Sample records for aberration detection algorithms

  1. 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

  2. Data-driven approach of CUSUM algorithm in temporal aberrant event detection using interactive web applications.

    PubMed

    Li, Ye; Whelan, Michael; Hobbs, Leigh; Fan, Wen Qi; Fung, Cecilia; Wong, Kenny; Marchand-Austin, Alex; Badiani, Tina; Johnson, Ian

    2016-06-27

    In 2014/2015, Public Health Ontario developed disease-specific, cumulative sum (CUSUM)-based statistical algorithms for detecting aberrant increases in reportable infectious disease incidence in Ontario. The objective of this study was to determine whether the prospective application of these CUSUM algorithms, based on historical patterns, have improved specificity and sensitivity compared to the currently used Early Aberration Reporting System (EARS) algorithm, developed by the US Centers for Disease Control and Prevention. A total of seven algorithms were developed for the following diseases: cyclosporiasis, giardiasis, influenza (one each for type A and type B), mumps, pertussis, invasive pneumococcal disease. Historical data were used as baseline to assess known outbreaks. Regression models were used to model seasonality and CUSUM was applied to the difference between observed and expected counts. An interactive web application was developed allowing program staff to directly interact with data and tune the parameters of CUSUM algorithms using their expertise on the epidemiology of each disease. Using these parameters, a CUSUM detection system was applied prospectively and the results were compared to the outputs generated by EARS. The outcome was the detection of outbreaks, or the start of a known seasonal increase and predicting the peak in activity. The CUSUM algorithms detected provincial outbreaks earlier than the EARS algorithm, identified the start of the influenza season in advance of traditional methods, and had fewer false positive alerts. Additionally, having staff involved in the creation of the algorithms improved their understanding of the algorithms and improved use in practice. Using interactive web-based technology to tune CUSUM improved the sensitivity and specificity of detection algorithms.

  3. LS-CAP: an algorithm for identifying cytogenetic aberrations in hepatocellular carcinoma using microarray data.

    PubMed

    He, Xianmin; Wei, Qing; Sun, Meiqian; Fu, Xuping; Fan, Sichang; Li, Yao

    2006-05-01

    Biological techniques such as Array-Comparative genomic hybridization (CGH), fluorescent in situ hybridization (FISH) and affymetrix single nucleotide pleomorphism (SNP) array have been used to detect cytogenetic aberrations. However, on genomic scale, these techniques are labor intensive and time consuming. Comparative genomic microarray analysis (CGMA) has been used to identify cytogenetic changes in hepatocellular carcinoma (HCC) using gene expression microarray data. However, CGMA algorithm can not give precise localization of aberrations, fails to identify small cytogenetic changes, and exhibits false negatives and positives. Locally un-weighted smoothing cytogenetic aberrations prediction (LS-CAP) based on local smoothing and binomial distribution can be expected to address these problems. LS-CAP algorithm was built and used on HCC microarray profiles. Eighteen cytogenetic abnormalities were identified, among them 5 were reported previously, and 12 were proven by CGH studies. LS-CAP effectively reduced the false negatives and positives, and precisely located small fragments with cytogenetic aberrations.

  4. Primary chromatic aberration elimination via optimization work with genetic algorithm

    NASA Astrophysics Data System (ADS)

    Wu, Bo-Wen; Liu, Tung-Kuan; Fang, Yi-Chin; Chou, Jyh-Horng; Tsai, Hsien-Lin; Chang, En-Hao

    2008-09-01

    Chromatic Aberration plays a part in modern optical systems, especially in digitalized and smart optical systems. Much effort has been devoted to eliminating specific chromatic aberration in order to match the demand for advanced digitalized optical products. Basically, the elimination of axial chromatic and lateral color aberration of an optical lens and system depends on the selection of optical glass. According to reports from glass companies all over the world, the number of various newly developed optical glasses in the market exceeds three hundred. However, due to the complexity of a practical optical system, optical designers have so far had difficulty in finding the right solution to eliminate small axial and lateral chromatic aberration except by the Damped Least Squares (DLS) method, which is limited in so far as the DLS method has not yet managed to find a better optical system configuration. In the present research, genetic algorithms are used to replace traditional DLS so as to eliminate axial and lateral chromatic, by combining the theories of geometric optics in Tessar type lenses and a technique involving Binary/Real Encoding, Multiple Dynamic Crossover and Random Gene Mutation to find a much better configuration for optical glasses. By implementing the algorithms outlined in this paper, satisfactory results can be achieved in eliminating axial and lateral color aberration.

  5. Detecting independent and recurrent copy number aberrations using interval graphs.

    PubMed

    Wu, Hsin-Ta; Hajirasouliha, Iman; Raphael, Benjamin J

    2014-06-15

    Somatic copy number aberrations SCNAS: are frequent in cancer genomes, but many of these are random, passenger events. A common strategy to distinguish functional aberrations from passengers is to identify those aberrations that are recurrent across multiple samples. However, the extensive variability in the length and position of SCNA: s makes the problem of identifying recurrent aberrations notoriously difficult. We introduce a combinatorial approach to the problem of identifying independent and recurrent SCNA: s, focusing on the key challenging of separating the overlaps in aberrations across individuals into independent events. We derive independent and recurrent SCNA: s as maximal cliques in an interval graph constructed from overlaps between aberrations. We efficiently enumerate all such cliques, and derive a dynamic programming algorithm to find an optimal selection of non-overlapping cliques, resulting in a very fast algorithm, which we call RAIG (Recurrent Aberrations from Interval Graphs). We show that RAIG outperforms other methods on simulated data and also performs well on data from three cancer types from The Cancer Genome Atlas (TCGA). In contrast to existing approaches that employ various heuristics to select independent aberrations, RAIG optimizes a well-defined objective function. We show that this allows RAIG to identify rare aberrations that are likely functional, but are obscured by overlaps with larger passenger aberrations. http://compbio.cs.brown.edu/software. © The Author 2014. Published by Oxford University Press.

  6. GeneBreak: detection of recurrent DNA copy number aberration-associated chromosomal breakpoints within genes.

    PubMed

    van den Broek, Evert; van Lieshout, Stef; Rausch, Christian; Ylstra, Bauke; van de Wiel, Mark A; Meijer, Gerrit A; Fijneman, Remond J A; Abeln, Sanne

    2016-01-01

    Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. 'GeneBreak' is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, 'GeneBreak' collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, 'GeneBreak', is implemented in R ( www.cran.r-project.org ) and is available from Bioconductor ( www.bioconductor.org/packages/release/bioc/html/GeneBreak.html ).

  7. Biological Terrorism Preparedness: Evaluating the Performance of the Early Aberration Reporting System (EARS) Syndromic Surveillance Algorithms

    DTIC Science & Technology

    2007-06-01

    PREPAREDNESS: EVALUATING THE PERFORMANCE OF THE EARLY ABERRATION REPORTING SYSTEM (EARS) SYNDROMIC SURVEILLANCE ALGORITHMS by David A...SUBTITLE Biological Terrorism Preparedness: Evaluating the Performance of the Early Aberration Reporting System (EARS) Syndromic Surveillance...Algorithms 6. AUTHOR(S) David Dunfee, Benjamin Hegler 5. FUNDING NUMBERS 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Postgraduate School

  8. Effects of ocular aberrations on contrast detection in noise.

    PubMed

    Liang, Bo; Liu, Rong; Dai, Yun; Zhou, Jiawei; Zhou, Yifeng; Zhang, Yudong

    2012-08-06

    We use adaptive optics (AO) techniques to manipulate the ocular aberrations and elucidate the effects of these ocular aberrations on contrast detection in a noisy background. The detectability of sine wave gratings at frequencies of 4, 8, and 16 circles per degree (cpd) was measured in a standard two-interval force-choice staircase procedure against backgrounds of various levels of white noise. The observer's ocular aberrations were either corrected with AO or left uncorrected. In low levels of external noise, contrast detection thresholds are always lowered by AO correction, whereas in high levels of external noise, they are generally elevated by AO correction. Higher levels of external noise are required to make this threshold elevation observable when signal spatial frequencies increase from 4 to 16 cpd. The linear-amplifier-model fit shows that mostly sampling efficiency and equivalent noise both decrease with AO correction. Our findings indicate that ocular aberrations could be beneficial for contrast detection in high-level noises. The implications of these findings are discussed.

  9. Detecting Outliers in Factor Analysis Using the Forward Search Algorithm

    ERIC Educational Resources Information Center

    Mavridis, Dimitris; Moustaki, Irini

    2008-01-01

    In this article we extend and implement the forward search algorithm for identifying atypical subjects/observations in factor analysis models. The forward search has been mainly developed for detecting aberrant observations in regression models (Atkinson, 1994) and in multivariate methods such as cluster and discriminant analysis (Atkinson, Riani,…

  10. [The comparison of two different types of baseline data regarding the performance of aberration detection algorithm for infectious disease outbreaks].

