?s Scientific Manuscript database

Thresholding is an important step in the segmentation of image features, and the existing methods are not all effective when the image histogram exhibits a unimodal pattern, which is common in defect detection of fruit. This study was aimed at developing a general automatic thresholding methodology ...

Multi-Target Tracking Using an Improved Gaussian Mixture CPHD Filter.

PubMed

Si, Weijian; Wang, Liwei; Qu, Zhiyu

2016-11-23

The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the full multi-target Bayesian filter for tracking multiple targets. However, although the joint propagation of the posterior intensity and cardinality distribution in its recursion allows more reliable estimates of the target number than the PHD filter, the CPHD filter suffers from the spooky effect where there exists arbitrary PHD mass shifting in the presence of missed detections. To address this issue in the Gaussian mixture (GM) implementation of the CPHD filter, this paper presents an improved GM-CPHD filter, which incorporates a weight redistribution scheme into the filtering process to modify the updated weights of the Gaussian components when missed detections occur. In addition, an efficient gating strategy that can adaptively adjust the gate sizes according to the number of missed detections of each Gaussian component is also presented to further improve the computational efficiency of the proposed filter. Simulation results demonstrate that the proposed method offers favorable performance in terms of both estimation accuracy and robustness to clutter and detection uncertainty over the existing methods.

QuateXelero: An Accelerated Exact Network Motif Detection Algorithm

PubMed Central

Khakabimamaghani, Sahand; Sharafuddin, Iman; Dichter, Norbert; Koch, Ina; Masoudi-Nejad, Ali

2013-01-01

Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks’ structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network. PMID:23874498

A Wireless Sensor Network-Based Portable Vehicle Detector Evaluation System

PubMed Central

Yoo, Seong-eun

2013-01-01

In an upcoming smart transportation environment, performance evaluations of existing Vehicle Detection Systems are crucial to maintain their accuracy. The existing evaluation method for Vehicle Detection Systems is based on a wired Vehicle Detection System reference and a video recorder, which must be operated and analyzed by capable traffic experts. However, this conventional evaluation system has many disadvantages. It is inconvenient to deploy, the evaluation takes a long time, and it lacks scalability and objectivity. To improve the evaluation procedure, this paper proposes a Portable Vehicle Detector Evaluation System based on wireless sensor networks. We describe both the architecture and design of a Vehicle Detector Evaluation System and the implementation results, focusing on the wireless sensor networks and methods for traffic information measurement. With the help of wireless sensor networks and automated analysis, our Vehicle Detector Evaluation System can evaluate a Vehicle Detection System conveniently and objectively. The extensive evaluations of our Vehicle Detector Evaluation System show that it can measure the traffic information such as volume counts and speed with over 98% accuracy. PMID:23344388

A wireless sensor network-based portable vehicle detector evaluation system.

PubMed

Yoo, Seong-eun

2013-01-17

In an upcoming smart transportation environment, performance evaluations of existing Vehicle Detection Systems are crucial to maintain their accuracy. The existing evaluation method for Vehicle Detection Systems is based on a wired Vehicle Detection System reference and a video recorder, which must be operated and analyzed by capable traffic experts. However, this conventional evaluation system has many disadvantages. It is inconvenient to deploy, the evaluation takes a long time, and it lacks scalability and objectivity. To improve the evaluation procedure, this paper proposes a Portable Vehicle Detector Evaluation System based on wireless sensor networks. We describe both the architecture and design of a Vehicle Detector Evaluation System and the implementation results, focusing on the wireless sensor networks and methods for traffic information measurement. With the help of wireless sensor networks and automated analysis, our Vehicle Detector Evaluation System can evaluate a Vehicle Detection System conveniently and objectively. The extensive evaluations of our Vehicle Detector Evaluation System show that it can measure the traffic information such as volume counts and speed with over 98% accuracy.

An overview of recent developments and current status of gluten ELISA methods

USDA-ARS?s Scientific Manuscript database

ELISA methods for detecting and quantitating allergens have been around for some time and they are continuously improved. In this context, the development of gluten methods is no exception. Around the turn of the millennium, doubts were raised whether the existing “Skerritt-ELISA” would meet the 20 ...

Project Cyclops: a Design Study of a System for Detecting Extraterrestrial Intelligent Life

NASA Technical Reports Server (NTRS)

1972-01-01

The requirements in hardware, manpower, time and funding to conduct a realistic effort aimed at detecting the existence of extraterrestrial intelligent life are examined. The methods used are limited to present or near term future state-of-the-art techniques. Subjects discussed include: (1) possible methods of contact, (2) communication by electromagnetic waves, (3) antenna array and system facilities, (4) antenna elements, (5) signal processing, (6) search strategy, and (7) radio and radar astronomy.

Trackline and Point Detection Probabilities for Acoustic Surveys of Cuvier’s and Blainville’s Beaked Whales

DTIC Science & Technology

2013-09-01

of sperm whales. Although the methods developed in those papers demonstrate feasibility, they are not applicable to a)Author to whom correspondence...information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and...location clicks (Marques et al., 2009) instead of detecting individual animals or groups of animals; these cue- counting methods will not be specifically

Survey of Anomaly Detection Methods

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

Ng, B

This survey defines the problem of anomaly detection and provides an overview of existing methods. The methods are categorized into two general classes: generative and discriminative. A generative approach involves building a model that represents the joint distribution of the input features and the output labels of system behavior (e.g., normal or anomalous) then applies the model to formulate a decision rule for detecting anomalies. On the other hand, a discriminative approach aims directly to find the decision rule, with the smallest error rate, that distinguishes between normal and anomalous behavior. For each approach, we will give an overview ofmore » popular techniques and provide references to state-of-the-art applications.« less

Preface to the Focus Issue: chaos detection methods and predictability.

PubMed

Gottwald, Georg A; Skokos, Charalampos

2014-06-01

This Focus Issue presents a collection of papers originating from the workshop Methods of Chaos Detection and Predictability: Theory and Applications held at the Max Planck Institute for the Physics of Complex Systems in Dresden, June 17-21, 2013. The main aim of this interdisciplinary workshop was to review comprehensively the theory and numerical implementation of the existing methods of chaos detection and predictability, as well as to report recent applications of these techniques to different scientific fields. The collection of twelve papers in this Focus Issue represents the wide range of applications, spanning mathematics, physics, astronomy, particle accelerator physics, meteorology and medical research. This Preface surveys the papers of this Issue.

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

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.

The advance of non-invasive detection methods in osteoarthritis

NASA Astrophysics Data System (ADS)

Dai, Jiao; Chen, Yanping

2011-06-01

Osteoarthritis (OA) is one of the most prevalent chronic diseases which badly affected the patients' living quality and economy. Detection and evaluation technology can provide basic information for early treatment. A variety of imaging methods in OA were reviewed, such as conventional X-ray, computed tomography (CT), ultrasound (US), magnetic resonance imaging (MRI) and near-infrared spectroscopy (NIRS). Among the existing imaging modalities, the spatial resolution of X-ray is extremely high; CT is a three-dimensional method, which has high density resolution; US as an evaluation method of knee OA discriminates lesions sensitively between normal cartilage and degenerative one; as a sensitive and nonionizing method, MRI is suitable for the detection of early OA, but the cost is too expensive for routine use; NIRS is a safe, low cost modality, and is also good at detecting early stage OA. In a word, each method has its own advantages, but NIRS is provided with broader application prospect, and it is likely to be used in clinical daily routine and become the golden standard for diagnostic detection.

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 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

The method of pulsed x-ray detection with a diode laser.

PubMed

Liu, Jun; Ouyang, Xiaoping; Zhang, Zhongbing; Sheng, Liang; Chen, Liang; Tan, Xinjian; Weng, Xiufeng

2016-12-01

A new class of pulsed X-ray detection methods by sensing carrier changes in a diode laser cavity has been presented and demonstrated. The proof-of-principle experiments on detecting pulsed X-ray temporal profile have been done through the diode laser with a multiple quantum well active layer. The result shows that our method can achieve the aim of detecting the temporal profile of a pulsed X-ray source. We predict that there is a minimum value for the pre-bias current of the diode laser by analyzing the carrier rate equation, which exists near the threshold current of the diode laser chip in experiments. This behaviour generally agrees with the characterizations of theoretical analysis. The relative sensitivity is estimated at about 3.3 × 10 -17 C ⋅ cm 2 . We have analyzed the time scale of about 10 ps response with both rate equation and Monte Carlo methods.

Nano-sized Contrast Agents to Non-Invasively Detect Renal Inflammation by Magnetic Resonance Imaging

PubMed Central

Thurman, Joshua M.; Serkova, Natalie J.

2013-01-01

Several molecular imaging methods have been developed that employ nano-sized contrast agents to detect markers of inflammation within tissues. Renal inflammation contributes to disease progression in a wide range of autoimmune and inflammatory diseases, and a biopsy is currently the only method of definitively diagnosing active renal inflammation. However, the development of new molecular imaging methods that employ contrast agents capable of detecting particular immune cells or protein biomarkers will allow clinicians to evaluate inflammation throughout the kidneys, and to assess a patient's response to immunomodulatory drugs. These imaging tools will improve our ability to validate new therapies and to optimize the treatment of individual patients with existing therapies. This review describes the clinical need for new methods of monitoring renal inflammation, and recent advances in the development of nano-sized contrast agents for detection of inflammatory markers of renal disease. PMID:24206601

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.

Overview of U.S. EPA Office of Research and Development’s planned research on analysis and monitoring in fresh and coastal/estuarine environments

EPA Science Inventory

This research plan has several objectives: 1) develop new or refine existing chemical, instrument and biological methods for the detection of cyanobacteria and their toxins; test such methods in field studies in both HAB and non HAB environments; 2) determine the method(s) that c...

Violence detection based on histogram of optical flow orientation

NASA Astrophysics Data System (ADS)

Yang, Zhijie; Zhang, Tao; Yang, Jie; Wu, Qiang; Bai, Li; Yao, Lixiu

2013-12-01

In this paper, we propose a novel approach for violence detection and localization in a public scene. Currently, violence detection is considerably under-researched compared with the common action recognition. Although existing methods can detect the presence of violence in a video, they cannot precisely locate the regions in the scene where violence is happening. This paper will tackle the challenge and propose a novel method to locate the violence location in the scene, which is important for public surveillance. The Gaussian Mixed Model is extended into the optical flow domain in order to detect candidate violence regions. In each region, a new descriptor, Histogram of Optical Flow Orientation (HOFO), is proposed to measure the spatial-temporal features. A linear SVM is trained based on the descriptor. The performance of the method is demonstrated on the publicly available data sets, BEHAVE and CAVIAR.

Online boosting for vehicle detection.

PubMed

Chang, Wen-Chung; Cho, Chih-Wei

2010-06-01

This paper presents a real-time vision-based vehicle detection system employing an online boosting algorithm. It is an online AdaBoost approach for a cascade of strong classifiers instead of a single strong classifier. Most existing cascades of classifiers must be trained offline and cannot effectively be updated when online tuning is required. The idea is to develop a cascade of strong classifiers for vehicle detection that is capable of being online trained in response to changing traffic environments. To make the online algorithm tractable, the proposed system must efficiently tune parameters based on incoming images and up-to-date performance of each weak classifier. The proposed online boosting method can improve system adaptability and accuracy to deal with novel types of vehicles and unfamiliar environments, whereas existing offline methods rely much more on extensive training processes to reach comparable results and cannot further be updated online. Our approach has been successfully validated in real traffic environments by performing experiments with an onboard charge-coupled-device camera in a roadway vehicle.

Multiuser receiver for DS-CDMA signals in multipath channels: an enhanced multisurface method.

PubMed

Mahendra, Chetan; Puthusserypady, Sadasivan

2006-11-01

This paper deals with the problem of multiuser detection in direct-sequence code-division multiple-access (DS-CDMA) systems in multipath environments. The existing multiuser detectors can be divided into two categories: (1) low-complexity poor-performance linear detectors and (2) high-complexity good-performance nonlinear detectors. In particular, in channels where the orthogonality of the code sequences is destroyed by multipath, detectors with linear complexity perform much worse than the nonlinear detectors. In this paper, we propose an enhanced multisurface method (EMSM) for multiuser detection in multipath channels. EMSM is an intermediate piecewise linear detection scheme with a run-time complexity linear in the number of users. Its bit error rate performance is compared with existing linear detectors, a nonlinear radial basis function detector trained by the new support vector learning algorithm, and Verdu's optimal detector. Simulations in multipath channels, for both synchronous and asynchronous cases, indicate that it always outperforms all other linear detectors, performing nearly as well as nonlinear detectors.

Walsh-Hadamard transform kernel-based feature vector for shot boundary detection.

PubMed

Lakshmi, Priya G G; Domnic, S

2014-12-01

Video shot boundary detection (SBD) is the first step of video analysis, summarization, indexing, and retrieval. In SBD process, videos are segmented into basic units called shots. In this paper, a new SBD method is proposed using color, edge, texture, and motion strength as vector of features (feature vector). Features are extracted by projecting the frames on selected basis vectors of Walsh-Hadamard transform (WHT) kernel and WHT matrix. After extracting the features, based on the significance of the features, weights are calculated. The weighted features are combined to form a single continuity signal, used as input for Procedure Based shot transition Identification process (PBI). Using the procedure, shot transitions are classified into abrupt and gradual transitions. Experimental results are examined using large-scale test sets provided by the TRECVID 2007, which has evaluated hard cut and gradual transition detection. To evaluate the robustness of the proposed method, the system evaluation is performed. The proposed method yields F1-Score of 97.4% for cut, 78% for gradual, and 96.1% for overall transitions. We have also evaluated the proposed feature vector with support vector machine classifier. The results show that WHT-based features can perform well than the other existing methods. In addition to this, few more video sequences are taken from the Openvideo project and the performance of the proposed method is compared with the recent existing SBD method.

Hybrid detection of lung nodules on CT scan images

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

Lu, Lin; Tan, Yongqiang; Schwartz, Lawrence H.

Purpose: The diversity of lung nodules poses difficulty for the current computer-aided diagnostic (CAD) schemes for lung nodule detection on computed tomography (CT) scan images, especially in large-scale CT screening studies. We proposed a novel CAD scheme based on a hybrid method to address the challenges of detection in diverse lung nodules. Methods: The hybrid method proposed in this paper integrates several existing and widely used algorithms in the field of nodule detection, including morphological operation, dot-enhancement based on Hessian matrix, fuzzy connectedness segmentation, local density maximum algorithm, geodesic distance map, and regression tree classification. All of the adopted algorithmsmore » were organized into tree structures with multi-nodes. Each node in the tree structure aimed to deal with one type of lung nodule. Results: The method has been evaluated on 294 CT scans from the Lung Image Database Consortium (LIDC) dataset. The CT scans were randomly divided into two independent subsets: a training set (196 scans) and a test set (98 scans). In total, the 294 CT scans contained 631 lung nodules, which were annotated by at least two radiologists participating in the LIDC project. The sensitivity and false positive per scan for the training set were 87% and 2.61%. The sensitivity and false positive per scan for the testing set were 85.2% and 3.13%. Conclusions: The proposed hybrid method yielded high performance on the evaluation dataset and exhibits advantages over existing CAD schemes. We believe that the present method would be useful for a wide variety of CT imaging protocols used in both routine diagnosis and screening studies.« less

Optic disc detection and boundary extraction in retinal images.

PubMed

Basit, A; Fraz, Muhammad Moazam

2015-04-10

With the development of digital image processing, analysis and modeling techniques, automatic retinal image analysis is emerging as an important screening tool for early detection of ophthalmologic disorders such as diabetic retinopathy and glaucoma. In this paper, a robust method for optic disc detection and extraction of the optic disc boundary is proposed to help in the development of computer-assisted diagnosis and treatment of such ophthalmic disease. The proposed method is based on morphological operations, smoothing filters, and the marker controlled watershed transform. Internal and external markers are used to first modify the gradient magnitude image and then the watershed transformation is applied on this modified gradient magnitude image for boundary extraction. This method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc. The proposed method has optic disc detection success rate of 100%, 100%, 100% and 98.9% for the DRIVE, Shifa, CHASE_DB1, and DIARETDB1 databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 61.88%, 70.96%, 45.61%, and 54.69% for these databases, respectively, which are higher than currents methods.

An integrated logit model for contamination event detection in water distribution systems.

PubMed

Housh, Mashor; Ostfeld, Avi

2015-05-15

The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies. Copyright © 2015 Elsevier Ltd. All rights reserved.

Detecting Payload Attacks on Programmable Logic Controllers (PLCs)

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

Yang, Huan

Programmable logic controllers (PLCs) play critical roles in industrial control systems (ICS). Providing hardware peripherals and firmware support for control programs (i.e., a PLC’s “payload”) written in languages such as ladder logic, PLCs directly receive sensor readings and control ICS physical processes. An attacker with access to PLC development software (e.g., by compromising an engineering workstation) can modify the payload program and cause severe physical damages to the ICS. To protect critical ICS infrastructure, we propose to model runtime behaviors of legitimate PLC payload program and use runtime behavior monitoring in PLC firmware to detect payload attacks. By monitoring themore » I/O access patterns, network access patterns, as well as payload program timing characteristics, our proposed firmware-level detection mechanism can detect abnormal runtime behaviors of malicious PLC payload. Using our proof-of-concept implementation, we evaluate the memory and execution time overhead of implementing our proposed method and find that it is feasible to incorporate our method into existing PLC firmware. In addition, our evaluation results show that a wide variety of payload attacks can be effectively detected by our proposed approach. The proposed firmware-level payload attack detection scheme complements existing bumpin- the-wire solutions (e.g., external temporal-logic-based model checkers) in that it can detect payload attacks that violate realtime requirements of ICS operations and does not require any additional apparatus.« less

Clustering Categorical Data Using Community Detection Techniques

PubMed Central

2017-01-01

With the advent of the k-modes algorithm, the toolbox for clustering categorical data has an efficient tool that scales linearly in the number of data items. However, random initialization of cluster centers in k-modes makes it hard to reach a good clustering without resorting to many trials. Recently proposed methods for better initialization are deterministic and reduce the clustering cost considerably. A variety of initialization methods differ in how the heuristics chooses the set of initial centers. In this paper, we address the clustering problem for categorical data from the perspective of community detection. Instead of initializing k modes and running several iterations, our scheme, CD-Clustering, builds an unweighted graph and detects highly cohesive groups of nodes using a fast community detection technique. The top-k detected communities by size will define the k modes. Evaluation on ten real categorical datasets shows that our method outperforms the existing initialization methods for k-modes in terms of accuracy, precision, and recall in most of the cases. PMID:29430249

ROKU: a novel method for identification of tissue-specific genes.

PubMed

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-06-12

One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes.

Spatial cluster detection using dynamic programming

PubMed Central

2012-01-01

Background The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. Methods We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. Results When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. Conclusions We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm. PMID:22443103

A Nested PCR Assay to Avoid False Positive Detection of the Microsporidian Enterocytozoon hepatopenaei (EHP) in Environmental Samples in Shrimp Farms

PubMed Central

Jaroenlak, Pattana; Sanguanrut, Piyachat; Williams, Bryony A. P.; Stentiford, Grant D.; Flegel, Timothy W.; Sritunyalucksana, Kallaya

2016-01-01

Hepatopancreatic microsporidiosis (HPM) caused by Enterocytozoon hepatopenaei (EHP) is an important disease of cultivated shrimp. Heavy infections may lead to retarded growth and unprofitable harvests. Existing PCR detection methods target the EHP small subunit ribosomal RNA (SSU rRNA) gene (SSU-PCR). However, we discovered that they can give false positive test results due to cross reactivity of the SSU-PCR primers with DNA from closely related microsporidia that infect other aquatic organisms. This is problematic for investigating and monitoring EHP infection pathways. To overcome this problem, a sensitive and specific nested PCR method was developed for detection of the spore wall protein (SWP) gene of EHP (SWP-PCR). The new SWP-PCR method did not produce false positive results from closely related microsporidia. The first PCR step of the SWP-PCR method was 100 times (104 plasmid copies per reaction vial) more sensitive than that of the existing SSU-PCR method (106 copies) but sensitivity was equal for both in the nested step (10 copies). Since the hepatopancreas of cultivated shrimp is not currently known to be infected with microsporidia other than EHP, the SSU-PCR methods are still valid for analyzing hepatopancreatic samples despite the lower sensitivity than the SWP-PCR method. However, due to its greater specificity and sensitivity, we recommend that the SWP-PCR method be used to screen for EHP in feces, feed and environmental samples for potential EHP carriers. PMID:27832178

A Nested PCR Assay to Avoid False Positive Detection of the Microsporidian Enterocytozoon hepatopenaei (EHP) in Environmental Samples in Shrimp Farms.

PubMed

Jaroenlak, Pattana; Sanguanrut, Piyachat; Williams, Bryony A P; Stentiford, Grant D; Flegel, Timothy W; Sritunyalucksana, Kallaya; Itsathitphaisarn, Ornchuma

2016-01-01

Hepatopancreatic microsporidiosis (HPM) caused by Enterocytozoon hepatopenaei (EHP) is an important disease of cultivated shrimp. Heavy infections may lead to retarded growth and unprofitable harvests. Existing PCR detection methods target the EHP small subunit ribosomal RNA (SSU rRNA) gene (SSU-PCR). However, we discovered that they can give false positive test results due to cross reactivity of the SSU-PCR primers with DNA from closely related microsporidia that infect other aquatic organisms. This is problematic for investigating and monitoring EHP infection pathways. To overcome this problem, a sensitive and specific nested PCR method was developed for detection of the spore wall protein (SWP) gene of EHP (SWP-PCR). The new SWP-PCR method did not produce false positive results from closely related microsporidia. The first PCR step of the SWP-PCR method was 100 times (104 plasmid copies per reaction vial) more sensitive than that of the existing SSU-PCR method (106 copies) but sensitivity was equal for both in the nested step (10 copies). Since the hepatopancreas of cultivated shrimp is not currently known to be infected with microsporidia other than EHP, the SSU-PCR methods are still valid for analyzing hepatopancreatic samples despite the lower sensitivity than the SWP-PCR method. However, due to its greater specificity and sensitivity, we recommend that the SWP-PCR method be used to screen for EHP in feces, feed and environmental samples for potential EHP carriers.

Hard exudates segmentation based on learned initial seeds and iterative graph cut.

PubMed

Kusakunniran, Worapan; Wu, Qiang; Ritthipravat, Panrasee; Zhang, Jian

2018-05-01

(Background and Objective): The occurrence of hard exudates is one of the early signs of diabetic retinopathy which is one of the leading causes of the blindness. Many patients with diabetic retinopathy lose their vision because of the late detection of the disease. Thus, this paper is to propose a novel method of hard exudates segmentation in retinal images in an automatic way. (Methods): The existing methods are based on either supervised or unsupervised learning techniques. In addition, the learned segmentation models may often cause miss-detection and/or fault-detection of hard exudates, due to the lack of rich characteristics, the intra-variations, and the similarity with other components in the retinal image. Thus, in this paper, the supervised learning based on the multilayer perceptron (MLP) is only used to identify initial seeds with high confidences to be hard exudates. Then, the segmentation is finalized by unsupervised learning based on the iterative graph cut (GC) using clusters of initial seeds. Also, in order to reduce color intra-variations of hard exudates in different retinal images, the color transfer (CT) is applied to normalize their color information, in the pre-processing step. (Results): The experiments and comparisons with the other existing methods are based on the two well-known datasets, e_ophtha EX and DIARETDB1. It can be seen that the proposed method outperforms the other existing methods in the literature, with the sensitivity in the pixel-level of 0.891 for the DIARETDB1 dataset and 0.564 for the e_ophtha EX dataset. The cross datasets validation where the training process is performed on one dataset and the testing process is performed on another dataset is also evaluated in this paper, in order to illustrate the robustness of the proposed method. (Conclusions): This newly proposed method integrates the supervised learning and unsupervised learning based techniques. It achieves the improved performance, when compared with the existing methods in the literature. The robustness of the proposed method for the scenario of cross datasets could enhance its practical usage. That is, the trained model could be more practical for unseen data in the real-world situation, especially when the capturing environments of training and testing images are not the same. Copyright © 2018 Elsevier B.V. All rights reserved.

Automatic QRS complex detection using two-level convolutional neural network.

PubMed

Xiang, Yande; Lin, Zhitao; Meng, Jianyi

2018-01-29

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

A scalable self-priming fractal branching microchannel net chip for digital PCR.

PubMed

Zhu, Qiangyuan; Xu, Yanan; Qiu, Lin; Ma, Congcong; Yu, Bingwen; Song, Qi; Jin, Wei; Jin, Qinhan; Liu, Jinyu; Mu, Ying

2017-05-02

As an absolute quantification method at the single-molecule level, digital PCR has been widely used in many bioresearch fields, such as next generation sequencing, single cell analysis, gene editing detection and so on. However, existing digital PCR methods still have some disadvantages, including high cost, sample loss, and complicated operation. In this work, we develop an exquisite scalable self-priming fractal branching microchannel net digital PCR chip. This chip with a special design inspired by natural fractal-tree systems has an even distribution and 100% compartmentalization of the sample without any sample loss, which is not available in existing chip-based digital PCR methods. A special 10 nm nano-waterproof layer was created to prevent the solution from evaporating. A vacuum pre-packaging method called self-priming reagent introduction is used to passively drive the reagent flow into the microchannel nets, so that this chip can realize sequential reagent loading and isolation within a couple of minutes, which is very suitable for point-of-care detection. When the number of positive microwells stays in the range of 100 to 4000, the relative uncertainty is below 5%, which means that one panel can detect an average of 101 to 15 374 molecules by the Poisson distribution. This chip is proved to have an excellent ability for single molecule detection and quantification of low expression of hHF-MSC stem cell markers. Due to its potential for high throughput, high density, low cost, lack of sample and reagent loss, self-priming even compartmentalization and simple operation, we envision that this device will significantly expand and extend the application range of digital PCR involving rare samples, liquid biopsy detection and point-of-care detection with higher sensitivity and accuracy.

Fuel cell flooding detection and correction

DOEpatents

DiPierno Bosco, Andrew; Fronk, Matthew Howard

2000-08-15

Method and apparatus for monitoring an H.sub.2 -O.sub.2 PEM fuel cells to detect and correct flooding. The pressure drop across a given H.sub.2 or O.sub.2 flow field is monitored and compared to predetermined thresholds of unacceptability. If the pressure drop exists a threshold of unacceptability corrective measures are automatically initiated.

Predicting carnivore occurrence with noninvasive surveys and occupancy modeling

Treesearch

Robert Long; Therese Donovan; Paula MacKay; William Zielinski; Jeffrey Buzas

2011-01-01

Terrestrial carnivores typically have large home ranges and exist at low population densities, thus presenting challenges to wildlife researchers. We employed multiple, noninvasive survey methods—scat detection dogs, remote cameras, and hair snares—to collect detection–nondetection data for elusive American black bears (Ursus americanus), fishers...

Unbiased Protein Association Study on the Public Human Proteome Reveals Biological Connections between Co-Occurring Protein Pairs

PubMed Central

2017-01-01

Mass-spectrometry-based, high-throughput proteomics experiments produce large amounts of data. While typically acquired to answer specific biological questions, these data can also be reused in orthogonal ways to reveal new biological knowledge. We here present a novel method for such orthogonal data reuse of public proteomics data. Our method elucidates biological relationships between proteins based on the co-occurrence of these proteins across human experiments in the PRIDE database. The majority of the significantly co-occurring protein pairs that were detected by our method have been successfully mapped to existing biological knowledge. The validity of our novel method is substantiated by the extremely few pairs that can be mapped to existing knowledge based on random associations between the same set of proteins. Moreover, using literature searches and the STRING database, we were able to derive meaningful biological associations for unannotated protein pairs that were detected using our method, further illustrating that as-yet unknown associations present highly interesting targets for follow-up analysis. PMID:28480704

Sub-surface defects detection of by using active thermography and advanced image edge detection

NASA Astrophysics Data System (ADS)

Tse, Peter W.; Wang, Gaochao

2017-05-01

Active or pulsed thermography is a popular non-destructive testing (NDT) tool for inspecting the integrity and anomaly of industrial equipment. One of the recent research trends in using active thermography is to automate the process in detecting hidden defects. As of today, human effort has still been using to adjust the temperature intensity of the thermo camera in order to visually observe the difference in cooling rates caused by a normal target as compared to that by a sub-surface crack exists inside the target. To avoid the tedious human-visual inspection and minimize human induced error, this paper reports the design of an automatic method that is capable of detecting subsurface defects. The method used the technique of active thermography, edge detection in machine vision and smart algorithm. An infrared thermo-camera was used to capture a series of temporal pictures after slightly heating up the inspected target by flash lamps. Then the Canny edge detector was employed to automatically extract the defect related images from the captured pictures. The captured temporal pictures were preprocessed by a packet of Canny edge detector and then a smart algorithm was used to reconstruct the whole sequences of image signals. During the processes, noise and irrelevant backgrounds exist in the pictures were removed. Consequently, the contrast of the edges of defective areas had been highlighted. The designed automatic method was verified by real pipe specimens that contains sub-surface cracks. After applying such smart method, the edges of cracks can be revealed visually without the need of using manual adjustment on the setting of thermo-camera. With the help of this automatic method, the tedious process in manually adjusting the colour contract and the pixel intensity in order to reveal defects can be avoided.

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 data-driven learning method for radar target detection in nonstationary environments

DOE PAGES

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

2016-04-12

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

Statistical Algorithms Accounting for Background Density in the Detection of UXO Target Areas at DoD Munitions Sites

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

Matzke, Brett D.; Wilson, John E.; Hathaway, J.

2008-02-12

Statistically defensible methods are presented for developing geophysical detector sampling plans and analyzing data for munitions response sites where unexploded ordnance (UXO) may exist. Detection methods for identifying areas of elevated anomaly density from background density are shown. Additionally, methods are described which aid in the choice of transect pattern and spacing to assure with degree of confidence that a target area (TA) of specific size, shape, and anomaly density will be identified using the detection methods. Methods for evaluating the sensitivity of designs to variation in certain parameters are also discussed. Methods presented have been incorporated into the Visualmore » Sample Plan (VSP) software (free at http://dqo.pnl.gov/vsp) and demonstrated at multiple sites in the United States. Application examples from actual transect designs and surveys from the previous two years are demonstrated.« less

Two-stage Keypoint Detection Scheme for Region Duplication Forgery Detection in Digital Images.

PubMed

Emam, Mahmoud; Han, Qi; Zhang, Hongli

2018-01-01

In digital image forensics, copy-move or region duplication forgery detection became a vital research topic recently. Most of the existing keypoint-based forgery detection methods fail to detect the forgery in the smooth regions, rather than its sensitivity to geometric changes. To solve these problems and detect points which cover all the regions, we proposed two steps for keypoint detection. First, we employed the scale-invariant feature operator to detect the spatially distributed keypoints from the textured regions. Second, the keypoints from the missing regions are detected using Harris corner detector with nonmaximal suppression to evenly distribute the detected keypoints. To improve the matching performance, local feature points are described using Multi-support Region Order-based Gradient Histogram descriptor. Based on precision-recall rates and commonly tested dataset, comprehensive performance evaluation is performed. The results demonstrated that the proposed scheme has better detection and robustness against some geometric transformation attacks compared with state-of-the-art methods. © 2017 American Academy of Forensic Sciences.

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

NASA Astrophysics Data System (ADS)

Tatar, Nurollah; Saadatseresht, Mohammad; Arefi, Hossein; Hadavand, Ahmad

2018-06-01

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C4 index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.

Seismic data fusion anomaly detection

NASA Astrophysics Data System (ADS)

Harrity, Kyle; Blasch, Erik; Alford, Mark; Ezekiel, Soundararajan; Ferris, David

2014-06-01

Detecting anomalies in non-stationary signals has valuable applications in many fields including medicine and meteorology. These include uses such as identifying possible heart conditions from an Electrocardiography (ECG) signals or predicting earthquakes via seismographic data. Over the many choices of anomaly detection algorithms, it is important to compare possible methods. In this paper, we examine and compare two approaches to anomaly detection and see how data fusion methods may improve performance. The first approach involves using an artificial neural network (ANN) to detect anomalies in a wavelet de-noised signal. The other method uses a perspective neural network (PNN) to analyze an arbitrary number of "perspectives" or transformations of the observed signal for anomalies. Possible perspectives may include wavelet de-noising, Fourier transform, peak-filtering, etc.. In order to evaluate these techniques via signal fusion metrics, we must apply signal preprocessing techniques such as de-noising methods to the original signal and then use a neural network to find anomalies in the generated signal. From this secondary result it is possible to use data fusion techniques that can be evaluated via existing data fusion metrics for single and multiple perspectives. The result will show which anomaly detection method, according to the metrics, is better suited overall for anomaly detection applications. The method used in this study could be applied to compare other signal processing algorithms.

A Novel Method for Remote Depth Estimation of Buried Radioactive Contamination.

PubMed

Ukaegbu, Ikechukwu Kevin; Gamage, Kelum A A

2018-02-08

Existing remote radioactive contamination depth estimation methods for buried radioactive wastes are either limited to less than 2 cm or are based on empirical models that require foreknowledge of the maximum penetrable depth of the contamination. These severely limits their usefulness in some real life subsurface contamination scenarios. Therefore, this work presents a novel remote depth estimation method that is based on an approximate three-dimensional linear attenuation model that exploits the benefits of using multiple measurements obtained from the surface of the material in which the contamination is buried using a radiation detector. Simulation results showed that the proposed method is able to detect the depth of caesium-137 and cobalt-60 contamination buried up to 40 cm in both sand and concrete. Furthermore, results from experiments show that the method is able to detect the depth of caesium-137 contamination buried up to 12 cm in sand. The lower maximum depth recorded in the experiment is due to limitations in the detector and the low activity of the caesium-137 source used. Nevertheless, both results demonstrate the superior capability of the proposed method compared to existing methods.

A Novel Method for Remote Depth Estimation of Buried Radioactive Contamination

PubMed Central

2018-01-01

Existing remote radioactive contamination depth estimation methods for buried radioactive wastes are either limited to less than 2 cm or are based on empirical models that require foreknowledge of the maximum penetrable depth of the contamination. These severely limits their usefulness in some real life subsurface contamination scenarios. Therefore, this work presents a novel remote depth estimation method that is based on an approximate three-dimensional linear attenuation model that exploits the benefits of using multiple measurements obtained from the surface of the material in which the contamination is buried using a radiation detector. Simulation results showed that the proposed method is able to detect the depth of caesium-137 and cobalt-60 contamination buried up to 40 cm in both sand and concrete. Furthermore, results from experiments show that the method is able to detect the depth of caesium-137 contamination buried up to 12 cm in sand. The lower maximum depth recorded in the experiment is due to limitations in the detector and the low activity of the caesium-137 source used. Nevertheless, both results demonstrate the superior capability of the proposed method compared to existing methods. PMID:29419759

Night Sky Weather Monitoring System Using Fish-Eye CCD

NASA Astrophysics Data System (ADS)

Tomida, Takayuki; Saito, Yasunori; Nakamura, Ryo; Yamazaki, Katsuya

Telescope Array (TA) is international joint experiment observing ultra-high energy cosmic rays. TA employs fluorescence detection technique to observe cosmic rays. In this technique, tho existence of cloud significantly affects quality of data. Therefore, cloud monitoring provides important information. We are developing two new methods for evaluating night sky weather with pictures taken by charge-coupled device (CCD) camera. One is evaluating the amount of cloud with pixels brightness. The other is counting the number of stars with contour detection technique. The results of these methods show clear correlation, and we concluded both the analyses are reasonable methods for weather monitoring. We discuss reliability of the star counting method.

Authorship Attribution of Source Code

ERIC Educational Resources Information Center

Tennyson, Matthew F.

2013-01-01

Authorship attribution of source code is the task of deciding who wrote a program, given its source code. Applications include software forensics, plagiarism detection, and determining software ownership. A number of methods for the authorship attribution of source code have been presented in the past. A review of those existing methods is…

Peak Detection Method Evaluation for Ion Mobility Spectrometry by Using Machine Learning Approaches

PubMed Central

Hauschild, Anne-Christin; Kopczynski, Dominik; D’Addario, Marianna; Baumbach, Jörg Ingo; Rahmann, Sven; Baumbach, Jan

2013-01-01

Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME). We manually generated a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors’ results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications. PMID:24957992

Peak detection method evaluation for ion mobility spectrometry by using machine learning approaches.

PubMed

Hauschild, Anne-Christin; Kopczynski, Dominik; D'Addario, Marianna; Baumbach, Jörg Ingo; Rahmann, Sven; Baumbach, Jan

2013-04-16

Ion mobility spectrometry with pre-separation by multi-capillary columns (MCC/IMS) has become an established inexpensive, non-invasive bioanalytics technology for detecting volatile organic compounds (VOCs) with various metabolomics applications in medical research. To pave the way for this technology towards daily usage in medical practice, different steps still have to be taken. With respect to modern biomarker research, one of the most important tasks is the automatic classification of patient-specific data sets into different groups, healthy or not, for instance. Although sophisticated machine learning methods exist, an inevitable preprocessing step is reliable and robust peak detection without manual intervention. In this work we evaluate four state-of-the-art approaches for automated IMS-based peak detection: local maxima search, watershed transformation with IPHEx, region-merging with VisualNow, and peak model estimation (PME).We manually generated Metabolites 2013, 3 278 a gold standard with the aid of a domain expert (manual) and compare the performance of the four peak calling methods with respect to two distinct criteria. We first utilize established machine learning methods and systematically study their classification performance based on the four peak detectors' results. Second, we investigate the classification variance and robustness regarding perturbation and overfitting. Our main finding is that the power of the classification accuracy is almost equally good for all methods, the manually created gold standard as well as the four automatic peak finding methods. In addition, we note that all tools, manual and automatic, are similarly robust against perturbations. However, the classification performance is more robust against overfitting when using the PME as peak calling preprocessor. In summary, we conclude that all methods, though small differences exist, are largely reliable and enable a wide spectrum of real-world biomedical applications.

A survey on object detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Cheng, Gong; Han, Junwei

2016-07-01

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

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

Compact Surface Plasmon Resonance Biosensor for Fieldwork Environmental Detection

NASA Astrophysics Data System (ADS)

Boyd, Margrethe; Drake, Madison; Stipe, Kristian; Serban, Monica; Turner, Ivana; Thomas, Aaron; Macaluso, David

2017-04-01

The ability to accurately and reliably detect biomolecular targets is important in innumerable applications, including the identification of food-borne parasites, viral pathogens in human tissue, and environmental pollutants. While detection methods do exist, they are typically slow, expensive, and restricted to laboratory use. The method of surface plasmon resonance based biosensing offers a unique opportunity to characterize molecular targets while avoiding these constraints. By incorporating a plasmon-supporting gold film within a prism/laser optical system, it is possible to reliably detect and quantify the presence of specific biomolecules of interest in real time. This detection is accomplished by observing shifts in plasmon formation energies corresponding to optical absorption due to changes in index of refraction near the gold-prism interface caused by the binding of target molecules. A compact, inexpensive, battery-powered surface plasmon resonance biosensor based on this method is being developed at the University of Montana to detect waterborne pollutants in field-based environmental research.

Portable evanescent wave fiber biosensor for highly sensitive detection of Shigella

NASA Astrophysics Data System (ADS)

Xiao, Rui; Rong, Zhen; Long, Feng; Liu, Qiqi

2014-11-01

A portable evanescent wave fiber biosensor was developed to achieve the rapid and highly sensitive detection of Shigella. In this study, a DNA probe was covalently immobilized onto fiber-optic biosensors that can hybridize with a fluorescently labeled complementary DNA. The sensitivity of detection for synthesized oligonucleotides can reach 10-10 M. The surface of the sensor can be regenerated with 0.5% sodium dodecyl sulfate solution (pH 1.9) for over 30 times without significant deterioration of performance. The total analysis time for a single sample, including the time for measurement and surface regeneration, was less than 6 min. We employed real-time polymerase chain reaction (PCR) and compared the results of both methods to investigate the actual Shigella DNA detection capability of the fiber-optic biosensor. The fiber-optic biosensor could detect as low as 102 colony-forming unit/mL Shigella. This finding was comparable with that by real-time PCR, which suggests that this method is a potential alternative to existing detection methods.

Trace gas detection in hyperspectral imagery using the wavelet packet subspace

NASA Astrophysics Data System (ADS)

Salvador, Mark A. Z.

This dissertation describes research into a new remote sensing method to detect trace gases in hyperspectral and ultra-spectral data. This new method is based on the wavelet packet transform. It attempts to improve both the computational tractability and the detection of trace gases in airborne and spaceborne spectral imagery. Atmospheric trace gas research supports various Earth science disciplines to include climatology, vulcanology, pollution monitoring, natural disasters, and intelligence and military applications. Hyperspectral and ultra-spectral data significantly increases the data glut of existing Earth science data sets. Spaceborne spectral data in particular significantly increases spectral resolution while performing daily global collections of the earth. Application of the wavelet packet transform to the spectral space of hyperspectral and ultra-spectral imagery data potentially improves remote sensing detection algorithms. It also facilities the parallelization of these methods for high performance computing. This research seeks two science goals, (1) developing a new spectral imagery detection algorithm, and (2) facilitating the parallelization of trace gas detection in spectral imagery data.

On-line Monitoring Device for High-voltage Switch Cabinet Partial Discharge Based on Pulse Current Method

NASA Astrophysics Data System (ADS)

Y Tao, S.; Zhang, X. Z.; Cai, H. W.; Li, P.; Feng, Y.; Zhang, T. C.; Li, J.; Wang, W. S.; Zhang, X. K.