    PubMed

    Lai, Sheng-jie; Li, Zhong-jie; Zhang, Hong-long; Lan, Ya-jia; Yang, Wei-zhong

    2011-06-01

    To compare the performance of aberration detection algorithm for infectious disease outbreaks, based on two different types of baseline data. Cases and outbreaks of hand-foot-and-mouth disease (HFMD) reported by six provinces of China in 2009 were used as the source of data. Two types of baseline data on algorithms of C1, C2 and C3 were tested, by distinguishing the baseline data of weekdays and weekends. Time to detection (TTD) and false alarm rate (FAR) were adopted as two evaluation indices to compare the performance of 3 algorithms based on these two types of baseline data. A total of 405 460 cases of HFMD were reported by 6 provinces in 2009. On average, each county reported 1.78 cases per day during the weekdays and 1.29 cases per day during weekends, with significant difference (P < 0.01) between them. When using the baseline data without distinguish weekdays and weekends, the optimal thresholds for C1, C2 and C3 was 0.2, 0.4 and 0.6 respectively while the TTD of C1, C2 and C3 was all 1 day and the FARs were 5.33%, 4.88% and 4.50% respectively. On the contrast, when using the baseline data to distinguish the weekdays and weekends, the optimal thresholds for C1, C2 and C3 became 0.4, 0.6 and 1.0 while the TTD of C1, C2 and C3 also appeared equally as 1 day. However, the FARs became 4.81%, 4.75% and 4.16% respectively, which were lower than the baseline data from the first type. The number of HFMD cases reported in weekdays and weekends were significantly different, suggesting that when using the baseline data to distinguish weekdays and weekends, the FAR of C1, C2 and C3 algorithm could effectively reduce so as to improve the accuracy of outbreak detection.

  11. Detecting Aberrant Response Patterns in the Rasch Model. Rapport 87-3.

    ERIC Educational Resources Information Center

    Kogut, Jan

    In this paper, the detection of response patterns aberrant from the Rasch model is considered. For this purpose, a new person fit index, recently developed by I. W. Molenaar (1987) and an iterative estimation procedure are used in a simulation study of Rasch model data mixed with aberrant data. Three kinds of aberrant response behavior are…

  12. Interpreting Chromosome Aberration Spectra

    NASA Technical Reports Server (NTRS)

    Levy, Dan; Reeder, Christopher; Loucas, Bradford; Hlatky, Lynn; Chen, Allen; Cornforth, Michael; Sachs, Rainer

    2007-01-01

    Ionizing radiation can damage cells by breaking both strands of DNA in multiple locations, essentially cutting chromosomes into pieces. The cell has enzymatic mechanisms to repair such breaks; however, these mechanisms are imperfect and, in an exchange process, may produce a large-scale rearrangement of the genome, called a chromosome aberration. Chromosome aberrations are important in killing cells, during carcinogenesis, in characterizing repair/misrepair pathways, in retrospective radiation biodosimetry, and in a number of other ways. DNA staining techniques such as mFISH ( multicolor fluorescent in situ hybridization) provide a means for analyzing aberration spectra by examining observed final patterns. Unfortunately, an mFISH observed final pattern often does not uniquely determine the underlying exchange process. Further, resolution limitations in the painting protocol sometimes lead to apparently incomplete final patterns. We here describe an algorithm for systematically finding exchange processes consistent with any observed final pattern. This algorithm uses aberration multigraphs, a mathematical formalism that links the various aspects of aberration formation. By applying a measure to the space of consistent multigraphs, we will show how to generate model-specific distributions of aberration processes from mFISH experimental data. The approach is implemented by software freely available over the internet. As a sample application, we apply these algorithms to an aberration data set, obtaining a distribution of exchange cycle sizes, which serves to measure aberration complexity. Estimating complexity, in turn, helps indicate how damaging the aberrations are and may facilitate identification of radiation type in retrospective biodosimetry.

  13. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

    PubMed

    Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

    2017-06-26

    We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

  14. Evaluating the utility of syndromic surveillance algorithms for screening to detect potentially clonal hospital infection outbreaks

    PubMed Central

    Talbot, Thomas R; Schaffner, William; Bloch, Karen C; Daniels, Titus L; Miller, Randolph A

    2011-01-01

    Objective The authors evaluated algorithms commonly used in syndromic surveillance for use as screening tools to detect potentially clonal outbreaks for review by infection control practitioners. Design Study phase 1 applied four aberrancy detection algorithms (CUSUM, EWMA, space-time scan statistic, and WSARE) to retrospective microbiologic culture data, producing a list of past candidate outbreak clusters. In phase 2, four infectious disease physicians categorized the phase 1 algorithm-identified clusters to ascertain algorithm performance. In phase 3, project members combined the algorithms to create a unified screening system and conducted a retrospective pilot evaluation. Measurements The study calculated recall and precision for each algorithm, and created precision-recall curves for various methods of combining the algorithms into a unified screening tool. Results Individual algorithm recall and precision ranged from 0.21 to 0.31 and from 0.053 to 0.29, respectively. Few candidate outbreak clusters were identified by more than one algorithm. The best method of combining the algorithms yielded an area under the precision-recall curve of 0.553. The phase 3 combined system detected all infection control-confirmed outbreaks during the retrospective evaluation period. Limitations Lack of phase 2 reviewers' agreement indicates that subjective expert review was an imperfect gold standard. Less conservative filtering of culture results and alternate parameter selection for each algorithm might have improved algorithm performance. Conclusion Hospital outbreak detection presents different challenges than traditional syndromic surveillance. Nevertheless, algorithms developed for syndromic surveillance have potential to form the basis of a combined system that might perform clinically useful hospital outbreak screening. PMID:21606134

  15. Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models.

    PubMed

    Chan, Ta-Chien; Teng, Yung-Chu; Hwang, Jing-Shiang

    2015-02-21

    Emerging novel influenza outbreaks have increasingly been a threat to the public and a major concern of public health departments. Real-time data in seamless surveillance systems such as health insurance claims data for influenza-like illnesses (ILI) are ready for analysis, making it highly desirable to develop practical techniques to analyze such readymade data for outbreak detection so that the public can receive timely influenza epidemic warnings. This study proposes a simple and effective approach to analyze area-based health insurance claims data including outpatient and emergency department (ED) visits for early detection of any aberrations of ILI. The health insurance claims data during 2004-2009 from a national health insurance research database were used for developing early detection methods. The proposed approach fitted the daily new ILI visits and monitored the Pearson residuals directly for aberration detection. First, negative binomial regression was used for both outpatient and ED visits to adjust for potentially influential factors such as holidays, weekends, seasons, temporal dependence and temperature. Second, if the Pearson residuals exceeded 1.96, aberration signals were issued. The empirical validation of the model was done in 2008 and 2009. In addition, we designed a simulation study to compare the time of outbreak detection, non-detection probability and false alarm rate between the proposed method and modified CUSUM. The model successfully detected the aberrations of 2009 pandemic (H1N1) influenza virus in northern, central and southern Taiwan. The proposed approach was more sensitive in identifying aberrations in ED visits than those in outpatient visits. Simulation studies demonstrated that the proposed approach could detect the aberrations earlier, and with lower non-detection probability and mean false alarm rate in detecting aberrations compared to modified CUSUM methods. The proposed simple approach was able to filter out temporal

  16. Comparison of 3-D Multi-Lag Cross-Correlation and Speckle Brightness Aberration Correction Algorithms on Static and Moving Targets

    PubMed Central

    Ivancevich, Nikolas M.; Dahl, Jeremy J.; Smith, Stephen W.

    2010-01-01

    Phase correction has the potential to increase the image quality of 3-D ultrasound, especially transcranial ultrasound. We implemented and compared 2 algorithms for aberration correction, multi-lag cross-correlation and speckle brightness, using static and moving targets. We corrected three 75-ns rms electronic aberrators with full-width at half-maximum (FWHM) auto-correlation lengths of 1.35, 2.7, and 5.4 mm. Cross-correlation proved the better algorithm at 2.7 and 5.4 mm correlation lengths (P < 0.05). Static cross-correlation performed better than moving-target cross-correlation at the 2.7 mm correlation length (P < 0.05). Finally, we compared the static and moving-target cross-correlation on a flow phantom with a skull casting aberrator. Using signal from static targets, the correction resulted in an average contrast increase of 22.2%, compared with 13.2% using signal from moving targets. The contrast-to-noise ratio (CNR) increased by 20.5% and 12.8% using static and moving targets, respectively. Doppler signal strength increased by 5.6% and 4.9% for the static and moving-targets methods, respectively. PMID:19942503

  17. Comparison of 3-D multi-lag cross- correlation and speckle brightness aberration correction algorithms on static and moving targets.

    PubMed

    Ivancevich, Nikolas M; Dahl, Jeremy J; Smith, Stephen W

    2009-10-01

    Phase correction has the potential to increase the image quality of 3-D ultrasound, especially transcranial ultrasound. We implemented and compared 2 algorithms for aberration correction, multi-lag cross-correlation and speckle brightness, using static and moving targets. We corrected three 75-ns rms electronic aberrators with full-width at half-maximum (FWHM) auto-correlation lengths of 1.35, 2.7, and 5.4 mm. Cross-correlation proved the better algorithm at 2.7 and 5.4 mm correlation lengths (P < 0.05). Static cross-correlation performed better than moving-target cross-correlation at the 2.7 mm correlation length (P < 0.05). Finally, we compared the static and moving-target cross-correlation on a flow phantom with a skull casting aberrator. Using signal from static targets, the correction resulted in an average contrast increase of 22.2%, compared with 13.2% using signal from moving targets. The contrast-to-noise ratio (CNR) increased by 20.5% and 12.8% using static and moving targets, respectively. Doppler signal strength increased by 5.6% and 4.9% for the static and moving-targets methods, respectively.

  18. [The spectrum of human chromosomal aberrations detected by routine and differential (GTG) staining].

    PubMed

    Ponomareva, A V; Matveeva, V G; Osipova, L P

    2001-01-01

    As a result of sample cytogenetic studies of 23 persons living on the territory of Yamal-Nentsy Autonomous District and chronically exposed to the small doses of radiation the data on the frequency and spectrum of chromosome aberrations, detected by the routine and differential (GTG) staining were obtained. Comparative efficiency of these methods was determined. The absence of significant differences for the spectrum and frequencies of chromosome aberrations revealed by both methods was shown.

  19. Linear feature detection algorithm for astronomical surveys - I. Algorithm description

    NASA Astrophysics Data System (ADS)

    Bektešević, Dino; Vinković, Dejan

    2017-11-01

    Computer vision algorithms are powerful tools in astronomical image analyses, especially when automation of object detection and extraction is required. Modern object detection algorithms in astronomy are oriented towards detection of stars and galaxies, ignoring completely the detection of existing linear features. With the emergence of wide-field sky surveys, linear features attract scientific interest as possible trails of fast flybys of near-Earth asteroids and meteors. In this work, we describe a new linear feature detection algorithm designed specifically for implementation in big data astronomy. The algorithm combines a series of algorithmic steps that first remove other objects (stars and galaxies) from the image and then enhance the line to enable more efficient line detection with the Hough algorithm. The rate of false positives is greatly reduced thanks to a step that replaces possible line segments with rectangles and then compares lines fitted to the rectangles with the lines obtained directly from the image. The speed of the algorithm and its applicability in astronomical surveys are also discussed.

  20. Wavelet-based identification of DNA focal genomic aberrations from single nucleotide polymorphism arrays

    PubMed Central

    2011-01-01

    Background Copy number aberrations (CNAs) are an important molecular signature in cancer initiation, development, and progression. However, these aberrations span a wide range of chromosomes, making it hard to distinguish cancer related genes from other genes that are not closely related to cancer but are located in broadly aberrant regions. With the current availability of high-resolution data sets such as single nucleotide polymorphism (SNP) microarrays, it has become an important issue to develop a computational method to detect driving genes related to cancer development located in the focal regions of CNAs. Results In this study, we introduce a novel method referred to as the wavelet-based identification of focal genomic aberrations (WIFA). The use of the wavelet analysis, because it is a multi-resolution approach, makes it possible to effectively identify focal genomic aberrations in broadly aberrant regions. The proposed method integrates multiple cancer samples so that it enables the detection of the consistent aberrations across multiple samples. We then apply this method to glioblastoma multiforme and lung cancer data sets from the SNP microarray platform. Through this process, we confirm the ability to detect previously known cancer related genes from both cancer types with high accuracy. Also, the application of this approach to a lung cancer data set identifies focal amplification regions that contain known oncogenes, though these regions are not reported using a recent CNAs detecting algorithm GISTIC: SMAD7 (chr18q21.1) and FGF10 (chr5p12). Conclusions Our results suggest that WIFA can be used to reveal cancer related genes in various cancer data sets. PMID:21569311

  1. Camera processing with chromatic aberration.

    PubMed

    Korneliussen, Jan Tore; Hirakawa, Keigo

    2014-10-01

    Since the refractive index of materials commonly used for lens depends on the wavelengths of light, practical camera optics fail to converge light to a single point on an image plane. Known as chromatic aberration, this phenomenon distorts image details by introducing magnification error, defocus blur, and color fringes. Though achromatic and apochromatic lens designs reduce chromatic aberration to a degree, they are complex and expensive and they do not offer a perfect correction. In this paper, we propose a new postcapture processing scheme designed to overcome these problems computationally. Specifically, the proposed solution is comprised of chromatic aberration-tolerant demosaicking algorithm and post-demosaicking chromatic aberration correction. Experiments with simulated and real sensor data verify that the chromatic aberration is effectively corrected.

  2. High speed wavefront sensorless aberration correction in digital micromirror based confocal microscopy.

    PubMed

    Pozzi, P; Wilding, D; Soloviev, O; Verstraete, H; Bliek, L; Vdovin, G; Verhaegen, M

    2017-01-23

    The quality of fluorescence microscopy images is often impaired by the presence of sample induced optical aberrations. Adaptive optical elements such as deformable mirrors or spatial light modulators can be used to correct aberrations. However, previously reported techniques either require special sample preparation, or time consuming optimization procedures for the correction of static aberrations. This paper reports a technique for optical sectioning fluorescence microscopy capable of correcting dynamic aberrations in any fluorescent sample during the acquisition. This is achieved by implementing adaptive optics in a non conventional confocal microscopy setup, with multiple programmable confocal apertures, in which out of focus light can be separately detected, and used to optimize the correction performance with a sampling frequency an order of magnitude faster than the imaging rate of the system. The paper reports results comparing the correction performances to traditional image optimization algorithms, and demonstrates how the system can compensate for dynamic changes in the aberrations, such as those introduced during a focal stack acquisition though a thick sample.

  3. An efficient parallel termination detection algorithm

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

    Baker, A. H.; Crivelli, S.; Jessup, E. R.

    2004-05-27

    Information local to any one processor is insufficient to monitor the overall progress of most distributed computations. Typically, a second distributed computation for detecting termination of the main computation is necessary. In order to be a useful computational tool, the termination detection routine must operate concurrently with the main computation, adding minimal overhead, and it must promptly and correctly detect termination when it occurs. In this paper, we present a new algorithm for detecting the termination of a parallel computation on distributed-memory MIMD computers that satisfies all of those criteria. A variety of termination detection algorithms have been devised. Ofmore » these, the algorithm presented by Sinha, Kale, and Ramkumar (henceforth, the SKR algorithm) is unique in its ability to adapt to the load conditions of the system on which it runs, thereby minimizing the impact of termination detection on performance. Because their algorithm also detects termination quickly, we consider it to be the most efficient practical algorithm presently available. The termination detection algorithm presented here was developed for use in the PMESC programming library for distributed-memory MIMD computers. Like the SKR algorithm, our algorithm adapts to system loads and imposes little overhead. Also like the SKR algorithm, ours is tree-based, and it does not depend on any assumptions about the physical interconnection topology of the processors or the specifics of the distributed computation. In addition, our algorithm is easier to implement and requires only half as many tree traverses as does the SKR algorithm. This paper is organized as follows. In section 2, we define our computational model. In section 3, we review the SKR algorithm. We introduce our new algorithm in section 4, and prove its correctness in section 5. We discuss its efficiency and present experimental results in section 6.« less

  4. Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation

    PubMed Central

    Dórea, Fernanda C.; McEwen, Beverly J.; McNab, W. Bruce; Revie, Crawford W.; Sanchez, Javier

    2013-01-01

    Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt–Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel. PMID:23576782

  5. Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation.

    PubMed

    Dórea, Fernanda C; McEwen, Beverly J; McNab, W Bruce; Revie, Crawford W; Sanchez, Javier

    2013-06-06

    Diagnostic test orders to an animal laboratory were explored as a data source for monitoring trends in the incidence of clinical syndromes in cattle. Four years of real data and over 200 simulated outbreak signals were used to compare pre-processing methods that could remove temporal effects in the data, as well as temporal aberration detection algorithms that provided high sensitivity and specificity. Weekly differencing demonstrated solid performance in removing day-of-week effects, even in series with low daily counts. For aberration detection, the results indicated that no single algorithm showed performance superior to all others across the range of outbreak scenarios simulated. Exponentially weighted moving average charts and Holt-Winters exponential smoothing demonstrated complementary performance, with the latter offering an automated method to adjust to changes in the time series that will likely occur in the future. Shewhart charts provided lower sensitivity but earlier detection in some scenarios. Cumulative sum charts did not appear to add value to the system; however, the poor performance of this algorithm was attributed to characteristics of the data monitored. These findings indicate that automated monitoring aimed at early detection of temporal aberrations will likely be most effective when a range of algorithms are implemented in parallel.

  6. Bio-ALIRT biosurveillance detection algorithm evaluation.

    PubMed

    Siegrist, David; Pavlin, J

    2004-09-24

    Early detection of disease outbreaks by a medical biosurveillance system relies on two major components: 1) the contribution of early and reliable data sources and 2) the sensitivity, specificity, and timeliness of biosurveillance detection algorithms. This paper describes an effort to assess leading detection algorithms by arranging a common challenge problem and providing a common data set. The objectives of this study were to determine whether automated detection algorithms can reliably and quickly identify the onset of natural disease outbreaks that are surrogates for possible terrorist pathogen releases, and do so at acceptable false-alert rates (e.g., once every 2-6 weeks). Historic de-identified data were obtained from five metropolitan areas over 23 months; these data included International Classification of Diseases, Ninth Revision (ICD-9) codes related to respiratory and gastrointestinal illness syndromes. An outbreak detection group identified and labeled two natural disease outbreaks in these data and provided them to analysts for training of detection algorithms. All outbreaks in the remaining test data were identified but not revealed to the detection groups until after their analyses. The algorithms established a probability of outbreak for each day's counts. The probability of outbreak was assessed as an "actual" alert for different false-alert rates. The best algorithms were able to detect all of the outbreaks at false-alert rates of one every 2-6 weeks. They were often able to detect for the same day human investigators had identified as the true start of the outbreak. Because minimal data exists for an actual biologic attack, determining how quickly an algorithm might detect such an attack is difficult. However, application of these algorithms in combination with other data-analysis methods to historic outbreak data indicates that biosurveillance techniques for analyzing syndrome counts can rapidly detect seasonal respiratory and gastrointestinal

  7. Performance of Dispersed Fringe Sensor in the Presence of Segmented Mirror Aberrations: Modeling and Simulation

    NASA Technical Reports Server (NTRS)

    Shi, Fang; Basinger, Scott A.; Redding, David C.