2017-12-01

The pulse current method for partial discharge detection is generally applied in type testing and other off-line tests of electrical equipment at delivery. After intensive analysis of the present situation and existing problems of partial discharge detection in switch cabinets, this paper designed the circuit principle and signal extraction method for partial discharge on-line detection based on a high-voltage presence indicating systems (VPIS), established a high voltage switch cabinet partial discharge on-line detection circuit based on the pulse current method, developed background software integrated with real-time monitoring, judging and analyzing functions, carried out a real discharge simulation test on a real-type partial discharge defect simulation platform of a 10KV switch cabinet, and verified the sensitivity and validity of the high-voltage switch cabinet partial discharge on-line monitoring device based on the pulse current method. The study presented in this paper is of great significance for switch cabinet maintenance and theoretical study on pulse current method on-line detection, and has provided a good implementation method for partial discharge on-line monitoring devices for 10KV distribution network equipment.

Supervised segmentation of microelectrode recording artifacts using power spectral density.

PubMed

Bakstein, Eduard; Schneider, Jakub; Sieger, Tomas; Novak, Daniel; Wild, Jiri; Jech, Robert

2015-08-01

Appropriate detection of clean signal segments in extracellular microelectrode recordings (MER) is vital for maintaining high signal-to-noise ratio in MER studies. Existing alternatives to manual signal inspection are based on unsupervised change-point detection. We present a method of supervised MER artifact classification, based on power spectral density (PSD) and evaluate its performance on a database of 95 labelled MER signals. The proposed method yielded test-set accuracy of 90%, which was close to the accuracy of annotation (94%). The unsupervised methods achieved accuracy of about 77% on both training and testing data.

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

Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection in MRI Guided Cervical Cancer Radiation Therapy

PubMed Central

Lu, Chao; Chelikani, Sudhakar; Jaffray, David A.; Milosevic, Michael F.; Staib, Lawrence H.; Duncan, James S.

2013-01-01

External beam radiation therapy (EBRT) for the treatment of cancer enables accurate placement of radiation dose on the cancerous region. However, the deformation of soft tissue during the course of treatment, such as in cervical cancer, presents significant challenges for the delineation of the target volume and other structures of interest. Furthermore, the presence and regression of pathologies such as tumors may violate registration constraints and cause registration errors. In this paper, automatic segmentation, nonrigid registration and tumor detection in cervical magnetic resonance (MR) data are addressed simultaneously using a unified Bayesian framework. The proposed novel method can generate a tumor probability map while progressively identifying the boundary of an organ of interest based on the achieved nonrigid transformation. The method is able to handle the challenges of significant tumor regression and its effect on surrounding tissues. The new method was compared to various currently existing algorithms on a set of 36 MR data from six patients, each patient has six T2-weighted MR cervical images. The results show that the proposed approach achieves an accuracy comparable to manual segmentation and it significantly outperforms the existing registration algorithms. In addition, the tumor detection result generated by the proposed method has a high agreement with manual delineation by a qualified clinician. PMID:22328178

Detection of shifted double JPEG compression by an adaptive DCT coefficient model

NASA Astrophysics Data System (ADS)

Wang, Shi-Lin; Liew, Alan Wee-Chung; Li, Sheng-Hong; Zhang, Yu-Jin; Li, Jian-Hua

2014-12-01

In many JPEG image splicing forgeries, the tampered image patch has been JPEG-compressed twice with different block alignments. Such phenomenon in JPEG image forgeries is called the shifted double JPEG (SDJPEG) compression effect. Detection of SDJPEG-compressed patches could help in detecting and locating the tampered region. However, the current SDJPEG detection methods do not provide satisfactory results especially when the tampered region is small. In this paper, we propose a new SDJPEG detection method based on an adaptive discrete cosine transform (DCT) coefficient model. DCT coefficient distributions for SDJPEG and non-SDJPEG patches have been analyzed and a discriminative feature has been proposed to perform the two-class classification. An adaptive approach is employed to select the most discriminative DCT modes for SDJPEG detection. The experimental results show that the proposed approach can achieve much better results compared with some existing approaches in SDJPEG patch detection especially when the patch size is small.

Infrared dim target detection based on visual attention

NASA Astrophysics Data System (ADS)

Wang, Xin; Lv, Guofang; Xu, Lizhong

2012-11-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. Based on human visual attention mechanisms, an automatic detection algorithm for infrared dim target is presented. After analyzing the characteristics of infrared dim target images, the method firstly designs Difference of Gaussians (DoG) filters to compute the saliency map. Then the salient regions where the potential targets exist in are extracted by searching through the saliency map with a control mechanism of winner-take-all (WTA) competition and inhibition-of-return (IOR). At last, these regions are identified by the characteristics of the dim IR targets, so the true targets are detected, and the spurious objects are rejected. The experiments are performed for some real-life IR images, and 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.

Objective-lens-free Fiber-based Position Detection with Nanometer Resolution in a Fiber Optical Trapping System.

PubMed

Ti, Chaoyang; Ho-Thanh, Minh-Tri; Wen, Qi; Liu, Yuxiang

2017-10-13

Position detection with high accuracy is crucial for force calibration of optical trapping systems. Most existing position detection methods require high-numerical-aperture objective lenses, which are bulky, expensive, and difficult to miniaturize. Here, we report an affordable objective-lens-free, fiber-based position detection scheme with 2 nm spatial resolution and 150 MHz bandwidth. This fiber based detection mechanism enables simultaneous trapping and force measurements in a compact fiber optical tweezers system. In addition, we achieved more reliable signal acquisition with less distortion compared with objective based position detection methods, thanks to the light guiding in optical fibers and small distance between the fiber tips and trapped particle. As a demonstration of the fiber based detection, we used the fiber optical tweezers to apply a force on a cell membrane and simultaneously measure the cellular response.

Direct detection of metal-insulator phase transitions using the modified Backus-Gilbert method

NASA Astrophysics Data System (ADS)

Ulybyshev, Maksim; Winterowd, Christopher; Zafeiropoulos, Savvas

2018-03-01

The detection of the (semi)metal-insulator phase transition can be extremely difficult if the local order parameter which characterizes the ordered phase is unknown. In some cases, it is even impossible to define a local order parameter: the most prominent example of such system is the spin liquid state. This state was proposed to exist in the Hubbard model on the hexagonal lattice in a region between the semimetal phase and the antiferromagnetic insulator phase. The existence of this phase has been the subject of a long debate. In order to detect these exotic phases we must use alternative methods to those used for more familiar examples of spontaneous symmetry breaking. We have modified the Backus-Gilbert method of analytic continuation which was previously used in the calculation of the pion quasiparticle mass in lattice QCD. The modification of the method consists of the introduction of the Tikhonov regularization scheme which was used to treat the ill-conditioned kernel. This modified Backus-Gilbert method is applied to the Euclidean propagators in momentum space calculated using the hybrid Monte Carlo algorithm. In this way, it is possible to reconstruct the full dispersion relation and to estimate the mass gap, which is a direct signal of the transition to the insulating state. We demonstrate the utility of this method in our calculations for the Hubbard model on the hexagonal lattice. We also apply the method to the metal-insulator phase transition in the Hubbard-Coulomb model on the square lattice.

Identifying Minefields and Verifying Clearance: Adapting Statistical Methods for UXO Target Detection

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

Gilbert, Richard O.; O'Brien, Robert F.; Wilson, John E.

2003-09-01

It may not be feasible to completely survey large tracts of land suspected of containing minefields. It is desirable to develop a characterization protocol that will confidently identify minefields within these large land tracts if they exist. Naturally, surveying areas of greatest concern and most likely locations would be necessary but will not provide the needed confidence that an unknown minefield had not eluded detection. Once minefields are detected, methods are needed to bound the area that will require detailed mine detection surveys. The US Department of Defense Strategic Environmental Research and Development Program (SERDP) is sponsoring the development ofmore » statistical survey methods and tools for detecting potential UXO targets. These methods may be directly applicable to demining efforts. Statistical methods are employed to determine the optimal geophysical survey transect spacing to have confidence of detecting target areas of a critical size, shape, and anomaly density. Other methods under development determine the proportion of a land area that must be surveyed to confidently conclude that there are no UXO present. Adaptive sampling schemes are also being developed as an approach for bounding the target areas. These methods and tools will be presented and the status of relevant research in this area will be discussed.« less

Detection of Memory B Activity Against a Therapeutic Protein in Treatment-Naïve Subjects.

PubMed

Liao, Karen; Derbyshire, Stacy; Wang, Kai-Fen; Caucci, Cherilyn; Tang, Shuo; Holland, Claire; Loercher, Amy; Gunn, George R

2018-03-16

Bridging immunoassays commonly used to detect and characterize immunogenicity during biologic development do not provide direct information on the presence or development of a memory anti-drug antibody (ADA) response. In this study, a B cell ELISPOT assay method was used to evaluate pre-existing ADA for anti-TNFR1 domain antibody, GSK1995057, an experimental biologic in treatment naive subjects. This assay utilized a 7-day activation of PBMCs by a combination of GSK1995057 (antigen) and polyclonal stimulator followed by GSK1995057-specific ELISPOT for the enumeration of memory B cells that have differentiated into antibody secreting cells (ASC) in vitro. We demonstrated that GSK1995057-specific ASC were detectable in treatment-naïve subjects with pre-existing ADA; the frequency of drug-specific ASC was low and ranged from 1 to 10 spot forming units (SFU) per million cells. Interestingly, the frequency of drug-specific ASC correlated with the ADA level measured using an in vitro ADA assay. We further confirmed that the ASC originated from CD27 + memory B cells, not from CD27 - -naïve B cells. Our data demonstrated the utility of the B cell ELISPOT method in therapeutic protein immunogenicity evaluation, providing a novel way to confirm and characterize the cell population producing pre-existing ADA. This novel application of a B cell ELISPOT assay informs and characterizes immune memory activity regarding incidence and magnitude associated with a pre-existing ADA response.

Technical Fact Sheet – Polybrominated biphenyls (PBBs)

EPA Pesticide Factsheets

This fact sheet provides a brief summary of polybrominated biphenyls (PBB), including physical and chemical properties; environmental and health impacts; existing federal and state guidelines; detection and treatment methods; and additional sources of info

External detection and localization of well leaks in aquifer zones

NASA Astrophysics Data System (ADS)

Haas, Allan K.

This dissertation presents a new methodology for monitoring, detecting, and localizing shallow, aquifer zone leaks in oil and gas wells. The rationale for this type of leak detection is to close the knowledge gap associated with public claims of subsurface water resource contamination caused by the oil and gas industry. A knowledge gap exists because there is no data, one way or the other, that can definitively prove or deny the existence of subsurface leakage pathways in oil and gas wells, new, old or abandoned. This dissertation begins with an overview of existing and future oil and gas well leak detection methods, and then presents three published papers, each describing a different phenomena that can be exploited for leak monitoring, detection, localization, and damage extent determination. The first paper describes the direct detection and localization of a leak that was discovered during a laboratory based hydraulic fracturing experiment. The second paper describes the laboratory measured electrical response that occurs during two phase flow inside of porous media. The third paper describes the detection and tracking of a gravity driven salt plume leak in a freshwater test tank in the laboratory. the three geophysical approaches that are presented, when combined together, provide a new, powerful, external to the well method to monitor, detect, localize, and assess the damage from leaks in the drinking water protection zone of oil and gas wells. This is a capability that is not available in any other leak detection and localization method. This dissertation also presents a chapter of Science, Technology and Society (STS), and Science, and Technology Policy (STP) as a final fulfillment requirement of the SmartGeo Fellowship program, and the Science, Technology, Engineering, and Policy minor. This chapter introduces a new STS/STP concept concerning the after effects of knowledge boundary disputes. This new concept is called the residual footprints of knowledge boundary disputes. This new concept is developed through the analysis of an oil and gas drilling controversy that climaxed in Erie, CO in 2012. Additional evidence of this residual footprint concept is also presented in a very brief form. It is hoped that this new concept will be further researched, and adopted by the STS/STP community.

Hyperspectral laser-induced autofluorescence imaging of dental caries

NASA Astrophysics Data System (ADS)

Bürmen, Miran; Fidler, Aleš; Pernuš, Franjo; Likar, Boštjan

2012-01-01

Dental caries is a disease characterized by demineralization of enamel crystals leading to the penetration of bacteria into the dentine and pulp. Early detection of enamel demineralization resulting in increased enamel porosity, commonly known as white spots, is a difficult diagnostic task. Laser induced autofluorescence was shown to be a useful method for early detection of demineralization. The existing studies involved either a single point spectroscopic measurements or imaging at a single spectral band. In the case of spectroscopic measurements, very little or no spatial information is acquired and the measured autofluorescence signal strongly depends on the position and orientation of the probe. On the other hand, single-band spectral imaging can be substantially affected by local spectral artefacts. Such effects can significantly interfere with automated methods for detection of early caries lesions. In contrast, hyperspectral imaging effectively combines the spatial information of imaging methods with the spectral information of spectroscopic methods providing excellent basis for development of robust and reliable algorithms for automated classification and analysis of hard dental tissues. In this paper, we employ 405 nm laser excitation of natural caries lesions. The fluorescence signal is acquired by a state-of-the-art hyperspectral imaging system consisting of a high-resolution acousto-optic tunable filter (AOTF) and a highly sensitive Scientific CMOS camera in the spectral range from 550 nm to 800 nm. The results are compared to the contrast obtained by near-infrared hyperspectral imaging technique employed in the existing studies on early detection of dental caries.

Detecting atrial fibrillation by deep convolutional neural networks.

PubMed

Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

2018-02-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

CNV detection method optimized for high-resolution arrayCGH by normality test.

PubMed

Ahn, Jaegyoon; Yoon, Youngmi; Park, Chihyun; Park, Sanghyun

2012-04-01

High-resolution arrayCGH platform makes it possible to detect small gains and losses which previously could not be measured. However, current CNV detection tools fitted to early low-resolution data are not applicable to larger high-resolution data. When CNV detection tools are applied to high-resolution data, they suffer from high false-positives, which increases validation cost. Existing CNV detection tools also require optimal parameter values. In most cases, obtaining these values is a difficult task. This study developed a CNV detection algorithm that is optimized for high-resolution arrayCGH data. This tool operates up to 1500 times faster than existing tools on a high-resolution arrayCGH of whole human chromosomes which has 42 million probes whose average length is 50 bases, while preserving false positive/negative rates. The algorithm also uses a normality test, thereby removing the need for optimal parameters. To our knowledge, this is the first formulation for CNV detecting problems that results in a near-linear empirical overall complexity for real high-resolution data. Copyright © 2012 Elsevier Ltd. All rights reserved.

High-Resolution Detection of Identity by Descent in Unrelated Individuals

PubMed Central

Browning, Sharon R.; Browning, Brian L.

2010-01-01

Detection of recent identity by descent (IBD) in population samples is important for population-based linkage mapping and for highly accurate genotype imputation and haplotype-phase inference. We present a method for detection of recent IBD in population samples. Our method accounts for linkage disequilibrium between SNPs to enable full use of high-density SNP data. We find that our method can detect segments of a length of 2 cM with moderate power and negligible false discovery rate in Illumina 550K data in Northwestern Europeans. We compare our method with GERMLINE and PLINK, and we show that our method has a level of resolution that is significantly better than these existing methods, thus extending the usefulness of recent IBD in analysis of high-density SNP data. We survey four genomic regions in a sample of UK individuals of European descent and find that on average, at a given location, our method detects IBD in 2.7 per 10,000 pairs of individuals in Illumina 550K data. We also present methodology and results for detection of homozygosity by descent (HBD) and survey the whole genome in a sample of 1373 UK individuals of European descent. We detect HBD in 4.7 individuals per 10,000 on average at a given location. Our methodology is implemented in the freely available BEAGLE software package. PMID:20303063

Scalable Track Detection in SAR CCD Images

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

Chow, James G; Quach, Tu-Thach

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images ta ken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are often too simple to capture natural track features such as continuity and parallelism. We present a simple convolutional network architecture consisting of a series of 3-by-3 convolutions to detect tracks. The network is trained end-to-end to learn natural track features entirely from data. The network is computationally efficient and improves the F-score on a standard dataset to 0.988,more » up fr om 0.907 obtained by the current state-of-the-art method.« less

Electrochemical and Infrared Absorption Spectroscopy Detection of SF₆ Decomposition Products.

PubMed

Dong, Ming; Zhang, Chongxing; Ren, Ming; Albarracín, Ricardo; Ye, Rixin

2017-11-15

Sulfur hexafluoride (SF₆) gas-insulated electrical equipment is widely used in high-voltage (HV) and extra-high-voltage (EHV) power systems. Partial discharge (PD) and local heating can occur in the electrical equipment because of insulation faults, which results in SF₆ decomposition and ultimately generates several types of decomposition products. These SF₆ decomposition products can be qualitatively and quantitatively detected with relevant detection methods, and such detection contributes to diagnosing the internal faults and evaluating the security risks of the equipment. At present, multiple detection methods exist for analyzing the SF₆ decomposition products, and electrochemical sensing (ES) and infrared (IR) spectroscopy are well suited for application in online detection. In this study, the combination of ES with IR spectroscopy is used to detect SF₆ gas decomposition. First, the characteristics of these two detection methods are studied, and the data analysis matrix is established. Then, a qualitative and quantitative analysis ES-IR model is established by adopting a two-step approach. A SF₆ decomposition detector is designed and manufactured by combining an electrochemical sensor and IR spectroscopy technology. The detector is used to detect SF₆ gas decomposition and is verified to reliably and accurately detect the gas components and concentrations.

TEAM: efficient two-locus epistasis tests in human genome-wide association study.

PubMed

Zhang, Xiang; Huang, Shunping; Zou, Fei; Wang, Wei

2010-06-15

As a promising tool for identifying genetic markers underlying phenotypic differences, genome-wide association study (GWAS) has been extensively investigated in recent years. In GWAS, detecting epistasis (or gene-gene interaction) is preferable over single locus study since many diseases are known to be complex traits. A brute force search is infeasible for epistasis detection in the genome-wide scale because of the intensive computational burden. Existing epistasis detection algorithms are designed for dataset consisting of homozygous markers and small sample size. In human study, however, the genotype may be heterozygous, and number of individuals can be up to thousands. Thus, existing methods are not readily applicable to human datasets. In this article, we propose an efficient algorithm, TEAM, which significantly speeds up epistasis detection for human GWAS. Our algorithm is exhaustive, i.e. it does not ignore any epistatic interaction. Utilizing the minimum spanning tree structure, the algorithm incrementally updates the contingency tables for epistatic tests without scanning all individuals. Our algorithm has broader applicability and is more efficient than existing methods for large sample study. It supports any statistical test that is based on contingency tables, and enables both family-wise error rate and false discovery rate controlling. Extensive experiments show that our algorithm only needs to examine a small portion of the individuals to update the contingency tables, and it achieves at least an order of magnitude speed up over the brute force approach.

Automatically Detecting Failures in Natural Language Processing Tools for Online Community Text.

PubMed

Park, Albert; Hartzler, Andrea L; Huh, Jina; McDonald, David W; Pratt, Wanda

2015-08-31

The prevalence and value of patient-generated health text are increasing, but processing such text remains problematic. Although existing biomedical natural language processing (NLP) tools are appealing, most were developed to process clinician- or researcher-generated text, such as clinical notes or journal articles. In addition to being constructed for different types of text, other challenges of using existing NLP include constantly changing technologies, source vocabularies, and characteristics of text. These continuously evolving challenges warrant the need for applying low-cost systematic assessment. However, the primarily accepted evaluation method in NLP, manual annotation, requires tremendous effort and time. The primary objective of this study is to explore an alternative approach-using low-cost, automated methods to detect failures (eg, incorrect boundaries, missed terms, mismapped concepts) when processing patient-generated text with existing biomedical NLP tools. We first characterize common failures that NLP tools can make in processing online community text. We then demonstrate the feasibility of our automated approach in detecting these common failures using one of the most popular biomedical NLP tools, MetaMap. Using 9657 posts from an online cancer community, we explored our automated failure detection approach in two steps: (1) to characterize the failure types, we first manually reviewed MetaMap's commonly occurring failures, grouped the inaccurate mappings into failure types, and then identified causes of the failures through iterative rounds of manual review using open coding, and (2) to automatically detect these failure types, we then explored combinations of existing NLP techniques and dictionary-based matching for each failure cause. Finally, we manually evaluated the automatically detected failures. From our manual review, we characterized three types of failure: (1) boundary failures, (2) missed term failures, and (3) word ambiguity failures. Within these three failure types, we discovered 12 causes of inaccurate mappings of concepts. We used automated methods to detect almost half of 383,572 MetaMap's mappings as problematic. Word sense ambiguity failure was the most widely occurring, comprising 82.22% of failures. Boundary failure was the second most frequent, amounting to 15.90% of failures, while missed term failures were the least common, making up 1.88% of failures. The automated failure detection achieved precision, recall, accuracy, and F1 score of 83.00%, 92.57%, 88.17%, and 87.52%, respectively. We illustrate the challenges of processing patient-generated online health community text and characterize failures of NLP tools on this patient-generated health text, demonstrating the feasibility of our low-cost approach to automatically detect those failures. Our approach shows the potential for scalable and effective solutions to automatically assess the constantly evolving NLP tools and source vocabularies to process patient-generated text.

CATCh, an Ensemble Classifier for Chimera Detection in 16S rRNA Sequencing Studies

PubMed Central

Mysara, Mohamed; Saeys, Yvan; Leys, Natalie; Raes, Jeroen

2014-01-01

In ecological studies, microbial diversity is nowadays mostly assessed via the detection of phylogenetic marker genes, such as 16S rRNA. However, PCR amplification of these marker genes produces a significant amount of artificial sequences, often referred to as chimeras. Different algorithms have been developed to remove these chimeras, but efforts to combine different methodologies are limited. Therefore, two machine learning classifiers (reference-based and de novo CATCh) were developed by integrating the output of existing chimera detection tools into a new, more powerful method. When comparing our classifiers with existing tools in either the reference-based or de novo mode, a higher performance of our ensemble method was observed on a wide range of sequencing data, including simulated, 454 pyrosequencing, and Illumina MiSeq data sets. Since our algorithm combines the advantages of different individual chimera detection tools, our approach produces more robust results when challenged with chimeric sequences having a low parent divergence, short length of the chimeric range, and various numbers of parents. Additionally, it could be shown that integrating CATCh in the preprocessing pipeline has a beneficial effect on the quality of the clustering in operational taxonomic units. PMID:25527546

[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.

Fringe order correction for the absolute phase recovered by two selected spatial frequency fringe projections in fringe projection profilometry.

PubMed

Ding, Yi; Peng, Kai; Yu, Miao; Lu, Lei; Zhao, Kun

2017-08-01

The performance of the two selected spatial frequency phase unwrapping methods is limited by a phase error bound beyond which errors will occur in the fringe order leading to a significant error in the recovered absolute phase map. In this paper, we propose a method to detect and correct the wrong fringe orders. Two constraints are introduced during the fringe order determination of two selected spatial frequency phase unwrapping methods. A strategy to detect and correct the wrong fringe orders is also described. Compared with the existing methods, we do not need to estimate the threshold associated with absolute phase values to determine the fringe order error, thus making it more reliable and avoiding the procedure of search in detecting and correcting successive fringe order errors. The effectiveness of the proposed method is validated by the experimental results.

A salient region detection model combining background distribution measure for indoor robots.

PubMed

Li, Na; Xu, Hui; Wang, Zhenhua; Sun, Lining; Chen, Guodong

2017-01-01

Vision system plays an important role in the field of indoor robot. Saliency detection methods, capturing regions that are perceived as important, are used to improve the performance of visual perception system. Most of state-of-the-art methods for saliency detection, performing outstandingly in natural images, cannot work in complicated indoor environment. Therefore, we propose a new method comprised of graph-based RGB-D segmentation, primary saliency measure, background distribution measure, and combination. Besides, region roundness is proposed to describe the compactness of a region to measure background distribution more robustly. To validate the proposed approach, eleven influential methods are compared on the DSD and ECSSD dataset. Moreover, we build a mobile robot platform for application in an actual environment, and design three different kinds of experimental constructions that are different viewpoints, illumination variations and partial occlusions. Experimental results demonstrate that our model outperforms existing methods and is useful for indoor mobile robots.

Spatial cluster detection using dynamic programming.

PubMed

Sverchkov, Yuriy; Jiang, Xia; Cooper, Gregory F

2012-03-25

The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm.

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.

Detection of the corrosion in reinforced concrete with GPR: the case study of the Park Guell

NASA Astrophysics Data System (ADS)

Sossa, Viviana; Perez-Gracia, Vega; Gonzalez-Drigo, Ramon; Caselles, Oriol; Clapes, Jaume

2017-04-01

Detection of corrosion is important in cultural heritage assessment. Many structures contain metallic targets embedded in masonry or mortar, and corrosion cab cause important damage. However, detection using non-destructive methods is difficult and highly localized, providing in most cases incomplete results. In order to obtain a more extended analysis, GPR was applied and evaluated to detect damage as consequence of corrosion. This technique is a non-destructive method that covers a large area of study while other methods are constrained to a small areas or specific points. Therefore, some controlled laboratory tests were designed to determine possible differences in radargrams obtained in the case of corroded and non-corroded targets. These analysis allowed to observe that the corrosion seems to increase the attenuation of the radar signal, being difficult to detect targets near the damaged bars. The results were applied to study the mosaic roofs in the Park Guell, in Barcelona. This park is one of the most important Modernista (Art Noveau) complex in Barcelona. It is characterized by structures with roofs and banks with tessellation. Some of these structures are most likely supported by metal elements, and seepage cause important damage observed over the tessellation. The objective of the study was to define the possible existence of those metallic targets, determining their location. And, in the case of existence of metallic elements, defining which are the zones more affected by corrosion. The results demonstrates the existence of metallic supports in many parts, as well as some defined areas that could be damaged. Acknowledgement: This work has been partially funded by the Spanish Government and by the European Commission with FEDER funds, through the research projects CGL2011-23621 and CGL2015-65913-P. The study is also a contribution to the EU funded COST Action TU1208, "Civil Engineering Applications of Ground Penetrating Radar", to the working group 2.2.

Image steganalysis using Artificial Bee Colony algorithm

NASA Astrophysics Data System (ADS)

Sajedi, Hedieh

2017-09-01

Steganography is the science of secure communication where the presence of the communication cannot be detected while steganalysis is the art of discovering the existence of the secret communication. Processing a huge amount of information takes extensive execution time and computational sources most of the time. As a result, it is needed to employ a phase of preprocessing, which can moderate the execution time and computational sources. In this paper, we propose a new feature-based blind steganalysis method for detecting stego images from the cover (clean) images with JPEG format. In this regard, we present a feature selection technique based on an improved Artificial Bee Colony (ABC). ABC algorithm is inspired by honeybees' social behaviour in their search for perfect food sources. In the proposed method, classifier performance and the dimension of the selected feature vector depend on using wrapper-based methods. The experiments are performed using two large data-sets of JPEG images. Experimental results demonstrate the effectiveness of the proposed steganalysis technique compared to the other existing techniques.

Radar fall detection using principal component analysis

NASA Astrophysics Data System (ADS)

Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

2016-05-01

Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

A primitive study on unsupervised anomaly detection with an autoencoder in emergency head CT volumes

NASA Astrophysics Data System (ADS)

Sato, Daisuke; Hanaoka, Shouhei; Nomura, Yukihiro; Takenaga, Tomomi; Miki, Soichiro; Yoshikawa, Takeharu; Hayashi, Naoto; Abe, Osamu

2018-02-01

Purpose: The target disorders of emergency head CT are wide-ranging. Therefore, people working in an emergency department desire a computer-aided detection system for general disorders. In this study, we proposed an unsupervised anomaly detection method in emergency head CT using an autoencoder and evaluated the anomaly detection performance of our method in emergency head CT. Methods: We used a 3D convolutional autoencoder (3D-CAE), which contains 11 layers in the convolution block and 6 layers in the deconvolution block. In the training phase, we trained the 3D-CAE using 10,000 3D patches extracted from 50 normal cases. In the test phase, we calculated abnormalities of each voxel in 38 emergency head CT volumes (22 abnormal cases and 16 normal cases) for evaluation and evaluated the likelihood of lesion existence. Results: Our method achieved a sensitivity of 68% and a specificity of 88%, with an area under the curve of the receiver operating characteristic curve of 0.87. It shows that this method has a moderate accuracy to distinguish normal CT cases to abnormal ones. Conclusion: Our method has potentialities for anomaly detection in emergency head CT.

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

Salient region detection by fusing bottom-up and top-down features extracted from a single image.

PubMed

Tian, Huawei; Fang, Yuming; Zhao, Yao; Lin, Weisi; Ni, Rongrong; Zhu, Zhenfeng

2014-10-01

Recently, some global contrast-based salient region detection models have been proposed based on only the low-level feature of color. It is necessary to consider both color and orientation features to overcome their limitations, and thus improve the performance of salient region detection for images with low-contrast in color and high-contrast in orientation. In addition, the existing fusion methods for different feature maps, like the simple averaging method and the selective method, are not effective sufficiently. To overcome these limitations of existing salient region detection models, we propose a novel salient region model based on the bottom-up and top-down mechanisms: the color contrast and orientation contrast are adopted to calculate the bottom-up feature maps, while the top-down cue of depth-from-focus from the same single image is used to guide the generation of final salient regions, since depth-from-focus reflects the photographer's preference and knowledge of the task. A more general and effective fusion method is designed to combine the bottom-up feature maps. According to the degree-of-scattering and eccentricities of feature maps, the proposed fusion method can assign adaptive weights to different feature maps to reflect the confidence level of each feature map. The depth-from-focus of the image as a significant top-down feature for visual attention in the image is used to guide the salient regions during the fusion process; with its aid, the proposed fusion method can filter out the background and highlight salient regions for the image. Experimental results show that the proposed model outperforms the state-of-the-art models on three public available data sets.

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

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

Searching Remote Homology with Spectral Clustering with Symmetry in Neighborhood Cluster Kernels

PubMed Central

Maulik, Ujjwal; Sarkar, Anasua

2013-01-01

Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of “recent” paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request. Contact: sarkar@labri.fr. PMID:23457439

Searching remote homology with spectral clustering with symmetry in neighborhood cluster kernels.

PubMed

Maulik, Ujjwal; Sarkar, Anasua

2013-01-01

Remote homology detection among proteins utilizing only the unlabelled sequences is a central problem in comparative genomics. The existing cluster kernel methods based on neighborhoods and profiles and the Markov clustering algorithms are currently the most popular methods for protein family recognition. The deviation from random walks with inflation or dependency on hard threshold in similarity measure in those methods requires an enhancement for homology detection among multi-domain proteins. We propose to combine spectral clustering with neighborhood kernels in Markov similarity for enhancing sensitivity in detecting homology independent of "recent" paralogs. The spectral clustering approach with new combined local alignment kernels more effectively exploits the unsupervised protein sequences globally reducing inter-cluster walks. When combined with the corrections based on modified symmetry based proximity norm deemphasizing outliers, the technique proposed in this article outperforms other state-of-the-art cluster kernels among all twelve implemented kernels. The comparison with the state-of-the-art string and mismatch kernels also show the superior performance scores provided by the proposed kernels. Similar performance improvement also is found over an existing large dataset. Therefore the proposed spectral clustering framework over combined local alignment kernels with modified symmetry based correction achieves superior performance for unsupervised remote homolog detection even in multi-domain and promiscuous domain proteins from Genolevures database families with better biological relevance. Source code available upon request. sarkar@labri.fr.

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.

Material identification based on electrostatic sensing technology

NASA Astrophysics Data System (ADS)

Liu, Kai; Chen, Xi; Li, Jingnan

2018-04-01

When the robot travels on the surface of different media, the uncertainty of the medium will seriously affect the autonomous action of the robot. In this paper, the distribution characteristics of multiple electrostatic charges on the surface of materials are detected, so as to improve the accuracy of the existing electrostatic signal material identification methods, which is of great significance to help the robot optimize the control algorithm. In this paper, based on the electrostatic signal material identification method proposed by predecessors, the multi-channel detection circuit is used to obtain the electrostatic charge distribution at different positions of the material surface, the weights are introduced into the eigenvalue matrix, and the weight distribution is optimized by the evolutionary algorithm, which makes the eigenvalue matrix more accurately reflect the surface charge distribution characteristics of the material. The matrix is used as the input of the k-Nearest Neighbor (kNN)classification algorithm to classify the dielectric materials. The experimental results show that the proposed method can significantly improve the recognition rate of the existing electrostatic signal material recognition methods.

An incremental community detection method for social tagging systems using locality-sensitive hashing.

PubMed

Wu, Zhenyu; Zou, Ming

2014-10-01

An increasing number of users interact, collaborate, and share information through social networks. Unprecedented growth in social networks is generating a significant amount of unstructured social data. From such data, distilling communities where users have common interests and tracking variations of users' interests over time are important research tracks in fields such as opinion mining, trend prediction, and personalized services. However, these tasks are extremely difficult considering the highly dynamic characteristics of the data. Existing community detection methods are time consuming, making it difficult to process data in real time. In this paper, dynamic unstructured data is modeled as a stream. Tag assignments stream clustering (TASC), an incremental scalable community detection method, is proposed based on locality-sensitive hashing. Both tags and latent interactions among users are incorporated in the method. In our experiments, the social dynamic behaviors of users are first analyzed. The proposed TASC method is then compared with state-of-the-art clustering methods such as StreamKmeans and incremental k-clique; results indicate that TASC can detect communities more efficiently and effectively. Copyright © 2014 Elsevier Ltd. All rights reserved.

Terahertz detection of alcohol using a photonic crystal fiber sensor.

PubMed

Sultana, Jakeya; Islam, Md Saiful; Ahmed, Kawsar; Dinovitser, Alex; Ng, Brian W-H; Abbott, Derek

2018-04-01

Ethanol is widely used in chemical industrial processes as well as in the food and beverage industry. Therefore, methods of detecting alcohol must be accurate, precise, and reliable. In this content, a novel Zeonex-based photonic crystal fiber (PCF) has been modeled and analyzed for ethanol detection in terahertz frequency range. A finite-element-method-based simulation of the PCF sensor shows a high relative sensitivity of 68.87% with negligible confinement loss of 7.79×10 -12    cm -1 at 1 THz frequency and x -polarization mode. Moreover, the core power fraction, birefringence, effective material loss, dispersion, and numerical aperture are also determined in the terahertz frequency range. Owing to the simple fiber structure, existing fabrication methods are feasible. With the outstanding waveguiding properties, the proposed sensor can potentially be used in ethanol detection, as well as polarization-preserving applications of terahertz waves.

Flag-based detection of weak gas signatures in long-wave infrared hyperspectral image sequences

NASA Astrophysics Data System (ADS)

Marrinan, Timothy; Beveridge, J. Ross; Draper, Bruce; Kirby, Michael; Peterson, Chris

2016-05-01

We present a flag manifold based method for detecting chemical plumes in long-wave infrared hyperspectral movies. The method encodes temporal and spatial information related to a hyperspectral pixel into a flag, or nested sequence of linear subspaces. The technique used to create the flags pushes information about the background clutter, ambient conditions, and potential chemical agents into the leading elements of the flags. Exploiting this temporal information allows for a detection algorithm that is sensitive to the presence of weak signals. This method is compared to existing techniques qualitatively on real data and quantitatively on synthetic data to show that the flag-based algorithm consistently performs better on data when the SINRdB is low, and beats the ACE and MF algorithms in probability of detection for low probabilities of false alarm even when the SINRdB is high.

Local Explosion Monitoring using Rg

NASA Astrophysics Data System (ADS)

O'Rourke, C. T.; Baker, G. E.

2016-12-01

Rg is the high-frequency fundamental-mode Rayleigh wave, which is only excited by near-surface events. As such, an Rg detection indicates that a seismic source is shallow, generally less than a few km depending on the velocity structure, and so likely man-made. Conversely, the absence of Rg can indicate that the source is deeper and so likely naturally occurring. We have developed a new automated method of detecting Rg arrivals from various explosion sources at local distances, and a process for estimating the likelihood that a source is not shallow when no Rg is detected. Our Rg detection method scans the spectrogram of a seismic signal for a characteristic frequency peak. We test this on the Bighorn Arch Seismic Experiment data, which includes earthquakes, active source explosions in boreholes, and mining explosions recorded on a dense network that spans the Bighorn Mountains and Powder River Basin. The Rg passbands used were 0.4-0.8 Hz for mining blasts and 0.8-1.2 Hz for borehole shots. We successfully detect Rg across the full network for most mining blasts. The lower-yield shots are detectable out to 50 km. We achieve <1% false-positive rate for the small-magnitude earthquakes in the region. Rg detections on known non-shallow earthquake seismograms indicates they are largely due to windowing leakage at very close distances or occasionally to cultural noise. We compare our results to existing methods that use cross-correlation to detect retrograde motion of the surface waves. Our method shows more complete detection across the network, especially in the Powder River Basin where Rg exhibits prograde motion that does not trigger the existing detector. We also estimate the likelihood that Rg would have been detected from a surface source, based on the measured P amplitude. For example, an event with a large P wave and no detectable Rg would have a high probability of being a deeper event, whereas we cannot confidently determine whether an event with a small P wave and no Rg detection is shallow or not. These results allow us to detect Rg arrivals, which indicate a shallow source, and to use the absence of Rg to estimate the likelihood that a source in a calibrated region is not shallow enough to be man-made.

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

Demos, S G; Gandour-Edwards, R; Ramsamooj, R

The feasibility of developing bladder cancer detection methods using intrinsic tissue optical properties is the focus of this investigation. In vitro experiments have been performed using polarized elastic light scattering in combination with tissue autofluorescence in the NIR spectral region under laser excitation in the green and red spectral regions. The experimental results obtained from a set of tissue specimens from 25 patients reveal the presence of optical fingerprint characteristics suitable for cancer detection with high contrast and accuracy. These photonic methods are compatible with existing endoscopic imaging modalities which make them suitable for in-vivo application.

[Determination of 10-HDA in honeybee body by HPLC].

PubMed

Fan, H; He, C; Han, H

1999-05-01

In the present work we found that in the honeybee body there exists an unsaturated fatty acid, trans-10-hydroxy-2-decenoic acid (10-HDA), which was known only to be present in royal jelly. We established the analytical method of 10-HDA in honeybee body by HPLC and simplified the extraction method of 10-HDA. In the optimum conditions the linear range of detection was 10-1,000 ng, the correlation coefficient was 0.9998, the recovery was 96.5%-99.2% and the detectable limit was 0.53 microgram/g.

High-speed event detector for embedded nanopore bio-systems.

PubMed

Huang, Yiyun; Magierowski, Sebastian; Ghafar-Zadeh, Ebrahim; Wang, Chengjie

2015-08-01

Biological measurements of microscopic phenomena often deal with discrete-event signals. The ability to automatically carry out such measurements at high-speed in a miniature embedded system is desirable but compromised by high-frequency noise along with practical constraints on filter quality and sampler resolution. This paper presents a real-time event-detection method in the context of nanopore sensing that helps to mitigate these drawbacks and allows accurate signal processing in an embedded system. Simulations show at least a 10× improvement over existing on-line detection methods.

Comparison of direct 13C and indirect 1H-[13C] MR detection methods for the study of dynamic metabolic turnover in the human brain

NASA Astrophysics Data System (ADS)

Chen, Hao; De Feyter, Henk M.; Brown, Peter B.; Rothman, Douglas L.; Cai, Shuhui; de Graaf, Robin A.

2017-10-01

A wide range of direct 13C and indirect 1H-[13C] MR detection methods exist to probe dynamic metabolic pathways in the human brain. Choosing an optimal detection method is difficult as sequence-specific features regarding spatial localization, broadband decoupling, spectral resolution, power requirements and sensitivity complicate a straightforward comparison. Here we combine density matrix simulations with experimentally determined values for intrinsic 1H and 13C sensitivity, T1 and T2 relaxation and transmit efficiency to allow selection of an optimal 13C MR detection method for a given application and magnetic field. The indirect proton-observed, carbon-edited (POCE) detection method provides the highest accuracy at reasonable RF power deposition both at 4 T and 7 T. The various polarization transfer methods all have comparable performances, but may become infeasible at 7 T due to the high RF power deposition. 2D MR methods have limited value for the metabolites considered (primarily glutamate, glutamine and γ-amino butyric acid (GABA)), but may prove valuable when additional information can be extracted, such as isotopomers or lipid composition. While providing the lowest accuracy, the detection of non-protonated carbons is the simplest to implement with the lowest RF power deposition. The magnetic field homogeneity is one of the most important parameters affecting the detection accuracy for all metabolites and all acquisition methods.

Unsupervised Anomaly Detection Based on Clustering and Multiple One-Class SVM

NASA Astrophysics Data System (ADS)

Song, Jungsuk; Takakura, Hiroki; Okabe, Yasuo; Kwon, Yongjin

Intrusion detection system (IDS) has played an important role as a device to defend our networks from cyber attacks. However, since it is unable to detect unknown attacks, i.e., 0-day attacks, the ultimate challenge in intrusion detection field is how we can exactly identify such an attack by an automated manner. Over the past few years, several studies on solving these problems have been made on anomaly detection using unsupervised learning techniques such as clustering, one-class support vector machine (SVM), etc. Although they enable one to construct intrusion detection models at low cost and effort, and have capability to detect unforeseen attacks, they still have mainly two problems in intrusion detection: a low detection rate and a high false positive rate. In this paper, we propose a new anomaly detection method based on clustering and multiple one-class SVM in order to improve the detection rate while maintaining a low false positive rate. We evaluated our method using KDD Cup 1999 data set. Evaluation results show that our approach outperforms the existing algorithms reported in the literature; especially in detection of unknown attacks.