    2006-01-01

    Dispersed Fringe Sensing (DFS) is an efficient and robust method for coarse phasing of a segmented primary mirror such as the James Webb Space Telescope (JWST). In this paper, modeling and simulations are used to study the effect of segmented mirror aberrations on the fringe image, DFS signals and DFS detection accuracy. The study has shown due to the pixilation spatial filter effect from DFS signal extraction the effect of wavefront error is reduced and DFS algorithm will be more robust against wavefront aberration by using multi-trace DFS approach. We also studied the JWST Dispersed Hartmann Sensor (DHS) performance in presence of wavefront aberrations caused by the gravity sag and we use the scaled gravity sag to explore the JWST DHS performance relationship with the level of the wavefront aberration. This also includes the effect from line-of-sight jitter.

  8. Detection of clonal aberrations by cytogenetic analysis after different culture methods and by FISH in 129 patients with Chronic Lymphocytic Leukemia.

    PubMed

    Jenderny, Jutta; Goldmann, Claudia; Thede, Rebekka; Ebrecht, Monika; Korioth, Frank

    2014-01-01

    There are only a few cytogenetic analysis (CA) studies that directly compare the novel cultivation technique using immunostimulatory CpG-oligonucleotide DSP30/interleukin-2 (DSP30/IL2) with other culture methods. Therefore, parallel cultures of peripheral blood of 129 chronic lymphocytic leukemia (CLL) patients were set up in unstimulated cultures, in the presence of pokeweed medium (PWM), and with DSP30/IL2. Furthermore, CA results were compared with data obtained by FISH. Clonal aberrations were observed by CA in 6% of the cases in unstimulated cultures, in 27% of the cases with PWM, and in 40% of the cases with DSP30/IL2. Some clonal aberrations were detected by CA only with one culture method. Using 3 different culture methods, clonal aberrations were detected in 41% of the cases by CA and in 71% of the cases by FISH. Altogether, 78% of the cases exhibited clonal aberrations discovered by CA and FISH. Also, CA detected clonal aberrations not targeted by FISH in 7% of the cases, and FISH identified clonal aberrations not detected by CA in 36% of the cases. Our study demonstrates that the combined use of CA with different culture methods together with FISH increases our knowledge of the genetic complexity and heterogeneity in CLL pathogenesis. © 2014 S. Karger AG, Basel.

  9. Effect of ocular transverse chromatic aberration on detection acuity for peripheral vision.

    PubMed

    Cheney, Frank; Thibos, Larry; Bradley, Arthur

    2015-01-01

    We examined the effect of transverse chromatic aberration (TCA) on detection acuity for white-light interference fringes seen in Maxwellian view at various orientations and locations in the visual field. A circular patch (3.5° diameter, 3.2 log Trolands) of nominally high-contrast fringes was produced on the retina by a commercial instrument (the Lotmar Visometer, Haag Streit) mounted on a gimbal for controlled positioning of the stimulus in the visual field from 0° to 35° eccentricity. Detection acuity for white light fringes for all meridians and eccentricities ≥15° was maximum when fringes were oriented parallel to the visual meridian line. This meridional effect disappeared when a narrow-band filter was used to eliminate TCA. The meridional effect also disappeared when the interferometric stimulator was displaced laterally to align the instrument with the eye's local achromatic axis. Modelling confirmed that TCA is the major factor responsible for white-light meridional bias, with minor contribution arising from higher-order monochromatic aberrations and neural factors. © 2014 The Authors Ophthalmic & Physiological Optics © 2014 The College of Optometrists.

  10. Multi-object Detection and Discrimination Algorithms

    DTIC Science & Technology

    2015-03-26

    with  an   algorithm  similar  to  a  depth-­‐first   search .   This  stage  of  the   algorithm  is  O(CN).  From...Multi-object Detection and Discrimination Algorithms This document contains an overview of research and work performed and published at the University...of Florida from October 1, 2009 to October 31, 2013 pertaining to proposal 57306CS: Multi-object Detection and Discrimination Algorithms

  11. Research on the filtering algorithm in speed and position detection of maglev trains.

    PubMed

    Dai, Chunhui; Long, Zhiqiang; Xie, Yunde; Xue, Song

    2011-01-01

    This paper introduces in brief the traction system of a permanent magnet electrodynamic suspension (EDS) train. The synchronous traction mode based on long stators and track cable is described. A speed and position detection system is recommended. It is installed on board and is used as the feedback end. Restricted by the maglev train's structure, the permanent magnet electrodynamic suspension (EDS) train uses the non-contact method to detect its position. Because of the shake and the track joints, the position signal sent by the position sensor is always aberrant and noisy. To solve this problem, a linear discrete track-differentiator filtering algorithm is proposed. The filtering characters of the track-differentiator (TD) and track-differentiator group are analyzed. The four series of TD are used in the signal processing unit. The result shows that the track-differentiator could have a good effect and make the traction system run normally.

  12. Chromosomal aberrations in chronic lymphocytic leukemia detected by conventional cytogenetics with DSP30 as a single agent: comparison with FISH.

    PubMed

    Kotkowska, Aleksandra; Wawrzyniak, Ewa; Blonski, Jerzy Z; Robak, Tadeusz; Korycka-Wolowiec, Anna

    2011-08-01

    The aim of our study was to estimate the usefulness for conventional cytogenetics (CC) of DSP30 as a single agent (CC-DSP30) for detecting the most important chromosomal aberrations revealed in CLL by FISH and to find other abnormalities possibly existing but undetected by FISH with standard probes. Using CC-DSP30, the metaphases suitable for analysis were obtained in 90% of patients. CC-DSP30 and FISH were similarly efficacious for detecting del(11)(q22) and trisomy 12, whereas FISH was more sensitive for del(13)(q14). Sole del(13)(q14) detected by FISH, in 50% of patients was associated with other aberrations revealed by CC-DSP30. Additionally, the most recurrent anomaly detected by CC-DSP30 were structural aberrations of chromosome 2. Copyright © 2011 Elsevier Ltd. All rights reserved.

  13. A new real-time tsunami detection algorithm

    NASA Astrophysics Data System (ADS)

    Chierici, F.; Embriaco, D.; Pignagnoli, L.

    2016-12-01

    Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection based on the real-time tide removal and real-time band-pass filtering of sea-bed pressure recordings. The algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability, at low computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. Pressure data sets acquired by Bottom Pressure Recorders in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event which occurred at Haida Gwaii on October 28th, 2012 using data recorded by the Bullseye underwater node of Ocean Networks Canada. The algorithm successfully ran for test purpose in year-long missions onboard the GEOSTAR stand-alone multidisciplinary abyssal observatory, deployed in the Gulf of Cadiz during the EC project NEAREST and on NEMO-SN1 cabled observatory deployed in the Western Ionian Sea, operational node of the European research infrastructure EMSO.

  14. Single-cell copy number variation detection

    PubMed Central

    2011-01-01

    Detection of chromosomal aberrations from a single cell by array comparative genomic hybridization (single-cell array CGH), instead of from a population of cells, is an emerging technique. However, such detection is challenging because of the genome artifacts and the DNA amplification process inherent to the single cell approach. Current normalization algorithms result in inaccurate aberration detection for single-cell data. We propose a normalization method based on channel, genome composition and recurrent genome artifact corrections. We demonstrate that the proposed channel clone normalization significantly improves the copy number variation detection in both simulated and real single-cell array CGH data. PMID:21854607

  15. Lens correction algorithm based on the see-saw diagram to correct Seidel aberrations employing aspheric surfaces

    NASA Astrophysics Data System (ADS)

    Rosete-Aguilar, Martha

    2000-06-01

    In this paper a lens correction algorithm based on the see- saw diagram developed by Burch is described. The see-saw diagram describes the image correction in rotationally symmetric systems over a finite field of view by means of aspherics surfaces. The algorithm is applied to the design of some basic telescopic configurations such as the classical Cassegrain telescope, the Dall-Kirkham telescope, the Pressman-Camichel telescope and the Ritchey-Chretien telescope in order to show a physically visualizable concept of image correction for optical systems that employ aspheric surfaces. By using the see-saw method the student can visualize the different possible configurations of such telescopes as well as their performances and also the student will be able to understand that it is not always possible to correct more primary aberrations by aspherizing more surfaces.