The Safety Course Design and Operations of Composite Overwrapped Pressure Vessels (COPV)

NASA Technical Reports Server (NTRS)

Saulsberry, Regor; Prosser, William

2015-01-01

Following a Commercial Launch Vehicle On-Pad COPV (Composite Overwrapped Pressure Vessels) failure, a request was received by the NESC (NASA Engineering and Safety Center) June 14, 2014. An assessment was approved July 10, 2014, to develop and assess the capability of scanning eddy current (EC) nondestructive evaluation (NDE) methods for mapping thickness and inspection for flaws. Current methods could not identify thickness reduction from necking and critical flaw detection was not possible with conventional dye penetrant (PT) methods, so sensitive EC scanning techniques were needed. Developmental methods existed, but had not been fully developed, nor had the requisite capability assessment (i.e., a POD (Probability of Detection) study) been performed.

Validation of a multi-analyte HPLC-DAD method for determination of uric acid, creatinine, homovanillic acid, niacinamide, hippuric acid, indole-3-acetic acid and 2-methylhippuric acid in human urine.

PubMed

Remane, Daniela; Grunwald, Soeren; Hoeke, Henrike; Mueller, Andrea; Roeder, Stefan; von Bergen, Martin; Wissenbach, Dirk K

2015-08-15

During the last decades exposure sciences and epidemiological studies attracts more attention to unravel the mechanisms for the development of chronic diseases. According to this an existing HPLC-DAD method for determination of creatinine in urine samples was expended for seven analytes and validated. Creatinine, uric acid, homovanillic acid, niacinamide, hippuric acid, indole-3-acetic acid, and 2-methylhippuric acid were separated by gradient elution (formate buffer/methanol) using an Eclipse Plus C18 Rapid Resolution column (4.6mm×100mm). No interfering signals were detected in mobile phase. After injection of blank urine samples signals for the endogenous compounds but no interferences were detected. All analytes were linear in the selected calibration range and a non weighted calibration model was chosen. Bias, intra-day and inter-day precision for all analytes were below 20% for quality control (QC) low and below 10% for QC medium and high. The limits of quantification in mobile phase were in line with reported reference values but had to be adjusted in urine for homovanillic acid (45mg/L), niacinamide 58.5(mg/L), and indole-3-acetic acid (63mg/L). Comparison of creatinine data obtained by the existing method with those of the developed method showing differences from -120mg/L to +110mg/L with a mean of differences of 29.0mg/L for 50 authentic urine samples. Analyzing 50 authentic urine samples, uric acid, creatinine, hippuric acid, and 2-methylhippuric acid were detected in (nearly) all samples. However, homovanillic acid was detected in 40%, niacinamide in 4% and indole-3-acetic acid was never detected within the selected samples. Copyright © 2015 Elsevier B.V. All rights reserved.

Using the EXIST Active Shields for Earth Occultation Observations of X-Ray Sources

NASA Technical Reports Server (NTRS)

Wilson, Colleen A.; Fishman, Gerald; Hong, Jae-Sub; Gridlay, Jonathan; Krawczynski, Henric

2005-01-01

The EXIST active shields, now being planned for the main detectors of the coded aperture telescope, will have approximately 15 times the area of the BATSE detectors; and they will have a good geometry on the spacecraft for viewing both the leading and training Earth's limb for occultation observations. These occultation observations will complement the imaging observations of EXIST and can extend them to higher energies. Earth occultatio observations of the hard X-ray sky with BATSE on the Compton Gamma Ray Observatory developed and demonstrated the capabilities of large, flat, uncollimated detectors for this method. With BATSE, a catalog of 179 X-ray sources was monitored twice every spacecraft orbit for 9 years at energies above about 25 keV, resulting in 83 definite detections and 36 possible detections with 5-sigma detection sensitivities of 3.5-20 mcrab (20-430 keV) depending on the sky location. This catalog included four transients discovered with this technique and many variable objects (galactic and extragalactic). This poster will describe the Earth occultation technique, summarize the BATSE occultation observations, and compare the basic observational parameters of the occultation detector elements of BATSE and EXIST.

Robust Statistical Approaches for RSS-Based Floor Detection in Indoor Localization.

PubMed

Razavi, Alireza; Valkama, Mikko; Lohan, Elena Simona

2016-05-31

Floor detection for indoor 3D localization of mobile devices is currently an important challenge in the wireless world. Many approaches currently exist, but usually the robustness of such approaches is not addressed or investigated. The goal of this paper is to show how to robustify the floor estimation when probabilistic approaches with a low number of parameters are employed. Indeed, such an approach would allow a building-independent estimation and a lower computing power at the mobile side. Four robustified algorithms are to be presented: a robust weighted centroid localization method, a robust linear trilateration method, a robust nonlinear trilateration method, and a robust deconvolution method. The proposed approaches use the received signal strengths (RSS) measured by the Mobile Station (MS) from various heard WiFi access points (APs) and provide an estimate of the vertical position of the MS, which can be used for floor detection. We will show that robustification can indeed increase the performance of the RSS-based floor detection algorithms.

Detection, identification and differentiation of Pectobacterium and Dickeya species causing potato blackleg and tuber soft rot: a review

PubMed Central

Czajkowski, R; Pérombelon, MCM; Jafra, S; Lojkowska, E; Potrykus, M; van der Wolf, JM; Sledz, W

2015-01-01

The soft rot Enterobacteriaceae (SRE) Pectobacterium and Dickeya species (formerly classified as pectinolytic Erwinia spp.) cause important diseases on potato and other arable and horticultural crops. They may affect the growing potato plant causing blackleg and are responsible for tuber soft rot in storage thereby reducing yield and quality. Efficient and cost-effective detection and identification methods are essential to investigate the ecology and pathogenesis of the SRE as well as in seed certification programmes. The aim of this review was to collect all existing information on methods available for SRE detection. The review reports on the sampling and preparation of plant material for testing and on over thirty methods to detect, identify and differentiate the soft rot and blackleg causing bacteria to species and subspecies level. These include methods based on biochemical characters, serology, molecular techniques which rely on DNA sequence amplification as well as several less-investigated ones. PMID:25684775

An accurate method of extracting fat droplets in liver images for quantitative evaluation

NASA Astrophysics Data System (ADS)

Ishikawa, Masahiro; Kobayashi, Naoki; Komagata, Hideki; Shinoda, Kazuma; Yamaguchi, Masahiro; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

2015-03-01

The steatosis in liver pathological tissue images is a promising indicator of nonalcoholic fatty liver disease (NAFLD) and the possible risk of hepatocellular carcinoma (HCC). The resulting values are also important for ensuring the automatic and accurate classification of HCC images, because the existence of many fat droplets is likely to create errors in quantifying the morphological features used in the process. In this study we propose a method that can automatically detect, and exclude regions with many fat droplets by using the feature values of colors, shapes and the arrangement of cell nuclei. We implement the method and confirm that it can accurately detect fat droplets and quantify the fat droplet ratio of actual images. This investigation also clarifies the effective characteristics that contribute to accurate detection.

Comparison of floods non-stationarity detection methods: an Austrian case study

NASA Astrophysics Data System (ADS)

Salinas, Jose Luis; Viglione, Alberto; Blöschl, Günter

2016-04-01

Non-stationarities in flood regimes have a huge impact in any mid and long term flood management strategy. In particular the estimation of design floods is very sensitive to any kind of flood non-stationarity, as they should be linked to a return period, concept that can be ill defined in a non-stationary context. Therefore it is crucial when analyzing existent flood time series to detect and, where possible, attribute flood non-stationarities to changing hydroclimatic and land-use processes. This works presents the preliminary results of applying different non-stationarity detection methods on annual peak discharges time series over more than 400 gauging stations in Austria. The kind of non-stationarities analyzed include trends (linear and non-linear), breakpoints, clustering beyond stochastic randomness, and detection of flood rich/flood poor periods. Austria presents a large variety of landscapes, elevations and climates that allow us to interpret the spatial patterns obtained with the non-stationarity detection methods in terms of the dominant flood generation mechanisms.

A line transect model for aerial surveys

USGS Publications Warehouse

Quang, Pham Xuan; Lanctot, Richard B.

1991-01-01

We employ a line transect method to estimate the density of the common and Pacific loon in the Yukon Flats National Wildlife Refuge from aerial survey data. Line transect methods have the advantage of automatically taking into account “visibility bias” due to detectability difference of animals at different distances from the transect line. However, line transect methods must overcome two difficulties when applied to inaccurate recording of sighting distances due to high travel speeds, so that in fact only a few reliable distance class counts are available. We propose a unimodal detection function that provides an estimate of the effective area lost due to the blind strip, under the assumption that a line of perfect detection exists parallel to the transect line. The unimodal detection function can also be applied when a blind strip is absent, and in certain instances when the maximum probability of detection is less than 100%. A simple bootstrap procedure to estimate standard error is illustrated. Finally, we present results from a small set of Monte Carlo experiments.

Multi-input multioutput orthogonal frequency division multiplexing radar waveform design for improving the detection performance of space-time adaptive processing

NASA Astrophysics Data System (ADS)

Wang, Hongyan

2017-04-01

This paper addresses the waveform optimization problem for improving the detection performance of multi-input multioutput (MIMO) orthogonal frequency division multiplexing (OFDM) radar-based space-time adaptive processing (STAP) in the complex environment. By maximizing the output signal-to-interference-and-noise-ratio (SINR) criterion, the waveform optimization problem for improving the detection performance of STAP, which is subjected to the constant modulus constraint, is derived. To tackle the resultant nonlinear and complicated optimization issue, a diagonal loading-based method is proposed to reformulate the issue as a semidefinite programming one; thereby, this problem can be solved very efficiently. In what follows, the optimized waveform can be obtained to maximize the output SINR of MIMO-OFDM such that the detection performance of STAP can be improved. The simulation results show that the proposed method can improve the output SINR detection performance considerably as compared with that of uncorrelated waveforms and the existing MIMO-based STAP method.

ROKU: a novel method for identification of tissue-specific genes

PubMed Central

Kadota, Koji; Ye, Jiazhen; Nakai, Yuji; Terada, Tohru; Shimizu, Kentaro

2006-01-01

Background One of the important goals of microarray research is the identification of genes whose expression is considerably higher or lower in some tissues than in others. We would like to have ways of identifying such tissue-specific genes. Results We describe a method, ROKU, which selects tissue-specific patterns from gene expression data for many tissues and thousands of genes. ROKU ranks genes according to their overall tissue specificity using Shannon entropy and detects tissues specific to each gene if any exist using an outlier detection method. We evaluated the capacity for the detection of various specific expression patterns using synthetic and real data. We observed that ROKU was superior to a conventional entropy-based method in its ability to rank genes according to overall tissue specificity and to detect genes whose expression pattern are specific only to objective tissues. Conclusion ROKU is useful for the detection of various tissue-specific expression patterns. The framework is also directly applicable to the selection of diagnostic markers for molecular classification of multiple classes. PMID:16764735

Edge-directed inference for microaneurysms detection in digital fundus images

NASA Astrophysics Data System (ADS)

Huang, Ke; Yan, Michelle; Aviyente, Selin

2007-03-01

Microaneurysms (MAs) detection is a critical step in diabetic retinopathy screening, since MAs are the earliest visible warning of potential future problems. A variety of algorithms have been proposed for MAs detection in mass screening. Different methods have been proposed for MAs detection. The core technology for most of existing methods is based on a directional mathematical morphological operation called "Top-Hat" filter that requires multiple filtering operations at each pixel. Background structure, uneven illumination and noise often cause confusion between MAs and some non-MA structures and limits the applicability of the filter. In this paper, a novel detection framework based on edge directed inference is proposed for MAs detection. The candidate MA regions are first delineated from the edge map of a fundus image. Features measuring shape, brightness and contrast are extracted for each candidate MA region to better exclude false detection from true MAs. Algorithmic analysis and empirical evaluation reveal that the proposed edge directed inference outperforms the "Top-Hat" based algorithm in both detection accuracy and computational speed.

A simplified implementation of edge detection in MATLAB is faster and more sensitive than fast fourier transform for actin fiber alignment quantification.

PubMed

Kemeny, Steven Frank; Clyne, Alisa Morss

2011-04-01

Fiber alignment plays a critical role in the structure and function of cells and tissues. While fiber alignment quantification is important to experimental analysis and several different methods for quantifying fiber alignment exist, many studies focus on qualitative rather than quantitative analysis perhaps due to the complexity of current fiber alignment methods. Speed and sensitivity were compared in edge detection and fast Fourier transform (FFT) for measuring actin fiber alignment in cells exposed to shear stress. While edge detection using matrix multiplication was consistently more sensitive than FFT, image processing time was significantly longer. However, when MATLAB functions were used to implement edge detection, MATLAB's efficient element-by-element calculations and fast filtering techniques reduced computation cost 100 times compared to the matrix multiplication edge detection method. The new computation time was comparable to the FFT method, and MATLAB edge detection produced well-distributed fiber angle distributions that statistically distinguished aligned and unaligned fibers in half as many sample images. When the FFT sensitivity was improved by dividing images into smaller subsections, processing time grew larger than the time required for MATLAB edge detection. Implementation of edge detection in MATLAB is simpler, faster, and more sensitive than FFT for fiber alignment quantification.

Improved Conflict Detection for Reducing Operational Errors in Air Traffic Control

NASA Technical Reports Server (NTRS)

Paielli, Russell A.; Erzberger, Hainz

2003-01-01

An operational error is an incident in which an air traffic controller allows the separation between two aircraft to fall below the minimum separation standard. The rates of such errors in the US have increased significantly over the past few years. This paper proposes new detection methods that can help correct this trend by improving on the performance of Conflict Alert, the existing software in the Host Computer System that is intended to detect and warn controllers of imminent conflicts. In addition to the usual trajectory based on the flight plan, a "dead-reckoning" trajectory (current velocity projection) is also generated for each aircraft and checked for conflicts. Filters for reducing common types of false alerts were implemented. The new detection methods were tested in three different ways. First, a simple flightpath command language was developed t o generate precisely controlled encounters for the purpose of testing the detection software. Second, written reports and tracking data were obtained for actual operational errors that occurred in the field, and these were "replayed" to test the new detection algorithms. Finally, the detection methods were used to shadow live traffic, and performance was analysed, particularly with regard to the false-alert rate. The results indicate that the new detection methods can provide timely warnings of imminent conflicts more consistently than Conflict Alert.

Segmentation of malignant lesions in 3D breast ultrasound using a depth-dependent model.

PubMed

Tan, Tao; Gubern-Mérida, Albert; Borelli, Cristina; Manniesing, Rashindra; van Zelst, Jan; Wang, Lei; Zhang, Wei; Platel, Bram; Mann, Ritse M; Karssemeijer, Nico

2016-07-01

Automated 3D breast ultrasound (ABUS) has been proposed as a complementary screening modality to mammography for early detection of breast cancers. To facilitate the interpretation of ABUS images, automated diagnosis and detection techniques are being developed, in which malignant lesion segmentation plays an important role. However, automated segmentation of cancer in ABUS is challenging since lesion edges might not be well defined. In this study, the authors aim at developing an automated segmentation method for malignant lesions in ABUS that is robust to ill-defined cancer edges and posterior shadowing. A segmentation method using depth-guided dynamic programming based on spiral scanning is proposed. The method automatically adjusts aggressiveness of the segmentation according to the position of the voxels relative to the lesion center. Segmentation is more aggressive in the upper part of the lesion (close to the transducer) than at the bottom (far away from the transducer), where posterior shadowing is usually visible. The authors used Dice similarity coefficient (Dice) for evaluation. The proposed method is compared to existing state of the art approaches such as graph cut, level set, and smart opening and an existing dynamic programming method without depth dependence. In a dataset of 78 cancers, our proposed segmentation method achieved a mean Dice of 0.73 ± 0.14. The method outperforms an existing dynamic programming method (0.70 ± 0.16) on this task (p = 0.03) and it is also significantly (p < 0.001) better than graph cut (0.66 ± 0.18), level set based approach (0.63 ± 0.20) and smart opening (0.65 ± 0.12). The proposed depth-guided dynamic programming method achieves accurate breast malignant lesion segmentation results in automated breast ultrasound.

Non-Negative Spherical Deconvolution (NNSD) for estimation of fiber Orientation Distribution Function in single-/multi-shell diffusion MRI.

PubMed

Cheng, Jian; Deriche, Rachid; Jiang, Tianzi; Shen, Dinggang; Yap, Pew-Thian

2014-11-01

Spherical Deconvolution (SD) is commonly used for estimating fiber Orientation Distribution Functions (fODFs) from diffusion-weighted signals. Existing SD methods can be classified into two categories: 1) Continuous Representation based SD (CR-SD), where typically Spherical Harmonic (SH) representation is used for convenient analytical solutions, and 2) Discrete Representation based SD (DR-SD), where the signal profile is represented by a discrete set of basis functions uniformly oriented on the unit sphere. A feasible fODF should be non-negative and should integrate to unity throughout the unit sphere S(2). However, to our knowledge, most existing SH-based SD methods enforce non-negativity only on discretized points and not the whole continuum of S(2). Maximum Entropy SD (MESD) and Cartesian Tensor Fiber Orientation Distributions (CT-FOD) are the only SD methods that ensure non-negativity throughout the unit sphere. They are however computational intensive and are susceptible to errors caused by numerical spherical integration. Existing SD methods are also known to overestimate the number of fiber directions, especially in regions with low anisotropy. DR-SD introduces additional error in peak detection owing to the angular discretization of the unit sphere. This paper proposes a SD framework, called Non-Negative SD (NNSD), to overcome all the limitations above. NNSD is significantly less susceptible to the false-positive peaks, uses SH representation for efficient analytical spherical deconvolution, and allows accurate peak detection throughout the whole unit sphere. We further show that NNSD and most existing SD methods can be extended to work on multi-shell data by introducing a three-dimensional fiber response function. We evaluated NNSD in comparison with Constrained SD (CSD), a quadratic programming variant of CSD, MESD, and an L1-norm regularized non-negative least-squares DR-SD. Experiments on synthetic and real single-/multi-shell data indicate that NNSD improves estimation performance in terms of mean difference of angles, peak detection consistency, and anisotropy contrast between isotropic and anisotropic regions. Copyright © 2014 Elsevier Inc. All rights reserved.

Current Technical Approaches for the Early Detection of Foodborne Pathogens: Challenges and Opportunities.

PubMed

Cho, Il-Hoon; Ku, Seockmo

2017-09-30

The development of novel and high-tech solutions for rapid, accurate, and non-laborious microbial detection methods is imperative to improve the global food supply. Such solutions have begun to address the need for microbial detection that is faster and more sensitive than existing methodologies (e.g., classic culture enrichment methods). Multiple reviews report the technical functions and structures of conventional microbial detection tools. These tools, used to detect pathogens in food and food homogenates, were designed via qualitative analysis methods. The inherent disadvantage of these analytical methods is the necessity for specimen preparation, which is a time-consuming process. While some literature describes the challenges and opportunities to overcome the technical issues related to food industry legal guidelines, there is a lack of reviews of the current trials to overcome technological limitations related to sample preparation and microbial detection via nano and micro technologies. In this review, we primarily explore current analytical technologies, including metallic and magnetic nanomaterials, optics, electrochemistry, and spectroscopy. These techniques rely on the early detection of pathogens via enhanced analytical sensitivity and specificity. In order to introduce the potential combination and comparative analysis of various advanced methods, we also reference a novel sample preparation protocol that uses microbial concentration and recovery technologies. This technology has the potential to expedite the pre-enrichment step that precedes the detection process.

The optimal community detection of software based on complex networks

NASA Astrophysics Data System (ADS)

Huang, Guoyan; Zhang, Peng; Zhang, Bing; Yin, Tengteng; Ren, Jiadong

2016-02-01

The community structure is important for software in terms of understanding the design patterns, controlling the development and the maintenance process. In order to detect the optimal community structure in the software network, a method Optimal Partition Software Network (OPSN) is proposed based on the dependency relationship among the software functions. First, by analyzing the information of multiple execution traces of one software, we construct Software Execution Dependency Network (SEDN). Second, based on the relationship among the function nodes in the network, we define Fault Accumulation (FA) to measure the importance of the function node and sort the nodes with measure results. Third, we select the top K(K=1,2,…) nodes as the core of the primal communities (only exist one core node). By comparing the dependency relationships between each node and the K communities, we put the node into the existing community which has the most close relationship. Finally, we calculate the modularity with different initial K to obtain the optimal division. With experiments, the method OPSN is verified to be efficient to detect the optimal community in various softwares.

A new iterative approach for multi-objective fault detection observer design and its application to a hypersonic vehicle

NASA Astrophysics Data System (ADS)

Huang, Di; Duan, Zhisheng

2018-03-01

This paper addresses the multi-objective fault detection observer design problems for a hypersonic vehicle. Owing to the fact that parameters' variations, modelling errors and disturbances are inevitable in practical situations, system uncertainty is considered in this study. By fully utilising the orthogonal space information of output matrix, some new understandings are proposed for the construction of Lyapunov matrix. Sufficient conditions for the existence of observers to guarantee the fault sensitivity and disturbance robustness in infinite frequency domain are presented. In order to further relax the conservativeness, slack matrices are introduced to fully decouple the observer gain with the Lyapunov matrices in finite frequency range. Iterative linear matrix inequality algorithms are proposed to obtain the solutions. The simulation examples which contain a Monte Carlo campaign illustrate that the new methods can effectively reduce the design conservativeness compared with the existing methods.

QQ-SNV: single nucleotide variant detection at low frequency by comparing the quality quantiles.

PubMed

Van der Borght, Koen; Thys, Kim; Wetzels, Yves; Clement, Lieven; Verbist, Bie; Reumers, Joke; van Vlijmen, Herman; Aerssens, Jeroen

2015-11-10

Next generation sequencing enables studying heterogeneous populations of viral infections. When the sequencing is done at high coverage depth ("deep sequencing"), low frequency variants can be detected. Here we present QQ-SNV (http://sourceforge.net/projects/qqsnv), a logistic regression classifier model developed for the Illumina sequencing platforms that uses the quantiles of the quality scores, to distinguish true single nucleotide variants from sequencing errors based on the estimated SNV probability. To train the model, we created a dataset of an in silico mixture of five HIV-1 plasmids. Testing of our method in comparison to the existing methods LoFreq, ShoRAH, and V-Phaser 2 was performed on two HIV and four HCV plasmid mixture datasets and one influenza H1N1 clinical dataset. For default application of QQ-SNV, variants were called using a SNV probability cutoff of 0.5 (QQ-SNV(D)). To improve the sensitivity we used a SNV probability cutoff of 0.0001 (QQ-SNV(HS)). To also increase specificity, SNVs called were overruled when their frequency was below the 80(th) percentile calculated on the distribution of error frequencies (QQ-SNV(HS-P80)). When comparing QQ-SNV versus the other methods on the plasmid mixture test sets, QQ-SNV(D) performed similarly to the existing approaches. QQ-SNV(HS) was more sensitive on all test sets but with more false positives. QQ-SNV(HS-P80) was found to be the most accurate method over all test sets by balancing sensitivity and specificity. When applied to a paired-end HCV sequencing study, with lowest spiked-in true frequency of 0.5%, QQ-SNV(HS-P80) revealed a sensitivity of 100% (vs. 40-60% for the existing methods) and a specificity of 100% (vs. 98.0-99.7% for the existing methods). In addition, QQ-SNV required the least overall computation time to process the test sets. Finally, when testing on a clinical sample, four putative true variants with frequency below 0.5% were consistently detected by QQ-SNV(HS-P80) from different generations of Illumina sequencers. We developed and successfully evaluated a novel method, called QQ-SNV, for highly efficient single nucleotide variant calling on Illumina deep sequencing virology data.

Evaluation of the immunogenicity of the dabigatran reversal agent idarucizumab during Phase I studies

PubMed Central

Norris, Stephen; Ramael, Steven; Ikushima, Ippei; Haazen, Wouter; Harada, Akiko; Moschetti, Viktoria; Imazu, Susumu; Reilly, Paul A.; Lang, Benjamin; Stangier, Joachim

2017-01-01

Aims Idarucizumab, a humanized monoclonal anti‐dabigatran antibody fragment, is effective in emergency reversal of dabigatran anticoagulation. Pre‐existing and treatment‐emergent anti‐idarucizumab antibodies (antidrug antibodies; ADA) may affect the safety and efficacy of idarucizumab. This analysis characterized the pre‐existing and treatment‐emergent ADA and assessed their impact on the pharmacokinetics and pharmacodynamics (PK/PD) of idarucizumab. Methods Data were pooled from three Phase I, randomized, double‐blind idarucizumab studies in healthy Caucasian subjects; elderly, renally impaired subjects; and healthy Japanese subjects. In plasma sampled before and after idarucizumab dosing, ADA were detected and titrated using a validated electrochemiluminescence method. ADA epitope specificities were examined using idarucizumab and two structurally related molecules. Idarucizumab PK/PD data were compared for subjects with and without pre‐existing ADA. Results Pre‐existing ADA were found in 33 out of 283 individuals (11.7%), seven of whom had intermittent ADA. Titres of pre‐existing and treatment‐emergent ADA were low, estimated equivalent to <0.3% of circulating idarucizumab after a 5 g dose. Pre‐existing ADA had no impact on dose‐normalized idarucizumab maximum plasma levels and exposure and, although data were limited, no impact on the reversal of dabigatran‐induced anticoagulation by idarucizumab. Treatment‐emergent ADA were detected in 20 individuals (19 out of 224 treated [8.5%]; 1 out of 59 received placebo [1.7%]) and were transient in ten. The majority had specificity primarily toward the C‐terminus of idarucizumab. There were no adverse events indicative of immunogenic reactions. Conclusion Pre‐existing and treatment‐emergent ADA were present at extremely low levels relative to the idarucizumab dosage under evaluation. The PK/PD of idarucizumab appeared to be unaffected by the presence of pre‐existing ADA. PMID:28230262

In-situ characterization of nanoparticle beams focused with an aerodynamic lens by Laser-Induced Breakdown Detection

PubMed Central

Barreda, F.-A.; Nicolas, C.; Sirven, J.-B.; Ouf, F.-X.; Lacour, J.-L.; Robert, E.; Benkoula, S.; Yon, J.; Miron, C.; Sublemontier, O.

2015-01-01

The Laser-Induced Breakdown Detection technique (LIBD) was adapted to achieve fast in-situ characterization of nanoparticle beams focused under vacuum by an aerodynamic lens. The method employs a tightly focused, 21 μm, scanning laser microprobe which generates a local plasma induced by the laser interaction with a single particle. A counting mode optical detection allows the achievement of 2D mappings of the nanoparticle beams with a reduced analysis time thanks to the use of a high repetition rate infrared pulsed laser. As an example, the results obtained with Tryptophan nanoparticles are presented and the advantages of this method over existing ones are discussed. PMID:26498694

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

FGWAS: Functional genome wide association analysis.

PubMed

Huang, Chao; Thompson, Paul; Wang, Yalin; Yu, Yang; Zhang, Jingwen; Kong, Dehan; Colen, Rivka R; Knickmeyer, Rebecca C; Zhu, Hongtu

2017-10-01

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs. Copyright © 2017 Elsevier Inc. All rights reserved.

Detection of Mycoplasma pneumoniae by real-time PCR.

PubMed

Winchell, Jonas M; Mitchell, Stephanie L

2013-01-01

Mycoplasma pneumoniae is a significant cause of respiratory disease, accounting for approximately 20% of cases of community-acquired pneumonia. Although several diagnostic methods exist to detect M. pneumoniae in respiratory specimens, real-time PCR has emerged as a significant improvement for the rapid diagnosis of this pathogen. The method described herein details the procedure for the detection of M. pneumoniae by real-time PCR (qPCR). The qPCR assay described can be performed with three targets specific for M. pneumoniae (Mp181, Mp3, and Mp7) and one marker for the detection of the RNaseP gene found in human nucleic acid as an internal control reaction. Recent studies have demonstrated the ability of this procedure to reliably identify this agent and facilitate the timely recognition of an outbreak.

A Colloidal Route to Detection of Organic Molecules Based on Surface-Enhanced Raman Spectroscopy Using Nanostructured Substrate Derived from Aerosols

NASA Astrophysics Data System (ADS)

Gen, Masao; Kakuta, Hideo; Kamimoto, Yoshihito; Wuled Lenggoro, I.

2011-06-01

A detection method based on the surface-enhanced Raman spectroscopy (SERS)-active substrate derived from aerosol nanoparticles and a colloidal suspension for detecting organic molecules of a model analyte (a pesticide) is proposed. This approach can detect the molecules of the derived from its solution with the concentration levels of ppb. For substrate fabrication, a gas-phase method is used to directly deposit Ag nanoparticles on to a silicon substrate having pyramidal structures. By mixing the target analyte with a suspension of Ag colloids purchased in advance, clotianidin analyte on Ag colloid can exist in junctions of co-aggregated Ag colloids. Using (i) a nanostructured substrate made from aerosol nanoparticles and (ii) colloidal suspension can increase the number of activity spots.

Novel Hyperspectral Anomaly Detection Methods Based on Unsupervised Nearest Regularized Subspace

NASA Astrophysics Data System (ADS)

Hou, Z.; Chen, Y.; Tan, K.; Du, P.

2018-04-01

Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels. In this paper, two methods of Unsupervised Nearest Regularized Subspace-based with Outlier Removal Anomaly Detector (UNRSORAD) and Local Summation UNRSORAD (LSUNRSORAD) are proposed, which are based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. Using a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination. The existence of outliers in the dual window will affect detection accuracy. Proposed detectors remove outlier pixels that are significantly different from majority of pixels. In order to make full use of various local spatial distributions information with the neighboring pixels of the pixels under test, we take the local summation dual-window sliding strategy. The residual image is constituted by subtracting the predicted background from the original hyperspectral imagery, and anomalies can be detected in the residual image. Experimental results show that the proposed methods have greatly improved the detection accuracy compared with other traditional detection method.

The combination design for open and endoscopic surgery using fluorescence molecular imaging technology

NASA Astrophysics Data System (ADS)

Mao, Yamin; Jiang, Shixin; Ye, Jinzuo; An, Yu; Yang, Xin; Chi, Chongwei; Tian, Jie

2015-03-01

For clinical surgery, it is still a challenge to objectively determine tumor margins during surgery. With the development of medical imaging technology, fluorescence molecular imaging (FMI) method can provide real-time intraoperative tumor margin information. Furthermore, surgical navigation system based on FMI technology plays an important role for the aid of surgeons' precise tumor margin decision. However, detection depth is the most limitation exists in the FMI technique and the method convenient for either macro superficial detection or micro deep tissue detection is needed. In this study, we combined advantages of both open surgery and endoscopic imaging systems with FMI technology. Indocyanine green (ICG) experiments were performed to confirm the feasibility of fluorescence detection in our system. Then, the ICG signal was photographed in the detection area with our system. When the system connected with endoscope lens, the minimum quantity of ICG detected by our system was 0.195 ug. For aspect of C mount lens, the sensitivity of ICG detection with our system was 0.195ug. Our experiments results proved that it was feasible to detect fluorescence images with this combination method. Our system shows great potential in the clinical applications of precise dissection of various tumors

Recombinase polymerase amplification (RPA) combined with lateral flow (LF) strip for detection of Toxoplasma gondii in the environment.

PubMed

Wu, Y D; Xu, M J; Wang, Q Q; Zhou, C X; Wang, M; Zhu, X Q; Zhou, D H

2017-08-30

Toxoplasma gondii infects all warm-blooded vertebrates, resulting in a great threat to human health and significant economic loss to the livestock industry. Ingestion of infectious oocysts of T. gondii from the environment is the major source of transmission. Detection of T. gondii oocysts by existing methods is laborious, time-consuming and expensive. The objective of the present study was to develop a recombinase polymerase amplification (RPA) method combined with a lateral flow (LF) strip for detection of T. gondii oocysts in the soil and water. The DNA of T. gondii oocysts was amplified by a pair of specific primers based on the T. gondii B1 gene over 15min at a constant temperature ranging from 30°C to 45°C using RPA. The amplification product was visualized by the lateral flow (LF) strip within 5min using the specific probe added to the RPA reaction system. The sensitivity of the established assay was 10 times higher than that of nested PCR with a lower detection limit of 0.1 oocyst per reaction, and there was no cross-reactivity with other closely related protozoan species. Fifty environmental samples were further assessed for the detection validity of the LF-RPA assay (B1-LF-RPA) and compared with nested PCR based on the B1 gene sequence. The B1-LF-RPA and nested PCR both showed that 5 out of the 50 environmental samples were positive. The B1-LF-RPA method was also proven to be sufficiently tolerant of existing inhibitors in the environment. In addition, the advantages of simple operation, speediness and cost-effectiveness make B1-LF-RPA a promising molecular detection tool for T. gondii. Copyright © 2017 Elsevier B.V. All rights reserved.

An automated and universal method for measuring mean grain size from a digital image of sediment

USGS Publications Warehouse

Buscombe, Daniel D.; Rubin, David M.; Warrick, Jonathan A.

2010-01-01

Existing methods for estimating mean grain size of sediment in an image require either complicated sequences of image processing (filtering, edge detection, segmentation, etc.) or statistical procedures involving calibration. We present a new approach which uses Fourier methods to calculate grain size directly from the image without requiring calibration. Based on analysis of over 450 images, we found the accuracy to be within approximately 16% across the full range from silt to pebbles. Accuracy is comparable to, or better than, existing digital methods. The new method, in conjunction with recent advances in technology for taking appropriate images of sediment in a range of natural environments, promises to revolutionize the logistics and speed at which grain-size data may be obtained from the field.

Automatic comic page image understanding based on edge segment analysis

NASA Astrophysics Data System (ADS)

Liu, Dong; Wang, Yongtao; Tang, Zhi; Li, Luyuan; Gao, Liangcai

2013-12-01

Comic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. It is the key technique to produce the digital comic documents suitable for reading on mobile devices. In this paper, we propose a novel comic page image understanding method based on edge segment analysis. First, we propose an efficient edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input comic page image; second, we propose a top-down scheme to detect line segments within each obtained edge segment; third, we develop a novel method to detect the storyboards by selecting the border lines and further identify the reading order of these storyboards. The proposed method is performed on a data set consisting of 2000 comic page images from ten printed comic series. The experimental results demonstrate that the proposed method achieves satisfactory results on different comics and outperforms the existing methods.

Lesion Border Detection in Dermoscopy Images

PubMed Central

Celebi, M. Emre; Schaefer, Gerald; Iyatomi, Hitoshi; Stoecker, William V.

2009-01-01

Background Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, computerized analysis of dermoscopy images has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. Methods In this article, we present a systematic overview of the recent border detection methods in the literature paying particular attention to computational issues and evaluation aspects. Conclusion Common problems with the existing approaches include the acquisition, size, and diagnostic distribution of the test image set, the evaluation of the results, and the inadequate description of the employed methods. Border determination by dermatologists appears to depend upon higher-level knowledge, therefore it is likely that the incorporation of domain knowledge in automated methods will enable them to perform better, especially in sets of images with a variety of diagnoses. PMID:19121917

Autoregressive statistical pattern recognition algorithms for damage detection in civil structures

NASA Astrophysics Data System (ADS)

Yao, Ruigen; Pakzad, Shamim N.

2012-08-01

Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms.

A nonlinear quality-related fault detection approach based on modified kernel partial least squares.

PubMed

Jiao, Jianfang; Zhao, Ning; Wang, Guang; Yin, Shen

2017-01-01

In this paper, a new nonlinear quality-related fault detection method is proposed based on kernel partial least squares (KPLS) model. To deal with the nonlinear characteristics among process variables, the proposed method maps these original variables into feature space in which the linear relationship between kernel matrix and output matrix is realized by means of KPLS. Then the kernel matrix is decomposed into two orthogonal parts by singular value decomposition (SVD) and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with relevant existing nonlinear approaches, the proposed method has the advantages of simple diagnosis logic and stable performance. A widely used literature example and an industrial process are used for the performance evaluation for the proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Conflict management based on belief function entropy in sensor fusion.

PubMed

Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

2016-01-01

Wireless sensor network plays an important role in intelligent navigation. It incorporates a group of sensors to overcome the limitation of single detection system. Dempster-Shafer evidence theory can combine the sensor data of the wireless sensor network by data fusion, which contributes to the improvement of accuracy and reliability of the detection system. However, due to different sources of sensors, there may be conflict among the sensor data under uncertain environment. Thus, this paper proposes a new method combining Deng entropy and evidence distance to address the issue. First, Deng entropy is adopted to measure the uncertain information. Then, evidence distance is applied to measure the conflict degree. The new method can cope with conflict effectually and improve the accuracy and reliability of the detection system. An example is illustrated to show the efficiency of the new method and the result is compared with that of the existing methods.

Application test of a Detection Method for the Enclosed Turbine Runner Chamber

NASA Astrophysics Data System (ADS)

Liu, Yunlong; Shen, Dingjie; Xie, Yi; Yang, Xiangwei; Long, Yi; Li, Wenbo

2017-06-01

At present, for the existing problems of the testing methods for the key hidden metal components of the turbine runner chamber, such as the poor reliability, the inaccurate locating and the larger detection blind spots of the detection device, under the downtime without opening the cover of the hydropower turbine runner chamber, an automatic detection method based on real-time image acquisition and simulation comparison techniques was proposed. By using the permanent magnet wheel, the magnetic crawler which carry the real-time image acquisition device, could complete the crawling work on the inner surface of the enclosed chamber. Then the image acquisition device completed the real-time collection of the scene image of the enclosed chamber. According to the obtained location by using the positioning auxiliary device, the position of the real-time detection image in a virtual 3D model was calibrated. Through comparing of the real-time detection images and the computer simulation images, the defects or foreign matter fall into could be accurately positioning, so as to repair and clean up conveniently.

Hyperspectral Image-Based Night-Time Vehicle Light Detection Using Spectral Normalization and Distance Mapper for Intelligent Headlight Control.

PubMed

Kim, Heekang; Kwon, Soon; Kim, Sungho

2016-07-08

This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD) or Complementary metal-Oxide-Semiconductor (CMOS) camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC), the vehicle headlights need to be detected. Headlights are comprised from a variety of lighting sources, such as Light Emitting Diodes (LEDs), High-intensity discharge (HID), and halogen lamps. In addition, rear lamps are made of LED and halogen lamp. This paper refers to the recent research in IHC. Some problems exist in the detection of headlights, such as erroneous detection of street lights or sign lights and the reflection plate of ego-car from CCD or CMOS images. To solve these problems, this study uses hyperspectral images because they have hundreds of bands and provide more information than a CCD or CMOS camera. Recent methods to detect headlights used the Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), and Euclidean Distance Mapper (EDM). The experimental results highlight the feasibility of the proposed method in three types of lights (LED, HID, and halogen).

Decision Fusion with Channel Errors in Distributed Decode-Then-Fuse Sensor Networks

PubMed Central

Yan, Yongsheng; Wang, Haiyan; Shen, Xiaohong; Zhong, Xionghu

2015-01-01

Decision fusion for distributed detection in sensor networks under non-ideal channels is investigated in this paper. Usually, the local decisions are transmitted to the fusion center (FC) and decoded, and a fusion rule is then applied to achieve a global decision. We propose an optimal likelihood ratio test (LRT)-based fusion rule to take the uncertainty of the decoded binary data due to modulation, reception mode and communication channel into account. The average bit error rate (BER) is employed to characterize such an uncertainty. Further, the detection performance is analyzed under both non-identical and identical local detection performance indices. In addition, the performance of the proposed method is compared with the existing optimal and suboptimal LRT fusion rules. The results show that the proposed fusion rule is more robust compared to these existing ones. PMID:26251908

Electrochemical and Infrared Absorption Spectroscopy Detection of SF6 Decomposition Products

PubMed Central

Dong, Ming; Ren, Ming; Ye, Rixin

2017-01-01

Sulfur hexafluoride (SF6) gas-insulated electrical equipment is widely used in high-voltage (HV) and extra-high-voltage (EHV) power systems. Partial discharge (PD) and local heating can occur in the electrical equipment because of insulation faults, which results in SF6 decomposition and ultimately generates several types of decomposition products. These SF6 decomposition products can be qualitatively and quantitatively detected with relevant detection methods, and such detection contributes to diagnosing the internal faults and evaluating the security risks of the equipment. At present, multiple detection methods exist for analyzing the SF6 decomposition products, and electrochemical sensing (ES) and infrared (IR) spectroscopy are well suited for application in online detection. In this study, the combination of ES with IR spectroscopy is used to detect SF6 gas decomposition. First, the characteristics of these two detection methods are studied, and the data analysis matrix is established. Then, a qualitative and quantitative analysis ES-IR model is established by adopting a two-step approach. A SF6 decomposition detector is designed and manufactured by combining an electrochemical sensor and IR spectroscopy technology. The detector is used to detect SF6 gas decomposition and is verified to reliably and accurately detect the gas components and concentrations. PMID:29140268

Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor.

PubMed

Kim, Dong Seop; Arsalan, Muhammad; Park, Kang Ryoung

2018-03-23

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works.

Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor

PubMed Central

Kim, Dong Seop; Arsalan, Muhammad; Park, Kang Ryoung

2018-01-01

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works. PMID:29570690

An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method

PubMed Central

Devasenapathy, Deepa; Kannan, Kathiravan

2015-01-01

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate. PMID:25793221

An energy-efficient cluster-based vehicle detection on road network using intention numeration method.

PubMed

Devasenapathy, Deepa; Kannan, Kathiravan

2015-01-01

The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

Fast frequency domain method to detect skew in a document image

NASA Astrophysics Data System (ADS)

Mehta, Sunita; Walia, Ekta; Dutta, Maitreyee

2015-12-01

In this paper, a new fast frequency domain method based on Discrete Wavelet Transform and Fast Fourier Transform has been implemented for the determination of the skew angle in a document image. Firstly, image size reduction is done by using two-dimensional Discrete Wavelet Transform and then skew angle is computed using Fast Fourier Transform. Skew angle error is almost negligible. The proposed method is experimented using a large number of documents having skew between -90° and +90° and results are compared with Moments with Discrete Wavelet Transform method and other commonly used existing methods. It has been determined that this method works more efficiently than the existing methods. Also, it works with typed, picture documents having different fonts and resolutions. It overcomes the drawback of the recently proposed method of Moments with Discrete Wavelet Transform that does not work with picture documents.