  16. Detection of Inter-chromosomal Stable Aberrations by Multiple Fluorescence In Situ Hybridization (mFISH) and Spectral Karyotyping (SKY) in Irradiated Mice

    PubMed Central

    Pathak, Rupak; Koturbash, Igor; Hauer-Jensen, Martin

    2017-01-01

    Ionizing radiation (IR) induces numerous stable and unstable chromosomal aberrations. Unstable aberrations, where chromosome morphology is substantially compromised, can easily be identified by conventional chromosome staining techniques. However, detection of stable aberrations, which involve exchange or translocation of genetic materials without considerable modification in the chromosome morphology, requires sophisticated chromosome painting techniques that rely on in situ hybridization of fluorescently labeled DNA probes, a chromosome painting technique popularly known as fluorescence in situ hybridization (FISH). FISH probes can be specific for whole chromosome/s or precise sub-region on chromosome/s. The method not only allows visualization of stable aberrations, but it can also allow detection of the chromosome/s or specific DNA sequence/s involved in a particular aberration formation. A variety of chromosome painting techniques are available in cytogenetics; here two highly sensitive methods, multiple fluorescence in situ hybridization (mFISH) and spectral karyotyping (SKY), are discussed to identify inter-chromosomal stable aberrations that form in the bone marrow cells of mice after exposure to total body irradiation. Although both techniques rely on fluorescent labeled DNA probes, the method of detection and the process of image acquisition of the fluorescent signals are different. These two techniques have been used in various research areas, such as radiation biology, cancer cytogenetics, retrospective radiation biodosimetry, clinical cytogenetics, evolutionary cytogenetics, and comparative cytogenetics. PMID:28117817

  17. Obstacle Detection Algorithms for Rotorcraft Navigation

    NASA Technical Reports Server (NTRS)

    Kasturi, Rangachar; Camps, Octavia I.; Huang, Ying; Narasimhamurthy, Anand; Pande, Nitin; Ahumada, Albert (Technical Monitor)

    2001-01-01

    In this research we addressed the problem of obstacle detection for low altitude rotorcraft flight. In particular, the problem of detecting thin wires in the presence of image clutter and noise was studied. Wires present a serious hazard to rotorcrafts. Since they are very thin, their detection early enough so that the pilot has enough time to take evasive action is difficult, as their images can be less than one or two pixels wide. After reviewing the line detection literature, an algorithm for sub-pixel edge detection proposed by Steger was identified as having good potential to solve the considered task. The algorithm was tested using a set of images synthetically generated by combining real outdoor images with computer generated wire images. The performance of the algorithm was evaluated both, at the pixel and the wire levels. It was observed that the algorithm performs well, provided that the wires are not too thin (or distant) and that some post processing is performed to remove false alarms due to clutter.

  18. Research on the Filtering Algorithm in Speed and Position Detection of Maglev Trains

    PubMed Central

    Dai, Chunhui; Long, Zhiqiang; Xie, Yunde; Xue, Song

    2011-01-01

    This paper introduces in brief the traction system of a permanent magnet electrodynamic suspension (EDS) train. The synchronous traction mode based on long stators and track cable is described. A speed and position detection system is recommended. It is installed on board and is used as the feedback end. Restricted by the maglev train’s structure, the permanent magnet electrodynamic suspension (EDS) train uses the non-contact method to detect its position. Because of the shake and the track joints, the position signal sent by the position sensor is always aberrant and noisy. To solve this problem, a linear discrete track-differentiator filtering algorithm is proposed. The filtering characters of the track-differentiator (TD) and track-differentiator group are analyzed. The four series of TD are used in the signal processing unit. The result shows that the track-differentiator could have a good effect and make the traction system run normally. PMID:22164012

  19. Measurement technique for in situ characterizing aberrations of projection optics in lithographic tools.

    PubMed

    Wang, Fan; Wang, Xiangzhao; Ma, Mingying

    2006-08-20

    As the feature size decreases, degradation of image quality caused by wavefront aberrations of projection optics in lithographic tools has become a serious problem in the low-k1 process. We propose a novel measurement technique for in situ characterizing aberrations of projection optics in lithographic tools. Considering the impact of the partial coherence illumination, we introduce a novel algorithm that accurately describes the pattern displacement and focus shift induced by aberrations. Employing the algorithm, the measurement condition is extended from three-beam interference to two-, three-, and hybrid-beam interferences. The experiments are performed to measure the aberrations of projection optics in an ArF scanner.

  20. Detection of algorithmic trading

    NASA Astrophysics Data System (ADS)

    Bogoev, Dimitar; Karam, Arzé

    2017-10-01

    We develop a new approach to reflect the behavior of algorithmic traders. Specifically, we provide an analytical and tractable way to infer patterns of quote volatility and price momentum consistent with different types of strategies employed by algorithmic traders, and we propose two ratios to quantify these patterns. Quote volatility ratio is based on the rate of oscillation of the best ask and best bid quotes over an extremely short period of time; whereas price momentum ratio is based on identifying patterns of rapid upward or downward movement in prices. The two ratios are evaluated across several asset classes. We further run a two-stage Artificial Neural Network experiment on the quote volatility ratio; the first stage is used to detect the quote volatility patterns resulting from algorithmic activity, while the second is used to validate the quality of signal detection provided by our measure.

  1. QRS Detection Algorithm for Telehealth Electrocardiogram Recordings.

    PubMed

    Khamis, Heba; Weiss, Robert; Xie, Yang; Chang, Chan-Wei; Lovell, Nigel H; Redmond, Stephen J

    2016-07-01

    QRS detection algorithms are needed to analyze electrocardiogram (ECG) recordings generated in telehealth environments. However, the numerous published QRS detectors focus on clean clinical data. Here, a "UNSW" QRS detection algorithm is described that is suitable for clinical ECG and also poorer quality telehealth ECG. The UNSW algorithm generates a feature signal containing information about ECG amplitude and derivative, which is filtered according to its frequency content and an adaptive threshold is applied. The algorithm was tested on clinical and telehealth ECG and the QRS detection performance is compared to the Pan-Tompkins (PT) and Gutiérrez-Rivas (GR) algorithm. For the MIT-BIH Arrhythmia database (virtually artifact free, clinical ECG), the overall sensitivity (Se) and positive predictivity (+P) of the UNSW algorithm was >99%, which was comparable to PT and GR. When applied to the MIT-BIH noise stress test database (clinical ECG with added calibrated noise) after artifact masking, all three algorithms had overall Se >99%, and the UNSW algorithm had higher +P (98%, p < 0.05) than PT and GR. For 250 telehealth ECG records (unsupervised recordings; dry metal electrodes), the UNSW algorithm had 98% Se and 95% +P which was superior to PT (+P: p < 0.001) and GR (Se and +P: p < 0.001). This is the first study to describe a QRS detection algorithm for telehealth data and evaluate it on clinical and telehealth ECG with superior results to published algorithms. The UNSW algorithm could be used to manage increasing telehealth ECG analysis workloads.

  2. Phase-aberration correction with a 3-D ultrasound scanner: feasibility study.

    PubMed

    Ivancevich, Nikolas M; Dahl, Jeremy J; Trahey, Gregg E; Smith, Stephen W

    2006-08-01

    We tested the feasibility of using adaptive imaging, namely phase-aberration correction, with two-dimensional (2-D) arrays and real-time, 3-D ultrasound. Because of the high spatial frequency content of aberrators, 2-D arrays, which generally have smaller pitch and thus higher spatial sampling frequency, and 3-D imaging show potential to improve the performance of adaptive imaging. Phase-correction algorithms improve image quality by compensating for tissue-induced errors in beamforming. Using the illustrative example of transcranial ultrasound, we have evaluated our ability to perform adaptive imaging with a real-time, 3-D scanner. We have used a polymer casting of a human temporal bone, root-mean-square (RMS) phase variation of 45.0 ns, full-width-half-maximum (FWHM) correlation length of 3.35 mm, and an electronic aberrator, 100 ns RMS, 3.76 mm correlation, with tissue phantoms as illustrative examples of near-field, phase-screen aberrators. Using the multilag, least-squares, cross-correlation method, we have shown the ability of 3-D adaptive imaging to increase anechoic cyst identification, image brightness, contrast-to-speckle ratio (CSR), and, in 3-D color Doppler experiments, the ability to visualize flow. For a physical aberrator skull casting we saw CSR increase by 13% from 1.01 to 1.14, while the number of detectable cysts increased from 4.3 to 7.7.

  3. Aberrant Learning Achievement Detection Based on Person-Fit Statistics in Personalized e-Learning Systems

    ERIC Educational Resources Information Center

    Liu, Ming-Tsung; Yu, Pao-Ta

    2011-01-01

    A personalized e-learning service provides learning content to fit learners' individual differences. Learning achievements are influenced by cognitive as well as non-cognitive factors such as mood, motivation, interest, and personal styles. This paper proposes the Learning Caution Indexes (LCI) to detect aberrant learning patterns. The philosophy…

  4. Toward an Objective Enhanced-V Detection Algorithm

    NASA Technical Reports Server (NTRS)

    Moses, John F.; Brunner,Jason C.; Feltz, Wayne F.; Ackerman, Steven A.; Moses, John F.; Rabin, Robert M.

    2007-01-01

    The area of coldest cloud tops above thunderstorms sometimes has a distinct V or U shape. This pattern, often referred to as an "enhanced-V signature, has been observed to occur during and preceding severe weather. This study describes an algorithmic approach to objectively detect overshooting tops, temperature couplets, and enhanced-V features with observations from the Geostationary Operational Environmental Satellite and Low Earth Orbit data. The methodology consists of temperature, temperature difference, and distance thresholds for the overshooting top and temperature couplet detection parts of the algorithm and consists of cross correlation statistics of pixels for the enhanced-V detection part of the algorithm. The effectiveness of the overshooting top and temperature couplet detection components of the algorithm is examined using GOES and MODIS image data for case studies in the 2003-2006 seasons. The main goal is for the algorithm to be useful for operations with future sensors, such as GOES-R.