A matched filter approach for blind joint detection of galaxy clusters in X-ray and SZ surveys

NASA Astrophysics Data System (ADS)

Tarrío, P.; Melin, J.-B.; Arnaud, M.

2018-06-01

The combination of X-ray and Sunyaev-Zeldovich (SZ) observations can potentially improve the cluster detection efficiency, when compared to using only one of these probes, since both probe the same medium, the hot ionized gas of the intra-cluster medium. We present a method based on matched multifrequency filters (MMF) for detecting galaxy clusters from SZ and X-ray surveys. This method builds on a previously proposed joint X-ray-SZ extraction method and allows the blind detection of clusters, that is finding new clusters without knowing their position, size, or redshift, by searching on SZ and X-ray maps simultaneously. The proposed method is tested using data from the ROSAT all-sky survey and from the Planck survey. The evaluation is done by comparison with existing cluster catalogues in the area of the sky covered by the deep SPT survey. Thanks to the addition of the X-ray information, the joint detection method is able to achieve simultaneously better purity, better detection efficiency, and better position accuracy than its predecessor Planck MMF, which is based on SZ maps alone. For a purity of 85%, the X-ray-SZ method detects 141 confirmed clusters in the SPT region; to detect the same number of confirmed clusters with Planck MMF, we would need to decrease its purity to 70%. We provide a catalogue of 225 sources selected by the proposed method in the SPT footprint, with masses ranging between 0.7 and 14.5 ×1014 M⊙ and redshifts between 0.01 and 1.2.

The Critical Power Model as a Potential Tool for Anti-doping

PubMed Central

Puchowicz, Michael J.; Mizelman, Eliran; Yogev, Assaf; Koehle, Michael S.; Townsend, Nathan E.; Clarke, David C.

2018-01-01

Existing doping detection strategies rely on direct and indirect biochemical measurement methods focused on detecting banned substances, their metabolites, or biomarkers related to their use. However, the goal of doping is to improve performance, and yet evidence from performance data is not considered by these strategies. The emergence of portable sensors for measuring exercise intensities and of player tracking technologies may enable the widespread collection of performance data. How these data should be used for doping detection is an open question. Herein, we review the basis by which performance models could be used for doping detection, followed by critically reviewing the potential of the critical power (CP) model as a prototypical performance model that could be used in this regard. Performance models are mathematical representations of performance data specific to the athlete. Some models feature parameters with physiological interpretations, changes to which may provide clues regarding the specific doping method. The CP model is a simple model of the power-duration curve and features two physiologically interpretable parameters, CP and W′. We argue that the CP model could be useful for doping detection mainly based on the predictable sensitivities of its parameters to ergogenic aids and other performance-enhancing interventions. However, our argument is counterbalanced by the existence of important limitations and unresolved questions that need to be addressed before the model is used for doping detection. We conclude by providing a simple worked example showing how it could be used and propose recommendations for its implementation. PMID:29928234

Oriented Markov random field based dendritic spine segmentation for fluorescence microscopy images.

PubMed

Cheng, Jie; Zhou, Xiaobo; Miller, Eric L; Alvarez, Veronica A; Sabatini, Bernardo L; Wong, Stephen T C

2010-10-01

Dendritic spines have been shown to be closely related to various functional properties of the neuron. Usually dendritic spines are manually labeled to analyze their morphological changes, which is very time-consuming and susceptible to operator bias, even with the assistance of computers. To deal with these issues, several methods have been recently proposed to automatically detect and measure the dendritic spines with little human interaction. However, problems such as degraded detection performance for images with larger pixel size (e.g. 0.125 μm/pixel instead of 0.08 μm/pixel) still exist in these methods. Moreover, the shapes of detected spines are also distorted. For example, the "necks" of some spines are missed. Here we present an oriented Markov random field (OMRF) based algorithm which improves spine detection as well as their geometric characterization. We begin with the identification of a region of interest (ROI) containing all the dendrites and spines to be analyzed. For this purpose, we introduce an adaptive procedure for identifying the image background. Next, the OMRF model is discussed within a statistical framework and the segmentation is solved as a maximum a posteriori estimation (MAP) problem, whose optimal solution is found by a knowledge-guided iterative conditional mode (KICM) algorithm. Compared with the existing algorithms, the proposed algorithm not only provides a more accurate representation of the spine shape, but also improves the detection performance by more than 50% with regard to reducing both the misses and false detection.

Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine

NASA Astrophysics Data System (ADS)

Gao, Feng; Dong, Junyu; Li, Bo; Xu, Qizhi; Xie, Cui

2016-10-01

Change detection is of high practical value to hazard assessment, crop growth monitoring, and urban sprawl detection. A synthetic aperture radar (SAR) image is the ideal information source for performing change detection since it is independent of atmospheric and sunlight conditions. Existing SAR image change detection methods usually generate a difference image (DI) first and use clustering methods to classify the pixels of DI into changed class and unchanged class. Some useful information may get lost in the DI generation process. This paper proposed an SAR image change detection method based on neighborhood-based ratio (NR) and extreme learning machine (ELM). NR operator is utilized for obtaining some interested pixels that have high probability of being changed or unchanged. Then, image patches centered at these pixels are generated, and ELM is employed to train a model by using these patches. Finally, pixels in both original SAR images are classified by the pretrained ELM model. The preclassification result and the ELM classification result are combined to form the final change map. The experimental results obtained on three real SAR image datasets and one simulated dataset show that the proposed method is robust to speckle noise and is effective to detect change information among multitemporal SAR images.

Change detection and classification in brain MR images using change vector analysis.

PubMed

Simões, Rita; Slump, Cornelis

2011-01-01

The automatic detection of longitudinal changes in brain images is valuable in the assessment of disease evolution and treatment efficacy. Most existing change detection methods that are currently used in clinical research to monitor patients suffering from neurodegenerative diseases--such as Alzheimer's--focus on large-scale brain deformations. However, such patients often have other brain impairments, such as infarcts, white matter lesions and hemorrhages, which are typically overlooked by the deformation-based methods. Other unsupervised change detection algorithms have been proposed to detect tissue intensity changes. The outcome of these methods is typically a binary change map, which identifies changed brain regions. However, understanding what types of changes these regions underwent is likely to provide equally important information about lesion evolution. In this paper, we present an unsupervised 3D change detection method based on Change Vector Analysis. We compute and automatically threshold the Generalized Likelihood Ratio map to obtain a binary change map. Subsequently, we perform histogram-based clustering to classify the change vectors. We obtain a Kappa Index of 0.82 using various types of simulated lesions. The classification error is 2%. Finally, we are able to detect and discriminate both small changes and ventricle expansions in datasets from Mild Cognitive Impairment patients.

Stratification-Based Outlier Detection over the Deep Web.

PubMed

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

A visual model for object detection based on active contours and level-set method.

PubMed

Satoh, Shunji

2006-09-01

A visual model for object detection is proposed. In order to make the detection ability comparable with existing technical methods for object detection, an evolution equation of neurons in the model is derived from the computational principle of active contours. The hierarchical structure of the model emerges naturally from the evolution equation. One drawback involved with initial values of active contours is alleviated by introducing and formulating convexity, which is a visual property. Numerical experiments show that the proposed model detects objects with complex topologies and that it is tolerant of noise. A visual attention model is introduced into the proposed model. Other simulations show that the visual properties of the model are consistent with the results of psychological experiments that disclose the relation between figure-ground reversal and visual attention. We also demonstrate that the model tends to perceive smaller regions as figures, which is a characteristic observed in human visual perception.

Fringe projection application for surface variation analysis on helical shaped silicon breast

NASA Astrophysics Data System (ADS)

Vairavan, R.; Ong, N. R.; Sauli, Z.; Shahimin, M. M.; Kirtsaeng, S.; Sakuntasathien, S.; Alcain, J. B.; Paitong, P.; Retnasamy, V.

2017-09-01

Breast carcinoma is rated as a second collective cause of cancer associated death among adult females. Detection of the disease at an early stage would enhance the chance for survival. Established detection methods such as mammography, ultrasound and MRI are classified as non invasive breast cancer detection modality, but however they are not entire non-invasive as physical contact still occurs to the breast. Thus requirement for a complete non invasive and non contact is evident. Therefore, in this work, a novel application of digital fringe projection for early detection of breast cancer based on breast surface analysis is reported. Phase shift fringe projection technique and pixel tracing method was utilized to analyze the breast surface change due to the incidence of breast lump. Results have shown that the digital fringe projection is capable in detecting the existence of 1 cm sized lump within the breast sample.

Penalty dynamic programming algorithm for dim targets detection in sensor systems.

PubMed

Huang, Dayu; Xue, Anke; Guo, Yunfei

2012-01-01

In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations.

Stratification-Based Outlier Detection over the Deep Web

PubMed Central

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S.; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web. PMID:27313603

Parallel Molecular Distributed Detection With Brownian Motion.

PubMed

Rogers, Uri; Koh, Min-Sung

2016-12-01

This paper explores the in vivo distributed detection of an undesired biological agent's (BAs) biomarkers by a group of biological sized nanomachines in an aqueous medium under drift. The term distributed, indicates that the system information relative to the BAs presence is dispersed across the collection of nanomachines, where each nanomachine possesses limited communication, computation, and movement capabilities. Using Brownian motion with drift, a probabilistic detection and optimal data fusion framework, coined molecular distributed detection, will be introduced that combines theory from both molecular communication and distributed detection. Using the optimal data fusion framework as a guide, simulation indicates that a sub-optimal fusion method exists, allowing for a significant reduction in implementation complexity while retaining BA detection accuracy.

Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.

PubMed

Tang, Tianyu; Zhou, Shilin; Deng, Zhipeng; Zou, Huanxin; Lei, Lin

2017-02-10

Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

The molecular adsorption-type endotoxin detection system using immobilized ɛ-polylysine

NASA Astrophysics Data System (ADS)

Ooe, Katsutoshi; Tsuji, Akihito; Nishishita, Naoki; Hirano, Yoshiaki

2007-12-01

Hemodialysis for chronic renal failure is the most popular treatment method with artificial organs. However, hemodialysis patients must continue the treatment throughout their life, the results of long term extracorporeal dialysis, those patients develop the various complications and diseases, for example, dialysis amyloidosis etc. Dialysis amyloidosis is one of the refractory complications, and endotoxin is thought to be the most likely cause of it, recently. Endotoxin is one of the major cell wall components of gram-negative bacteria, and it has various biological activities. In addition, it is known that a mount of endotoxin exists in living environment, and medicine is often contaminated with endotoxin. When contaminated dialyzing fluids are used to hemodialysis, above-mentioned dialysis amyloidosis is developed. Therefore, it is important that the detection and removal of endotoxin from dialyzing fluids. Until now, the measurement methods using Limulus Amebosyte Lysate (LAL) reagent were carried out as the tests for the presence of endtoxin. However, these methods include several different varieties of measurement techniques. The following are examples of them, gelatinization method, turbidimetric assay method, colorimetric assay method and fluoroscopic method. However, these techniques needed 30-60 minutes for the measurement. From these facts, they are not able to use as a "real-time endotoxin detector". The detection of endotoxin has needed to carry out immediately, for that reason, a new detection method is desired. In this research, we focused attention to adsorption reaction between ɛ-polylysine and endotoxin. ɛ-polylysine has the structure of straight chain molecule composed by 25-30 residues made by lysine, and it is used as an antimicrobial agent, moreover, cellulose beads with immobilized ɛ-polylysine is used as the barrier filter for endotoxin removal. The endotoxin is adsorbed to immobilized ɛ-polylysine, as the result of this reaction, the mass incrementation is occurred, and the existence of endtoxin can be detected immediately, by using of Quartz Crystal Microbalance (QCM). In this report, the immobilization of ɛ-polylysine onto the Au and Si substrate and its adsorptive activity are described. We use X-ray Photoelectron Spectroscopy (XPS) to confirm the ɛ-polylysine immobilization, and the adsorptive activity of immobilized ɛ-polylysine is measured by AFM and QCM. This molecular adsorption type endotoxin sensor aims to the realization of "real-time endotoxin detection system".

Development of a novel single tube nested PCR for enhanced detection of cytomegalovirus DNA from dried blood spots.

PubMed

Atkinson, C; Emery, V C; Griffiths, P D

2014-02-01

Newborn screening for congenital cytomegalovirus (CCMV) using dried blood spots (DBS) has been proposed because many developed countries have DBS screening programmes in place for other diseases. The aim of this study was to develop a rapid, single tube nested polymerase chain reaction (PCR) method for enhanced detection of CMV from DBS compared to existing (single target) real time PCRs. The new method was compared with existing real time PCRs for sensitivity and specificity. Overall sensitivity of the single target PCR assays in both asymptomatic and symptomatic infants with laboratory confirmed congenital CMV was 69% (CMV PCR or culture positive before day 21 of life). In contrast, the single tube nested assay had an increased sensitivity of 81% with100% specificity. Overall the assay detected CMV from a DBS equivalent to an original blood sample which contained 500IU/ml. In conclusion this single tube nested methodology allows simultaneous amplification and detection of CMV DNA in 1.5h removing the associated contamination risk of a two step nested PCR. Owing to its increased sensitivity, it has the potential to be used as a screening assay and ultimately allow early identification and intervention for children with congenital CMV. Copyright © 2013 Elsevier B.V. All rights reserved.

Stellar Wakes from Dark Matter Subhalos

NASA Astrophysics Data System (ADS)

Buschmann, Malte; Kopp, Joachim; Safdi, Benjamin R.; Wu, Chih-Liang

2018-05-01

We propose a novel method utilizing stellar kinematic data to detect low-mass substructure in the Milky Way's dark matter halo. By probing characteristic wakes that a passing dark matter subhalo leaves in the phase-space distribution of ambient halo stars, we estimate sensitivities down to subhalo masses of ˜107 M⊙ or below. The detection of such subhalos would have implications for dark matter and cosmological models that predict modifications to the halo-mass function at low halo masses. We develop an analytic formalism for describing the perturbed stellar phase-space distributions, and we demonstrate through idealized simulations the ability to detect subhalos using the phase-space model and a likelihood framework. Our method complements existing methods for low-mass subhalo searches, such as searches for gaps in stellar streams, in that we can localize the positions and velocities of the subhalos today.

Detecting Horizontal Gene Transfer between Closely Related Taxa

PubMed Central

Adato, Orit; Ninyo, Noga; Gophna, Uri; Snir, Sagi

2015-01-01

Horizontal gene transfer (HGT), the transfer of genetic material between organisms, is crucial for genetic innovation and the evolution of genome architecture. Existing HGT detection algorithms rely on a strong phylogenetic signal distinguishing the transferred sequence from ancestral (vertically derived) genes in its recipient genome. Detecting HGT between closely related species or strains is challenging, as the phylogenetic signal is usually weak and the nucleotide composition is normally nearly identical. Nevertheless, there is a great importance in detecting HGT between congeneric species or strains, especially in clinical microbiology, where understanding the emergence of new virulent and drug-resistant strains is crucial, and often time-sensitive. We developed a novel, self-contained technique named Near HGT, based on the synteny index, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the constant relative mutability (CRM). Using CRM, the algorithm assigns a confidence score based on “unusual” sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three E. coli strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set. When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain. PMID:26439115

Method of Testing Oxygen Regulators

NASA Technical Reports Server (NTRS)

Sontag, Harcourt; Borlik, E L

1935-01-01

Oxygen regulators are used in aircraft to regulate automatically the flow of oxygen to the pilot from a cylinder at pressures ranging up to 150 atmospheres. The instruments are adjusted to open at an altitude of about 15,000 ft. and thereafter to deliver oxygen at a rate which increases with the altitude. The instruments are tested to determine the rate of flow of oxygen delivered at various altitudes and to detect any mechanical defects which may exist. A method of testing oxygen regulators was desired in which the rate of flow could be determined more accurately than by the test method previously used (reference 1) and by which instruments defective mechanically could be detected. The new method of test fulfills these requirements.

Determination of 2-alkylcyclobutanones by combining precolumn derivatization with 1-naphthalenyl hydrazine and ultra-performance liquid chromatography with fluorescence detection.

PubMed

Meng, Xiangpeng; Tong, Tong; Wang, Lianrong; Liu, Hanxia; Chan, Wan

2016-05-01

2-Alkylcyclobutanones (2-ACBs) are uniquely formed when triglycerides-containing food products are exposed to ionizing radiation. Thus, 2-ACBs have been used as marker molecules to identify irradiated food. Most methods to determine 2-ACBs involve mass spectrometric detection after chromatographic separation. The spectrofluorometer is rarely used to determine 2-ACBs because these molecules do not fluoresce. In this study, we developed an ultra-performance liquid chromatography (UPLC) method to determine 2-ACBs. 2-ACBs were converted into fluorophores after reacting with 1-naphthalenyl hydrazine to facilitate their sensitive and selective detection using a fluorescence detector (FLD). Analysis of 2-ACBs using our developed UPLC-FLD method allows sensitive determination of 2-ACBs at a detection limit of 2 ng 2-ACBs per g of fat (30 pg/injection), which is significantly lower than that of existing analytical methods. After validation for trueness and precision, the method was applied to γ-irradiated chicken samples to determine their 2-ACB content. Comparative studies employing liquid chromatography-tandem mass spectrometric method revealed no systematic difference between the two methods, thereby demonstrating that the proposed UPLC-FLD method can be suitably used to determine 2-ACBs in irradiated foodstuffs. Graphical Abstract Determination of radiation-induced food-borne 2-dodecylcyclobutanone and 2-tetradecylcyclobutanone by combining 1-naphthalenyl hydrazine derivatization and ultra-performance liquid chromatography with fluorescence detection.

The invisible AGN catalogue: a mid-infrared-radio selection method for optically faint active galactic nuclei

NASA Astrophysics Data System (ADS)

Truebenbach, Alexandra E.; Darling, Jeremy

2017-06-01

A large fraction of active galactic nuclei (AGN) are 'invisible' in extant optical surveys due to either distance or dust-obscuration. The existence of this large population of dust-obscured, infrared (IR)-bright AGN is predicted by models of galaxy-supermassive black hole coevolution and is required to explain the observed X-ray and IR backgrounds. Recently, IR colour cuts with Wide-field Infrared Survey Explorer have identified a portion of this missing population. However, as the host galaxy brightness relative to that of the AGN increases, it becomes increasingly difficult to differentiate between IR emission originating from the AGN and from its host galaxy. As a solution, we have developed a new method to select obscured AGN using their 20-cm continuum emission to identify the objects as AGN. We created the resulting invisible AGN catalogue by selecting objects that are detected in AllWISE (mid-IR) and FIRST (20 cm), but are not detected in SDSS (optical) or 2MASS (near-IR), producing a final catalogue of 46 258 objects. 30 per cent of the objects are selected by existing selection methods, while the remaining 70 per cent represent a potential previously unidentified population of candidate AGN that are missed by mid-IR colour cuts. Additionally, by relying on a radio continuum detection, this technique is efficient at detecting radio-loud AGN at z ≥ 0.29, regardless of their level of dust obscuration or their host galaxy's relative brightness.

Robust one pot synthesis of colloidal silver nanoparticles by simple redox method and absorbance recovered sensing.

PubMed

Salman, Muhammad; Iqbal, Mahwish; El Ashry, El Sayed H; Kanwal, Shamsa

2012-01-01

Conventional synthesis of silver nanoparticles employs a reducing agent and a capping agent. In this report water-soluble silver nanoparticles (AgNPs) were prepared facilely by chemical reduction of Ag(I) ions. 4-Amino-3-(d-gluco-pentitol-1-yl)-4,5-dihydro-1,2,4-triazole-5-thione (AGTT) was used both as reducing and stabilizing agent. Direct heating methodology was found to be more suitable for achieving particles with a hydrodynamic diameter of ~20 nm. AGTT exists as tautomer in solution form and our studies indicate that -NH(2) group is involved in the reduction and stabilization of Ag(+) and thione (Δ=S) group of AGTT is possibly involved in stabilizing the nanoparticles via coordinate covalent linkage. Characterization of synthesized silver nanoparticles was performed by UV-vis, FT-IR and by FESEM. Based on the absorption properties of synthesized AgNPs, we used AgNPs to detect bovine serum albumin (BSA) and AgNPs-BSA composite nanoprobe was further applied to detect Cu(2+) based on absorbance recovery. The proposed method has advantages over existing methods in terms of rapid synthesis and stability of AgNPs and their applications. Analysis is reproducible, cost effective and highly sensitive. The lowest detectable concentration of BSA in this approach is 3 nM, and for Cu(2+) it can detect upto 200 pM. Copyright © 2012 Elsevier B.V. All rights reserved.

Low Base-Substitution Mutation Rate in the Germline Genome of the Ciliate Tetrahymena thermophila

DTIC Science & Technology

2016-09-15

generations of mutation accumulation (MA). We applied an existing mutation-calling pipeline and developed a new probabilistic mutation detection approach...noise introduced by mismapped reads. We used both our new method and an existing mutation-calling pipeline (Sung, Tucker, et al. 2012) to analyse the...and larger MA experiments will be required to confidently estimate the mutational spectrum of a species with such a low mutation rate. Materials and

Mapping quantitative trait loci for traits defined as ratios.

PubMed

Yang, Runqing; Li, Jiahan; Xu, Shizhong

2008-03-01

Many traits are defined as ratios of two quantitative traits. Methods of QTL mapping for regular quantitative traits are not optimal when applied to ratios due to lack of normality for traits defined as ratios. We develop a new method of QTL mapping for traits defined as ratios. The new method uses a special linear combination of the two component traits, and thus takes advantage of the normal property of the new variable. Simulation study shows that the new method can substantially increase the statistical power of QTL detection relative to the method which treats ratios as regular quantitative traits. The new method also outperforms the method that uses Box-Cox transformed ratio as the phenotype. A real example of QTL mapping for relative growth rate in soybean demonstrates that the new method can detect more QTL than existing methods of QTL mapping for traits defined as ratios.

Automated Segmentation Methods of Drusen to Diagnose Age-Related Macular Degeneration Screening in Retinal Images.

PubMed

Kim, Young Jae; Kim, Kwang Gi

2018-01-01

Existing drusen measurement is difficult to use in clinic because it requires a lot of time and effort for visual inspection. In order to resolve this problem, we propose an automatic drusen detection method to help clinical diagnosis of age-related macular degeneration. First, we changed the fundus image to a green channel and extracted the ROI of the macular area based on the optic disk. Next, we detected the candidate group using the difference image of the median filter within the ROI. We also segmented vessels and removed them from the image. Finally, we detected the drusen through Renyi's entropy threshold algorithm. We performed comparisons and statistical analysis between the manual detection results and automatic detection results for 30 cases in order to verify validity. As a result, the average sensitivity was 93.37% (80.95%~100%) and the average DSC was 0.73 (0.3~0.98). In addition, the value of the ICC was 0.984 (CI: 0.967~0.993, p < 0.01), showing the high reliability of the proposed automatic method. We expect that the automatic drusen detection helps clinicians to improve the diagnostic performance in the detection of drusen on fundus image.

The COLA Collision Avoidance Method

NASA Astrophysics Data System (ADS)

Assmann, K.; Berger, J.; Grothkopp, S.

2009-03-01

In the following we present a collision avoidance method named COLA. The method has been designed to predict collisions for Earth orbiting spacecraft on any orbits, including orbit changes, with other space-born objects. The point in time of a collision and the collision probability are determined. To guarantee effective processing the COLA method uses a modular design and is composed of several components which are either developed within this work or deduced from existing algorithms: A filtering module, the close approach determination, the collision detection and the collision probability calculation. A software tool which implements the COLA method has been verified using various test cases built from sample missions. This software has been implemented in the C++ programming language and serves as a universal collision detection tool at LSE Space Engineering & Operations AG.

Methods for detecting, quantifying, and adjusting for dissemination bias in meta-analysis are described.

PubMed

Mueller, Katharina Felicitas; Meerpohl, Joerg J; Briel, Matthias; Antes, Gerd; von Elm, Erik; Lang, Britta; Motschall, Edith; Schwarzer, Guido; Bassler, Dirk

2016-12-01

To systematically review methodological articles which focus on nonpublication of studies and to describe methods of detecting and/or quantifying and/or adjusting for dissemination in meta-analyses. To evaluate whether the methods have been applied to an empirical data set for which one can be reasonably confident that all studies conducted have been included. We systematically searched Medline, the Cochrane Library, and Web of Science, for methodological articles that describe at least one method of detecting and/or quantifying and/or adjusting for dissemination bias in meta-analyses. The literature search retrieved 2,224 records, of which we finally included 150 full-text articles. A great variety of methods to detect, quantify, or adjust for dissemination bias were described. Methods included graphical methods mainly based on funnel plot approaches, statistical methods, such as regression tests, selection models, sensitivity analyses, and a great number of more recent statistical approaches. Only few methods have been validated in empirical evaluations using unpublished studies obtained from regulators (Food and Drug Administration, European Medicines Agency). We present an overview of existing methods to detect, quantify, or adjust for dissemination bias. It remains difficult to advise which method should be used as they are all limited and their validity has rarely been assessed. Therefore, a thorough literature search remains crucial in systematic reviews, and further steps to increase the availability of all research results need to be taken. Copyright © 2016 Elsevier Inc. All rights reserved.

Saliency Detection for Stereoscopic 3D Images in the Quaternion Frequency Domain

NASA Astrophysics Data System (ADS)

Cai, Xingyu; Zhou, Wujie; Cen, Gang; Qiu, Weiwei

2018-06-01

Recent studies have shown that a remarkable distinction exists between human binocular and monocular viewing behaviors. Compared with two-dimensional (2D) saliency detection models, stereoscopic three-dimensional (S3D) image saliency detection is a more challenging task. In this paper, we propose a saliency detection model for S3D images. The final saliency map of this model is constructed from the local quaternion Fourier transform (QFT) sparse feature and global QFT log-Gabor feature. More specifically, the local QFT feature measures the saliency map of an S3D image by analyzing the location of a similar patch. The similar patch is chosen using a sparse representation method. The global saliency map is generated by applying the wake edge-enhanced gradient QFT map through a band-pass filter. The results of experiments on two public datasets show that the proposed model outperforms existing computational saliency models for estimating S3D image saliency.

Integrated analysis of error detection and recovery

NASA Technical Reports Server (NTRS)

Shin, K. G.; Lee, Y. H.

1985-01-01

An integrated modeling and analysis of error detection and recovery is presented. When fault latency and/or error latency exist, the system may suffer from multiple faults or error propagations which seriously deteriorate the fault-tolerant capability. Several detection models that enable analysis of the effect of detection mechanisms on the subsequent error handling operations and the overall system reliability were developed. Following detection of the faulty unit and reconfiguration of the system, the contaminated processes or tasks have to be recovered. The strategies of error recovery employed depend on the detection mechanisms and the available redundancy. Several recovery methods including the rollback recovery are considered. The recovery overhead is evaluated as an index of the capabilities of the detection and reconfiguration mechanisms.

The biometric-based module of smart grid system

NASA Astrophysics Data System (ADS)

Engel, E.; Kovalev, I. V.; Ermoshkina, A.

2015-10-01

Within Smart Grid concept the flexible biometric-based module base on Principal Component Analysis (PCA) and selective Neural Network is developed. The formation of the selective Neural Network the biometric-based module uses the method which includes three main stages: preliminary processing of the image, face localization and face recognition. Experiments on the Yale face database show that (i) selective Neural Network exhibits promising classification capability for face detection, recognition problems; and (ii) the proposed biometric-based module achieves near real-time face detection, recognition speed and the competitive performance, as compared to some existing subspaces-based methods.

Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs

PubMed Central

Gómez-Adorno, Helena; Sidorov, Grigori; Pinto, David; Vilariño, Darnes; Gelbukh, Alexander

2016-01-01

We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents. On average, our method outperforms the state of the art approaches and gives consistently high results across different corpora, unlike existing methods. Our results show that our textual patterns are useful for the task of authorship attribution. PMID:27589740

A hidden two-locus disease association pattern in genome-wide association studies

PubMed Central

2011-01-01

Background Recent association analyses in genome-wide association studies (GWAS) mainly focus on single-locus association tests (marginal tests) and two-locus interaction detections. These analysis methods have provided strong evidence of associations between genetics variances and complex diseases. However, there exists a type of association pattern, which often occurs within local regions in the genome and is unlikely to be detected by either marginal tests or interaction tests. This association pattern involves a group of correlated single-nucleotide polymorphisms (SNPs). The correlation among SNPs can lead to weak marginal effects and the interaction does not play a role in this association pattern. This phenomenon is due to the existence of unfaithfulness: the marginal effects of correlated SNPs do not express their significant joint effects faithfully due to the correlation cancelation. Results In this paper, we develop a computational method to detect this association pattern masked by unfaithfulness. We have applied our method to analyze seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). The analysis for each data set takes about one week to finish the examination of all pairs of SNPs. Based on the empirical result of these real data, we show that this type of association masked by unfaithfulness widely exists in GWAS. Conclusions These newly identified associations enrich the discoveries of GWAS, which may provide new insights both in the analysis of tagSNPs and in the experiment design of GWAS. Since these associations may be easily missed by existing analysis tools, we can only connect some of them to publicly available findings from other association studies. As independent data set is limited at this moment, we also have difficulties to replicate these findings. More biological implications need further investigation. Availability The software is freely available at http://bioinformatics.ust.hk/hidden_pattern_finder.zip. PMID:21569557

Sensing Methods for Detecting Analog Television Signals

NASA Astrophysics Data System (ADS)

Rahman, Mohammad Azizur; Song, Chunyi; Harada, Hiroshi

This paper introduces a unified method of spectrum sensing for all existing analog television (TV) signals including NTSC, PAL and SECAM. We propose a correlation based method (CBM) with a single reference signal for sensing any analog TV signals. In addition we also propose an improved energy detection method. The CBM approach has been implemented in a hardware prototype specially designed for participating in Singapore TV white space (WS) test trial conducted by Infocomm Development Authority (IDA) of the Singapore government. Analytical and simulation results of the CBM method will be presented in the paper, as well as hardware testing results for sensing various analog TV signals. Both AWGN and fading channels will be considered. It is shown that the theoretical results closely match with those from simulations. Sensing performance of the hardware prototype will also be presented in fading environment by using a fading simulator. We present performance of the proposed techniques in terms of probability of false alarm, probability of detection, sensing time etc. We also present a comparative study of the various techniques.

Leakage detection in galvanized iron pipelines using ensemble empirical mode decomposition analysis

NASA Astrophysics Data System (ADS)

Amin, Makeen; Ghazali, M. Fairusham

2015-05-01

There are many numbers of possible approaches to detect leaks. Some leaks are simply noticeable when the liquids or water appears on the surface. However many leaks do not find their way to the surface and the existence has to be check by analysis of fluid flow in the pipeline. The first step is to determine the approximate position of leak. This can be done by isolate the sections of the mains in turn and noting which section causes a drop in the flow. Next approach is by using sensor to locate leaks. This approach are involves strain gauge pressure transducers and piezoelectric sensor. the occurrence of leaks and know its exact location in the pipeline by using specific method which are Acoustic leak detection method and transient method. The objective is to utilize the signal processing technique in order to analyse leaking in the pipeline. With this, an EEMD method will be applied as the analysis method to collect and analyse the data.

[Aging explosive detection using terahertz time-domain spectroscopy].

PubMed

Meng, Kun; Li, Ze-ren; Liu, Qiao

2011-05-01

Detecting the aging situation of stock explosive is essentially meaningful to the research on the capability, security and stability of explosive. Existing aging explosive detection techniques, such as scan microscope technique, Fourier transfer infrared spectrum technique, gas chromatogram mass spectrum technique and so on, are either not able to differentiate whether the explosive is aging or not, or not able to image the structure change of the molecule. In the present paper, using the density functional theory (DFT), the absorb spectrum changes after the explosive aging were calculated, from which we can clearly find the difference of spectrum between explosive molecule and aging ones in the terahertz band. The terahertz time-domain spectrum (THz-TDS) system as well as its frequency spectrum resolution and measured range are analyzed. Combined with the existing experimental results and the essential characters of the terahertz wave, the application of THz-TDS technique to the detection of aging explosive was demonstrated from the aspects of feasibility, veracity and practicability. On the base of that, the authors advance the new method of aging explosive detection using the terahertz time-domain spectrum technique.

Is This the Carbapenemase Test We've Been Waiting for? A Multicenter Evaluation of the Modified Carbapenem Inactivation Method.

PubMed

Butler-Wu, Susan M; Abbott, April N

2017-08-01

A plethora of phenotypic methods exist for the detection of carbapenemases; however, clinical laboratories have struggled for years with accurate, objective phenotypic detection of carbapenemase activity in Enterobacteriaceae In this issue of the Journal of Clinical Microbiology , V. M. Pierce et al. (J Clin Microbiol 55:2321-2333, 2017, https://doi.org/10.1128/JCM.00193-17) report on a multicenter evaluation of the modified carbapenem inactivation method (mCIM). The high sensitivity, specificity, reproducibility, and ease of interpretation associated with the mCIM for Enterobacteriaceae will likely lead to its adoption by clinical laboratories. Copyright © 2017 American Society for Microbiology.

Global Detection of Live Virtual Machine Migration Based on Cellular Neural Networks

PubMed Central

Xie, Kang; Yang, Yixian; Zhang, Ling; Jing, Maohua; Xin, Yang; Li, Zhongxian

2014-01-01

In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM) migration detection algorithm based on the cellular neural networks (CNNs), is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation allowing the VM migration detection to be performed better. PMID:24959631

Global detection of live virtual machine migration based on cellular neural networks.

PubMed

Xie, Kang; Yang, Yixian; Zhang, Ling; Jing, Maohua; Xin, Yang; Li, Zhongxian

2014-01-01

In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM) migration detection algorithm based on the cellular neural networks (CNNs), is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation allowing the VM migration detection to be performed better.

[Determination of iodine and its species in plant samples using ion chromatography-inductively coupled plasma mass spectrometry].

PubMed

Lin, Li; Chen, Guang; Chen, Yuhong

2011-07-01

A method was established for the determination of iodine and its species in plant samples using ion chromatography-inductively coupled plasma mass spectrometry (IC-ICP/ MS). Alkaline extraction and IC-ICP/MS were applied as the sample pre-treatment method and the detection technique respectively, for iodate and iodide determination. Moreover, high-temperature pyrolysis absorption was adopted as the pre-treatment method for total iodine analysis, which finally converted all the iodine species into iodide and measured the iodide by IC-ICP/MS. The recoveries of iodine for alkaline extraction and high-temperature pyrolysis absorption were 89.6%-97.5% and 95.2%-111.2%, respectively. The results were satisfactory. The detection limit of iodine was 0.010 mg/kg. The iodine and its speciation contents in several kinds of plant samples such as seaweeds, kelp, cabbage, tea leaf and spinach were investigated. It was shown that the iodine in seaweeds mainly existed as organic iodine; while the ones in kelp, cabbage, tea leaf and spinach mainly existed as inorganic iodine.

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.

Test of multi-object exoplanet search spectral interferometer

NASA Astrophysics Data System (ADS)

Zhang, Kai; Wang, Liang; Jiang, Haijiao; Zhu, Yongtian; Hou, Yonghui; Dai, Songxin; Tang, Jin; Tang, Zhen; Zeng, Yizhong; Chen, Yi; Wang, Lei; Hu, Zhongwen

2014-07-01

Exoplanet detection, a highlight in the current astronomy, will be part of puzzle in astronomical and astrophysical future, which contains dark energy, dark matter, early universe, black hole, galactic evolution and so on. At present, most of the detected Exoplanets are confirmed through methods of radial velocity and transit. Guo shoujing Telescope well known as LAMOST is an advanced multi-object spectral survey telescope equipped with 4000 fibers and 16 low resolution fiber spectrographs. To explore its potential in different astronomical activities, a new radial velocity method named Externally Dispersed Interferometry (EDI) is applied to serve Exoplanet detection through combining a fixed-delay interferometer with the existing spectrograph in medium spectral resolution mode (R=5,000-10,000). This new technology has an impressive feature to enhance radial velocity measuring accuracy of the existing spectrograph through installing a fixed-delay interferometer in front of spectrograph. This way produces an interference spectrum with higher sensitivity to Doppler Effect by interference phase and fixed delay. This relative system named Multi-object Exoplanet Search Spectral Interferometer (MESSI) is composed of a few parts, including a pair of multi-fiber coupling sockets, a remote control iodine subsystem, a multi-object fixed delay interferometer and the existing spectrograph. It covers from 500 to 550 nm and simultaneously observes up to 21 stars. Even if it's an experimental instrument at present, it's still well demonstrated in paper that how MESSI does explore an effective way to build its own system under the existing condition of LAMOST and get its expected performance for multi-object Exoplanet detection, especially instrument stability and its special data reduction. As a result of test at lab, inside temperature of its instrumental chamber is stable in a range of +/-0.5degree Celsius within 12 hours, and the direct instrumental stability without further observation correction is equivalent to be +/-50m/s every 20mins.

Detection prospects of the cosmic neutrino background

NASA Astrophysics Data System (ADS)

Li, Yu-Feng

2015-04-01

The existence of the cosmic neutrino background (CνB) is a fundamental prediction of the standard Big Bang cosmology. Although current cosmological probes provide indirect observational evidence, the direct detection of the CνB in a laboratory experiment is a great challenge to the present experimental techniques. We discuss the future prospects for the direct detection of the CνB, with the emphasis on the method of captures on beta-decaying nuclei and the PTOLEMY project. Other possibilities using the electron-capture (EC) decaying nuclei, the annihilation of extremely high-energy cosmic neutrinos (EHECνs) at the Z-resonance, and the atomic de-excitation method are also discussed in this review (talk given at the International Conference on Massive Neutrinos, Singapore, 9-13 February 2015).

Tagged Neutron Source for API Inspection Systems with Greatly Enhanced Spatial Resolution

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

None

2012-06-04

We recently developed induced fission and transmission imaging methods with time- and directionally-tagged neutrons offer new capabilities for characterization of fissile material configurations and enhanced detection of special nuclear materials (SNM). An Advanced Associated Particle Imaging (API) generator with higher angular resolution and neutron yield than existing systems is needed to fully exploit these methods.

Surface scattering plasmon resonance fibre sensors: demonstration of rapid influenza A virus detection

NASA Astrophysics Data System (ADS)

Franςois, A.; Boehm, J.; Oh, S. Y.; Kok, T.; Monro, T. M.

2011-06-01

The management of threats such as pandemics and explosives, and of health and the environment requires the rapid deployment of highly sensitive detection tools. Sensors based on Surface Plasmon Resonance (SPR) allow rapid, labelfree, highly sensitive detection, and indeed this phenomenon underpins the only label-free optical biosensing technology that is available commercially. In these sensors, the existence of surface plasmons is inferred indirectly from absorption features that correspond to the coupling of light to the surface plasmon. Although SPR is not intrinsically a radiative process, under certain conditions the surface plasmon can itself couple to the local photon states, and emit light as first described byKretschmann. Here we show that by collecting and characterising this re-emitted light, it is possible to realise new SPR sensing architectures that are more compact, versatile and robust than existing approaches. This approach addresses existing practical limitations associated with current SPR technologies, including bulk, cost and calibration. It is applicable to a range of SPR geometries, including optical fibres, planar waveguides and prism configurations, and is in principle capable of detecting multiple analytes simultaneously. Moreover, this technique allows to combine SPR sensing and fluorescence sensing into a single platform which has never been demonstrated before and consequently use these two methods for a more reliable diagnostic. As an example, this approach has been used to demonstrate the rapid detection of the seasonal influenza virus.

Automated Detection of Electroencephalography Artifacts in Human, Rodent and Canine Subjects using Machine Learning.

PubMed

Levitt, Joshua; Nitenson, Adam; Koyama, Suguru; Heijmans, Lonne; Curry, James; Ross, Jason T; Kamerling, Steven; Saab, Carl Y

2018-06-23

Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation. We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of 'eyes open/closed' states in human subjects. The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance. Comparison with Existing Methods: Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does. We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra. Copyright © 2018. Published by Elsevier B.V.

Intraoperative imaging-guided cancer surgery: from current fluorescence molecular imaging methods to future multi-modality imaging technology.

PubMed

Chi, Chongwei; Du, Yang; Ye, Jinzuo; Kou, Deqiang; Qiu, Jingdan; Wang, Jiandong; Tian, Jie; Chen, Xiaoyuan

2014-01-01

Cancer is a major threat to human health. Diagnosis and treatment using precision medicine is expected to be an effective method for preventing the initiation and progression of cancer. Although anatomical and functional imaging techniques such as radiography, computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) have played an important role for accurate preoperative diagnostics, for the most part these techniques cannot be applied intraoperatively. Optical molecular imaging is a promising technique that provides a high degree of sensitivity and specificity in tumor margin detection. Furthermore, existing clinical applications have proven that optical molecular imaging is a powerful intraoperative tool for guiding surgeons performing precision procedures, thus enabling radical resection and improved survival rates. However, detection depth limitation exists in optical molecular imaging methods and further breakthroughs from optical to multi-modality intraoperative imaging methods are needed to develop more extensive and comprehensive intraoperative applications. Here, we review the current intraoperative optical molecular imaging technologies, focusing on contrast agents and surgical navigation systems, and then discuss the future prospects of multi-modality imaging technology for intraoperative imaging-guided cancer surgery.

Intraoperative Imaging-Guided Cancer Surgery: From Current Fluorescence Molecular Imaging Methods to Future Multi-Modality Imaging Technology

PubMed Central

Chi, Chongwei; Du, Yang; Ye, Jinzuo; Kou, Deqiang; Qiu, Jingdan; Wang, Jiandong; Tian, Jie; Chen, Xiaoyuan

2014-01-01

Cancer is a major threat to human health. Diagnosis and treatment using precision medicine is expected to be an effective method for preventing the initiation and progression of cancer. Although anatomical and functional imaging techniques such as radiography, computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET) have played an important role for accurate preoperative diagnostics, for the most part these techniques cannot be applied intraoperatively. Optical molecular imaging is a promising technique that provides a high degree of sensitivity and specificity in tumor margin detection. Furthermore, existing clinical applications have proven that optical molecular imaging is a powerful intraoperative tool for guiding surgeons performing precision procedures, thus enabling radical resection and improved survival rates. However, detection depth limitation exists in optical molecular imaging methods and further breakthroughs from optical to multi-modality intraoperative imaging methods are needed to develop more extensive and comprehensive intraoperative applications. Here, we review the current intraoperative optical molecular imaging technologies, focusing on contrast agents and surgical navigation systems, and then discuss the future prospects of multi-modality imaging technology for intraoperative imaging-guided cancer surgery. PMID:25250092

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.

Two methods of Haustral fold detection from computed tomographic virtual colonoscopy images

NASA Astrophysics Data System (ADS)

Chowdhury, Ananda S.; Tan, Sovira; Yao, Jianhua; Linguraru, Marius G.; Summers, Ronald M.

2009-02-01

Virtual colonoscopy (VC) has gained popularity as a new colon diagnostic method over the last decade. VC is a new, less invasive alternative to the usually practiced optical colonoscopy for colorectal polyp and cancer screening, the second major cause of cancer related deaths in industrial nations. Haustral (colonic) folds serve as important landmarks for virtual endoscopic navigation in the existing computer-aided-diagnosis (CAD) system. In this paper, we propose and compare two different methods of haustral fold detection from volumetric computed tomographic virtual colonoscopy images. The colon lumen is segmented from the input using modified region growing and fuzzy connectedness. The first method for fold detection uses a level set that evolves on a mesh representation of the colon surface. The colon surface is obtained from the segmented colon lumen using the Marching Cubes algorithm. The second method for fold detection, based on a combination of heat diffusion and fuzzy c-means algorithm, is employed on the segmented colon volume. Folds obtained on the colon volume using this method are then transferred to the corresponding colon surface. After experimentation with different datasets, results are found to be promising. The results also demonstrate that the first method has a tendency of slight under-segmentation while the second method tends to slightly over-segment the folds.

The ADE scorecards: a tool for adverse drug event detection in electronic health records.

PubMed

Chazard, Emmanuel; Băceanu, Adrian; Ferret, Laurie; Ficheur, Grégoire

2011-01-01

Although several methods exist for Adverse Drug events (ADE) detection due to past hospitalizations, a tool that could display those ADEs to the physicians does not exist yet. This article presents the ADE Scorecards, a Web tool that enables to screen past hospitalizations extracted from Electronic Health Records (EHR), using a set of ADE detection rules, presently rules discovered by data mining. The tool enables the physicians to (1) get contextualized statistics about the ADEs that happen in their medical department, (2) see the rules that are useful in their department, i.e. the rules that could have enabled to prevent those ADEs and (3) review in detail the ADE cases, through a comprehensive interface displaying the diagnoses, procedures, lab results, administered drugs and anonymized records. The article shows a demonstration of the tool through a use case.

Premature ventricular contraction detection combining deep neural networks and rules inference.

PubMed

Zhou, Fei-Yan; Jin, Lin-Peng; Dong, Jun

2017-06-01

Premature ventricular contraction (PVC), which is a common form of cardiac arrhythmia caused by ectopic heartbeat, can lead to life-threatening cardiac conditions. Computer-aided PVC detection is of considerable importance in medical centers or outpatient ECG rooms. In this paper, we proposed a new approach that combined deep neural networks and rules inference for PVC detection. The detection performance and generalization were studied using publicly available databases: the MIT-BIH arrhythmia database (MIT-BIH-AR) and the Chinese Cardiovascular Disease Database (CCDD). The PVC detection accuracy on the MIT-BIH-AR database was 99.41%, with a sensitivity and specificity of 97.59% and 99.54%, respectively, which were better than the results from other existing methods. To test the generalization capability, the detection performance was also evaluated on the CCDD. The effectiveness of the proposed method was confirmed by the accuracy (98.03%), sensitivity (96.42%) and specificity (98.06%) with the dataset over 140,000 ECG recordings of the CCDD. Copyright © 2017 Elsevier B.V. All rights reserved.

Research and Design of Rootkit Detection Method

NASA Astrophysics Data System (ADS)

Liu, Leian; Yin, Zuanxing; Shen, Yuli; Lin, Haitao; Wang, Hongjiang

Rootkit is one of the most important issues of network communication systems, which is related to the security and privacy of Internet users. Because of the existence of the back door of the operating system, a hacker can use rootkit to attack and invade other people's computers and thus he can capture passwords and message traffic to and from these computers easily. With the development of the rootkit technology, its applications are more and more extensive and it becomes increasingly difficult to detect it. In addition, for various reasons such as trade secrets, being difficult to be developed, and so on, the rootkit detection technology information and effective tools are still relatively scarce. In this paper, based on the in-depth analysis of the rootkit detection technology, a new kind of the rootkit detection structure is designed and a new method (software), X-Anti, is proposed. Test results show that software designed based on structure proposed is much more efficient than any other rootkit detection software.

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.

Eyelid contour detection and tracking for startle research related eye-blink measurements from high-speed video records.

PubMed

Bernard, Florian; Deuter, Christian Eric; Gemmar, Peter; Schachinger, Hartmut

2013-10-01

Using the positions of the eyelids is an effective and contact-free way for the measurement of startle induced eye-blinks, which plays an important role in human psychophysiological research. To the best of our knowledge, no methods for an efficient detection and tracking of the exact eyelid contours in image sequences captured at high-speed exist that are conveniently usable by psychophysiological researchers. In this publication a semi-automatic model-based eyelid contour detection and tracking algorithm for the analysis of high-speed video recordings from an eye tracker is presented. As a large number of images have been acquired prior to method development it was important that our technique is able to deal with images that are recorded without any special parametrisation of the eye tracker. The method entails pupil detection, specular reflection removal and makes use of dynamic model adaption. In a proof-of-concept study we could achieve a correct detection rate of 90.6%. With this approach, we provide a feasible method to accurately assess eye-blinks from high-speed video recordings. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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

The analysis and detection of hypernasality based on a formant extraction algorithm

NASA Astrophysics Data System (ADS)

Qian, Jiahui; Fu, Fanglin; Liu, Xinyi; He, Ling; Yin, Heng; Zhang, Han

2017-08-01

In the clinical practice, the effective assessment of cleft palate speech disorders is important. For hypernasal speech, the resonance between nasal cavity and oral cavity causes an additional nasal formant. Thus, the formant frequency is a crucial cue for the judgment of hypernasality in cleft palate speech. Due to the existence of nasal formant, the peak merger occurs to the spectrum of nasal speech more often. However, the peak merger could not be solved by classical linear prediction coefficient root extraction method. In this paper, a method is proposed to detect the additional nasal formant in low-frequency region and obtain the formant frequency. The experiment results show that the proposed method could locate the nasal formant preferably. Moreover, the formants are regarded as the extraction features to proceed the detection of hypernasality. 436 phonemes, which are collected from Hospital of Stomatology, are used to carry out the experiment. The detection accuracy of hypernasality in cleft palate speech is 95.2%.

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.

PubMed

Chu, Catherine J; Chan, Arthur; Song, Dan; Staley, Kevin J; Stufflebeam, Steven M; Kramer, Mark A

2017-02-01

High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. Copyright © 2016 Elsevier B.V. All rights reserved.

Effect of parameters in moving average method for event detection enhancement using phase sensitive OTDR

NASA Astrophysics Data System (ADS)

Kwon, Yong-Seok; Naeem, Khurram; Jeon, Min Yong; Kwon, Il-bum

2017-04-01

We analyze the relations of parameters in moving average method to enhance the event detectability of phase sensitive optical time domain reflectometer (OTDR). If the external events have unique frequency of vibration, then the control parameters of moving average method should be optimized in order to detect these events efficiently. A phase sensitive OTDR was implemented by a pulsed light source, which is composed of a laser diode, a semiconductor optical amplifier, an erbium-doped fiber amplifier, a fiber Bragg grating filter, and a light receiving part, which has a photo-detector and high speed data acquisition system. The moving average method is operated with the control parameters: total number of raw traces, M, number of averaged traces, N, and step size of moving, n. The raw traces are obtained by the phase sensitive OTDR with sound signals generated by a speaker. Using these trace data, the relation of the control parameters is analyzed. In the result, if the event signal has one frequency, then the optimal values of N, n are existed to detect the event efficiently.

A scan statistic for binary outcome based on hypergeometric probability model, with an application to detecting spatial clusters of Japanese encephalitis.

PubMed

Zhao, Xing; Zhou, Xiao-Hua; Feng, Zijian; Guo, Pengfei; He, Hongyan; Zhang, Tao; Duan, Lei; Li, Xiaosong

2013-01-01

As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff's methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff's statistics for clusters of high population density or large size; otherwise Kulldorff's statistics are superior.

Sky Detection in Hazy Image.

PubMed

Song, Yingchao; Luo, Haibo; Ma, Junkai; Hui, Bin; Chang, Zheng

2018-04-01

Sky detection plays an essential role in various computer vision applications. Most existing sky detection approaches, being trained on ideal dataset, may lose efficacy when facing unfavorable conditions like the effects of weather and lighting conditions. In this paper, a novel algorithm for sky detection in hazy images is proposed from the perspective of probing the density of haze. We address the problem by an image segmentation and a region-level classification. To characterize the sky of hazy scenes, we unprecedentedly introduce several haze-relevant features that reflect the perceptual hazy density and the scene depth. Based on these features, the sky is separated by two imbalance SVM classifiers and a similarity measurement. Moreover, a sky dataset (named HazySky) with 500 annotated hazy images is built for model training and performance evaluation. To evaluate the performance of our method, we conducted extensive experiments both on our HazySky dataset and the SkyFinder dataset. The results demonstrate that our method performs better on the detection accuracy than previous methods, not only under hazy scenes, but also under other weather conditions.

Nucleic Acids for Ultra-Sensitive Protein Detection

PubMed Central

Janssen, Kris P. F.; Knez, Karel; Spasic, Dragana; Lammertyn, Jeroen

2013-01-01

Major advancements in molecular biology and clinical diagnostics cannot be brought about strictly through the use of genomics based methods. Improved methods for protein detection and proteomic screening are an absolute necessity to complement to wealth of information offered by novel, high-throughput sequencing technologies. Only then will it be possible to advance insights into clinical processes and to characterize the importance of specific protein biomarkers for disease detection or the realization of “personalized medicine”. Currently however, large-scale proteomic information is still not as easily obtained as its genomic counterpart, mainly because traditional antibody-based technologies struggle to meet the stringent sensitivity and throughput requirements that are required whereas mass-spectrometry based methods might be burdened by significant costs involved. However, recent years have seen the development of new biodetection strategies linking nucleic acids with existing antibody technology or replacing antibodies with oligonucleotide recognition elements altogether. These advancements have unlocked many new strategies to lower detection limits and dramatically increase throughput of protein detection assays. In this review, an overview of these new strategies will be given. PMID:23337338

Wavelet based detection of manatee vocalizations

NASA Astrophysics Data System (ADS)

Gur, Berke M.; Niezrecki, Christopher

2005-04-01

The West Indian manatee (Trichechus manatus latirostris) has become endangered partly because of watercraft collisions in Florida's coastal waterways. Several boater warning systems, based upon manatee vocalizations, have been proposed to reduce the number of collisions. Three detection methods based on the Fourier transform (threshold, harmonic content and autocorrelation methods) were previously suggested and tested. In the last decade, the wavelet transform has emerged as an alternative to the Fourier transform and has been successfully applied in various fields of science and engineering including the acoustic detection of dolphin vocalizations. As of yet, no prior research has been conducted in analyzing manatee vocalizations using the wavelet transform. Within this study, the wavelet transform is used as an alternative to the Fourier transform in detecting manatee vocalizations. The wavelet coefficients are analyzed and tested against a specified criterion to determine the existence of a manatee call. The performance of the method presented is tested on the same data previously used in the prior studies, and the results are compared. Preliminary results indicate that using the wavelet transform as a signal processing technique to detect manatee vocalizations shows great promise.

Sky Detection in Hazy Image

PubMed Central

Song, Yingchao; Luo, Haibo; Ma, Junkai; Hui, Bin; Chang, Zheng

2018-01-01

Sky detection plays an essential role in various computer vision applications. Most existing sky detection approaches, being trained on ideal dataset, may lose efficacy when facing unfavorable conditions like the effects of weather and lighting conditions. In this paper, a novel algorithm for sky detection in hazy images is proposed from the perspective of probing the density of haze. We address the problem by an image segmentation and a region-level classification. To characterize the sky of hazy scenes, we unprecedentedly introduce several haze-relevant features that reflect the perceptual hazy density and the scene depth. Based on these features, the sky is separated by two imbalance SVM classifiers and a similarity measurement. Moreover, a sky dataset (named HazySky) with 500 annotated hazy images is built for model training and performance evaluation. To evaluate the performance of our method, we conducted extensive experiments both on our HazySky dataset and the SkyFinder dataset. The results demonstrate that our method performs better on the detection accuracy than previous methods, not only under hazy scenes, but also under other weather conditions. PMID:29614778

Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

PubMed Central

Li, Gang; He, Bin; Huang, Hongwei; Tang, Limin

2016-01-01

The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs). Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection. These two parts are pursued separately in existing works. In this work, the combination of temporal data-driven sleep scheduling (TDSS) and spatial data-driven anomaly detection is proposed, where TDSS can reduce data redundancy. The TDSS model is inspired by transmission control protocol (TCP) congestion control. Based on long and linear cluster structure in the tunnel monitoring system, cooperative TDSS and spatial data-driven anomaly detection are then proposed. To realize synchronous acquisition in the same ring for analyzing the situation of every ring, TDSS is implemented in a cooperative way in the cluster. To keep the precision of sensor data, spatial data-driven anomaly detection based on the spatial correlation and Kriging method is realized to generate an anomaly indicator. The experiment results show that cooperative TDSS can realize non-uniform sensing effectively to reduce the energy consumption. In addition, spatial data-driven anomaly detection is quite significant for maintaining and improving the precision of sensor data. PMID:27690035

The Application of Sensors on Guardrails for the Purpose of Real Time Impact Detection

DTIC Science & Technology

2012-03-01

collection methods ; however, there are major differences in the measures of performance for policy goals and objectives (U.S. DOT, 2002). The goal here is...seriousness of this issue has motivated the US Department of Transportation and Transportation Research Board to develop and deploy new methods and... methods to integrate new sensing capabilities into existing Intelligent Transportation Systems in a time efficient and cost effective manner. In

Zero leakage separable and semipermanent ducting joints

NASA Technical Reports Server (NTRS)

Mischel, H. T.

1973-01-01

A study program has been conducted to explore new methods of achieving zero leakage, separable and semipermanent, ducting joints for space flight vehicles. The study consisted of a search of literature of existing zero leakage methods, the generation of concepts of new methods of achieving the desired zero leakage criteria and the development of detailed analysis and design of a selected concept. Other techniques of leak detection were explored with a view toward improving this area.

Online Detection of Broken Rotor Bar Fault in Induction Motors by Combining Estimation of Signal Parameters via Min-norm Algorithm and Least Square Method

NASA Astrophysics Data System (ADS)

Wang, Pan-Pan; Yu, Qiang; Hu, Yong-Jun; Miao, Chang-Xin

2017-11-01

Current research in broken rotor bar (BRB) fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estimation cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the frequencies of the fundamental and fault characteristic components with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.

Design and Characterization of a Microfabricated Hydrogen Clearance Blood Flow Sensor

PubMed Central

Walton, Lindsay R.; Edwards, Martin A.; McCarty, Gregory S.; Wightman, R. Mark

2016-01-01

Background Modern cerebral blood flow (CBF) detection favors the use of either optical technologies that are limited to cortical brain regions, or expensive magnetic resonance. Decades ago, inhalation gas clearance was the choice method of quantifying CBF, but this suffered from poor temporal resolution. Electrolytic H2 clearance (EHC) generates and collects gas in situ at an electrode pair, which improves temporal resolution, but the probe size has prohibited meaningful subcortical use. New Method We microfabricated EHC electrodes to an order of magnitude smaller than those existing, on the scale of 100 µm, to permit use deep within the brain. Results Novel EHC probes were fabricated. The devices offered exceptional signal-to-noise, achieved high collection efficiencies (40 – 50%) in vitro, and agreed with theoretical modeling. An in vitro chemical reaction model was used to confirm that our devices detected flow rates higher than those expected physiologically. Computational modeling that incorporated realistic noise levels demonstrated devices would be sensitive to physiological CBF rates. Comparison with Existing Method The reduced size of our arrays makes them suitable for subcortical EHC measurements, as opposed to the larger, existing EHC electrodes that would cause substantial tissue damage. Our array can collect multiple CBF measurements per minute, and can thus resolve physiological changes occurring on a shorter timescale than existing gas clearance measurements. Conclusion We present and characterize microfabricated EHC electrodes and an accompanying theoretical model to interpret acquired data. Microfabrication allows for the high-throughput production of reproducible devices that are capable of monitoring deep brain CBF with sub-minute resolution. PMID:27102042

[Place of indocyanine green coupled with fluorescence imaging in research of breast cancer sentinel node].

PubMed

Vermersch, Charlotte; Raia Barjat, Tiphaine; Perrot, Marianne; Lima, Suzanne; Chauleur, Céline

2016-04-01

The sentinel node has a fundamental role in the management of early breast cancer. Currently, the double detection of blue and radioisotope is recommended. But in common practice, many centers use a single method. However, with a single detection, the risk of false negatives and the identification failure rate increase to a significant extent and the number of sentinel lymph node detected and removed is not enough. Furthermore, the tracers used until now show inconveniences. The purpose of this work is to present a new method of detection, using the green of indocyanine coupled with fluorescence imaging, and to compare it with the already existing methods. The method combined by fluorescence and isotopic is reliable, sure, of fast learning and could constitute a good strategy of detection. The major interest is to obtain a satisfactory number of sentinel nodes. The profit could be even more important for overweight patients. The fluorescence used alone is at the moment not possible. Wide ranging studies are necessary. The FLUOTECH, randomized study of 100 patients, comparing the isotopic method of double isotope technique and fluorescence, is underway to confirm these data. Copyright © 2016 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

A New Cloud and Aerosol Layer Detection Method Based on Micropulse Lidar Measurements

NASA Astrophysics Data System (ADS)

Wang, Q.; Zhao, C.; Wang, Y.; Li, Z.; Wang, Z.; Liu, D.

2014-12-01

A new algorithm is developed to detect aerosols and clouds based on micropulse lidar (MPL) measurements. In this method, a semi-discretization processing (SDP) technique is first used to inhibit the impact of increasing noise with distance, then a value distribution equalization (VDE) method is introduced to reduce the magnitude of signal variations with distance. Combined with empirical threshold values, clouds and aerosols are detected and separated. This method can detect clouds and aerosols with high accuracy, although classification of aerosols and clouds is sensitive to the thresholds selected. Compared with the existing Atmospheric Radiation Measurement (ARM) program lidar-based cloud product, the new method detects more high clouds. The algorithm was applied to a year of observations at both the U.S. Southern Great Plains (SGP) and China Taihu site. At SGP, the cloud frequency shows a clear seasonal variation with maximum values in winter and spring, and shows bi-modal vertical distributions with maximum frequency at around 3-6 km and 8-12 km. The annual averaged cloud frequency is about 50%. By contrast, the cloud frequency at Taihu shows no clear seasonal variation and the maximum frequency is at around 1 km. The annual averaged cloud frequency is about 15% higher than that at SGP.

Robust foreground detection: a fusion of masked grey world, probabilistic gradient information and extended conditional random field approach.

PubMed

Zulkifley, Mohd Asyraf; Moran, Bill; Rawlinson, David

2012-01-01

Foreground detection has been used extensively in many applications such as people counting, traffic monitoring and face recognition. However, most of the existing detectors can only work under limited conditions. This happens because of the inability of the detector to distinguish foreground and background pixels, especially in complex situations. Our aim is to improve the robustness of foreground detection under sudden and gradual illumination change, colour similarity issue, moving background and shadow noise. Since it is hard to achieve robustness using a single model, we have combined several methods into an integrated system. The masked grey world algorithm is introduced to handle sudden illumination change. Colour co-occurrence modelling is then fused with the probabilistic edge-based background modelling. Colour co-occurrence modelling is good in filtering moving background and robust to gradual illumination change, while an edge-based modelling is used for solving a colour similarity problem. Finally, an extended conditional random field approach is used to filter out shadow and afterimage noise. Simulation results show that our algorithm performs better compared to the existing methods, which makes it suitable for higher-level applications.

Comparison the percentage of detection of periarthritis in patients with rheumatoid arthritis using clinical examination or ultrasound methods

PubMed Central

Karimzadeh, Hadi; Seyedbonakdar, Zahra; Mousavi, Maryam; Karami, Mehdi

2016-01-01

Background: This study aimed to compare the percentage of detection of periarthritis in patients with rheumatoid arthritis using clinical examination and ultrasound methods. Materials and Methods: This study is a cross-sectional study which was conducted in Al-Zahra Hospital (Isfahan, Iran) during 2014–2015. In our study, ninety patients were selected based on the American College of Rheumatology 2010 criteria. All patients were examined by a rheumatologist to find the existence of effusion, and the data were filled in the checklist. The ultrasonography for detecting effusion in periarticular structures was done by an expert radiologist with two methods, including high-resolution ultrasonography and power Doppler. The percentage of effusion existence found by physical examination was compared by sonography, and the Chi-square and t-tests were used for data analysis. Results: The percentage of effusion found in areas with physical examination by rheumatologist was lower than the frequency distribution of effusions found by sonography (8.3% VS 14.2%) (P < 0.001). In sonography, rotator cuff tendonitis is the most common periarthritis. Other findings in sonography were biceps tendinitis (10 cases), wrist tendonitis (13 cases), olecranon bursitis (9 cases), golfers elbow (4 cases), tennis elbow (4 cases), trochanteric bursitis (6 cases), anserine bursitis (6 cases), prepatellar bursitis (11 cases), and ankle tendonitis (7 cases). Tenderness on physical examination was found in 15% of the cases, and the evidence of periarthritis was found in 21/7% through sonography (P < 0.001) and 34% through Doppler sonography (P < 0.001). Conclusion: The percentage of periarthritis detection by ultrasonography and power Doppler sonography was higher than clinical examination. Hence, the ultrasonography is more accurate than physical examination. PMID:28331520

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.

Design of temperature detection device for drum of belt conveyor

NASA Astrophysics Data System (ADS)

Zhang, Li; He, Rongjun

2018-03-01

For difficult wiring and big measuring error existed in the traditional temperature detection method for drum of belt conveyor, a temperature detection device for drum of belt conveyor based on Radio Frequency(RF) communication is designed. In the device, detection terminal can collect temperature data through tire pressure sensor chip SP370 which integrates temperature detection and RF emission. The receiving terminal which is composed of RF receiver chip and microcontroller receives the temperature data and sends it to Controller Area Network(CAN) bus. The test results show that the device meets requirements of field application with measuring error ±3.73 ° and single button battery can provide continuous current for the detection terminal over 1.5 years.

Predicting chaos in memristive oscillator via harmonic balance method.

PubMed

Wang, Xin; Li, Chuandong; Huang, Tingwen; Duan, Shukai

2012-12-01

This paper studies the possible chaotic behaviors in a memristive oscillator with cubic nonlinearities via harmonic balance method which is also called the method of describing function. This method was proposed to detect chaos in classical Chua's circuit. We first transform the considered memristive oscillator system into Lur'e model and present the prediction of the existence of chaotic behaviors. To ensure the prediction result is correct, the distortion index is also measured. Numerical simulations are presented to show the effectiveness of theoretical results.

Unsupervised detection of salt marsh platforms: a topographic method

NASA Astrophysics Data System (ADS)

Goodwin, Guillaume C. H.; Mudd, Simon M.; Clubb, Fiona J.

2018-03-01

Salt marshes filter pollutants, protect coastlines against storm surges, and sequester carbon, yet are under threat from sea level rise and anthropogenic modification. The sustained existence of the salt marsh ecosystem depends on the topographic evolution of marsh platforms. Quantifying marsh platform topography is vital for improving the management of these valuable landscapes. The determination of platform boundaries currently relies on supervised classification methods requiring near-infrared data to detect vegetation, or demands labour-intensive field surveys and digitisation. We propose a novel, unsupervised method to reproducibly isolate salt marsh scarps and platforms from a digital elevation model (DEM), referred to as Topographic Identification of Platforms (TIP). Field observations and numerical models show that salt marshes mature into subhorizontal platforms delineated by subvertical scarps. Based on this premise, we identify scarps as lines of local maxima on a slope raster, then fill landmasses from the scarps upward, thus isolating mature marsh platforms. We test the TIP method using lidar-derived DEMs from six salt marshes in England with varying tidal ranges and geometries, for which topographic platforms were manually isolated from tidal flats. Agreement between manual and unsupervised classification exceeds 94 % for DEM resolutions of 1 m, with all but one site maintaining an accuracy superior to 90 % for resolutions up to 3 m. For resolutions of 1 m, platforms detected with the TIP method are comparable in surface area to digitised platforms and have similar elevation distributions. We also find that our method allows for the accurate detection of local block failures as small as 3 times the DEM resolution. Detailed inspection reveals that although tidal creeks were digitised as part of the marsh platform, unsupervised classification categorises them as part of the tidal flat, causing an increase in false negatives and overall platform perimeter. This suggests our method may benefit from combination with existing creek detection algorithms. Fallen blocks and high tidal flat portions, associated with potential pioneer zones, can also lead to differences between our method and supervised mapping. Although pioneer zones prove difficult to classify using a topographic method, we suggest that these transition areas should be considered when analysing erosion and accretion processes, particularly in the case of incipient marsh platforms. Ultimately, we have shown that unsupervised classification of marsh platforms from high-resolution topography is possible and sufficient to monitor and analyse topographic evolution.

Design of on line detection system for static evaporation rate of LNG vehicle cylinders

NASA Astrophysics Data System (ADS)

Tang, P.; Wang, M.; Tan, W. H.; Ling, Z. W.; Li, F.

2017-06-01

In order to solve the problems existing in the regular inspection of LNG vehicle cylinders, the static evaporation rate on line detection system of LNG cylinders is discussed in this paper. A non-disassembling, short-term and high-efficiency on line detection system for LNG vehicle cylinders is proposed, which can meet the requirement of evaporation rate test under different media and different test pressures. And then test methods under the experimental conditions, atmospheric pressure and pressure are given respectively. This online detection system designed in this paper can effectively solve the technical problems during the inspection of the cylinder.

Mutual Comparative Filtering for Change Detection in Videos with Unstable Illumination Conditions

NASA Astrophysics Data System (ADS)

Sidyakin, Sergey V.; Vishnyakov, Boris V.; Vizilter, Yuri V.; Roslov, Nikolay I.

2016-06-01

In this paper we propose a new approach for change detection and moving objects detection in videos with unstable, abrupt illumination changes. This approach is based on mutual comparative filters and background normalization. We give the definitions of mutual comparative filters and outline their strong advantage for change detection purposes. Presented approach allows us to deal with changing illumination conditions in a simple and efficient way and does not have drawbacks, which exist in models that assume different color transformation laws. The proposed procedure can be used to improve a number of background modelling methods, which are not specifically designed to work under illumination changes.

Recent advances in immunosensor for narcotic drug detection

PubMed Central

Gandhi, Sonu; Suman, Pankaj; Kumar, Ashok; Sharma, Prince; Capalash, Neena; Suri, C. Raman

2015-01-01

Introduction: Immunosensor for illicit drugs have gained immense interest and have found several applications for drug abuse monitoring. This technology has offered a low cost detection of narcotics; thereby, providing a confirmatory platform to compliment the existing analytical methods. Methods: In this minireview, we define the basic concept of transducer for immunosensor development that utilizes antibodies and low molecular mass hapten (opiate) molecules. Results: This article emphasizes on recent advances in immunoanalytical techniques for monitoring of opiate drugs. Our results demonstrate that high quality antibodies can be used for immunosensor development against target analyte with greater sensitivity, specificity and precision than other available analytical methods. Conclusion: In this review we highlight the fundamentals of different transducer technologies and its applications for immunosensor development currently being developed in our laboratory using rapid screening via immunochromatographic kit, label free optical detection via enzyme, fluorescence, gold nanoparticles and carbon nanotubes based immunosensing for sensitive and specific monitoring of opiates. PMID:26929925

Automatically generated acceptance test: A software reliability experiment

NASA Technical Reports Server (NTRS)

Protzel, Peter W.

1988-01-01

This study presents results of a software reliability experiment investigating the feasibility of a new error detection method. The method can be used as an acceptance test and is solely based on empirical data about the behavior of internal states of a program. The experimental design uses the existing environment of a multi-version experiment previously conducted at the NASA Langley Research Center, in which the launch interceptor problem is used as a model. This allows the controlled experimental investigation of versions with well-known single and multiple faults, and the availability of an oracle permits the determination of the error detection performance of the test. Fault interaction phenomena are observed that have an amplifying effect on the number of error occurrences. Preliminary results indicate that all faults examined so far are detected by the acceptance test. This shows promise for further investigations, and for the employment of this test method on other applications.

Sensitive indoor air monitoring of formaldehyde and other carbonyl compounds using the 2,4-dinitrophenylhydrazine method.

PubMed

Sandner, F; Dott, W; Hollender, J

2001-03-01

The toxic potential of formaldehyde and other aliphatic/aromatic carbonyl compounds requires the determination of even low amounts of these compounds in indoor air. The existing DFG-method for workplace monitoring using adsorption at 2,4-dinitrophenylhydrazine (DNPH)-coated sorbents followed by HPLC-UV/DAD analysis of the extract was modified in order to decrease detection limits. The improvement included an increase in volume and rate of the air sampling, testing applicability of different adsorption materials and a decrease of the extraction volume of the hydrazones. 13 DNPH-derivatives could be separated well on a RP18-column followed by UV/DAD-detection at 365 nm. Recovery rates of 70-100% were determined (apart from acetone with 19%) using dynamically produced artifical carbonyl atmospheres. Detection limits of 0.05-0.4 microgram/m3 were reached by this method which are sufficient for indoor air monitoring.

Stellar Wakes from Dark Matter Subhalos.

PubMed

Buschmann, Malte; Kopp, Joachim; Safdi, Benjamin R; Wu, Chih-Liang

2018-05-25

We propose a novel method utilizing stellar kinematic data to detect low-mass substructure in the Milky Way's dark matter halo. By probing characteristic wakes that a passing dark matter subhalo leaves in the phase-space distribution of ambient halo stars, we estimate sensitivities down to subhalo masses of ∼10^{7}  M_{⊙} or below. The detection of such subhalos would have implications for dark matter and cosmological models that predict modifications to the halo-mass function at low halo masses. We develop an analytic formalism for describing the perturbed stellar phase-space distributions, and we demonstrate through idealized simulations the ability to detect subhalos using the phase-space model and a likelihood framework. Our method complements existing methods for low-mass subhalo searches, such as searches for gaps in stellar streams, in that we can localize the positions and velocities of the subhalos today.

Vision-Based Real-Time Traversable Region Detection for Mobile Robot in the Outdoors.

PubMed

Deng, Fucheng; Zhu, Xiaorui; He, Chao

2017-09-13

Environment perception is essential for autonomous mobile robots in human-robot coexisting outdoor environments. One of the important tasks for such intelligent robots is to autonomously detect the traversable region in an unstructured 3D real world. The main drawback of most existing methods is that of high computational complexity. Hence, this paper proposes a binocular vision-based, real-time solution for detecting traversable region in the outdoors. In the proposed method, an appearance model based on multivariate Gaussian is quickly constructed from a sample region in the left image adaptively determined by the vanishing point and dominant borders. Then, a fast, self-supervised segmentation scheme is proposed to classify the traversable and non-traversable regions. The proposed method is evaluated on public datasets as well as a real mobile robot. Implementation on the mobile robot has shown its ability in the real-time navigation applications.

Single-channel EEG-based mental fatigue detection based on deep belief network.

PubMed

Pinyi Li; Wenhui Jiang; Fei Su

2016-08-01

Mental fatigue has a pernicious influence on road and work place safety as well as a negative symptom of many acute and chronic illnesses, since the ability of concentrating, responding and judging quickly decreases during the fatigue or drowsiness stage. Electroencephalography (EEG) has been proven to be a robust physiological indicator of human cognitive state over the last few decades. But most existing EEG-based fatigue detection methods have poor performance in accuracy. This paper proposed a single-channel EEG-based mental fatigue detection method based on Deep Belief Network (DBN). The fused nonliear features from specified sub-bands and dynamic analysis, a total of 21 features are extracted as the input of the DBN to discriminate three classes of mental state including alert, slight fatigue and severe fatigue. Experimental results show the good performance of the proposed model comparing with those state-of-art methods.

Circulating tumour cells, their role in metastasis and their clinical utility in lung cancer.

PubMed

O'Flaherty, John D; Gray, Steven; Richard, Derek; Fennell, Dean; O'Leary, John J; Blackhall, Fiona H; O'Byrne, Kenneth J

2012-04-01

Circulating tumour cells (CTCs) have attracted much recent interest in cancer research as a potential biomarker and as a means of studying the process of metastasis. It has long been understood that metastasis is a hallmark of malignancy, and conceptual theories on the basis of metastasis from the nineteenth century foretold the existence of a tumour "seed" which is capable of establishing discrete tumours in the "soil" of distant organs. This prescient "seed and soil" hypothesis accurately predicted the existence of CTCs; microscopic tumour fragments in the blood, at least some of which are capable of forming metastases. However, it is only in recent years that reliable, reproducible methods of CTC detection and analysis have been developed. To date, the majority of studies have employed the CellSearch™ system (Veridex LLC), which is an immunomagnetic purification method. Other promising techniques include microfluidic filters, isolation of tumour cells by size using microporous polycarbonate filters and flow cytometry-based approaches. While many challenges still exist, the detection of CTCs in blood is becoming increasingly feasible, giving rise to some tantalizing questions about the use of CTCs as a potential biomarker. CTC enumeration has been used to guide prognosis in patients with metastatic disease, and to act as a surrogate marker for disease response during therapy. Other possible uses for CTC detection include prognostication in early stage patients, identifying patients requiring adjuvant therapy, or in surveillance, for the detection of relapsing disease. Another exciting possible use for CTC detection assays is the molecular and genetic characterization of CTCs to act as a "liquid biopsy" representative of the primary tumour. Indeed it has already been demonstrated that it is possible to detect HER2, KRAS and EGFR mutation status in breast, colon and lung cancer CTCs respectively. In the course of this review, we shall discuss the biology of CTCs and their role in metastagenesis, the most commonly used techniques for their detection and the evidence to date of their clinical utility, with particular reference to lung cancer. Copyright © 2012. Published by Elsevier Ireland Ltd.

A novel alignment-free method for detection of lateral genetic transfer based on TF-IDF.

PubMed

Cong, Yingnan; Chan, Yao-Ban; Ragan, Mark A

2016-07-25

Lateral genetic transfer (LGT) plays an important role in the evolution of microbes. Existing computational methods for detecting genomic regions of putative lateral origin scale poorly to large data. Here, we propose a novel method based on TF-IDF (Term Frequency-Inverse Document Frequency) statistics to detect not only regions of lateral origin, but also their origin and direction of transfer, in sets of hierarchically structured nucleotide or protein sequences. This approach is based on the frequency distributions of k-mers in the sequences. If a set of contiguous k-mers appears sufficiently more frequently in another phyletic group than in its own, we infer that they have been transferred from the first group to the second. We performed rigorous tests of TF-IDF using simulated and empirical datasets. With the simulated data, we tested our method under different parameter settings for sequence length, substitution rate between and within groups and post-LGT, deletion rate, length of transferred region and k size, and found that we can detect LGT events with high precision and recall. Our method performs better than an established method, ALFY, which has high recall but low precision. Our method is efficient, with runtime increasing approximately linearly with sequence length.

Challenging a bioinformatic tool's ability to detect microbial contaminants using in silico whole genome sequencing data.

PubMed

Olson, Nathan D; Zook, Justin M; Morrow, Jayne B; Lin, Nancy J

2017-01-01

High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants. Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant. Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need for a priori assumptions about the contaminant. Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives. Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm. Simulated WGS datasets from ten genera as individuals and binary mixtures of eight organisms at varying ratios were analyzed to evaluate the role of contaminant concentration and taxonomy on detection. For the individual genomes the false positive contaminants reported depended on the genus, with Staphylococcus , Escherichia , and Shigella having the highest proportion of false positives. For nearly all binary mixtures the contaminant was detected in the in-silico datasets at the equivalent of 1 in 1,000 cells, though F. tularensis was not detected in any of the simulated contaminant mixtures and Y. pestis was only detected at the equivalent of one in 10 cells. Once a WGS method for detecting contaminants is characterized, it can be applied to evaluate microbial material purity, in efforts to ensure that contaminants are characterized in microbial materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods.

Earthquake recording at the Stanford DAS Array with fibers in existing telecomm conduits

NASA Astrophysics Data System (ADS)

Biondi, B. C.; Martin, E. R.; Yuan, S.; Cole, S.; Karrenbach, M. H.

2017-12-01

The Stanford Distributed Acoustic Sensing Array (SDASA-1) has been continuously recording seismic data since September 2016 on 2.5 km of single mode fiber optics in existing telecommunications conduits under Stanford's campus. The array is figure-eight shaped and roughly 600 m along its widest side with a channel spacing of roughly 8 m. This array is easy to maintain and is nonintrusive, making it well suited to urban environments, but it sacrifices some cable-to-ground coupling compared to more traditional seismometers. We have been testing its utility for earthquake recording, active seismic, and ambient noise interferometry. This talk will focus on earthquake observations. We will show comparisons between the strain rates measured throughout the DAS array and the particle velocities measured at the nearby Jasper Ridge Seismic Station (JRSC). In some of these events, we will point out directionality features specific to DAS that can require slight modifications in data processing. We also compare repeatability of DAS and JRSC recordings of blasts from a nearby quarry. Using existing earthquake databases, we have created a small catalog of DAS earthquake observations by pulling records of over 700 Northern California events spanning Sep. 2016 to Jul. 2017 from both the DAS data and JRSC. On these events we have tested common array methods for earthquake detection and location including beamforming and STA/LTA analysis in time and frequency. We have analyzed these events to approximate thresholds on what distances and magnitudes are clearly detectible by the DAS array. Further analysis should be done on detectability with methods tailored to small events (for example, template matching). In creating this catalog, we have developed open source software available for free download that can manage large sets of continuous seismic data files (both existing files, and files as they stream in). This software can both interface with existing earthquake networks, and efficiently extract earthquake recordings from many continuous recordings saved on the users machines.

PolyaPeak: Detecting Transcription Factor Binding Sites from ChIP-seq Using Peak Shape Information

PubMed Central

Wu, Hao; Ji, Hongkai

2014-01-01

ChIP-seq is a powerful technology for detecting genomic regions where a protein of interest interacts with DNA. ChIP-seq data for mapping transcription factor binding sites (TFBSs) have a characteristic pattern: around each binding site, sequence reads aligned to the forward and reverse strands of the reference genome form two separate peaks shifted away from each other, and the true binding site is located in between these two peaks. While it has been shown previously that the accuracy and resolution of binding site detection can be improved by modeling the pattern, efficient methods are unavailable to fully utilize that information in TFBS detection procedure. We present PolyaPeak, a new method to improve TFBS detection by incorporating the peak shape information. PolyaPeak describes peak shapes using a flexible Pólya model. The shapes are automatically learnt from the data using Minorization-Maximization (MM) algorithm, then integrated with the read count information via a hierarchical model to distinguish true binding sites from background noises. Extensive real data analyses show that PolyaPeak is capable of robustly improving TFBS detection compared with existing methods. An R package is freely available. PMID:24608116

The performance of matched-field track-before-detect methods using shallow-water Pacific data.

PubMed

Tantum, Stacy L; Nolte, Loren W; Krolik, Jeffrey L; Harmanci, Kerem

2002-07-01

Matched-field track-before-detect processing, which extends the concept of matched-field processing to include modeling of the source dynamics, has recently emerged as a promising approach for maintaining the track of a moving source. In this paper, optimal Bayesian and minimum variance beamforming track-before-detect algorithms which incorporate a priori knowledge of the source dynamics in addition to the underlying uncertainties in the ocean environment are presented. A Markov model is utilized for the source motion as a means of capturing the stochastic nature of the source dynamics without assuming uniform motion. In addition, the relationship between optimal Bayesian track-before-detect processing and minimum variance track-before-detect beamforming is examined, revealing how an optimal tracking philosophy may be used to guide the modification of existing beamforming techniques to incorporate track-before-detect capabilities. Further, the benefits of implementing an optimal approach over conventional methods are illustrated through application of these methods to shallow-water Pacific data collected as part of the SWellEX-1 experiment. The results show that incorporating Markovian dynamics for the source motion provides marked improvement in the ability to maintain target track without the use of a uniform velocity hypothesis.

Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals

PubMed Central

Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

2015-01-01

The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments. PMID:26473858

Detecting Surgical Tools by Modelling Local Appearance and Global Shape.