  5. A robust human face detection algorithm

    NASA Astrophysics Data System (ADS)

    Raviteja, Thaluru; Karanam, Srikrishna; Yeduguru, Dinesh Reddy V.

    2012-01-01

    Human face detection plays a vital role in many applications like video surveillance, managing a face image database, human computer interface among others. This paper proposes a robust algorithm for face detection in still color images that works well even in a crowded environment. The algorithm uses conjunction of skin color histogram, morphological processing and geometrical analysis for detecting human faces. To reinforce the accuracy of face detection, we further identify mouth and eye regions to establish the presence/absence of face in a particular region of interest.

  6. MUSIC algorithms for rebar detection

    NASA Astrophysics Data System (ADS)

    Solimene, Raffaele; Leone, Giovanni; Dell'Aversano, Angela

    2013-12-01

    The MUSIC (MUltiple SIgnal Classification) algorithm is employed to detect and localize an unknown number of scattering objects which are small in size as compared to the wavelength. The ensemble of objects to be detected consists of both strong and weak scatterers. This represents a scattering environment challenging for detection purposes as strong scatterers tend to mask the weak ones. Consequently, the detection of more weakly scattering objects is not always guaranteed and can be completely impaired when the noise corrupting data is of a relatively high level. To overcome this drawback, here a new technique is proposed, starting from the idea of applying a two-stage MUSIC algorithm. In the first stage strong scatterers are detected. Then, information concerning their number and location is employed in the second stage focusing only on the weak scatterers. The role of an adequate scattering model is emphasized to improve drastically detection performance in realistic scenarios.

  7. A hardware-algorithm co-design approach to optimize seizure detection algorithms for implantable applications.

    PubMed

    Raghunathan, Shriram; Gupta, Sumeet K; Markandeya, Himanshu S; Roy, Kaushik; Irazoqui, Pedro P

    2010-10-30

    Implantable neural prostheses that deliver focal electrical stimulation upon demand are rapidly emerging as an alternate therapy for roughly a third of the epileptic patient population that is medically refractory. Seizure detection algorithms enable feedback mechanisms to provide focally and temporally specific intervention. Real-time feasibility and computational complexity often limit most reported detection algorithms to implementations using computers for bedside monitoring or external devices communicating with the implanted electrodes. A comparison of algorithms based on detection efficacy does not present a complete picture of the feasibility of the algorithm with limited computational power, as is the case with most battery-powered applications. We present a two-dimensional design optimization approach that takes into account both detection efficacy and hardware cost in evaluating algorithms for their feasibility in an implantable application. Detection features are first compared for their ability to detect electrographic seizures from micro-electrode data recorded from kainate-treated rats. Circuit models are then used to estimate the dynamic and leakage power consumption of the compared features. A score is assigned based on detection efficacy and the hardware cost for each of the features, then plotted on a two-dimensional design space. An optimal combination of compared features is used to construct an algorithm that provides maximal detection efficacy per unit hardware cost. The methods presented in this paper would facilitate the development of a common platform to benchmark seizure detection algorithms for comparison and feasibility analysis in the next generation of implantable neuroprosthetic devices to treat epilepsy. Copyright © 2010 Elsevier B.V. All rights reserved.

  8. Optimum conditions for detecting hepatic micronuclei caused by numerical chromosome aberration inducers in mice.

    PubMed

    Igarashi, Miyuki; Setoguchi, Mayumi; Takada, Sanae; Itoh, Satoru; Furuhama, Kazuhisa

    2007-08-15

    To ascertain an optimum condition for detecting micronuclei in the liver caused by numerical aberration inducers, either carbendazim (125-1000mg/kg, p.o.), colchicine (0.375-1.5mg/kg, i.v.), cytochalasin B (2.5-20mg/kg, i.v.), diazepam (3.13-25mg/kg, i.v.), noscapine (7.8-62.5mg/kg, i.v.), paclitaxel (1-100mg/kg, i.v.) or trichlorfon (18.75-150mg/kg, i.v.) was administered once to male Slc:ddY mice 1 day before or after partial hepatectomy (PH, Day 1). Five days after PH (on Day 6), hepatic micronuclei were determined in conjunction with classifications of the main nuclei and relative liver weights as a proliferative indicator or a dysfunction marker of cell division. Additionally, hepatocyte proliferation index (HPI) was calculated by using mono-, bi- and multinucleated cell counts. Treatment of mice with six compounds, except for colchicine, after PH showed higher incidence of micronucleated hepatocytes (MNH) than that before PH, and also increases in binucleated and multinucleated cells. Especially for carbendazim, diazepam, noscapine and trichlorfon, the dosing after PH was essential for the detecting numerical aberration. Colchicine evidently increased HPI and decreased relative liver weights without MNH induction on Day 6. On Day 8 when HPI and relative liver weights almost returned to the basal range, a significant increase in MNH was noted. This implied that the strong inhibition of colchicine on hepatocyte proliferation may obstruct the induction of MNH on Day 6. In conclusion, to detect the potential numerical aberration, exposure of mice to test chemicals should be performed 1 day after PH, during which enhanced proliferation of hepatocytes was seen, and it would be better to analyze the liver specimens on Day 6 or more post-PH.

  9. Detection of SHOX gene aberrations in routine diagnostic practice and evaluation of phenotype scoring form effectiveness.

    PubMed

    Hirschfeldova, Katerina; Florianova, Martina; Kebrdlova, Vera; Urbanova, Marketa; Stekrova, Jitka

    2017-02-01

    Heterozygous aberrations of SHOX gene have been reported to be responsible for Léri-Weill dyschondrosteosis (LWD) and small portion of idiopathic short stature. The study was established to assess effectiveness of using phenotype 'scoring form' in patients indicated for SHOX gene defect analysis. The submitted study is based on a retrospective group of 352 unrelated patients enrolled as a part of the routine diagnostic practice and analyzed for aberrations affecting the SHOX gene. All participants were scanned for deletion/duplication within the main pseudoautosomal region (PAR1) using the multiplex ligation-dependent probe amplification (MLPA) method. The phenotype 'scoring form' is used in our laboratory practice to preselect patients for subsequent mutation analysis of SHOX gene-coding sequences. The overall detection rate was 11.1% but there was a significant increase in frequency of SHOX gene defect positive with increasing achieved score (P<0.0001). The most frequent aberration was a causal deletion within PAR1. In three probands, MLPA analysis indicated a more complex rearrangement. Madelung deformity or co-occurrence of disproportionate short stature, short forearm and muscular hypertrophy had represented the most potent markers to determine the likelihood of SHOX gene defect detection. We conclude that appliance of phenotype 'scoring form' had saved excessive sample analysis and enabled effective routine diagnostic testing.

  10. A community detection algorithm based on structural similarity

    NASA Astrophysics Data System (ADS)

    Guo, Xuchao; Hao, Xia; Liu, Yaqiong; Zhang, Li; Wang, Lu

    2017-09-01

    In order to further improve the efficiency and accuracy of community detection algorithm, a new algorithm named SSTCA (the community detection algorithm based on structural similarity with threshold) is proposed. In this algorithm, the structural similarities are taken as the weights of edges, and the threshold k is considered to remove multiple edges whose weights are less than the threshold, and improve the computational efficiency. Tests were done on the Zachary’s network, Dolphins’ social network and Football dataset by the proposed algorithm, and compared with GN and SSNCA algorithm. The results show that the new algorithm is superior to other algorithms in accuracy for the dense networks and the operating efficiency is improved obviously.

  11. AdaBoost-based algorithm for network intrusion detection.

    PubMed

    Hu, Weiming; Hu, Wei; Maybank, Steve

    2008-04-01

    Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.

  12. Comparison of human and algorithmic target detection in passive infrared imagery

    NASA Astrophysics Data System (ADS)

    Weber, Bruce A.; Hutchinson, Meredith

    2003-09-01

    We have designed an experiment that compares the performance of human observers and a scale-insensitive target detection algorithm that uses pixel level information for the detection of ground targets in passive infrared imagery. The test database contains targets near clutter whose detectability ranged from easy to very difficult. Results indicate that human observers detect more "easy-to-detect" targets, and with far fewer false alarms, than the algorithm. For "difficult-to-detect" targets, human and algorithm detection rates are considerably degraded, and algorithm false alarms excessive. Analysis of detections as a function of observer confidence shows that algorithm confidence attribution does not correspond to human attribution, and does not adequately correlate with correct detections. The best target detection score for any human observer was 84%, as compared to 55% for the algorithm for the same false alarm rate. At 81%, the maximum detection score for the algorithm, the same human observer had 6 false alarms per frame as compared to 29 for the algorithm. Detector ROC curves and observer-confidence analysis benchmarks the algorithm and provides insights into algorithm deficiencies and possible paths to improvement.

  13. Wire Detection Algorithms for Navigation

    NASA Technical Reports Server (NTRS)

    Kasturi, Rangachar; Camps, Octavia I.