PubMed

Bouget, David; Benenson, Rodrigo; Omran, Mohamed; Riffaud, Laurent; Schiele, Bernt; Jannin, Pierre

2015-12-01

Detecting tools in surgical videos is an important ingredient for context-aware computer-assisted surgical systems. To this end, we present a new surgical tool detection dataset and a method for joint tool detection and pose estimation in 2d images. Our two-stage pipeline is data-driven and relaxes strong assumptions made by previous works regarding the geometry, number, and position of tools in the image. The first stage classifies each pixel based on local appearance only, while the second stage evaluates a tool-specific shape template to enforce global shape. Both local appearance and global shape are learned from training data. Our method is validated on a new surgical tool dataset of 2 476 images from neurosurgical microscopes, which is made freely available. It improves over existing datasets in size, diversity and detail of annotation. We show that our method significantly improves over competitive baselines from the computer vision field. We achieve 15% detection miss-rate at 10(-1) false positives per image (for the suction tube) over our surgical tool dataset. Results indicate that performing semantic labelling as an intermediate task is key for high quality detection.

Hyperspectral Image-Based Night-Time Vehicle Light Detection Using Spectral Normalization and Distance Mapper for Intelligent Headlight Control

PubMed Central

Kim, Heekang; Kwon, Soon; Kim, Sungho

2016-01-01

This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD) or Complementary metal-Oxide-Semiconductor (CMOS) camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC), the vehicle headlights need to be detected. Headlights are comprised from a variety of lighting sources, such as Light Emitting Diodes (LEDs), High-intensity discharge (HID), and halogen lamps. In addition, rear lamps are made of LED and halogen lamp. This paper refers to the recent research in IHC. Some problems exist in the detection of headlights, such as erroneous detection of street lights or sign lights and the reflection plate of ego-car from CCD or CMOS images. To solve these problems, this study uses hyperspectral images because they have hundreds of bands and provide more information than a CCD or CMOS camera. Recent methods to detect headlights used the Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), and Euclidean Distance Mapper (EDM). The experimental results highlight the feasibility of the proposed method in three types of lights (LED, HID, and halogen). PMID:27399720

A Hopfield neural network for image change detection.

PubMed

Pajares, Gonzalo

2006-09-01

This paper outlines an optimization relaxation approach based on the analog Hopfield neural network (HNN) for solving the image change detection problem between two images. A difference image is obtained by subtracting pixel by pixel both images. The network topology is built so that each pixel in the difference image is a node in the network. Each node is characterized by its state, which determines if a pixel has changed. An energy function is derived, so that the network converges to stable states. The analog Hopfield's model allows each node to take on analog state values. Unlike most widely used approaches, where binary labels (changed/unchanged) are assigned to each pixel, the analog property provides the strength of the change. The main contribution of this paper is reflected in the customization of the analog Hopfield neural network to derive an automatic image change detection approach. When a pixel is being processed, some existing image change detection procedures consider only interpixel relations on its neighborhood. The main drawback of such approaches is the labeling of this pixel as changed or unchanged according to the information supplied by its neighbors, where its own information is ignored. The Hopfield model overcomes this drawback and for each pixel allows a tradeoff between the influence of its neighborhood and its own criterion. This is mapped under the energy function to be minimized. The performance of the proposed method is illustrated by comparative analysis against some existing image change detection methods.

Wavefront reconstruction method based on wavelet fractal interpolation for coherent free space optical communication

NASA Astrophysics Data System (ADS)

Zhang, Dai; Hao, Shiqi; Zhao, Qingsong; Zhao, Qi; Wang, Lei; Wan, Xiongfeng

2018-03-01

Existing wavefront reconstruction methods are usually low in resolution, restricted by structure characteristics of the Shack Hartmann wavefront sensor (SH WFS) and the deformable mirror (DM) in the adaptive optics (AO) system, thus, resulting in weak homodyne detection efficiency for free space optical (FSO) communication. In order to solve this problem, we firstly validate the feasibility of liquid crystal spatial light modulator (LC SLM) using in an AO system. Then, wavefront reconstruction method based on wavelet fractal interpolation is proposed after self-similarity analysis of wavefront distortion caused by atmospheric turbulence. Fast wavelet decomposition is operated to multiresolution analyze the wavefront phase spectrum, during which soft threshold denoising is carried out. The resolution of estimated wavefront phase is then improved by fractal interpolation. Finally, fast wavelet reconstruction is taken to recover wavefront phase. Simulation results reflect the superiority of our method in homodyne detection. Compared with minimum variance estimation (MVE) method based on interpolation techniques, the proposed method could obtain superior homodyne detection efficiency with lower operation complexity. Our research findings have theoretical significance in the design of coherent FSO communication system.

A PROBABILISTIC METHOD FOR ESTIMATING MONITORING POINT DENSITY FOR CONTAINMENT SYSTEM LEAK DETECTION

EPA Science Inventory

The use of physical and hydraulic containment systems for the isolation of contaminated ground water and aquifer materials ssociated with hazardous waste sites has increased during the last decade. The existing methodologies for monitoring and evaluating leakage from hazardous w...

Multiplexed biosensors for detection of mycotoxins

USDA-ARS?s Scientific Manuscript database

As analytical methods have improved it has become apparent that mycotoxins exist in many forms within a commodity or food. For the established toxins there has been increased interest in the presence of metabolites that might also harbor toxicity. These include biosynthetic precursors as well as pro...

Spatio-temporal cluster detection of chickenpox in Valencia, Spain in the period 2008-2012.

PubMed

Iftimi, Adina; Martínez-Ruiz, Francisco; Míguez Santiyán, Ana; Montes, Francisco

2015-05-18

Chickenpox is a highly contagious airborne disease caused by Varicella zoster, which affects nearly all non-immune children worldwide with an annual incidence estimated at 80-90 million cases. To analyze the spatiotemporal pattern of the chickenpox incidence in the city of Valencia, Spain two complementary statistical approaches were used. First, we evaluated the existence of clusters and spatio-temporal interaction; secondly, we used this information to find the locations of the spatio-temporal clusters via the space-time permutation model. The first method used detects any aggregation in our data but does not provide the spatial and temporal information. The second method gives the locations, areas and time-frame for the spatio-temporal clusters. An overall decreasing time trend, a pronounced 12-monthly periodicity and two complementary periods were observed. Several areas with high incidence, surrounding the center of the city were identified. The existence of aggregation in time and space was observed, and a number of spatio-temporal clusters were located.

A real-time PCR diagnostic method for detection of Naegleria fowleri.

PubMed

Madarová, Lucia; Trnková, Katarína; Feiková, Sona; Klement, Cyril; Obernauerová, Margita

2010-09-01

Naegleria fowleri is a free-living amoeba that can cause primary amoebic meningoencephalitis (PAM). While, traditional methods for diagnosing PAM still rely on culture, more current laboratory diagnoses exist based on conventional PCR methods; however, only a few real-time PCR processes have been described as yet. Here, we describe a real-time PCR-based diagnostic method using hybridization fluorescent labelled probes, with a LightCycler instrument and accompanying software (Roche), targeting the Naegleria fowleriMp2Cl5 gene sequence. Using this method, no cross reactivity with other tested epidemiologically relevant prokaryotic and eukaryotic organisms was found. The reaction detection limit was 1 copy of the Mp2Cl5 DNA sequence. This assay could become useful in the rapid laboratory diagnostic assessment of the presence or absence of Naegleria fowleri. Copyright 2009 Elsevier Inc. All rights reserved.

Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens

PubMed Central

Yin, Zheng; Zhou, Xiaobo; Bakal, Chris; Li, Fuhai; Sun, Youxian; Perrimon, Norbert; Wong, Stephen TC

2008-01-01

Background The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens. Results Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms. Conclusion We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens. PMID:18534020

Smart accelerometer

NASA Astrophysics Data System (ADS)

Bozeman, Richard J., Jr.

1992-02-01

The invention discloses methods and apparatus for detecting vibrations from machines which indicate an impending malfunction for the purpose of preventing additional damage and allowing for an orderly shutdown or a change in mode of operation. The method and apparatus is especially suited for reliable operation in providing thruster control data concerning unstable vibration in an electrical environment which is typically noisy and in which unrecognized ground loops may exist.

Smart accelerometer

NASA Astrophysics Data System (ADS)

Bozeman, Richard J., Jr.

1994-05-01

The invention discloses methods and apparatus for detecting vibrations from machines which indicate an impending malfunction for the purpose of preventing additional damage and allowing for an orderly shutdown or a change in mode of operation. The method and apparatus is especially suited for reliable operation in providing thruster control data concerning unstable vibration in an electrical environment which is typically noisy and in which unrecognized ground loops may exist.

Emissivity corrected infrared method for imaging anomalous structural heat flows

DOEpatents

Del Grande, Nancy K.; Durbin, Philip F.; Dolan, Kenneth W.; Perkins, Dwight E.

1995-01-01

A method for detecting flaws in structures using dual band infrared radiation. Heat is applied to the structure being evaluated. The structure is scanned for two different wavelengths and data obtained in the form of images. Images are used to remove clutter to form a corrected image. The existence and nature of a flaw is determined by investigating a variety of features.

A conceptual guide to detection probability for point counts and other count-based survey methods

Treesearch

D. Archibald McCallum

2005-01-01

Accurate and precise estimates of numbers of animals are vitally needed both to assess population status and to evaluate management decisions. Various methods exist for counting birds, but most of those used with territorial landbirds yield only indices, not true estimates of population size. The need for valid density estimates has spawned a number of models for...

Ion mobility spectrometer, spectrometer analyte detection and identification verification system, and method

DOEpatents

Atkinson, David A.

2002-01-01

Methods and apparatus for ion mobility spectrometry and analyte detection and identification verification system are disclosed. The apparatus is configured to be used in an ion mobility spectrometer and includes a plurality of reactant reservoirs configured to contain a plurality of reactants which can be reacted with the sample to form adducts having varying ion mobilities. A carrier fluid, such as air or nitrogen, is used to carry the sample into the spectrometer. The plurality of reactants are configured to be selectively added to the carrier stream by use inlet and outlet manifolds in communication with the reagent reservoirs, the reservoirs being selectively isolatable by valves. The invention further includes a spectrometer having the reagent system described. In the method, a first reactant is used with the sample. Following a positive result, a second reactant is used to determine whether a predicted response occurs. The occurrence of the second predicted response tends to verify the existence of a component of interest within the sample. A third reactant can also be used to provide further verification of the existence of a component of interest. A library can be established of known responses of compounds of interest with various reactants and the results of a specific multi-reactant survey of a sample can be compared against the library to determine whether a component detected in the sample is likely to be a specific component of interest.

Depleted uranium contamination by inhalation exposure and its detection after approximately 20 years: implications for human health assessment.

PubMed

Parrish, Randall R; Horstwood, Matthew; Arnason, John G; Chenery, Simon; Brewer, Tim; Lloyd, Nicholas S; Carpenter, David O

2008-02-01

Inhaled depleted uranium (DU) aerosols are recognised as a distinct human health hazard and DU has been suggested to be responsible in part for illness in both military and civilian populations that may have been exposed. This study aimed to develop and use a testing procedure capable of detecting an individual's historic milligram-quantity aerosol exposure to DU up to 20 years after the event. This method was applied to individuals associated with or living proximal to a DU munitions plant in Colonie New York that were likely to have had a significant DU aerosol inhalation exposure, in order to improve DU-exposure screening reliability and gain insight into the residence time of DU in humans. We show using sensitive mass spectrometric techniques that when exposure to aerosol has been unambiguous and in sufficient quantity, urinary excretion of DU can be detected more than 20 years after primary DU inhalation contamination ceased, even when DU constitutes only approximately 1% of the total excreted uranium. It seems reasonable to conclude that a chronically DU-exposed population exists within the contamination 'footprint' of the munitions plant in Colonie, New York. The method allows even a modest DU exposure to be identified where other less sensitive methods would have failed entirely. This should allow better assessment of historical exposure incidence than currently exists.

An effective and efficient compression algorithm for ECG signals with irregular periods.

PubMed

Chou, Hsiao-Hsuan; Chen, Ying-Jui; Shiau, Yu-Chien; Kuo, Te-Son

2006-06-01

This paper presents an effective and efficient preprocessing algorithm for two-dimensional (2-D) electrocardiogram (ECG) compression to better compress irregular ECG signals by exploiting their inter- and intra-beat correlations. To better reveal the correlation structure, we first convert the ECG signal into a proper 2-D representation, or image. This involves a few steps including QRS detection and alignment, period sorting, and length equalization. The resulting 2-D ECG representation is then ready to be compressed by an appropriate image compression algorithm. We choose the state-of-the-art JPEG2000 for its high efficiency and flexibility. In this way, the proposed algorithm is shown to outperform some existing arts in the literature by simultaneously achieving high compression ratio (CR), low percent root mean squared difference (PRD), low maximum error (MaxErr), and low standard derivation of errors (StdErr). In particular, because the proposed period sorting method rearranges the detected heartbeats into a smoother image that is easier to compress, this algorithm is insensitive to irregular ECG periods. Thus either the irregular ECG signals or the QRS false-detection cases can be better compressed. This is a significant improvement over existing 2-D ECG compression methods. Moreover, this algorithm is not tied exclusively to JPEG2000. It can also be combined with other 2-D preprocessing methods or appropriate codecs to enhance the compression performance in irregular ECG cases.

Subpixel Mapping of Hyperspectral Image Based on Linear Subpixel Feature Detection and Object Optimization

NASA Astrophysics Data System (ADS)

Liu, Zhaoxin; Zhao, Liaoying; Li, Xiaorun; Chen, Shuhan

2018-04-01

Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning

PubMed Central

Zhang, Yudong; Dong, Zhengchao; Phillips, Preetha; Wang, Shuihua; Ji, Genlin; Yang, Jiquan; Yuan, Ti-Fei

2015-01-01

Purpose: Early diagnosis or detection of Alzheimer's disease (AD) from the normal elder control (NC) is very important. However, the computer-aided diagnosis (CAD) was not widely used, and the classification performance did not reach the standard of practical use. We proposed a novel CAD system for MR brain images based on eigenbrains and machine learning with two goals: accurate detection of both AD subjects and AD-related brain regions. Method: First, we used maximum inter-class variance (ICV) to select key slices from 3D volumetric data. Second, we generated an eigenbrain set for each subject. Third, the most important eigenbrain (MIE) was obtained by Welch's t-test (WTT). Finally, kernel support-vector-machines with different kernels that were trained by particle swarm optimization, were used to make an accurate prediction of AD subjects. Coefficients of MIE with values higher than 0.98 quantile were highlighted to obtain the discriminant regions that distinguish AD from NC. Results: The experiments showed that the proposed method can predict AD subjects with a competitive performance with existing methods, especially the accuracy of the polynomial kernel (92.36 ± 0.94) was better than the linear kernel of 91.47 ± 1.02 and the radial basis function (RBF) kernel of 86.71 ± 1.93. The proposed eigenbrain-based CAD system detected 30 AD-related brain regions (Anterior Cingulate, Caudate Nucleus, Cerebellum, Cingulate Gyrus, Claustrum, Inferior Frontal Gyrus, Inferior Parietal Lobule, Insula, Lateral Ventricle, Lentiform Nucleus, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterial Cingulate, Precentral Gyrus, Precuneus, Subcallosal Gyrus, Sub-Gyral, Superior Frontal Gyrus, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, Thalamus, Transverse Temporal Gyrus, and Uncus). The results were coherent with existing literatures. Conclusion: The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning. PMID:26082713

Need for new technologies for detection of adventitious agents in vaccines and other biological products.

PubMed

Mallet, Laurent; Gisonni-Lex, Lucy

2014-01-01

From an industrial perspective, the conventional in vitro and in vivo assays used for detection of viral contaminants have shown their limitations, as illustrated by the unfortunate detection of porcine circovirus contamination in a licensed rotavirus vaccine. This contamination event illustrates the gaps within the existing adventitious agent strategy and the potential use of new broader molecular detection methods. This paper serves to summarize current testing approaches and challenges, along with opportunities for the use of these new technologies. Testing of biological products is required to ensure the safety of patients. Recently, a licensed vaccine was found to be contaminated with a virus. This contamination did not cause a safety concern to the patients; however, it highlights the need for using new testing methods to control our biological products. This paper introduces the benefits of these new tests and outlines the challenges with the current tests. © PDA, Inc. 2014.

Computer assisted diagnostic system in tumor radiography.

PubMed

Faisal, Ahmed; Parveen, Sharmin; Badsha, Shahriar; Sarwar, Hasan; Reza, Ahmed Wasif

2013-06-01

An improved and efficient method is presented in this paper to achieve a better trade-off between noise removal and edge preservation, thereby detecting the tumor region of MRI brain images automatically. Compass operator has been used in the fourth order Partial Differential Equation (PDE) based denoising technique to preserve the anatomically significant information at the edges. A new morphological technique is also introduced for stripping skull region from the brain images, which consequently leading to the process of detecting tumor accurately. Finally, automatic seeded region growing segmentation based on an improved single seed point selection algorithm is applied to detect the tumor. The method is tested on publicly available MRI brain images and it gives an average PSNR (Peak Signal to Noise Ratio) of 36.49. The obtained results also show detection accuracy of 99.46%, which is a significant improvement than that of the existing results.

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

Detecting long-term growth trends using tree rings: a critical evaluation of methods.

PubMed

Peters, Richard L; Groenendijk, Peter; Vlam, Mart; Zuidema, Pieter A

2015-05-01

Tree-ring analysis is often used to assess long-term trends in tree growth. A variety of growth-trend detection methods (GDMs) exist to disentangle age/size trends in growth from long-term growth changes. However, these detrending methods strongly differ in approach, with possible implications for their output. Here, we critically evaluate the consistency, sensitivity, reliability and accuracy of four most widely used GDMs: conservative detrending (CD) applies mathematical functions to correct for decreasing ring widths with age; basal area correction (BAC) transforms diameter into basal area growth; regional curve standardization (RCS) detrends individual tree-ring series using average age/size trends; and size class isolation (SCI) calculates growth trends within separate size classes. First, we evaluated whether these GDMs produce consistent results applied to an empirical tree-ring data set of Melia azedarach, a tropical tree species from Thailand. Three GDMs yielded similar results - a growth decline over time - but the widely used CD method did not detect any change. Second, we assessed the sensitivity (probability of correct growth-trend detection), reliability (100% minus probability of detecting false trends) and accuracy (whether the strength of imposed trends is correctly detected) of these GDMs, by applying them to simulated growth trajectories with different imposed trends: no trend, strong trends (-6% and +6% change per decade) and weak trends (-2%, +2%). All methods except CD, showed high sensitivity, reliability and accuracy to detect strong imposed trends. However, these were considerably lower in the weak or no-trend scenarios. BAC showed good sensitivity and accuracy, but low reliability, indicating uncertainty of trend detection using this method. Our study reveals that the choice of GDM influences results of growth-trend studies. We recommend applying multiple methods when analysing trends and encourage performing sensitivity and reliability analysis. Finally, we recommend SCI and RCS, as these methods showed highest reliability to detect long-term growth trends. © 2014 John Wiley & Sons Ltd.

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.

ULTRASONIC FLAW DETECTION METHOD AND MEANS

DOEpatents

Worlton, D.C.

1961-08-15

A method of detecting subsurface flaws in an object using ultrasonic waves is described. An ultnasonic wave of predetermined velocity and frequency is transmitted to engage the surface of the object at a predetermined angle of inci dence thereto. The incident angle of the wave to the surface is determined with respect to phase velocity, incident wave velocity, incident wave frequency, and the estimated depth of the flaw so that Lamb waves of a particular type and mode are induced only in the portion of the object between the flaw and the surface. These Lamb waves are then detected as they leave the object at an angle of exit equal to the angle of incidence. No waves wlll be generated in the object and hence received if no flaw exists beneath the surface. (AEC)

Breast cancer early detection via tracking of skin back-scattered secondary speckle patterns

NASA Astrophysics Data System (ADS)

Bennett, Aviya; Sirkis, Talia; Beiderman, Yevgeny; Agdarov, Sergey; Beiderman, Yafim; Zalevsky, Zeev

2018-02-01

Breast cancer has become a major cause of death among women. The lifetime risk of a woman developing this disease has been established as one in eight. The most useful way to reduce breast cancer death is to treat the disease as early as possible. The existing methods of early diagnostics of breast cancer are mainly based on screening mammography or Magnetic Resonance Imaging (MRI) periodically conducted at medical facilities. In this paper the authors proposing a new approach for simple breast cancer detection. It is based on skin stimulation by sound waves, illuminating it by laser beam and tracking the reflected secondary speckle patterns. As first approach, plastic balls of different sizes were placed under the skin of chicken breast and detected by the proposed method.

Investigation of an automatic trim algorithm for restructurable aircraft control

NASA Technical Reports Server (NTRS)

Weiss, J.; Eterno, J.; Grunberg, D.; Looze, D.; Ostroff, A.

1986-01-01

This paper develops and solves an automatic trim problem for restructurable aircraft control. The trim solution is applied as a feed-forward control to reject measurable disturbances following control element failures. Disturbance rejection and command following performances are recovered through the automatic feedback control redesign procedure described by Looze et al. (1985). For this project the existence of a failure detection mechanism is assumed, and methods to cope with potential detection and identification inaccuracies are addressed.

Exact extraction method for road rutting laser lines

NASA Astrophysics Data System (ADS)

Hong, Zhiming

2018-02-01

This paper analyzes the importance of asphalt pavement rutting detection in pavement maintenance and pavement administration in today's society, the shortcomings of the existing rutting detection methods are presented and a new rutting line-laser extraction method based on peak intensity characteristic and peak continuity is proposed. The intensity of peak characteristic is enhanced by a designed transverse mean filter, and an intensity map of peak characteristic based on peak intensity calculation for the whole road image is obtained to determine the seed point of the rutting laser line. Regarding the seed point as the starting point, the light-points of a rutting line-laser are extracted based on the features of peak continuity, which providing exact basic data for subsequent calculation of pavement rutting depths.

diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data.

PubMed

Lun, Aaron T L; Smyth, Gordon K

2015-08-19

Chromatin conformation capture with high-throughput sequencing (Hi-C) is a technique that measures the in vivo intensity of interactions between all pairs of loci in the genome. Most conventional analyses of Hi-C data focus on the detection of statistically significant interactions. However, an alternative strategy involves identifying significant changes in the interaction intensity (i.e., differential interactions) between two or more biological conditions. This is more statistically rigorous and may provide more biologically relevant results. Here, we present the diffHic software package for the detection of differential interactions from Hi-C data. diffHic provides methods for read pair alignment and processing, counting into bin pairs, filtering out low-abundance events and normalization of trended or CNV-driven biases. It uses the statistical framework of the edgeR package to model biological variability and to test for significant differences between conditions. Several options for the visualization of results are also included. The use of diffHic is demonstrated with real Hi-C data sets. Performance against existing methods is also evaluated with simulated data. On real data, diffHic is able to successfully detect interactions with significant differences in intensity between biological conditions. It also compares favourably to existing software tools on simulated data sets. These results suggest that diffHic is a viable approach for differential analyses of Hi-C data.

Mal-Xtract: Hidden Code Extraction using Memory Analysis

NASA Astrophysics Data System (ADS)

Lim, Charles; Syailendra Kotualubun, Yohanes; Suryadi; Ramli, Kalamullah

2017-01-01

Software packer has been used effectively to hide the original code inside a binary executable, making it more difficult for existing signature based anti malware software to detect malicious code inside the executable. A new method of written and rewritten memory section is introduced to to detect the exact end time of unpacking routine and extract original code from packed binary executable using Memory Analysis running in an software emulated environment. Our experiment results show that at least 97% of the original code from the various binary executable packed with different software packers could be extracted. The proposed method has also been successfully extracted hidden code from recent malware family samples.

A proven and highly cost-effective method of early detection of breast cancer for developing countries.

PubMed

Rebentisch, D P; Rebentisch, H D; Thomas, K; Karat, S; Jadhav, A J

1995-12-01

Carcinoma of the breast is the third most common cancer in Indian women. With rapid industrialization and effective control of communicable diseases, better diagnostic and treatment facilities, cancer is emerging as a major health problem. Since early detection is the only way to reduce morbidity and mortality from breast cancer, we undertook a pilot project to evaluate efficacy of using existing manpower and resources for screening women in the high risk group. Methodology pros and cons, results, and recommendations are presented. Our method can be adopted by any developing country interested in a screening programme for malignant disease.

Recent Progresses in Nanobiosensing for Food Safety Analysis

PubMed Central

Yang, Tao; Huang, Huifen; Zhu, Fang; Lin, Qinlu; Zhang, Lin; Liu, Junwen

2016-01-01

With increasing adulteration, food safety analysis has become an important research field. Nanomaterials-based biosensing holds great potential in designing highly sensitive and selective detection strategies necessary for food safety analysis. This review summarizes various function types of nanomaterials, the methods of functionalization of nanomaterials, and recent (2014–present) progress in the design and development of nanobiosensing for the detection of food contaminants including pathogens, toxins, pesticides, antibiotics, metal contaminants, and other analytes, which are sub-classified according to various recognition methods of each analyte. The existing shortcomings and future perspectives of the rapidly growing field of nanobiosensing addressing food safety issues are also discussed briefly. PMID:27447636

Noninvasive control of dental calculus removal: qualification of two fluorescence methods

NASA Astrophysics Data System (ADS)

Gonchukov, S.; Sukhinina, A.; Bakhmutov, D.; Biryukova, T.

2013-02-01

The main condition of periodontitis prevention is the full calculus removal from the teeth surface. This procedure should be fulfilled without harming adjacent unaffected tooth tissues. Nevertheless the problem of sensitive and precise estimating of tooth-calculus interface exists and potential risk of hard tissue damage remains. In this work it was shown that fluorescence diagnostics during calculus removal can be successfully used for precise noninvasive detection of calculus-tooth interface. In so doing the simple implementation of this method free from the necessity of spectrometer using can be employed. Such a simple implementation of calculus detection set-up can be aggregated with the devices of calculus removing.

Recent Progresses in Nanobiosensing for Food Safety Analysis.

PubMed

Yang, Tao; Huang, Huifen; Zhu, Fang; Lin, Qinlu; Zhang, Lin; Liu, Junwen

2016-07-19

With increasing adulteration, food safety analysis has become an important research field. Nanomaterials-based biosensing holds great potential in designing highly sensitive and selective detection strategies necessary for food safety analysis. This review summarizes various function types of nanomaterials, the methods of functionalization of nanomaterials, and recent (2014-present) progress in the design and development of nanobiosensing for the detection of food contaminants including pathogens, toxins, pesticides, antibiotics, metal contaminants, and other analytes, which are sub-classified according to various recognition methods of each analyte. The existing shortcomings and future perspectives of the rapidly growing field of nanobiosensing addressing food safety issues are also discussed briefly.

Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data.

PubMed

Schouten, Kim; van der Weijde, Onne; Frasincar, Flavius; Dekker, Rommert

2018-04-01

Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods. In contrast to most existing approaches, the first method presented is an unsupervised method that applies association rule mining on co-occurrence frequency data obtained from a corpus to find these aspect categories. While not on par with state-of-the-art supervised methods, the proposed unsupervised method performs better than several simple baselines, a similar but supervised method, and a supervised baseline, with an -score of 67%. The second method is a supervised variant that outperforms existing methods with an -score of 84%.

Information dynamics algorithm for detecting communities in networks

NASA Astrophysics Data System (ADS)

Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro

2012-11-01

The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.

Detection of adenosine triphosphate in HeLa cell using capillary electrophoresis-laser induced fluorescence detection based on aptamer and graphene oxide.

PubMed

Fang, Bi-Yun; Yao, Ming-Hao; Wang, Chun-Yuan; Wang, Chao-Yang; Zhao, Yuan-Di; Chen, Fang

2016-04-01

A method for ATP quantification based on dye-labeled aptamer/graphene oxide (aptamer/GO) using capillary electrophoresis-laser induced fluorescence (CE-LIF) detecting technique has been established. In this method, the carboxyfluorescein (FAM)-labelled ATP aptamers were adsorbed onto the surface of GO, leading to the fluorescence quenching of FAM; after the incubation with a limited amount of ATP, stronger affinity between ATP aptamer and ATP resulted in the desorption of aptamers and the fluorescence restoration of FAM. Then, aptamer-ATP complex and excess of aptamer/GO and GO were separated and quantified by CE-LIF detection. It was shown that a linear relation was existing in the CE-LIF peak intensity of aptamer-ATP and ATP concentration in range of 10-700 μM, the regression equation was F=1.50+0.0470C(ATP) (R(2)=0.990), and the limit of detection was 1.28 μM (3S/N, n=5), which was one order magnitude lower than that of detection in solution by fluorescence method. The approach with excellent specificity and reproducibility has been successfully applied to detecting concentration of ATP in HeLa cell. Copyright © 2015 Elsevier B.V. All rights reserved.

Development of analytical method for ultrasensitive detection of salbutamol utilizing DNA labeled-immunoprobe.

PubMed

Lei, Yajing; Li, Xiaoyi; Akash, Muhammad Sajid Hamid; Zhou, Lifang; Tang, Xiaojing; Shi, Weixing; Liu, Zhiming; Chen, Shuqing

2015-03-25

Salbutamol (SAL) is known as quick-acting β-2 receptor agonist and its use in humans for pulmonary diseases and/or in animal feed is limited because of associated potential hazardous effects on health. Several techniques are available for the detection of SAL, but are expensive and time consuming. Here for the first time, a novel pre-assembled DNA immunoprobe-based immuno-PCR assay was developed to investigate the levels of SAL in human urine samples and compared the proposed immuno-PCR assay for the detection of SAL with that of direct competitive ELISA. Under the optimized conditions, the assay showed a linear range over seven orders of magnitude, whereas the limit of detection of SAL in buffer was found to be 21 fg/mL. Current immuno-PCR assay exhibited a 300-fold better detection limit for SAL as compared to one achieved with direct competitive ELISA using the same antibody. In conclusion, it has been found that the developed method is highly sensitive and simple method that can significantly enhance the detection sensitivity for small molecules. Moreover it can be modified and used for detecting other small molecules and/or chemicals that exist in the environment and are responsible for the environmental pollution. Copyright © 2014 Elsevier B.V. All rights reserved.

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

JRC GMO-Matrix: a web application to support Genetically Modified Organisms detection strategies.

PubMed

Angers-Loustau, Alexandre; Petrillo, Mauro; Bonfini, Laura; Gatto, Francesco; Rosa, Sabrina; Patak, Alexandre; Kreysa, Joachim

2014-12-30

The polymerase chain reaction (PCR) is the current state of the art technique for DNA-based detection of Genetically Modified Organisms (GMOs). A typical control strategy starts by analyzing a sample for the presence of target sequences (GM-elements) known to be present in many GMOs. Positive findings from this "screening" are then confirmed with GM (event) specific test methods. A reliable knowledge of which GMOs are detected by combinations of GM-detection methods is thus crucial to minimize the verification efforts. In this article, we describe a novel platform that links the information of two unique databases built and maintained by the European Union Reference Laboratory for Genetically Modified Food and Feed (EU-RL GMFF) at the Joint Research Centre (JRC) of the European Commission, one containing the sequence information of known GM-events and the other validated PCR-based detection and identification methods. The new platform compiles in silico determinations of the detection of a wide range of GMOs by the available detection methods using existing scripts that simulate PCR amplification and, when present, probe binding. The correctness of the information has been verified by comparing the in silico conclusions to experimental results for a subset of forty-nine GM events and six methods. The JRC GMO-Matrix is unique for its reliance on DNA sequence data and its flexibility in integrating novel GMOs and new detection methods. Users can mine the database using a set of web interfaces that thus provide a valuable support to GMO control laboratories in planning and evaluating their GMO screening strategies. The platform is accessible at http://gmo-crl.jrc.ec.europa.eu/jrcgmomatrix/ .

CAFFEINE SPECIFICITY OF VARIOUS NON-IMPRINTED POLYMERS IN AQUEOUS MEDIA

EPA Science Inventory

Limitations exist in applying the conventional microbial methods to the detection of human fecal contamination in water. Certain organic compounds such as caffeine, have been reported by the U.S. Geological Survey as a more suitable tracer. The employment of caffeine has been h...

Automatic image orientation detection via confidence-based integration of low-level and semantic cues.

PubMed

Luo, Jiebo; Boutell, Matthew

2005-05-01

Automatic image orientation detection for natural images is a useful, yet challenging research topic. Humans use scene context and semantic object recognition to identify the correct image orientation. However, it is difficult for a computer to perform the task in the same way because current object recognition algorithms are extremely limited in their scope and robustness. As a result, existing orientation detection methods were built upon low-level vision features such as spatial distributions of color and texture. Discrepant detection rates have been reported for these methods in the literature. We have developed a probabilistic approach to image orientation detection via confidence-based integration of low-level and semantic cues within a Bayesian framework. Our current accuracy is 90 percent for unconstrained consumer photos, impressive given the findings of a psychophysical study conducted recently. The proposed framework is an attempt to bridge the gap between computer and human vision systems and is applicable to other problems involving semantic scene content understanding.

LOSITAN: a workbench to detect molecular adaptation based on a Fst-outlier method.

PubMed

Antao, Tiago; Lopes, Ana; Lopes, Ricardo J; Beja-Pereira, Albano; Luikart, Gordon

2008-07-28

Testing for selection is becoming one of the most important steps in the analysis of multilocus population genetics data sets. Existing applications are difficult to use, leaving many non-trivial, error-prone tasks to the user. Here we present LOSITAN, a selection detection workbench based on a well evaluated Fst-outlier detection method. LOSITAN greatly facilitates correct approximation of model parameters (e.g., genome-wide average, neutral Fst), provides data import and export functions, iterative contour smoothing and generation of graphics in a easy to use graphical user interface. LOSITAN is able to use modern multi-core processor architectures by locally parallelizing fdist, reducing computation time by half in current dual core machines and with almost linear performance gains in machines with more cores. LOSITAN makes selection detection feasible to a much wider range of users, even for large population genomic datasets, by both providing an easy to use interface and essential functionality to complete the whole selection detection process.

Penalty Dynamic Programming Algorithm for Dim Targets Detection in Sensor Systems

PubMed Central

Huang, Dayu; Xue, Anke; Guo, Yunfei

2012-01-01

In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations. PMID:22666074

Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

NASA Astrophysics Data System (ADS)

Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah H.; Prince, Jerry L.

2016-03-01

Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.

Non-invasive detection of the freezing of gait in Parkinson's disease using spectral and wavelet features.

PubMed

Nazarzadeh, Kimia; Arjunan, Sridhar P; Kumar, Dinesh K; Das, Debi Prasad

2016-08-01

In this study, we have analyzed the accelerometer data recorded during gait analysis of Parkinson disease patients for detecting freezing of gait (FOG) episodes. The proposed method filters the recordings for noise reduction of the leg movement changes and computes the wavelet coefficients to detect FOG events. Publicly available FOG database was used and the technique was evaluated using receiver operating characteristic (ROC) analysis. Results show a higher performance of the wavelet feature in discrimination of the FOG events from the background activity when compared with the existing technique.

Assessment of the Utility of the Advanced Himawari Imager to Detect Active Fire Over Australia

NASA Astrophysics Data System (ADS)

Hally, B.; Wallace, L.; Reinke, K.; Jones, S.

2016-06-01

Wildfire detection and attribution is an issue of importance due to the socio-economic impact of fires in Australia. Early detection of fires allows emergency response agencies to make informed decisions in order to minimise loss of life and protect strategic resources in threatened areas. Until recently, the ability of land management authorities to accurately assess fire through satellite observations of Australia was limited to those made by polar orbiting satellites. The launch of the Japan Meteorological Agency (JMA) Himawari-8 satellite, with the 16-band Advanced Himawari Imager (AHI-8) onboard, in October 2014 presents a significant opportunity to improve the timeliness of satellite fire detection across Australia. The near real-time availability of images, at a ten minute frequency, may also provide contextual information (background temperature) leading to improvements in the assessment of fire characteristics. This paper investigates the application of the high frequency observation data supplied by this sensor for fire detection and attribution. As AHI-8 is a new sensor we have performed an analysis of the noise characteristics of the two spectral bands used for fire attribution across various land use types which occur in Australia. Using this information we have adapted existing algorithms, based upon least squares error minimisation and Kalman filtering, which utilise high frequency observations of surface temperature to detect and attribute fire. The fire detection and attribution information provided by these algorithms is then compared to existing satellite based fire products as well as in-situ information provided by land management agencies. These comparisons were made Australia-wide for an entire fire season - including many significant fire events (wildfires and prescribed burns). Preliminary detection results suggest that these methods for fire detection perform comparably to existing fire products and fire incident reporting from relevant fire authorities but with the advantage of being near-real time. Issues remain for detection due to cloud and smoke obscuration, along with validation of the attribution of fire characteristics using these algorithms.

Estimating clinical chemistry reference values based on an existing data set of unselected animals.

PubMed

Dimauro, Corrado; Bonelli, Piero; Nicolussi, Paola; Rassu, Salvatore P G; Cappio-Borlino, Aldo; Pulina, Giuseppe

2008-11-01

In an attempt to standardise the determination of biological reference values, the International Federation of Clinical Chemistry (IFCC) has published a series of recommendations on developing reference intervals. The IFCC recommends the use of an a priori sampling of at least 120 healthy individuals. However, such a high number of samples and laboratory analysis is expensive, time-consuming and not always feasible, especially in veterinary medicine. In this paper, an alternative (a posteriori) method is described and is used to determine reference intervals for biochemical parameters of farm animals using an existing laboratory data set. The method used was based on the detection and removal of outliers to obtain a large sample of animals likely to be healthy from the existing data set. This allowed the estimation of reliable reference intervals for biochemical parameters in Sarda dairy sheep. This method may also be useful for the determination of reference intervals for different species, ages and gender.

Detecting Visually Observable Disease Symptoms from Faces.

PubMed

Wang, Kuan; Luo, Jiebo

2016-12-01

Recent years have witnessed an increasing interest in the application of machine learning to clinical informatics and healthcare systems. A significant amount of research has been done on healthcare systems based on supervised learning. In this study, we present a generalized solution to detect visually observable symptoms on faces using semi-supervised anomaly detection combined with machine vision algorithms. We rely on the disease-related statistical facts to detect abnormalities and classify them into multiple categories to narrow down the possible medical reasons of detecting. Our method is in contrast with most existing approaches, which are limited by the availability of labeled training data required for supervised learning, and therefore offers the major advantage of flagging any unusual and visually observable symptoms.

Global-Context Based Salient Region Detection in Nature Images

NASA Astrophysics Data System (ADS)

Bao, Hong; Xu, De; Tang, Yingjun

Visually saliency detection provides an alternative methodology to image description in many applications such as adaptive content delivery and image retrieval. One of the main aims of visual attention in computer vision is to detect and segment the salient regions in an image. In this paper, we employ matrix decomposition to detect salient object in nature images. To efficiently eliminate high contrast noise regions in the background, we integrate global context information into saliency detection. Therefore, the most salient region can be easily selected as the one which is globally most isolated. The proposed approach intrinsically provides an alternative methodology to model attention with low implementation complexity. Experiments show that our approach achieves much better performance than that from the existing state-of-art methods.

Enzymatically Regulated Peptide Pairing and Catalysis for the Bioanalysis of Extracellular Prometastatic Activities of Functionally Linked Enzymes

NASA Astrophysics Data System (ADS)

Li, Hao; Huang, Yue; Yu, Yue; Li, Tianqi; Li, Genxi; Anzai, Jun-Ichi

2016-05-01

Diseases such as cancer arise from systematical reconfiguration of interactions of exceedingly large numbers of proteins in cell signaling. The study of such complicated molecular mechanisms requires multiplexed detection of the inter-connected activities of several proteins in a disease-associated context. However, the existing methods are generally not well-equipped for this kind of application. Here a method for analyzing functionally linked protein activities is developed based on enzyme controlled pairing between complementary peptide helix strands, which simultaneously enables elaborate regulation of catalytic activity of the paired peptides. This method has been used to detect three different types of protein modification enzymes that participate in the modification of extracellular matrix and the formation of invasion front in tumour. In detecting breast cancer tissue samples using this method, up-regulated activity can be observed for two of the assessed enzymes, while the third enzyme is found to have a subtle fluctuation of activity. These results may point to the application of this method in evaluating prometastatic activities of proteins in tumour.

Advances in methods for detection of anaerobic ammonium oxidizing (anammox) bacteria.

PubMed

Li, Meng; Gu, Ji-Dong

2011-05-01

Anaerobic ammonium oxidation (anammox), the biochemical process oxidizing ammonium into dinitrogen gas using nitrite as an electron acceptor, has only been recognized for its significant role in the global nitrogen cycle not long ago, and its ubiquitous distribution in a wide range of environments has changed our knowledge about the contributors to the global nitrogen cycle. Currently, several groups of methods are used in detection of anammox bacteria based on their physiological and biochemical characteristics, cellular chemical composition, and both 16S rRNA gene and selective functional genes as biomarkers, including hydrazine oxidoreductase and nitrite reductase encoding genes hzo and nirS, respectively. Results from these methods coupling with advances in quantitative PCR, reverse transcription of mRNA genes and stable isotope labeling have improved our understanding on the distribution, diversity, and activity of anammox bacteria in different environments both natural and engineered ones. In this review, we summarize these methods used in detection of anammox bacteria from various environments, highlight the strengths and weakness of these methods, and also discuss the new development potentials on the existing and new techniques in the future.

Wave field features of shallow vertical discontinuity and their application in non-destructive detection

USGS Publications Warehouse

Liu, J.; Xia, J.; Luo, Y.; Chen, C.; Li, X.; Huang, Y.