    2002-01-01

    In this research we addressed the problem of obstacle detection for low altitude rotorcraft flight. In particular, the problem of detecting thin wires in the presence of image clutter and noise was studied. Wires present a serious hazard to rotorcrafts. Since they are very thin, their detection early enough so that the pilot has enough time to take evasive action is difficult, as their images can be less than one or two pixels wide. Two approaches were explored for this purpose. The first approach involved a technique for sub-pixel edge detection and subsequent post processing, in order to reduce the false alarms. After reviewing the line detection literature, an algorithm for sub-pixel edge detection proposed by Steger was identified as having good potential to solve the considered task. The algorithm was tested using a set of images synthetically generated by combining real outdoor images with computer generated wire images. The performance of the algorithm was evaluated both, at the pixel and the wire levels. It was observed that the algorithm performs well, provided that the wires are not too thin (or distant) and that some post processing is performed to remove false alarms due to clutter. The second approach involved the use of an example-based learning scheme namely, Support Vector Machines. The purpose of this approach was to explore the feasibility of an example-based learning based approach for the task of detecting wires from their images. Support Vector Machines (SVMs) have emerged as a promising pattern classification tool and have been used in various applications. It was found that this approach is not suitable for very thin wires and of course, not suitable at all for sub-pixel thick wires. High dimensionality of the data as such does not present a major problem for SVMs. However it is desirable to have a large number of training examples especially for high dimensional data. The main difficulty in using SVMs (or any other example-based learning

  14. A Motion Detection Algorithm Using Local Phase Information

    PubMed Central

    Lazar, Aurel A.; Ukani, Nikul H.; Zhou, Yiyin

    2016-01-01

    Previous research demonstrated that global phase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithm by using only local phase information. We also demonstrate that local phase alone can be effectively used to detect local motion. The local phase-based motion detector is akin to models employed to detect motion in biological vision, for example, the Reichardt detector. The local phase-based motion detection algorithm introduced here consists of two building blocks. The first building block measures/evaluates the temporal change of the local phase. The temporal derivative of the local phase is shown to exhibit the structure of a second order Volterra kernel with two normalized inputs. We provide an efficient, FFT-based algorithm for implementing the change of the local phase. The second processing building block implements the detector; it compares the maximum of the Radon transform of the local phase derivative with a chosen threshold. We demonstrate examples of applying the local phase-based motion detection algorithm on several video sequences. We also show how the locally detected motion can be used for segmenting moving objects in video scenes and compare our local phase-based algorithm to segmentation achieved with a widely used optic flow algorithm. PMID:26880882

  15. Model-based sensor-less wavefront aberration correction in optical coherence tomography.

    PubMed

    Verstraete, Hans R G W; Wahls, Sander; Kalkman, Jeroen; Verhaegen, Michel

    2015-12-15

    Several sensor-less wavefront aberration correction methods that correct nonlinear wavefront aberrations by maximizing the optical coherence tomography (OCT) signal are tested on an OCT setup. A conventional coordinate search method is compared to two model-based optimization methods. The first model-based method takes advantage of the well-known optimization algorithm (NEWUOA) and utilizes a quadratic model. The second model-based method (DONE) is new and utilizes a random multidimensional Fourier-basis expansion. The model-based algorithms achieve lower wavefront errors with up to ten times fewer measurements. Furthermore, the newly proposed DONE method outperforms the NEWUOA method significantly. The DONE algorithm is tested on OCT images and shows a significantly improved image quality.

  16. Improvement and implementation for Canny edge detection algorithm

    NASA Astrophysics Data System (ADS)

    Yang, Tao; Qiu, Yue-hong

    2015-07-01

    Edge detection is necessary for image segmentation and pattern recognition. In this paper, an improved Canny edge detection approach is proposed due to the defect of traditional algorithm. A modified bilateral filter with a compensation function based on pixel intensity similarity judgment was used to smooth image instead of Gaussian filter, which could preserve edge feature and remove noise effectively. In order to solve the problems of sensitivity to the noise in gradient calculating, the algorithm used 4 directions gradient templates. Finally, Otsu algorithm adaptively obtain the dual-threshold. All of the algorithm simulated with OpenCV 2.4.0 library in the environments of vs2010, and through the experimental analysis, the improved algorithm has been proved to detect edge details more effectively and with more adaptability.

  17. A TCAS-II Resolution Advisory Detection Algorithm

    NASA Technical Reports Server (NTRS)

    Munoz, Cesar; Narkawicz, Anthony; Chamberlain, James

    2013-01-01

    The Traffic Alert and Collision Avoidance System (TCAS) is a family of airborne systems designed to reduce the risk of mid-air collisions between aircraft. TCASII, the current generation of TCAS devices, provides resolution advisories that direct pilots to maintain or increase vertical separation when aircraft distance and time parameters are beyond designed system thresholds. This paper presents a mathematical model of the TCASII Resolution Advisory (RA) logic that assumes accurate aircraft state information. Based on this model, an algorithm for RA detection is also presented. This algorithm is analogous to a conflict detection algorithm, but instead of predicting loss of separation, it predicts resolution advisories. It has been formally verified that for a kinematic model of aircraft trajectories, this algorithm completely and correctly characterizes all encounter geometries between two aircraft that lead to a resolution advisory within a given lookahead time interval. The RA detection algorithm proposed in this paper is a fundamental component of a NASA sense and avoid concept for the integration of Unmanned Aircraft Systems in civil airspace.

  18. Saliency detection algorithm based on LSC-RC

    NASA Astrophysics Data System (ADS)

    Wu, Wei; Tian, Weiye; Wang, Ding; Luo, Xin; Wu, Yingfei; Zhang, Yu

    2018-02-01

    Image prominence is the most important region in an image, which can cause the visual attention and response of human beings. Preferentially allocating the computer resources for the image analysis and synthesis by the significant region is of great significance to improve the image area detecting. As a preprocessing of other disciplines in image processing field, the image prominence has widely applications in image retrieval and image segmentation. Among these applications, the super-pixel segmentation significance detection algorithm based on linear spectral clustering (LSC) has achieved good results. The significance detection algorithm proposed in this paper is better than the regional contrast ratio by replacing the method of regional formation in the latter with the linear spectral clustering image is super-pixel block. After combining with the latest depth learning method, the accuracy of the significant region detecting has a great promotion. At last, the superiority and feasibility of the super-pixel segmentation detection algorithm based on linear spectral clustering are proved by the comparative test.

  19. Immunostimulatory oligonucleotide-induced metaphase cytogenetics detect chromosomal aberrations in 80% of CLL patients: A study of 132 CLL cases with correlation to FISH, IgVH status, and CD38 expression.

    PubMed

    Dicker, Frank; Schnittger, Susanne; Haferlach, Torsten; Kern, Wolfgang; Schoch, Claudia

    2006-11-01

    Compared with fluorescence in situ hybridization (FISH), conventional metaphase cytogenetics play only a minor prognostic role in chronic lymphocytic leukemia (CLL) so far, due to technical problems resulting from limited proliferation of CLL cells in vitro. Here, we present a simple method for in vitro stimulation of CLL cells that overcomes this limitation. In our unselected patient population, 125 of 132 cases could be successfully stimulated for metaphase generation by culture with the immunostimulatory CpG-oligonucleotide DSP30 plus interleukin 2. Of 125 cases, 101 showed chromosomal aberrations. The aberration rate is comparable to the rate detected by parallel interphase FISH. In 47 patients, conventional cytogenetics detected additional aberrations not detected by FISH analysis. A complex aberrant karyotype, defined as one having at least 3 aberrations, was detected in 30 of 125 patients, compared with only one such case as defined by FISH. Conventional cytogenetics frequently detected balanced and unbalanced translocations. A significant correlation of the poor-prognosis unmutated IgV(H) status with unbalanced translocations and of the likewise poor-prognosis CD38 expression to balanced translocations and complex aberrant karyotype was found. We demonstrate that FISH analysis underestimates the complexity of chromosomal aberrations in CLL. Therefore, conventional cytogenetics may define subgroups of patients with high risk of progression.

  20. Acoustic change detection algorithm using an FM radio

    NASA Astrophysics Data System (ADS)

    Goldman, Geoffrey H.; Wolfe, Owen

    2012-06-01

    The U.S. Army is interested in developing low-cost, low-power, non-line-of-sight sensors for monitoring human activity. One modality that is often overlooked is active acoustics using sources of opportunity such as speech or music. Active acoustics can be used to detect human activity by generating acoustic images of an area at different times, then testing for changes among the imagery. A change detection algorithm was developed to detect physical changes in a building, such as a door changing positions or a large box being moved using acoustics sources of opportunity. The algorithm is based on cross correlating the acoustic signal measured from two microphones. The performance of the algorithm was shown using data generated with a hand-held FM radio as a sound source and two microphones. The algorithm could detect a door being opened in a hallway.

  1. A Formally Verified Conflict Detection Algorithm for Polynomial Trajectories

    NASA Technical Reports Server (NTRS)

    Narkawicz, Anthony; Munoz, Cesar

    2015-01-01

    In air traffic management, conflict detection algorithms are used to determine whether or not aircraft are predicted to lose horizontal and vertical separation minima within a time interval assuming a trajectory model. In the case of linear trajectories, conflict detection algorithms have been proposed that are both sound, i.e., they detect all conflicts, and complete, i.e., they do not present false alarms. In general, for arbitrary nonlinear trajectory models, it is possible to define detection algorithms that are either sound or complete, but not both. This paper considers the case of nonlinear aircraft trajectory models based on polynomial functions. In particular, it proposes a conflict detection algorithm that precisely determines whether, given a lookahead time, two aircraft flying polynomial trajectories are in conflict. That is, it has been formally verified that, assuming that the aircraft trajectories are modeled as polynomial functions, the proposed algorithm is both sound and complete.