2007-01-01

The geotechnical integrity of critical infrastructure can be seriously compromised by the presence of fractures or crevices. Non-destructive techniques to accurately detect fractures in critical infrastructure such as dams and highways could be of significant benefit to the geotechnical industry. This paper investigates the application of shallow seismic and georadar methods to the detection of a vertical discontinuity using numerical simulations. The objective is to address the kinematical analysis of a vertical discontinuity, determine the resulting wave field characteristics, and provide the basis for determining the existence of vertical discontinuities based on the recorded signals. Simulation results demonstrate that: (1) A reflection from a vertical discontinuity produces a hyperbolic feature on a seismic or georadar profile; (2) In order for a reflection from a vertical discontinuity to be produced, a reflecting horizon below the discontinuity must exist, the offset between source and receiver (x0) must be non-zero, on the same side of the vertical discontinuity; (3) The range of distances from the vertical discontinuity where a reflection event is observed is proportional to its length and to x0; (4) Should the vertical crevice (or fracture) pass through a reflecting horizon, dual hyperbolic features can be observed on the records, and this can be used as a determining factor that the vertical crevice passes through the interface; and (5) diffractions from the edges of the discontinuity can be recorded with relatively smaller amplitude than reflections and their ranges are not constrained by the length of discontinuity. If the length of discontinuity is short enough, diffractions are the dominant feature. Real-world examples show that the shallow seismic reflection method and the georadar method are capable of recording the hyperbolic feature, which can be interpreted as vertical discontinuity. Thus, these methods show some promise as effective non-destructive detection methods for locating vertical discontinuities (e.g., fractures or crevices) in infrastructure such as dams and highway pavement. ?? 2007 Elsevier B.V. All rights reserved.

Theoretical Background and Prognostic Modeling for Benchmarking SHM Sensors for Composite Structures

DTIC Science & Technology

2010-10-01

minimum flaw size can be detected by the existing SHM based monitoring methods. Sandwich panels with foam , WebCore and honeycomb structures were...Whether it be hat stiffened, corrugated sandwich, honeycomb sandwich, or foam filled sandwich, all composite structures have one basic handicap in...based monitoring methods. Sandwich panels with foam , WebCore and honeycomb structures were considered for use in this study. Eigenmode frequency

Emissivity corrected infrared method for imaging anomalous structural heat flows

DOEpatents

Del Grande, N.K.; Durbin, P.F.; Dolan, K.W.; Perkins, D.E.

1995-08-22

A method for detecting flaws in structures using dual band infrared radiation is disclosed. Heat is applied to the structure being evaluated. The structure is scanned for two different wavelengths and data obtained in the form of images. Images are used to remove clutter to form a corrected image. The existence and nature of a flaw is determined by investigating a variety of features. 1 fig.

Hepatitis disease detection using Bayesian theory

NASA Astrophysics Data System (ADS)

Maseleno, Andino; Hidayati, Rohmah Zahroh

2017-02-01

This paper presents hepatitis disease diagnosis using a Bayesian theory for better understanding of the theory. In this research, we used a Bayesian theory for detecting hepatitis disease and displaying the result of diagnosis process. Bayesian algorithm theory is rediscovered and perfected by Laplace, the basic idea is using of the known prior probability and conditional probability density parameter, based on Bayes theorem to calculate the corresponding posterior probability, and then obtained the posterior probability to infer and make decisions. Bayesian methods combine existing knowledge, prior probabilities, with additional knowledge derived from new data, the likelihood function. The initial symptoms of hepatitis which include malaise, fever and headache. The probability of hepatitis given the presence of malaise, fever, and headache. The result revealed that a Bayesian theory has successfully identified the existence of hepatitis disease.

Current limitations and recommendations to improve testing for the environmental assessment of endocrine active substances

USGS Publications Warehouse

Coady, Katherine K.; Biever, Ronald C.; Denslow, Nancy D.; Gross, Melanie; Guiney, Patrick D.; Holbech, Henrik; Karouna-Renier, Natalie K.; Katsiadaki, Ioanna; Krueger, Hank; Levine, Steven L.; Maack, Gerd; Williams, Mike; Wolf, Jeffrey C.; Ankley, Gerald T.

2017-01-01

In the present study, existing regulatory frameworks and test systems for assessing potential endocrine active chemicals are described, and associated challenges are discussed, along with proposed approaches to address these challenges. Regulatory frameworks vary somewhat across geographies, but all basically evaluate whether a chemical possesses endocrine activity and whether this activity can result in adverse outcomes either to humans or to the environment. Current test systems include in silico, in vitro, and in vivo techniques focused on detecting potential endocrine activity, and in vivo tests that collect apical data to detect possible adverse effects. These test systems are currently designed to robustly assess endocrine activity and/or adverse effects in the estrogen, androgen, and thyroid hormone signaling pathways; however, there are some limitations of current test systems for evaluating endocrine hazard and risk. These limitations include a lack of certainty regarding: 1) adequately sensitive species and life stages; 2) mechanistic endpoints that are diagnostic for endocrine pathways of concern; and 3) the linkage between mechanistic responses and apical, adverse outcomes. Furthermore, some existing test methods are resource intensive with regard to time, cost, and use of animals. However, based on recent experiences, there are opportunities to improve approaches to and guidance for existing test methods and to reduce uncertainty. For example, in vitro high-throughput screening could be used to prioritize chemicals for testing and provide insights as to the most appropriate assays for characterizing hazard and risk. Other recommendations include adding endpoints for elucidating connections between mechanistic effects and adverse outcomes, identifying potentially sensitive taxa for which test methods currently do not exist, and addressing key endocrine pathways of possible concern in addition to those associated with estrogen, androgen, and thyroid signaling. 

A PILOT STUDY TO COMPARE MICROBIAL AND CHEMICAL INDICATORS OF HUMAN FECAL CONTAMINATION IN WATER

EPA Science Inventory

Limitations exist in applying traditional microbial methods for the detection of human fecal contamination of water. A pilot study was undertaken to compare the microbial and chemical indicators of human fecal contamination of water. Sixty-four water samples were collected in O...

Using Qualitative Methods for Revising Items in the Hispanic Stress Inventory

ERIC Educational Resources Information Center

Cervantes, Richard C.; Goldbach, Jeremy T.; Padilla, Amado M.

2012-01-01

Despite progress in the development of measures to assess psychosocial stress experiences in the general population, a lack of culturally informed assessment instruments exist to enable clinicians and researchers to detect and accurately diagnosis mental health concerns among Hispanics. The Hispanic Stress Inventory (HSI) was developed…

DETECTION OF PROTOZOAN PARASITES IN SOURCE AND FINISHED WATER - 3RD EDITION ASM'S METHODS IN ENVIRONMENTAL MICROBIOLOGY

EPA Science Inventory

Protozoans are eukaryotic organisms which can live either a free-living or parasitic existence. Some free-living forms, under the right conditions, can become opportunistic parasites. Enteric pathogenic protozoans, like Giardia and Cryptosporidium, which are now known to be tra...

QUANTIFICATION OF STACHYBOTRYS CHARTARUM CONIDIA IN INDOOR DUST USING REAL TIME, FLUORESCENT PROBE-BASED DETECTION OF PCR PRODUCTS

EPA Science Inventory

Analyses of fungal spores or conidia in indoor dust samples can be useful for determining the contamination status of building interiors and in signaling instances where potentially harmful exposures of building occupants to these organisms may exist. A recently developed method ...

Real-time fluorescence loop mediated isothermal amplification for the diagnosis of malaria.

PubMed

Lucchi, Naomi W; Demas, Allison; Narayanan, Jothikumar; Sumari, Deborah; Kabanywanyi, Abdunoor; Kachur, S Patrick; Barnwell, John W; Udhayakumar, Venkatachalam

2010-10-29

Molecular diagnostic methods can complement existing tools to improve the diagnosis of malaria. However, they require good laboratory infrastructure thereby restricting their use to reference laboratories and research studies. Therefore, adopting molecular tools for routine use in malaria endemic countries will require simpler molecular platforms. The recently developed loop-mediated isothermal amplification (LAMP) method is relatively simple and can be improved for better use in endemic countries. In this study, we attempted to improve this method for malaria diagnosis by using a simple and portable device capable of performing both the amplification and detection (by fluorescence) of LAMP in one platform. We refer to this as the RealAmp method. Published genus-specific primers were used to test the utility of this method. DNA derived from different species of malaria parasites was used for the initial characterization. Clinical samples of P. falciparum were used to determine the sensitivity and specificity of this system compared to microscopy and a nested PCR method. Additionally, directly boiled parasite preparations were compared with a conventional DNA isolation method. The RealAmp method was found to be simple and allowed real-time detection of DNA amplification. The time to amplification varied but was generally less than 60 minutes. All human-infecting Plasmodium species were detected. The sensitivity and specificity of RealAmp in detecting P. falciparum was 96.7% and 91.7% respectively, compared to microscopy and 98.9% and 100% respectively, compared to a standard nested PCR method. In addition, this method consistently detected P. falciparum from directly boiled blood samples. This RealAmp method has great potential as a field usable molecular tool for diagnosis of malaria. This tool can provide an alternative to conventional PCR based diagnostic methods for field use in clinical and operational programs.

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic

PubMed Central

Qi, Jin-Peng; Qi, Jie; Zhang, Qing

2016-01-01

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals. PMID:27413364

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic.

PubMed

Qi, Jin-Peng; Qi, Jie; Zhang, Qing

2016-01-01

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals.

Active link selection for efficient semi-supervised community detection

NASA Astrophysics Data System (ADS)

Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

2015-03-01

Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches.

Molecular Dynamics Information Improves cis-Peptide-Based Function Annotation of Proteins.

PubMed

Das, Sreetama; Bhadra, Pratiti; Ramakumar, Suryanarayanarao; Pal, Debnath

2017-08-04

cis-Peptide bonds, whose occurrence in proteins is rare but evolutionarily conserved, are implicated to play an important role in protein function. This has led to their previous use in a homology-independent, fragment-match-based protein function annotation method. However, proteins are not static molecules; dynamics is integral to their activity. This is nicely epitomized by the geometric isomerization of cis-peptide to trans form for molecular activity. Hence we have incorporated both static (cis-peptide) and dynamics information to improve the prediction of protein molecular function. Our results show that cis-peptide information alone cannot detect functional matches in cases where cis-trans isomerization exists but 3D coordinates have been obtained for only the trans isomer or when the cis-peptide bond is incorrectly assigned as trans. On the contrary, use of dynamics information alone includes false-positive matches for cases where fragments with similar secondary structure show similar dynamics, but the proteins do not share a common function. Combining the two methods reduces errors while detecting the true matches, thereby enhancing the utility of our method in function annotation. A combined approach, therefore, opens up new avenues of improving existing automated function annotation methodologies.

Active link selection for efficient semi-supervised community detection

PubMed Central

Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

2015-01-01

Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches. PMID:25761385

Porphyrin-induced photogeneration of hydrogen peroxide determined using the luminol chemiluminescence method in aqueous solution: A structure-activity relationship study related to the aggregation of porphyrin.

PubMed

Komagoe, Keiko; Katsu, Takashi

2006-02-01

A luminol chemiluminescence method was used to evaluate the porphyrin-induced photogeneration of hydrogen peroxide (H2O2). This method enabled us to detect H202 in the presence of a high concentration of porphyrin, which was not possible using conventional colorimetry. The limit of detection was about 1 microM. We compared the ability to generate H2O2, using uroporphyrin (UP), hexacarboxylporphyrin (HCP), coproporphyrin (CP), hematoporphyrin (HP), mesoporphyrin (MP), and protoporphyrin (PP). The amount of H2O2 photoproduced was strongly related to the state of the porphyrin in the aqueous solution. UP and HCP, which existed predominantly in a monomeric form, had a good ability to produce H2O2. HP and MP, existing as dimers, showed weak activity. CP, forming a mixture of monomer and dimer, had a moderate ability to produce H2O2. PP, which was highly aggregated, had a good ability. These results demonstrated that the efficiency of porphyrins to produce H2O2 was strongly dependent on their aggregated form, and the dimer suppressed the production of H2O2.

Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data

PubMed Central

Zook, Justin M.; Morrow, Jayne B.; Lin, Nancy J.

2017-01-01

High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants. Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant. Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need for a priori assumptions about the contaminant. Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives. Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm. Simulated WGS datasets from ten genera as individuals and binary mixtures of eight organisms at varying ratios were analyzed to evaluate the role of contaminant concentration and taxonomy on detection. For the individual genomes the false positive contaminants reported depended on the genus, with Staphylococcus, Escherichia, and Shigella having the highest proportion of false positives. For nearly all binary mixtures the contaminant was detected in the in-silico datasets at the equivalent of 1 in 1,000 cells, though F. tularensis was not detected in any of the simulated contaminant mixtures and Y. pestis was only detected at the equivalent of one in 10 cells. Once a WGS method for detecting contaminants is characterized, it can be applied to evaluate microbial material purity, in efforts to ensure that contaminants are characterized in microbial materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods. PMID:28924496

Current limitations and recommendations to improve testing ...

EPA Pesticide Factsheets

In this paper existing regulatory frameworks and test systems for assessing potential endocrine-active chemicals are described, and associated challenges discussed, along with proposed approaches to address these challenges. Regulatory frameworks vary somewhat across organizations, but all basically evaluate whether a chemical possesses endocrine activity and whether this activity can result in adverse outcomes either to humans or the environment. Current test systems include in silico, in vitro and in vivo techniques focused on detecting potential endocrine activity, and in vivo tests that collect apical data to detect possible adverse effects. These test systems are currently designed to robustly assess endocrine activity and/or adverse effects in the estrogen, androgen, and thyroid hormonal pathways; however, there are some limitations of current test systems for evaluating endocrine hazard and risk. These limitations include a lack of certainty regarding: 1)adequately sensitive species and life-stages, 2) mechanistic endpoints that are diagnostic for endocrine pathways of concern, and 3) the linkage between mechanistic responses and apical, adverse outcomes. Furthermore, some existing test methods are resource intensive in regard to time, cost, and use of animals. However, based on recent experiences, there are opportunities to improve approaches to, and guidance for existing test methods, and to reduce uncertainty. For example, in vitro high throughput

A Track Initiation Method for the Underwater Target Tracking Environment

NASA Astrophysics Data System (ADS)

Li, Dong-dong; Lin, Yang; Zhang, Yao

2018-04-01

A novel efficient track initiation method is proposed for the harsh underwater target tracking environment (heavy clutter and large measurement errors): track splitting, evaluating, pruning and merging method (TSEPM). Track initiation demands that the method should determine the existence and initial state of a target quickly and correctly. Heavy clutter and large measurement errors certainly pose additional difficulties and challenges, which deteriorate and complicate the track initiation in the harsh underwater target tracking environment. There are three primary shortcomings for the current track initiation methods to initialize a target: (a) they cannot eliminate the turbulences of clutter effectively; (b) there may be a high false alarm probability and low detection probability of a track; (c) they cannot estimate the initial state for a new confirmed track correctly. Based on the multiple hypotheses tracking principle and modified logic-based track initiation method, in order to increase the detection probability of a track, track splitting creates a large number of tracks which include the true track originated from the target. And in order to decrease the false alarm probability, based on the evaluation mechanism, track pruning and track merging are proposed to reduce the false tracks. TSEPM method can deal with the track initiation problems derived from heavy clutter and large measurement errors, determine the target's existence and estimate its initial state with the least squares method. What's more, our method is fully automatic and does not require any kind manual input for initializing and tuning any parameter. Simulation results indicate that our new method improves significantly the performance of the track initiation in the harsh underwater target tracking environment.

Simulation and modeling of return waveforms from a ladar beam footprint in USU LadarSIM

NASA Astrophysics Data System (ADS)

Budge, Scott; Leishman, Brad; Pack, Robert

2006-05-01

Ladar systems are an emerging technology with applications in many fields. Consequently, simulations for these systems have become a valuable tool in the improvement of existing systems and the development of new ones. This paper discusses the theory and issues involved in reliably modeling the return waveform of a ladar beam footprint in the Utah State University LadarSIM simulation software. Emphasis is placed on modeling system-level effects that allow an investigation of engineering tradeoffs in preliminary designs, and validation of behaviors in fabricated designs. Efforts have been made to decrease the necessary computation time while still maintaining a usable model. A full waveform simulation is implemented that models optical signals received on detector followed by electronic signals and discriminators commonly encountered in contemporary direct-detection ladar systems. Waveforms are modeled using a novel hexagonal sampling process applied across the ladar beam footprint. Each sample is weighted using a Gaussian spatial profile for a well formed laser footprint. Model fidelity is also improved by using a bidirectional reflectance distribution function (BRDF) for target reflectance. Once photons are converted to electrons, waveform processing is used to detect first, last or multiple return pulses. The detection methods discussed in this paper are a threshold detection method, a constant fraction method, and a derivative zero-crossing method. Various detection phenomena, such as range error, walk error, drop outs and false alarms, can be studied using these detection methods.

Highly sensitive and selective liquid crystal optical sensor for detection of ammonia.

PubMed

Niu, Xiaofang; Zhong, Yuanbo; Chen, Rui; Wang, Fei; Luo, Dan

2017-06-12

Ammonia detection technologies are very important in environment monitoring. However, most existing technologies are complex and expensive, which limit the useful range of real-time application. Here, we propose a highly sensitive and selective optical sensor for detection of ammonia (NH 3 ) based on liquid crystals (LCs). This optical sensor is realized through the competitive binding between ammonia and liquid crystals on chitosan-Cu 2+ that decorated on glass substrate. We achieve a broad detection range of ammonia from 50 ppm to 1250 ppm, with a low detection limit of 16.6 ppm. This sensor is low-cost, simple, fast, and highly sensitive and selective for detection of ammonia. The proposal LC sensing method can be a sensitive detection platform for other molecule monitors such as proteins, DNAs and other heavy metal ions by modifying sensing molecules.

Detection scheme for a partially occluded pedestrian based on occluded depth in lidar-radar sensor fusion

NASA Astrophysics Data System (ADS)

Kwon, Seong Kyung; Hyun, Eugin; Lee, Jin-Hee; Lee, Jonghun; Son, Sang Hyuk

2017-11-01

Object detections are critical technologies for the safety of pedestrians and drivers in autonomous vehicles. Above all, occluded pedestrian detection is still a challenging topic. We propose a new detection scheme for occluded pedestrian detection by means of lidar-radar sensor fusion. In the proposed method, the lidar and radar regions of interest (RoIs) have been selected based on the respective sensor measurement. Occluded depth is a new means to determine whether an occluded target exists or not. The occluded depth is a region projected out by expanding the longitudinal distance with maintaining the angle formed by the outermost two end points of the lidar RoI. The occlusion RoI is the overlapped region made by superimposing the radar RoI and the occluded depth. The object within the occlusion RoI is detected by the radar measurement information and the occluded object is estimated as a pedestrian based on human Doppler distribution. Additionally, various experiments are performed in detecting a partially occluded pedestrian in outdoor as well as indoor environments. According to experimental results, the proposed sensor fusion scheme has much better detection performance compared to the case without our proposed method.

Detection of shallow buried objects using an autoregressive model on the ground penetrating radar signal

NASA Astrophysics Data System (ADS)

Nabelek, Daniel P.; Ho, K. C.

2013-06-01

The detection of shallow buried low-metal content objects using ground penetrating radar (GPR) is a challenging task. This is because these targets are right underneath the ground and the ground bounce reflection interferes with their detections. They do not create distinctive hyperbolic signatures as required by most existing GPR detection algorithms due to their special geometric shapes and low metal content. This paper proposes the use of the Autoregressive (AR) modeling method for the detection of these targets. We fit an A-scan of the GPR data to an AR model. It is found that the fitting error will be small when such a target is present and large when it is absent. The ratio of the energy in an Ascan before and after AR model fitting is used as the confidence value for detection. We also apply AR model fitting over scans and utilize the fitting residual energies over several scans to form a feature vector for improving the detections. Using the data collected from a government test site, the proposed method can improve the detection of this kind of targets by 30% compared to the pre-screener, at a false alarm rate of 0.002/m2.

Spot counting on fluorescence in situ hybridization in suspension images using Gaussian mixture model

NASA Astrophysics Data System (ADS)

Liu, Sijia; Sa, Ruhan; Maguire, Orla; Minderman, Hans; Chaudhary, Vipin

2015-03-01

Cytogenetic abnormalities are important diagnostic and prognostic criteria for acute myeloid leukemia (AML). A flow cytometry-based imaging approach for FISH in suspension (FISH-IS) was established that enables the automated analysis of several log-magnitude higher number of cells compared to the microscopy-based approaches. The rotational positioning can occur leading to discordance between spot count. As a solution of counting error from overlapping spots, in this study, a Gaussian Mixture Model based classification method is proposed. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) of GMM are used as global image features of this classification method. Via Random Forest classifier, the result shows that the proposed method is able to detect closely overlapping spots which cannot be separated by existing image segmentation based spot detection methods. The experiment results show that by the proposed method we can obtain a significant improvement in spot counting accuracy.

[Application of Stata software to test heterogeneity in meta-analysis method].

PubMed

Wang, Dan; Mou, Zhen-yun; Zhai, Jun-xia; Zong, Hong-xia; Zhao, Xiao-dong

2008-07-01

To introduce the application of Stata software to heterogeneity test in meta-analysis. A data set was set up according to the example in the study, and the corresponding commands of the methods in Stata 9 software were applied to test the example. The methods used were Q-test and I2 statistic attached to the fixed effect model forest plot, H statistic and Galbraith plot. The existence of the heterogeneity among studies could be detected by Q-test and H statistic and the degree of the heterogeneity could be detected by I2 statistic. The outliers which were the sources of the heterogeneity could be spotted from the Galbraith plot. Heterogeneity test in meta-analysis can be completed by the four methods in Stata software simply and quickly. H and I2 statistics are more robust, and the outliers of the heterogeneity can be clearly seen in the Galbraith plot among the four methods.

Speech sound classification and detection of articulation disorders with support vector machines and wavelets.

PubMed

Georgoulas, George; Georgopoulos, Voula C; Stylios, Chrysostomos D

2006-01-01

This paper proposes a novel integrated methodology to extract features and classify speech sounds with intent to detect the possible existence of a speech articulation disorder in a speaker. Articulation, in effect, is the specific and characteristic way that an individual produces the speech sounds. A methodology to process the speech signal, extract features and finally classify the signal and detect articulation problems in a speaker is presented. The use of support vector machines (SVMs), for the classification of speech sounds and detection of articulation disorders is introduced. The proposed method is implemented on a data set where different sets of features and different schemes of SVMs are tested leading to satisfactory performance.

Structured product labeling improves detection of drug-intolerance issues.

PubMed

Schadow, Gunther

2009-01-01

This study sought to assess the value of the Health Level 7/U.S. Food and Drug Administration Structured Product Labeling (SPL) drug knowledge representation standard and its associated terminology sources for drug-intolerance (allergy) decision support in computerized provider order entry (CPOE) systems. The Regenstrief Institute CPOE drug-intolerance issue detection system and its knowledge base was compared with a method based on existing SPL label content enriched with knowledge sources used with SPL (NDF-RT/MeSH). Both methods were applied to a large set of drug-intolerance (allergy) records, drug orders, and medication dispensing records covering >50,000 patients over 30 years. The number of drug-intolerance issues detected by both methods was counted, as well as the number of patients with issues, number of distinct drugs, and number of distinct intolerances. The difference between drug-intolerance issues detected or missed by either method was qualitatively analyzed. Although <70% of terms were mapped to SPL, the new approach detected four times as many drug-intolerance issues on twice as many patients. The SPL-based approach is more sensitive and suggests that mapping local dictionaries to SPL, and enhancing the depth and breadth of coverage of SPL content are worth accelerating. The study also highlights specificity problems known to trouble drug-intolerance decision support and suggests how terminology and methods of recording drug intolerances could be improved.

Structured Product Labeling Improves Detection of Drug-intolerance Issues

PubMed Central

Schadow, Gunther

2009-01-01

Objectives This study sought to assess the value of the Health Level 7/U.S. Food and Drug Administration Structured Product Labeling (SPL) drug knowledge representation standard and its associated terminology sources for drug-intolerance (allergy) decision support in computerized provider order entry (CPOE) systems. Design The Regenstrief Institute CPOE drug-intolerance issue detection system and its knowledge base was compared with a method based on existing SPL label content enriched with knowledge sources used with SPL (NDF-RT/MeSH). Both methods were applied to a large set of drug-intolerance (allergy) records, drug orders, and medication dispensing records covering >50,000 patients over 30 years. Measurements The number of drug-intolerance issues detected by both methods was counted, as well as the number of patients with issues, number of distinct drugs, and number of distinct intolerances. The difference between drug-intolerance issues detected or missed by either method was qualitatively analyzed. Results Although <70% of terms were mapped to SPL, the new approach detected four times as many drug-intolerance issues on twice as many patients. Conclusion The SPL-based approach is more sensitive and suggests that mapping local dictionaries to SPL, and enhancing the depth and breadth of coverage of SPL content are worth accelerating. The study also highlights specificity problems known to trouble drug-intolerance decision support and suggests how terminology and methods of recording drug intolerances could be improved. PMID:18952933

Validation of Satellite-Based Objective Overshooting Cloud-Top Detection Methods Using CloudSat Cloud Profiling Radar Observations

NASA Technical Reports Server (NTRS)

Bedka, Kristopher M.; Dworak, Richard; Brunner, Jason; Feltz, Wayne

2012-01-01

Two satellite infrared-based overshooting convective cloud-top (OT) detection methods have recently been described in the literature: 1) the 11-mm infrared window channel texture (IRW texture) method, which uses IRW channel brightness temperature (BT) spatial gradients and thresholds, and 2) the water vapor minus IRW BT difference (WV-IRW BTD). While both methods show good performance in published case study examples, it is important to quantitatively validate these methods relative to overshooting top events across the globe. Unfortunately, no overshooting top database currently exists that could be used in such study. This study examines National Aeronautics and Space Administration CloudSat Cloud Profiling Radar data to develop an OT detection validation database that is used to evaluate the IRW-texture and WV-IRW BTD OT detection methods. CloudSat data were manually examined over a 1.5-yr period to identify cases in which the cloud top penetrates above the tropopause height defined by a numerical weather prediction model and the surrounding cirrus anvil cloud top, producing 111 confirmed overshooting top events. When applied to Moderate Resolution Imaging Spectroradiometer (MODIS)-based Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager proxy data, the IRW-texture (WV-IRW BTD) method offered a 76% (96%) probability of OT detection (POD) and 16% (81%) false-alarm ratio. Case study examples show that WV-IRW BTD.0 K identifies much of the deep convective cloud top, while the IRW-texture method focuses only on regions with a spatial scale near that of commonly observed OTs. The POD decreases by 20% when IRW-texture is applied to current geostationary imager data, highlighting the importance of imager spatial resolution for observing and detecting OT regions.

Analysis of 2-alkylcyclobutanones in cashew nut, nutmeg, apricot kernel, and pine nut samples: re-evaluating the uniqueness of 2-alkylcyclobutanones for irradiated food identification.

PubMed

Leung, Elvis M K; Tang, Phyllis N Y; Ye, Yuran; Chan, Wan

2013-10-16

2-Alkylcyclobutanones (2-ACBs) have long been considered as unique radiolytic products that can be used as indicators for irradiated food identification. A recent report on the natural existence of 2-ACB in non-irradiated nutmeg and cashew nut samples aroused worldwide concern because it contradicts the general belief that 2-ACBs are specific to irradiated food. The goal of this study is to test the natural existence of 2-ACBs in nut samples using our newly developed liquid chromatography-tandem mass spectrometry (LC-MS/MS) method with enhanced analytical sensitivity and selectivity ( Ye , Y. ; Liu , H. ; Horvatovich , P. ; Chan , W. Liquid chromatography-electrospray ionization tandem mass spectrometric analysis of 2-alkylcyclobutanones in irradiated chicken by precolumn derivatization with hydroxylamine . J. Agric. Food Chem. 2013 , 61 , 5758 - 5763 ). The validated method was applied to identify 2-dodecylcyclobutanone (2-DCB) and 2-tetradecylcyclobutanone (2-TCB) in nutmeg, cashew nut, pine nut, and apricot kernel samples (n = 22) of different origins. Our study reveals that 2-DCB and 2-TCB either do not exist naturally or exist at concentrations below the detection limit of the existing method. Thus, 2-DCB and 2-TCB are still valid to be used as biomarkers for identifying irradiated food.

Study of SEM induced current and voltage contrast modes to assess semiconductor reliability

NASA Technical Reports Server (NTRS)

Beall, J. R.

1976-01-01

The purpose of the scanning electron microscopy study was to review the failure history of existing integrated circuit technologies to identify predominant failure mechanisms, and to evaluate the feasibility of their detection using SEM application techniques. The study investigated the effects of E-beam irradiation damage and contamination deposition rates; developed the necessary methods for applying the techniques to the detection of latent defects and weaknesses in integrated circuits; and made recommendations for applying the techniques.

Development and application of deep convolutional neural network in target detection

NASA Astrophysics Data System (ADS)

Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

2018-04-01

With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.

Detection of Extraterrestrial Life. Method II- Optical Rotatory Dispersion

NASA Technical Reports Server (NTRS)

1963-01-01

The object of this study is to develop polarimetric methods to detect the presence of DNA (deoxyribonucleic acid) or its congeners in soil suspensions, and through these methods determine the existence of life (as known terrestrially) on other planets. The cotton region associated with optically active organic compounds is being used to detect and characterize the compounds above. An apparatus has been designed and assembled which can measure optical rotations in systems which strongly attenuate incident-polarized, monochromatic light. This instrument was used to measure the optical rotatory dispersion spectra of nucleosides, a polynucleotide, and proteins whose optical density at 260 microns approached 1.0. This work is discussed in the final report on Contract NASR-85, Detection of Extraterrestrial Life, Method II: Optical Rotatory Dispersion. Recent work in Melpar laboratories has reaffirmed these rotatory dispersion spectra. Based upon the analysis of the optical components associated with this apparatus, however, these measurements must be considered as qualitative rather than quantitative. The reason for this is discussed in greater detail subsequently in this report. In addition, an evaluation of the theoretical and instrumental aspects of making rotatory-dispersion measurements in the cotton region has resulted in a procedure for measuring optical rotation.

A review of damage detection methods for wind turbine blades

NASA Astrophysics Data System (ADS)

Li, Dongsheng; Ho, Siu-Chun M.; Song, Gangbing; Ren, Liang; Li, Hongnan

2015-03-01

Wind energy is one of the most important renewable energy sources and many countries are predicted to increase wind energy portion of their whole national energy supply to about twenty percent in the next decade. One potential obstacle in the use of wind turbines to harvest wind energy is the maintenance of the wind turbine blades. The blades are a crucial and costly part of a wind turbine and over their service life can suffer from factors such as material degradation and fatigue, which can limit their effectiveness and safety. Thus, the ability to detect damage in wind turbine blades is of great significance for planning maintenance and continued operation of the wind turbine. This paper presents a review of recent research and development in the field of damage detection for wind turbine blades. Specifically, this paper reviews frequently employed sensors including fiber optic and piezoelectric sensors, and four promising damage detection methods, namely, transmittance function, wave propagation, impedance and vibration based methods. As a note towards the future development trend for wind turbine sensing systems, the necessity for wireless sensing and energy harvesting is briefly presented. Finally, existing problems and promising research efforts for online damage detection of turbine blades are discussed.

Unmanned Aerial Vehicles for Alien Plant Species Detection and Monitoring

NASA Astrophysics Data System (ADS)

Dvořák, P.; Müllerová, J.; Bartaloš, T.; Brůna, J.

2015-08-01

Invasive species spread rapidly and their eradication is difficult. New methods enabling fast and efficient monitoring are urgently needed for their successful control. Remote sensing can improve early detection of invading plants and make their management more efficient and less expensive. In an ongoing project in the Czech Republic, we aim at developing innovative methods of mapping invasive plant species (semi-automatic detection algorithms) by using purposely designed unmanned aircraft (UAV). We examine possibilities for detection of two tree and two herb invasive species. Our aim is to establish fast, repeatable and efficient computer-assisted method of timely monitoring, reducing the costs of extensive field campaigns. For finding the best detection algorithm we test various classification approaches (object-, pixel-based and hybrid). Thanks to its flexibility and low cost, UAV enables assessing the effect of phenological stage and spatial resolution, and is most suitable for monitoring the efficiency of eradication efforts. However, several challenges exist in UAV application, such as geometrical and radiometric distortions, high amount of data to be processed and legal constrains for the UAV flight missions over urban areas (often highly invaded). The newly proposed UAV approach shall serve invasive species researchers, management practitioners and policy makers.

Identification of influential users by neighbors in online social networks

NASA Astrophysics Data System (ADS)

Sheikhahmadi, Amir; Nematbakhsh, Mohammad Ali; Zareie, Ahmad

2017-11-01

Identification and ranking of influential users in social networks for the sake of news spreading and advertising has recently become an attractive field of research. Given the large number of users in social networks and also the various relations that exist among them, providing an effective method to identify influential users has been gradually considered as an essential factor. In most of the already-provided methods, those users who are located in an appropriate structural position of the network are regarded as influential users. These methods do not usually pay attention to the interactions among users, and also consider those relations as being binary in nature. This paper, therefore, proposes a new method to identify influential users in a social network by considering those interactions that exist among the users. Since users tend to act within the frame of communities, the network is initially divided into different communities. Then the amount of interaction among users is used as a parameter to set the weight of relations existing within the network. Afterward, by determining the neighbors' role for each user, a two-level method is proposed for both detecting users' influence and also ranking them. Simulation and experimental results on twitter data shows that those users who are selected by the proposed method, comparing to other existing ones, are distributed in a more appropriate distance. Moreover, the proposed method outperforms the other ones in terms of both the influential speed and capacity of the users it selects.

Application of the modified transient plane source technique for early detection of liquid explosives

NASA Astrophysics Data System (ADS)

Bateman, Robert; Harris, Adam; Lee, Linda; Howle, Christopher R.; Ackermann, Sarah L. G.

2016-05-01

The paper will review the feasibility of adapting the Modified Transient Plane Source (MTPS) method as a screening tool for early-detection of explosives and hazardous materials. Materials can be distinguished from others based on their inherent thermal properties (e.g. thermal effusivity) in testing through different types of barrier materials. A complimentary advantage to this technique relative to other traditional detection technologies is that it can penetrate reflective barrier materials, such as aluminum, easily. A strong proof-of-principle is presented on application of the MTPS transient thermal property measuring in the early-screening of liquid explosives. The work demonstrates a significant sensitivity to distinguishing a wide range of fluids based on their thermal properties through a barrier material. The work covers various complicating factors to the longer-term adoption of such a method including the impact of carbonization and viscosity. While some technical challenges remain, the technique offers significant advantages in complimenting existing detection methods in being able to penetrate reflective metal containers (e.g. aluminum soft drinkscans) with ease.

Ultrasensitive determination of jasmonic acid in plant tissues using high-performance liquid chromatography with fluorescence detection.

PubMed

Xiong, Xu-Jie; Rao, Wan-Bing; Guo, Xiao-Feng; Wang, Hong; Zhang, Hua-Shan

2012-05-23

An ultrasensitive and selective high-performance liquid chromatographic method for the volatile signaling hormone, jasmonic acid, has been developed based on precolumn derivatization with 1,3,5,7-tetramethyl-8-aminozide-difluoroboradiaza-s-indacene (BODIPY-aminozide). The derivatization reaction was carried out at 60 °C for 30 min in the presence of phosphoric acid. The formed jasmonic acid derivative was eluted using a mobile phase of methanol/pH 6.50 ammonium formate buffer/tetrahydrofuran (67:30:3, v/v/v) in 10 min on a C(18) column and detected with fluorescence detection at excitation and emission wavelengths of 495 and 505 nm, respectively. The detection limit (signal-to-noise ratio = 4) reached 1.14 × 10(-10) M or 2.29 fmol per injection (20 μL), which is the lowest of the existing methods. The proposed method has been successfully applied to the direct determination of trace jasmonic acid in the crude extracts of soybean leaves from soybean mosaic virus-infected and normal plants with recoveries of 95-104%.

A Fast Method for Embattling Optimization of Ground-Based Radar Surveillance Network

NASA Astrophysics Data System (ADS)

Jiang, H.; Cheng, H.; Zhang, Y.; Liu, J.

A growing number of space activities have created an orbital debris environment that poses increasing impact risks to existing space systems and human space flight. For the safety of in-orbit spacecraft, a lot of observation facilities are needed to catalog space objects, especially in low earth orbit. Surveillance of Low earth orbit objects are mainly rely on ground-based radar, due to the ability limitation of exist radar facilities, a large number of ground-based radar need to build in the next few years in order to meet the current space surveillance demands. How to optimize the embattling of ground-based radar surveillance network is a problem to need to be solved. The traditional method for embattling optimization of ground-based radar surveillance network is mainly through to the detection simulation of all possible stations with cataloged data, and makes a comprehensive comparative analysis of various simulation results with the combinational method, and then selects an optimal result as station layout scheme. This method is time consuming for single simulation and high computational complexity for the combinational analysis, when the number of stations increases, the complexity of optimization problem will be increased exponentially, and cannot be solved with traditional method. There is no better way to solve this problem till now. In this paper, target detection procedure was simplified. Firstly, the space coverage of ground-based radar was simplified, a space coverage projection model of radar facilities in different orbit altitudes was built; then a simplified objects cross the radar coverage model was established according to the characteristics of space objects orbit motion; after two steps simplification, the computational complexity of the target detection was greatly simplified, and simulation results shown the correctness of the simplified results. In addition, the detection areas of ground-based radar network can be easily computed with the simplified model, and then optimized the embattling of ground-based radar surveillance network with the artificial intelligent algorithm, which can greatly simplifies the computational complexities. Comparing with the traditional method, the proposed method greatly improved the computational efficiency.

Efficient method for events detection in phonocardiographic signals

NASA Astrophysics Data System (ADS)

Martinez-Alajarin, Juan; Ruiz-Merino, Ramon

2005-06-01

The auscultation of the heart is still the first basic analysis tool used to evaluate the functional state of the heart, as well as the first indicator used to submit the patient to a cardiologist. In order to improve the diagnosis capabilities of auscultation, signal processing algorithms are currently being developed to assist the physician at primary care centers for adult and pediatric population. A basic task for the diagnosis from the phonocardiogram is to detect the events (main and additional sounds, murmurs and clicks) present in the cardiac cycle. This is usually made by applying a threshold and detecting the events that are bigger than the threshold. However, this method usually does not allow the detection of the main sounds when additional sounds and murmurs exist, or it may join several events into a unique one. In this paper we present a reliable method to detect the events present in the phonocardiogram, even in the presence of heart murmurs or additional sounds. The method detects relative maxima peaks in the amplitude envelope of the phonocardiogram, and computes a set of parameters associated with each event. Finally, a set of characteristics is extracted from each event to aid in the identification of the events. Besides, the morphology of the murmurs is also detected, which aids in the differentiation of different diseases that can occur in the same temporal localization. The algorithms have been applied to real normal heart sounds and murmurs, achieving satisfactory results.

Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images Based on an End-to-End Deep Neural Network.

PubMed

Wang, Zhiwei; Liu, Chaoyue; Cheng, Danpeng; Wang, Liang; Yang, Xin; Cheng, Kwang-Ting

2018-05-01

Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network. The proposed neural network consists of concatenated subnets: 1) a novel tissue deformation network (TDN) for automated prostate detection and multimodal registration and 2) a dual-path convolutional neural network (CNN) for CS PCa detection. Three types of loss functions, i.e., classification loss, inconsistency loss, and overlap loss, are employed for optimizing all parameters of the proposed TDN and CNN. In the training phase, the two nets mutually affect each other and effectively guide registration and extraction of representative CS PCa-relevant features to achieve results with sufficient accuracy. The entire network is trained in a weakly supervised manner by providing only image-level annotations (i.e., presence/absence of PCa) without exact priors of lesions' locations. Compared with most existing systems which require supervised labels, e.g., manual delineation of PCa lesions, it is much more convenient for clinical usage. Comprehensive evaluation based on fivefold cross validation using 360 patient data demonstrates that our system achieves a high accuracy for CS PCa detection, i.e., a sensitivity of 0.6374 and 0.8978 at 0.1 and 1 false positives per normal/benign patient.

New S control chart using skewness correction method for monitoring process dispersion of skewed distributions

NASA Astrophysics Data System (ADS)

Atta, Abdu; Yahaya, Sharipah; Zain, Zakiyah; Ahmed, Zalikha

2017-11-01

Control chart is established as one of the most powerful tools in Statistical Process Control (SPC) and is widely used in industries. The conventional control charts rely on normality assumption, which is not always the case for industrial data. This paper proposes a new S control chart for monitoring process dispersion using skewness correction method for skewed distributions, named as SC-S control chart. Its performance in terms of false alarm rate is compared with various existing control charts for monitoring process dispersion, such as scaled weighted variance S chart (SWV-S); skewness correction R chart (SC-R); weighted variance R chart (WV-R); weighted variance S chart (WV-S); and standard S chart (STD-S). Comparison with exact S control chart with regards to the probability of out-of-control detections is also accomplished. The Weibull and gamma distributions adopted in this study are assessed along with the normal distribution. Simulation study shows that the proposed SC-S control chart provides good performance of in-control probabilities (Type I error) in almost all the skewness levels and sample sizes, n. In the case of probability of detection shift the proposed SC-S chart is closer to the exact S control chart than the existing charts for skewed distributions, except for the SC-R control chart. In general, the performance of the proposed SC-S control chart is better than all the existing control charts for monitoring process dispersion in the cases of Type I error and probability of detection shift.

The Occurrence of Veterinary Pharmaceuticals in the Environment: A Review

PubMed Central

Kaczala, Fabio; Blum, Shlomo E.

2016-01-01

It is well known that there is a widespread use of veterinary pharmaceuticals and consequent release into different ecosystems such as freshwater bodies and groundwater systems. Furthermore, the use of organic fertilizers produced from animal waste manure has been also responsible for the occurrence of veterinary pharmaceuticals in agricultural soils. This article is a review of different studies focused on the detection and quantification of such compounds in environmental compartments using different analytical techniques. Furthermore, this paper reports the main challenges regarding veterinary pharmaceuticals in terms of analytical methods, detection/quantification of parent compounds and metabolites, and risks/toxicity to human health and aquatic ecosystems. Based on the existing literature, it is clear that only limited data is available regarding veterinary compounds and there are still considerable gaps to be bridged in order to remediate existing problems and prevent future ones. In terms of analytical methods, there are still considerable challenges to overcome considering the large number of existing compounds and respective metabolites. A number of studies highlight the lack of attention given to the detection and quantification of transformation products and metabolites. Furthermore more attention needs to be given in relation to the toxic effects and potential risks that veterinary compounds pose to environmental and human health. To conclude, the more research investigations focused on these subjects take place in the near future, more rapidly we will get a better understanding about the behavior of these compounds and the real risks they pose to aquatic and terrestrial environments and how to properly tackle them. PMID:28579931

Laser-induced photo emission detection: data acquisition based on light intensity counting

NASA Astrophysics Data System (ADS)

Yulianto, N.; Yudasari, N.; Putri, K. Y.

2017-04-01

Laser Induced Breakdown Detection (LIBD) is one of the quantification techniques for colloids. There are two ways of detection in LIBD: optical detection and acoustic detection. LIBD is based on the detection of plasma emission due to the interaction between particle and laser beam. In this research, the changing of light intensity during plasma formations was detected by a photodiode sensor. A photo emission data acquisition system was built to collect and transform them into digital counts. The real-time system used data acquisition device National Instrument DAQ 6009 and LABVIEW software. The system has been tested on distilled water and tap water samples. The result showed 99.8% accuracy by using counting technique in comparison to the acoustic detection with sample rate of 10 Hz, thus the acquisition system can be applied as an alternative method to the existing LIBD acquisition system.

Vessel extraction in retinal images using automatic thresholding and Gabor Wavelet.

PubMed

Ali, Aziah; Hussain, Aini; Wan Zaki, Wan Mimi Diyana

2017-07-01

Retinal image analysis has been widely used for early detection and diagnosis of multiple systemic diseases. Accurate vessel extraction in retinal image is a crucial step towards a fully automated diagnosis system. This work affords an efficient unsupervised method for extracting blood vessels from retinal images by combining existing Gabor Wavelet (GW) method with automatic thresholding. Green channel image is extracted from color retinal image and used to produce Gabor feature image using GW. Both green channel image and Gabor feature image undergo vessel-enhancement step in order to highlight blood vessels. Next, the two vessel-enhanced images are transformed to binary images using automatic thresholding before combined to produce the final vessel output. Combining the images results in significant improvement of blood vessel extraction performance compared to using individual image. Effectiveness of the proposed method was proven via comparative analysis with existing methods validated using publicly available database, DRIVE.

ScanIndel: a hybrid framework for indel detection via gapped alignment, split reads and de novo assembly.

PubMed

Yang, Rendong; Nelson, Andrew C; Henzler, Christine; Thyagarajan, Bharat; Silverstein, Kevin A T

2015-12-07

Comprehensive identification of insertions/deletions (indels) across the full size spectrum from second generation sequencing is challenging due to the relatively short read length inherent in the technology. Different indel calling methods exist but are limited in detection to specific sizes with varying accuracy and resolution. We present ScanIndel, an integrated framework for detecting indels with multiple heuristics including gapped alignment, split reads and de novo assembly. Using simulation data, we demonstrate ScanIndel's superior sensitivity and specificity relative to several state-of-the-art indel callers across various coverage levels and indel sizes. ScanIndel yields higher predictive accuracy with lower computational cost compared with existing tools for both targeted resequencing data from tumor specimens and high coverage whole-genome sequencing data from the human NIST standard NA12878. Thus, we anticipate ScanIndel will improve indel analysis in both clinical and research settings. ScanIndel is implemented in Python, and is freely available for academic use at https://github.com/cauyrd/ScanIndel.

Bad data detection in two stage estimation using phasor measurements

NASA Astrophysics Data System (ADS)

Tarali, Aditya

The ability of the Phasor Measurement Unit (PMU) to directly measure the system state, has led to steady increase in the use of PMU in the past decade. However, in spite of its high accuracy and the ability to measure the states directly, they cannot completely replace the conventional measurement units due to high cost. Hence it is necessary for the modern estimators to use both conventional and phasor measurements together. This thesis presents an alternative method to incorporate the new PMU measurements into the existing state estimator in a systematic manner such that no major modification is necessary to the existing algorithm. It is also shown that if PMUs are placed appropriately, the phasor measurements can be used to detect and identify the bad data associated with critical measurements by using this model, which cannot be detected by conventional state estimation algorithm. The developed model is tested on IEEE 14, IEEE 30 and IEEE 118 bus under various conditions.

Scalable detection of statistically significant communities and hierarchies, using message passing for modularity

PubMed Central

Zhang, Pan; Moore, Cristopher

2014-01-01

Modularity is a popular measure of community structure. However, maximizing the modularity can lead to many competing partitions, with almost the same modularity, that are poorly correlated with each other. It can also produce illusory ‘‘communities’’ in random graphs where none exist. We address this problem by using the modularity as a Hamiltonian at finite temperature and using an efficient belief propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it. We show analytically and numerically that the proposed algorithm works all of the way down to the detectability transition in networks generated by the stochastic block model. It also performs well on real-world networks, revealing large communities in some networks where previous work has claimed no communities exist. Finally we show that by applying our algorithm recursively, subdividing communities until no statistically significant subcommunities can be found, we can detect hierarchical structure in real-world networks more efficiently than previous methods. PMID:25489096

Comparative analysis of methods for detecting interacting loci

PubMed Central

2011-01-01

Background Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted. Results We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs. Conclusion This comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at: http://code.google.com/p/simulation-tool-bmc-ms9169818735220977/downloads/list. PMID:21729295

Comparative analysis of methods for detecting interacting loci.

PubMed

Chen, Li; Yu, Guoqiang; Langefeld, Carl D; Miller, David J; Guy, Richard T; Raghuram, Jayaram; Yuan, Xiguo; Herrington, David M; Wang, Yue

2011-07-05

Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted. We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs. This comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at: http://code.google.com/p/simulation-tool-bmc-ms9169818735220977/downloads/list.

Detection of Dry Intrusion on Water Vapor Images Over Central Europe - June 2010 TO September 2011

NASA Astrophysics Data System (ADS)

Novotny, J.; Dejmal, K.; Hudec, F.; Kolar, P.

2016-06-01

The knowledge of evaluation of the intensity of cyclogenesis which could be connected with the weather having a significant impact on Earth's surface is quite useful. If, as one of the basic assumptions, the existence of connection between dry intrusions, dry bands, tropopause height and warm dark areas distribution on water vapor images (WV images) is considered, it is possible to set up a method of detecting dry intrusions on searching and tracking areas with higher brightness temperature compared with the surrounding environment. This paper covers the period between June 2010 and September 2011 over Central Europe. The ISIS method (Instrument de Suivi dans I'Imagerie satellitaire), originally developed for detection of cold cloud tops, was used as an initial ideological point. Subsequently, this method was modified by Michel and Bouttier for usage on WV images. Some of the applied criteria and parameters were chosen with reference to the results published by Michel and Bouttier as well as by Novotny. The procedure can be divided into two steps: detection of warm areas and their tracking. Cases of detection of areas not evidently connected with dry intrusions can be solved by filtering off based on the connection between detected warm areas to the cyclonic side of jet streams and significant lowering of the tropopause.

Detection of protein complex from protein-protein interaction network using Markov clustering

NASA Astrophysics Data System (ADS)

Ochieng, P. J.; Kusuma, W. A.; Haryanto, T.

2017-05-01

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.

Rapid high-throughput analysis of ochratoxin A by the self-assembly of DNAzyme-aptamer conjugates in wine.

PubMed

Yang, Cheng; Lates, Vasilica; Prieto-Simón, Beatriz; Marty, Jean-Louis; Yang, Xiurong

2013-11-15

We report a new label-free colorimetric aptasensor based on DNAzyme-aptamer conjugate for rapid and high-throughput detection of Ochratoxin A (OTA, a possible human carcinogen, group 2B) in wine. Two oligonucleotides were designed for this detection. One is N1 for biorecognition, which includes two adjacent sequences: the OTA-specific aptamer sequence and the horseradish peroxidase (HRP)-mimicking DNAzyme sequence. The other is a blocking DNA (B2), which is partially complementary to a part of the OTA aptamer and partially complementary to a part of the DNAzyme. The existence of OTA reduces the hybridization between N1 and B2. Thus, the activity of the non-hybridized DNAzyme is linearly correlated with the concentration of OTA up to 30 nM with a limit of detection of 4 nM (3σ). Meanwhile, a double liquid-liquid extraction (LLE) method is accordingly developed to purify OTA from wine. Compared with the existing HPLC-FD or immunoassay methods, the proposed strategy presents the most appropriate balance between accuracy and facility, resulting in a considerable improvement of real-time quality control, and thereby, preventing chronic poisoning caused by OTA contained red wine. Copyright © 2013 Elsevier B.V. All rights reserved.

Tracking Object Existence From an Autonomous Patrol Vehicle

NASA Technical Reports Server (NTRS)

Wolf, Michael; Scharenbroich, Lucas

2011-01-01

An autonomous vehicle patrols a large region, during which an algorithm receives measurements of detected potential objects within its sensor range. The goal of the algorithm is to track all objects in the region over time. This problem differs from traditional multi-target tracking scenarios because the region of interest is much larger than the sensor range and relies on the movement of the sensor through this region for coverage. The goal is to know whether anything has changed between visits to the same location. In particular, two kinds of alert conditions must be detected: (1) a previously detected object has disappeared and (2) a new object has appeared in a location already checked. For the time an object is within sensor range, the object can be assumed to remain stationary, changing position only between visits. The problem is difficult because the upstream object detection processing is likely to make many errors, resulting in heavy clutter (false positives) and missed detections (false negatives), and because only noisy, bearings-only measurements are available. This work has three main goals: (1) Associate incoming measurements with known objects or mark them as new objects or false positives, as appropriate. For this, a multiple hypothesis tracker was adapted to this scenario. (2) Localize the objects using multiple bearings-only measurements to provide estimates of global position (e.g., latitude and longitude). A nonlinear Kalman filter extension provides these 2D position estimates using the 1D measurements. (3) Calculate the probability that a suspected object truly exists (in the estimated position), and determine whether alert conditions have been triggered (for new objects or disappeared objects). The concept of a probability of existence was created, and a new Bayesian method for updating this probability at each time step was developed. A probabilistic multiple hypothesis approach is chosen because of its superiority in handling the uncertainty arising from errors in sensors and upstream processes. However, traditional target tracking methods typically assume a stationary detection volume of interest, whereas in this case, one must make adjustments for being able to see only a small portion of the region of interest and understand when an alert situation has occurred. To track object existence inside and outside the vehicle's sensor range, a probability of existence was defined for each hypothesized object, and this value was updated at every time step in a Bayesian manner based on expected characteristics of the sensor and object and whether that object has been detected in the most recent time step. Then, this value feeds into a sequential probability ratio test (SPRT) to determine the status of the object (suspected, confirmed, or deleted). Alerts are sent upon selected status transitions. Additionally, in order to track objects that move in and out of sensor range and update the probability of existence appropriately a variable probability detection has been defined and the hypothesis probability equations have been re-derived to accommodate this change. Unsupervised object tracking is a pervasive issue in automated perception systems. This work could apply to any mobile platform (ground vehicle, sea vessel, air vehicle, or orbiter) that intermittently revisits regions of interest and needs to determine whether anything interesting has changed.

A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data

PubMed Central

Feng, Hao; Conneely, Karen N.; Wu, Hao

2014-01-01

DNA methylation is an important epigenetic modification that has essential roles in cellular processes including gene regulation, development and disease and is widely dysregulated in most types of cancer. Recent advances in sequencing technology have enabled the measurement of DNA methylation at single nucleotide resolution through methods such as whole-genome bisulfite sequencing and reduced representation bisulfite sequencing. In DNA methylation studies, a key task is to identify differences under distinct biological contexts, for example, between tumor and normal tissue. A challenge in sequencing studies is that the number of biological replicates is often limited by the costs of sequencing. The small number of replicates leads to unstable variance estimation, which can reduce accuracy to detect differentially methylated loci (DML). Here we propose a novel statistical method to detect DML when comparing two treatment groups. The sequencing counts are described by a lognormal-beta-binomial hierarchical model, which provides a basis for information sharing across different CpG sites. A Wald test is developed for hypothesis testing at each CpG site. Simulation results show that the proposed method yields improved DML detection compared to existing methods, particularly when the number of replicates is low. The proposed method is implemented in the Bioconductor package DSS. PMID:24561809

TRANSAT-- method for detecting the conserved helices of functional RNA structures, including transient, pseudo-knotted and alternative structures.

PubMed

Wiebe, Nicholas J P; Meyer, Irmtraud M

2010-06-24

The prediction of functional RNA structures has attracted increased interest, as it allows us to study the potential functional roles of many genes. RNA structure prediction methods, however, assume that there is a unique functional RNA structure and also do not predict functional features required for in vivo folding. In order to understand how functional RNA structures form in vivo, we require sophisticated experiments or reliable prediction methods. So far, there exist only a few, experimentally validated transient RNA structures. On the computational side, there exist several computer programs which aim to predict the co-transcriptional folding pathway in vivo, but these make a range of simplifying assumptions and do not capture all features known to influence RNA folding in vivo. We want to investigate if evolutionarily related RNA genes fold in a similar way in vivo. To this end, we have developed a new computational method, Transat, which detects conserved helices of high statistical significance. We introduce the method, present a comprehensive performance evaluation and show that Transat is able to predict the structural features of known reference structures including pseudo-knotted ones as well as those of known alternative structural configurations. Transat can also identify unstructured sub-sequences bound by other molecules and provides evidence for new helices which may define folding pathways, supporting the notion that homologous RNA sequence not only assume a similar reference RNA structure, but also fold similarly. Finally, we show that the structural features predicted by Transat differ from those assuming thermodynamic equilibrium. Unlike the existing methods for predicting folding pathways, our method works in a comparative way. This has the disadvantage of not being able to predict features as function of time, but has the considerable advantage of highlighting conserved features and of not requiring a detailed knowledge of the cellular environment.

Rapid identification and quantification of tumor cells using an electrocatalytic method based on gold nanoparticles.

PubMed

de la Escosura-Muñiz, Alfredo; Sánchez-Espinel, Christian; Díaz-Freitas, Belén; González-Fernández, Africa; Maltez-da Costa, Marisa; Merkoçi, Arben

2009-12-15

There is a high demand for simple, rapid, efficient, and user-friendly alternative methods for the detection of cells in general and, in particular, for the detection of cancer cells. A biosensor able to detect cells would be an all-in-one dream device for such applications. The successful integration of nanoparticles into cell detection assays could allow for the development of this novel class of cell sensors. Indeed, their application could well have a great future in diagnostics, as well as other fields. As an example of a novel biosensor, we report here an electrocatalytic device for the specific identification of tumor cells that quantifies gold nanoparticles (AuNPs) coupled with an electrotransducing platform/sensor. Proliferation and adherence of tumor cells are achieved on the electrotransducer/detector, which consists of a mass-produced screen-printed carbon electrode (SPCE). In situ identification/quantification of tumor cells is achieved with a detection limit of 4000 cells per 700 microL of suspension. This novel and selective cell-sensing device is based on the reaction of cell surface proteins with specific antibodies conjugated with AuNPs. Final detection requires only a couple of minutes, taking advantage of the catalytic properties of AuNPs on hydrogen evolution. The proposed detection method does not require the chemical agents used in most existing assays for the detection of AuNPs. It allows for the miniaturization of the system and is much cheaper than other expensive and sophisticated methods used for tumor cell detection. We envisage that this device could operate in a simple way as an immunosensor or DNA sensor. Moreover, it could be used, even by inexperienced staff, for the detection of protein molecules or DNA strands.

Detection of Echinoderm Microtubule Associated Protein Like 4-Anaplastic Lymphoma Kinase Fusion Genes in Non-small Cell Lung Cancer Clinical Samples by a Real-time Quantitative Reverse Transcription Polymerase Chain Reaction Method.

PubMed

Zhao, Jing; Zhao, Jin-Yin; Chen, Zhi-Xia; Zhong, Wei; Li, Long-Yun; Liu, Li-Cheng; Hu, Xiao-Xu; Chen, Wei-Jun; Wang, Meng-Zhao

2016-12-20

Objective To establish a real-time quantitative reverse transcription polymerase chain reaction assay (qRT-PCR) for the rapid, sensitive, and specific detection of echinoderm microtubule associated protein like 4-anaplastic lymphoma kinase (EML4-ALK) fusion genes in non-small cell lung cancer. Methods The specific primers for the four variants of EML4-ALK fusion genes (V1, V2, V3a, and V3b) and Taqman fluorescence probes for the detection of the target sequences were carefully designed by the Primer Premier 5.0 software. Then, using pseudovirus containing EML4-ALK fusion genes variants (V1, V2, V3a, and V3b) as the study objects, we further analyzed the lower limit, sensitivity, and specificity of this method. Finally, 50 clinical samples, including 3 ALK-fluorescence in situ hybridization (FISH) positive specimens, were collected and used to detect EML4-ALK fusion genes using this method. Results The lower limit of this method for the detection of EML4-ALK fusion genes was 10 copies/μl if no interference of background RNA existed. Regarding the method's sensitivity, the detection resolution was as high as 1% and 0.5% in the background of 500 and 5000 copies/μl wild-type ALK gene, respectively. Regarding the method's specificity, no non-specific amplification was found when it was used to detect EML4-ALK fusion genes in leukocyte and plasma RNA samples from healthy volunteers. Among the 50 clinical samples, 47 ALK-FISH negative samples were also negative. Among 3 ALK-FISH positive samples, 2 cases were detected positive using this method, but another was not detected because of the failure of RNA extraction. Conclusion The proposed qRT-PCR assay for the detection of EML4-ALK fusion genes is rapid, simple, sensitive, and specific, which is deserved to be validated and widely used in clinical settings.

Developing new mathematical method for search of the time series periodicity with deletions and insertions

NASA Astrophysics Data System (ADS)

Korotkov, E. V.; Korotkova, M. A.

2017-01-01

The purpose of this study was to detect latent periodicity in the presence of deletions or insertions in the analyzed data, when the points of deletions or insertions are unknown. A mathematical method was developed to search for periodicity in the numerical series, using dynamic programming and random matrices. The developed method was applied to search for periodicity in the Euro/Dollar (Eu/) exchange rate, since 2001. The presence of periodicity within the period length equal to 24 h in the analyzed financial series was shown. Periodicity can be detected only with insertions and deletions. The results of this study show that periodicity phase shifts, depend on the observation time. The reasons for the existence of the periodicity in the financial ranks are discussed.

Identification of Successive ``Unobservable'' Cyber Data Attacks in Power Systems Through Matrix Decomposition

NASA Astrophysics Data System (ADS)

Gao, Pengzhi; Wang, Meng; Chow, Joe H.; Ghiocel, Scott G.; Fardanesh, Bruce; Stefopoulos, George; Razanousky, Michael P.

2016-11-01

This paper presents a new framework of identifying a series of cyber data attacks on power system synchrophasor measurements. We focus on detecting "unobservable" cyber data attacks that cannot be detected by any existing method that purely relies on measurements received at one time instant. Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we formulate the identification problem of successive unobservable cyber attacks as a matrix decomposition problem of a low-rank matrix plus a transformed column-sparse matrix. We propose a convex-optimization-based method and provide its theoretical guarantee in the data identification. Numerical experiments on actual PMU data from the Central New York power system and synthetic data are conducted to verify the effectiveness of the proposed method.

Torque-mixing magnetic resonance spectroscopy (Conference Presentation)

NASA Astrophysics Data System (ADS)

Losby, Joseph; Fani Sani, Fatemeh; Grandmont, Dylan T.; Diao, Zhu; Belov, Miro; Burgess, Jacob A.; Compton, Shawn R.; Hiebert, Wayne K.; Vick, Doug; Mohammad, Kaveh; Salimi, Elham; Bridges, Gregory E.; Thomson, Douglas J.; Freeman, Mark R.

2016-10-01

An optomechanical platform for magnetic resonance spectroscopy will be presented. The method relies on frequency mixing of orthogonal RF fields to yield a torque amplitude (arising from the transverse component of a precessing dipole moment, in analogy to magnetic resonance detection by electromagnetic induction) on a miniaturized resonant mechanical torsion sensor. In contrast to induction, the method is fully broadband and allows for simultaneous observation of the equilibrium net magnetic moment alongside the associated magnetization dynamics. To illustrate the method, comprehensive electron spin resonance spectra of a mesoscopic, single-crystal YIG disk at room temperature will be presented, along with situations where torque spectroscopy can offer complimentary information to existing magnetic resonance detection techniques. The authors are very grateful for support from NSERC, CRC, AITF, and NINT. Reference: Science 350, 798 (2015).

Cross validation of gas chromatography-flame photometric detection and gas chromatography-mass spectrometry methods for measuring dialkylphosphate metabolites of organophosphate pesticides in human urine.

PubMed

Prapamontol, Tippawan; Sutan, Kunrunya; Laoyang, Sompong; Hongsibsong, Surat; Lee, Grace; Yano, Yukiko; Hunter, Ronald Elton; Ryan, P Barry; Barr, Dana Boyd; Panuwet, Parinya

2014-01-01

We report two analytical methods for the measurement of dialkylphosphate (DAP) metabolites of organophosphate pesticides in human urine. These methods were independently developed/modified and implemented in two separate laboratories and cross validated. The aim was to develop simple, cost effective, and reliable methods that could use available resources and sample matrices in Thailand and the United States. While several methods already exist, we found that direct application of these methods required modification of sample preparation and chromatographic conditions to render accurate, reliable data. The problems encountered with existing methods were attributable to urinary matrix interferences, and differences in the pH of urine samples and reagents used during the extraction and derivatization processes. Thus, we provide information on key parameters that require attention during method modification and execution that affect the ruggedness of the methods. The methods presented here employ gas chromatography (GC) coupled with either flame photometric detection (FPD) or electron impact ionization-mass spectrometry (EI-MS) with isotopic dilution quantification. The limits of detection were reported from 0.10ng/mL urine to 2.5ng/mL urine (for GC-FPD), while the limits of quantification were reported from 0.25ng/mL urine to 2.5ng/mL urine (for GC-MS), for all six common DAP metabolites (i.e., dimethylphosphate, dimethylthiophosphate, dimethyldithiophosphate, diethylphosphate, diethylthiophosphate, and diethyldithiophosphate). Each method showed a relative recovery range of 94-119% (for GC-FPD) and 92-103% (for GC-MS), and relative standard deviations (RSD) of less than 20%. Cross-validation was performed on the same set of urine samples (n=46) collected from pregnant women residing in the agricultural areas of northern Thailand. The results from split sample analysis from both laboratories agreed well for each metabolite, suggesting that each method can produce comparable data. In addition, results from analyses of specimens from the German External Quality Assessment Scheme (G-EQUAS) suggested that the GC-FPD method produced accurate results that can be reasonably compared to other studies. Copyright © 2013 Elsevier GmbH. All rights reserved.

Identification of Matra Region and Overlapping Characters for OCR of Printed Bengali Scripts

NASA Astrophysics Data System (ADS)

Goswami, Subhra Sundar

One of the important reasons for poor recognition rate in optical character recognition (OCR) system is the error in character segmentation. In case of Bangla scripts, the errors occur due to several reasons, which include incorrect detection of matra (headline), over-segmentation and under-segmentation. We have proposed a robust method for detecting the headline region. Existence of overlapping characters (in under-segmented parts) in scanned printed documents is a major problem in designing an effective character segmentation procedure for OCR systems. In this paper, a predictive algorithm is developed for effectively identifying overlapping characters and then selecting the cut-borders for segmentation. Our method can be successfully used in achieving high recognition result.

Use of lidar for the evaluation of traffic-related urban pollution

NASA Astrophysics Data System (ADS)

Eichinger, William E.; Cooper, D. I.; Buttler, William T.; Cottingame, William; Tellier, Larry

1994-03-01

Lidar (Light Detection and Ranging) is demonstrated as a tool for the detection and tracking of sources of aerosol pollution. Existing elastic lidars have been used to demonstrate the potential of the application of this technology in urban areas. Data from several experiments is shown along with analysis methods used for interpretation of the data. The goal of the project is to develop a light-weight, low-cost, lidar system and data analysis methods which can be used by urban planners and local air quality managers. The ability to determine the sources, i.e., causes, of non-attainment may lead to more effective use of tax dollars. Future directions for the project are also discussed.

A powerful score-based test statistic for detecting gene-gene co-association.

PubMed

Xu, Jing; Yuan, Zhongshang; Ji, Jiadong; Zhang, Xiaoshuai; Li, Hongkai; Wu, Xuesen; Xue, Fuzhong; Liu, Yanxun

2016-01-29

The genetic variants identified by Genome-wide association study (GWAS) can only account for a small proportion of the total heritability for complex disease. The existence of gene-gene joint effects which contains the main effects and their co-association is one of the possible explanations for the "missing heritability" problems. Gene-gene co-association refers to the extent to which the joint effects of two genes differ from the main effects, not only due to the traditional interaction under nearly independent condition but the correlation between genes. Generally, genes tend to work collaboratively within specific pathway or network contributing to the disease and the specific disease-associated locus will often be highly correlated (e.g. single nucleotide polymorphisms (SNPs) in linkage disequilibrium). Therefore, we proposed a novel score-based statistic (SBS) as a gene-based method for detecting gene-gene co-association. Various simulations illustrate that, under different sample sizes, marginal effects of causal SNPs and co-association levels, the proposed SBS has the better performance than other existed methods including single SNP-based and principle component analysis (PCA)-based logistic regression model, the statistics based on canonical correlations (CCU), kernel canonical correlation analysis (KCCU), partial least squares path modeling (PLSPM) and delta-square (δ (2)) statistic. The real data analysis of rheumatoid arthritis (RA) further confirmed its advantages in practice. SBS is a powerful and efficient gene-based method for detecting gene-gene co-association.

[Investigation of concentration levels of chromium(VI) in bottled mineral and spring waters by high performance ion chromatography technique with application of postcolumn reaction with 1,5-diphenylcarbazide and VIS detection].

PubMed

Swiecicka, Dorota; Garboś, Sławomir

2008-01-01

The aim of this work was optimization and validation of the method of determination of Cr(VI) existing in the form of chromate(VI) in mineral and spring waters by High Performance Ion Chromatography (HPIC) technique with application of postcolumn reaction with 1,5-diphenylcarbazide and VIS detection. Optimization of the method performed with the use of initial apparatus parameters and chromatographic conditions from the Method 218.6 allowed to lowering detection limit for Cr(VI) from 400 ng/l to 2 ng/l. Thanks to very low detection limit achieved it was possible to determine of Cr(VI) concentrations in 25 mineral and spring waters presented at Polish market. In the cases of four mineral and spring waters analyzed, determined Cr(VI) concentrations were below of quantification limit (< 4 ng/l) but simultaneously in another mineral and spring waters the concentrations of chromium(VI) were determined in the range of 5.6 - 1281 ng/l. The fact of existence of different Cr(VI) concentrations in investigated waters could be connected with secondary contamination of mineral and spring waters by chromium coming from metal installations and fittings. One should be underlined that even the highest determined concentration level of chromium(VI) was below of the maximum admissible concentration of total chromium presented in Polish Decree of Minister of Health from April 29th 2004. Therefore after taking into account determined in this work concentration of Cr(VI), the consumption of all waters analyzed in this study does not lead to essential human health risk.

Comparison the percentage of detection of periarthritis in patients with rheumatoid arthritis using clinical examination or ultrasound methods.

PubMed

Karimzadeh, Hadi; Seyedbonakdar, Zahra; Mousavi, Maryam; Karami, Mehdi

2016-01-01

This study aimed to compare the percentage of detection of periarthritis in patients with rheumatoid arthritis using clinical examination and ultrasound methods. This study is a cross-sectional study which was conducted in Al-Zahra Hospital (Isfahan, Iran) during 2014-2015. In our study, ninety patients were selected based on the American College of Rheumatology 2010 criteria. All patients were examined by a rheumatologist to find the existence of effusion, and the data were filled in the checklist. The ultrasonography for detecting effusion in periarticular structures was done by an expert radiologist with two methods, including high-resolution ultrasonography and power Doppler. The percentage of effusion existence found by physical examination was compared by sonography, and the Chi-square and t -tests were used for data analysis. The percentage of effusion found in areas with physical examination by rheumatologist was lower than the frequency distribution of effusions found by sonography (8.3% VS 14.2%) ( P < 0.001). In sonography, rotator cuff tendonitis is the most common periarthritis. Other findings in sonography were biceps tendinitis (10 cases), wrist tendonitis (13 cases), olecranon bursitis (9 cases), golfers elbow (4 cases), tennis elbow (4 cases), trochanteric bursitis (6 cases), anserine bursitis (6 cases), prepatellar bursitis (11 cases), and ankle tendonitis (7 cases). Tenderness on physical examination was found in 15% of the cases, and the evidence of periarthritis was found in 21/7% through sonography ( P < 0.001) and 34% through Doppler sonography ( P < 0.001). The percentage of periarthritis detection by ultrasonography and power Doppler sonography was higher than clinical examination. Hence, the ultrasonography is more accurate than physical examination.

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.

Application of Non-destructive Methods of Stress-strain State at Hazardous Production Facilities

NASA Astrophysics Data System (ADS)

Shram, V.; Kravtsova, Ye; Selsky, A.; Bezborodov, Yu; Lysyannikova, N.; Lysyannikov, A.

2016-06-01

The paper deals with the sources of accidents in distillation columns, on the basis of which the most dangerous defects are detected. The analysis of the currently existing methods of non-destructive testing of the stress-strain state is performed. It is proposed to apply strain and acoustic emission techniques to continuously monitor dangerous objects, which helps prevent the possibility of accidents, as well as reduce the work.

Joint detection and localization of multiple anatomical landmarks through learning

NASA Astrophysics Data System (ADS)

Dikmen, Mert; Zhan, Yiqiang; Zhou, Xiang Sean

2008-03-01

Reliable landmark detection in medical images provides the essential groundwork for successful automation of various open problems such as localization, segmentation, and registration of anatomical structures. In this paper, we present a learning-based system to jointly detect (is it there?) and localize (where?) multiple anatomical landmarks in medical images. The contributions of this work exist in two aspects. First, this method takes the advantage from the learning scenario that is able to automatically extract the most distinctive features for multi-landmark detection. Therefore, it is easily adaptable to detect arbitrary landmarks in various kinds of imaging modalities, e.g., CT, MRI and PET. Second, the use of multi-class/cascaded classifier architecture in different phases of the detection stage combined with robust features that are highly efficient in terms of computation time enables a seemingly real time performance, with very high localization accuracy. This method is validated on CT scans of different body sections, e.g., whole body scans, chest scans and abdominal scans. Aside from improved robustness (due to the exploitation of spatial correlations), it gains a run time efficiency in landmark detection. It also shows good scalability performance under increasing number of landmarks.

Collaborative Wideband Compressed Signal Detection in Interplanetary Internet

NASA Astrophysics Data System (ADS)

Wang, Yulin; Zhang, Gengxin; Bian, Dongming; Gou, Liang; Zhang, Wei

2014-07-01

As the development of autonomous radio in deep space network, it is possible to actualize communication between explorers, aircrafts, rovers and satellites, e.g. from different countries, adopting different signal modes. The first mission to enforce the autonomous radio is to detect signals of the explorer autonomously without disturbing the original communication. This paper develops a collaborative wideband compressed signal detection approach for InterPlaNetary (IPN) Internet where there exist sparse active signals in the deep space environment. Compressed sensing (CS) can be utilized by exploiting the sparsity of IPN Internet communication signal, whose useful frequency support occupies only a small portion of an entirely wide spectrum. An estimate of the signal spectrum can be obtained by using reconstruction algorithms. Against deep space shadowing and channel fading, multiple satellites collaboratively sense and make a final decision according to certain fusion rule to gain spatial diversity. A couple of novel discrete cosine transform (DCT) and walsh-hadamard transform (WHT) based compressed spectrum detection methods are proposed which significantly improve the performance of spectrum recovery and signal detection. Finally, extensive simulation results are presented to show the effectiveness of our proposed collaborative scheme for signal detection in IPN Internet. Compared with the conventional discrete fourier transform (DFT) based method, our DCT and WHT based methods reduce computational complexity, decrease processing time, save energy and enhance probability of detection.

Multidrug-resistant tuberculosis treatment failure detection depends on monitoring interval and microbiological method

PubMed Central

White, Richard A.; Lu, Chunling; Rodriguez, Carly A.; Bayona, Jaime; Becerra, Mercedes C.; Burgos, Marcos; Centis, Rosella; Cohen, Theodore; Cox, Helen; D'Ambrosio, Lia; Danilovitz, Manfred; Falzon, Dennis; Gelmanova, Irina Y.; Gler, Maria T.; Grinsdale, Jennifer A.; Holtz, Timothy H.; Keshavjee, Salmaan; Leimane, Vaira; Menzies, Dick; Milstein, Meredith B.; Mishustin, Sergey P.; Pagano, Marcello; Quelapio, Maria I.; Shean, Karen; Shin, Sonya S.; Tolman, Arielle W.; van der Walt, Martha L.; Van Deun, Armand; Viiklepp, Piret

2016-01-01

Debate persists about monitoring method (culture or smear) and interval (monthly or less frequently) during treatment for multidrug-resistant tuberculosis (MDR-TB). We analysed existing data and estimated the effect of monitoring strategies on timing of failure detection. We identified studies reporting microbiological response to MDR-TB treatment and solicited individual patient data from authors. Frailty survival models were used to estimate pooled relative risk of failure detection in the last 12 months of treatment; hazard of failure using monthly culture was the reference. Data were obtained for 5410 patients across 12 observational studies. During the last 12 months of treatment, failure detection occurred in a median of 3 months by monthly culture; failure detection was delayed by 2, 7, and 9 months relying on bimonthly culture, monthly smear and bimonthly smear, respectively. Risk (95% CI) of failure detection delay resulting from monthly smear relative to culture is 0.38 (0.34–0.42) for all patients and 0.33 (0.25–0.42) for HIV-co-infected patients. Failure detection is delayed by reducing the sensitivity and frequency of the monitoring method. Monthly monitoring of sputum cultures from patients receiving MDR-TB treatment is recommended. Expanded laboratory capacity is needed for high-quality culture, and for smear microscopy and rapid molecular tests. PMID:27587552

Existing and emerging detection technologies for DNA (Deoxyribonucleic Acid) finger printing, sequencing, bio- and analytical chips: a multidisciplinary development unifying molecular biology, chemical and electronics engineering.

PubMed

Kumar Khanna, Vinod

2007-01-01

The current status and research trends of detection techniques for DNA-based analysis such as DNA finger printing, sequencing, biochips and allied fields are examined. An overview of main detectors is presented vis-à-vis these DNA operations. The biochip method is explained, the role of micro- and nanoelectronic technologies in biochip realization is highlighted, various optical and electrical detection principles employed in biochips are indicated, and the operational mechanisms of these detection devices are described. Although a diversity of biochips for diagnostic and therapeutic applications has been demonstrated in research laboratories worldwide, only some of these chips have entered the clinical market, and more chips are awaiting commercialization. The necessity of tagging is eliminated in refractive-index change based devices, but the basic flaw of indirect nature of most detection methodologies can only be overcome by generic and/or reagentless DNA sensors such as the conductance-based approach and the DNA-single electron transistor (DNA-SET) structure. Devices of the electrical detection-based category are expected to pave the pathway for the next-generation DNA chips. The review provides a comprehensive coverage of the detection technologies for DNA finger printing, sequencing and related techniques, encompassing a variety of methods from the primitive art to the state-of-the-art scenario as well as promising methods for the future.

Earthquake detection through computationally efficient similarity search

PubMed Central

Yoon, Clara E.; O’Reilly, Ossian; Bergen, Karianne J.; Beroza, Gregory C.

2015-01-01

Seismology is experiencing rapid growth in the quantity of data, which has outpaced the development of processing algorithms. Earthquake detection—identification of seismic events in continuous data—is a fundamental operation for observational seismology. We developed an efficient method to detect earthquakes using waveform similarity that overcomes the disadvantages of existing detection methods. Our method, called Fingerprint And Similarity Thresholding (FAST), can analyze a week of continuous seismic waveform data in less than 2 hours, or 140 times faster than autocorrelation. FAST adapts a data mining algorithm, originally designed to identify similar audio clips within large databases; it first creates compact “fingerprints” of waveforms by extracting key discriminative features, then groups similar fingerprints together within a database to facilitate fast, scalable search for similar fingerprint pairs, and finally generates a list of earthquake detections. FAST detected most (21 of 24) cataloged earthquakes and 68 uncataloged earthquakes in 1 week of continuous data from a station located near the Calaveras Fault in central California, achieving detection performance comparable to that of autocorrelation, with some additional false detections. FAST is expected to realize its full potential when applied to extremely long duration data sets over a distributed network of seismic stations. The widespread application of FAST has the potential to aid in the discovery of unexpected seismic signals, improve seismic monitoring, and promote a greater understanding of a variety of earthquake processes. PMID:26665176

Blind technique using blocking artifacts and entropy of histograms for image tampering detection

NASA Astrophysics Data System (ADS)

Manu, V. T.; Mehtre, B. M.

2017-06-01

The tremendous technological advancements in recent times has enabled people to create, edit and circulate images easily than ever before. As a result of this, ensuring the integrity and authenticity of the images has become challenging. Malicious editing of images to deceive the viewer is referred to as image tampering. A widely used image tampering technique is image splicing or compositing, in which regions from different images are copied and pasted. In this paper, we propose a tamper detection method utilizing the blocking and blur artifacts which are the footprints of splicing. The classification of images as tampered or not, is done based on the standard deviations of the entropy histograms and block discrete cosine transformations. We can detect the exact boundaries of the tampered area in the image, if the image is classified as tampered. Experimental results on publicly available image tampering datasets show that the proposed method outperforms the existing methods in terms of accuracy.

A general purpose feature extractor for light detection and ranging data.

PubMed

Li, Yangming; Olson, Edwin B

2010-01-01

Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset.

A General Purpose Feature Extractor for Light Detection and Ranging Data

PubMed Central

Li, Yangming; Olson, Edwin B.

2010-01-01

Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset. PMID:22163474

Using fuzzy fractal features of digital images for the material surface analisys

NASA Astrophysics Data System (ADS)

Privezentsev, D. G.; Zhiznyakov, A. L.; Astafiev, A. V.; Pugin, E. V.

2018-01-01

Edge detection is an important task in image processing. There are a lot of approaches in this area: Sobel, Canny operators and others. One of the perspective techniques in image processing is the use of fuzzy logic and fuzzy sets theory. They allow us to increase processing quality by representing information in its fuzzy form. Most of the existing fuzzy image processing methods switch to fuzzy sets on very late stages, so this leads to some useful information loss. In this paper, a novel method of edge detection based on fuzzy image representation and fuzzy pixels is proposed. With this approach, we convert the image to fuzzy form on the first step. Different approaches to this conversion are described. Several membership functions for fuzzy pixel description and requirements for their form and view are given. A novel approach to edge detection based on Sobel operator and fuzzy image representation is proposed. Experimental testing of developed method was performed on remote sensing images.

A fast community detection method in bipartite networks by distance dynamics

NASA Astrophysics Data System (ADS)

Sun, Hong-liang; Ch'ng, Eugene; Yong, Xi; Garibaldi, Jonathan M.; See, Simon; Chen, Duan-bing

2018-04-01

Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extension from unipartite to bipartite networks. Since Jaccard coefficient of distance dynamics model is incapable to measure distances of different types of vertices in bipartite networks, our main contribution is to extend distance dynamics model from unipartite to bipartite networks using a novel measure Local Jaccard Distance (LJD). Furthermore, distances between different types of vertices are not affected by common neighbors in the original method. This new idea makes clear assumptions and yields interpretable results in linear time complexity O(| E |) in sparse networks, where | E | is the number of edges. Experiments on synthetic networks demonstrate it is capable to overcome resolution limit compared with existing other methods. Further research on real networks shows that this model can accurately detect interpretable community structures in a short time.

Pathological brain detection based on wavelet entropy and Hu moment invariants.

PubMed

Zhang, Yudong; Wang, Shuihua; Sun, Ping; Phillips, Preetha

2015-01-01

With the aim of developing an accurate pathological brain detection system, we proposed a novel automatic computer-aided diagnosis (CAD) to detect pathological brains from normal brains obtained by magnetic resonance imaging (MRI) scanning. The problem still remained a challenge for technicians and clinicians, since MR imaging generated an exceptionally large information dataset. A new two-step approach was proposed in this study. We used wavelet entropy (WE) and Hu moment invariants (HMI) for feature extraction, and the generalized eigenvalue proximal support vector machine (GEPSVM) for classification. To further enhance classification accuracy, the popular radial basis function (RBF) kernel was employed. The 10 runs of k-fold stratified cross validation result showed that the proposed "WE + HMI + GEPSVM + RBF" method was superior to existing methods w.r.t. classification accuracy. It obtained the average classification accuracies of 100%, 100%, and 99.45% over Dataset-66, Dataset-160, and Dataset-255, respectively. The proposed method is effective and can be applied to realistic use.

Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map.

PubMed

Tan, Yihua; Li, Qingyun; Li, Yansheng; Tian, Jinwen

2015-09-11

This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradient distribution detector is used to obtain a gradient textural saliency map in the favor of the candidate regions. In addition, the final targets will be detected by segmenting the saliency map using CFAR-type algorithm. The real high-resolution airborne SAR image data is used to verify the proposed algorithm. The results demonstrate that this algorithm can detect aircraft targets quickly and accurately, and decrease the false alarm rate.