  2. Algorithmic detectability threshold of the stochastic block model

    NASA Astrophysics Data System (ADS)

    Kawamoto, Tatsuro

    2018-03-01

    The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation-maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

  3. STREAMFINDER - I. A new algorithm for detecting stellar streams

    NASA Astrophysics Data System (ADS)

    Malhan, Khyati; Ibata, Rodrigo A.

    2018-07-01

    We have designed a powerful new algorithm to detect stellar streams in an automated and systematic way. The algorithm, which we call the STREAMFINDER, is well suited for finding dynamically cold and thin stream structures that may lie along any simple or complex orbits in Galactic stellar surveys containing any combination of positional and kinematic information. In the present contribution, we introduce the algorithm, lay out the ideas behind it, explain the methodology adopted to detect streams, and detail its workings by running it on a suite of simulations of mock Galactic survey data of similar quality to that expected from the European Space Agency/Gaia mission. We show that our algorithm is able to detect even ultra-faint stream features lying well below previous detection limits. Tests show that our algorithm will be able to detect distant halo stream structures >10° long containing as few as ˜15 members (ΣG ˜ 33.6 mag arcsec-2) in the Gaia data set.

  4. Image based method for aberration measurement of lithographic tools

    NASA Astrophysics Data System (ADS)

    Xu, Shuang; Tao, Bo; Guo, Yongxing; Li, Gongfa

    2018-01-01

    Information of lens aberration of lithographic tools is important as it directly affects the intensity distribution in the image plane. Zernike polynomials are commonly used for a mathematical description of lens aberrations. Due to the advantage of lower cost and easier implementation of tools, image based measurement techniques have been widely used. Lithographic tools are typically partially coherent systems that can be described by a bilinear model, which entails time consuming calculations and does not lend a simple and intuitive relationship between lens aberrations and the resulted images. Previous methods for retrieving lens aberrations in such partially coherent systems involve through-focus image measurements and time-consuming iterative algorithms. In this work, we propose a method for aberration measurement in lithographic tools, which only requires measuring two images of intensity distribution. Two linear formulations are derived in matrix forms that directly relate the measured images to the unknown Zernike coefficients. Consequently, an efficient non-iterative solution is obtained.

  5. Evaluation schemes for video and image anomaly detection algorithms

    NASA Astrophysics Data System (ADS)

    Parameswaran, Shibin; Harguess, Josh; Barngrover, Christopher; Shafer, Scott; Reese, Michael

    2016-05-01

    Video anomaly detection is a critical research area in computer vision. It is a natural first step before applying object recognition algorithms. There are many algorithms that detect anomalies (outliers) in videos and images that have been introduced in recent years. However, these algorithms behave and perform differently based on differences in domains and tasks to which they are subjected. In order to better understand the strengths and weaknesses of outlier algorithms and their applicability in a particular domain/task of interest, it is important to measure and quantify their performance using appropriate evaluation metrics. There are many evaluation metrics that have been used in the literature such as precision curves, precision-recall curves, and receiver operating characteristic (ROC) curves. In order to construct these different metrics, it is also important to choose an appropriate evaluation scheme that decides when a proposed detection is considered a true or a false detection. Choosing the right evaluation metric and the right scheme is very critical since the choice can introduce positive or negative bias in the measuring criterion and may favor (or work against) a particular algorithm or task. In this paper, we review evaluation metrics and popular evaluation schemes that are used to measure the performance of anomaly detection algorithms on videos and imagery with one or more anomalies. We analyze the biases introduced by these by measuring the performance of an existing anomaly detection algorithm.

  6. 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.

  7. Smell Detection Agent Based Optimization Algorithm

    NASA Astrophysics Data System (ADS)

    Vinod Chandra, S. S.

    2016-09-01

    In this paper, a novel nature-inspired optimization algorithm has been employed and the trained behaviour of dogs in detecting smell trails is adapted into computational agents for problem solving. The algorithm involves creation of a surface with smell trails and subsequent iteration of the agents in resolving a path. This algorithm can be applied in different computational constraints that incorporate path-based problems. Implementation of the algorithm can be treated as a shortest path problem for a variety of datasets. The simulated agents have been used to evolve the shortest path between two nodes in a graph. This algorithm is useful to solve NP-hard problems that are related to path discovery. This algorithm is also useful to solve many practical optimization problems. The extensive derivation of the algorithm can be enabled to solve shortest path problems.

  8. Space Object Maneuver Detection Algorithms Using TLE Data

    NASA Astrophysics Data System (ADS)

    Pittelkau, M.

    2016-09-01

    An important aspect of Space Situational Awareness (SSA) is detection of deliberate and accidental orbit changes of space objects. Although space surveillance systems detect orbit maneuvers within their tracking algorithms, maneuver data are not readily disseminated for general use. However, two-line element (TLE) data is available and can be used to detect maneuvers of space objects. This work is an attempt to improve upon existing TLE-based maneuver detection algorithms. Three adaptive maneuver detection algorithms are developed and evaluated: The first is a fading-memory Kalman filter, which is equivalent to the sliding-window least-squares polynomial fit, but computationally more efficient and adaptive to the noise in the TLE data. The second algorithm is based on a sample cumulative distribution function (CDF) computed from a histogram of the magnitude-squared |V|2 of change-in-velocity vectors (V), which is computed from the TLE data. A maneuver detection threshold is computed from the median estimated from the CDF, or from the CDF and a specified probability of false alarm. The third algorithm is a median filter. The median filter is the simplest of a class of nonlinear filters called order statistics filters, which is within the theory of robust statistics. The output of the median filter is practically insensitive to outliers, or large maneuvers. The median of the |V|2 data is proportional to the variance of the V, so the variance is estimated from the output of the median filter. A maneuver is detected when the input data exceeds a constant times the estimated variance.

  9. Obstacle Detection Algorithms for Aircraft Navigation: Performance Characterization of Obstacle Detection Algorithms for Aircraft Navigation

    NASA Technical Reports Server (NTRS)

    Kasturi, Rangachar; Camps, Octavia; Coraor, Lee

    2000-01-01

    The research reported here is a part of NASA's Synthetic Vision System (SVS) project for the development of a High Speed Civil Transport Aircraft (HSCT). One of the components of the SVS is a module for detection of potential obstacles in the aircraft's flight path by analyzing the images captured by an on-board camera in real-time. Design of such a module includes the selection and characterization of robust, reliable, and fast techniques and their implementation for execution in real-time. This report describes the results of our research in realizing such a design. It is organized into three parts. Part I. Data modeling and camera characterization; Part II. Algorithms for detecting airborne obstacles; and Part III. Real time implementation of obstacle detection algorithms on the Datacube MaxPCI architecture. A list of publications resulting from this grant as well as a list of relevant publications resulting from prior NASA grants on this topic are presented.

  10. An Algorithm for Pedestrian Detection in Multispectral Image Sequences

    NASA Astrophysics Data System (ADS)

    Kniaz, V. V.; Fedorenko, V. V.

    2017-05-01

    The growing interest for self-driving cars provides a demand for scene understanding and obstacle detection algorithms. One of the most challenging problems in this field is the problem of pedestrian detection. Main difficulties arise from a diverse appearances of pedestrians. Poor visibility conditions such as fog and low light conditions also significantly decrease the quality of pedestrian detection. This paper presents a new optical flow based algorithm BipedDetet that provides robust pedestrian detection on a single-borad computer. The algorithm is based on the idea of simplified Kalman filtering suitable for realization on modern single-board computers. To detect a pedestrian a synthetic optical flow of the scene without pedestrians is generated using slanted-plane model. The estimate of a real optical flow is generated using a multispectral image sequence. The difference of the synthetic optical flow and the real optical flow provides the optical flow induced by pedestrians. The final detection of pedestrians is done by the segmentation of the difference of optical flows. To evaluate the BipedDetect algorithm a multispectral dataset was collected using a mobile robot.

  11. Lidar detection algorithm for time and range anomalies.

    PubMed

    Ben-David, Avishai; Davidson, Charles E; Vanderbeek, Richard G

    2007-10-10

    A new detection algorithm for lidar applications has been developed. The detection is based on hyperspectral anomaly detection that is implemented for time anomaly where the question "is a target (aerosol cloud) present at range R within time t(1) to t(2)" is addressed, and for range anomaly where the question "is a target present at time t within ranges R(1) and R(2)" is addressed. A detection score significantly different in magnitude from the detection scores for background measurements suggests that an anomaly (interpreted as the presence of a target signal in space/time) exists. The algorithm employs an option for a preprocessing stage where undesired oscillations and artifacts are filtered out with a low-rank orthogonal projection technique. The filtering technique adaptively removes the one over range-squared dependence of the background contribution of the lidar signal and also aids visualization of features in the data when the signal-to-noise ratio is low. A Gaussian-mixture probability model for two hypotheses (anomaly present or absent) is computed with an expectation-maximization algorithm to produce a detection threshold and probabilities of detection and false alarm. Results of the algorithm for CO(2) lidar measurements of bioaerosol clouds Bacillus atrophaeus (formerly known as Bacillus subtilis niger, BG) and Pantoea agglomerans, Pa (formerly known as Erwinia herbicola, Eh) are shown and discussed.

  12. Negative Selection Algorithm for Aircraft Fault Detection

    NASA Technical Reports Server (NTRS)

    Dasgupta, D.; KrishnaKumar, K.; Wong, D.; Berry, M.

    2004-01-01

    We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.

  13. SA-SOM algorithm for detecting communities in complex networks

    NASA Astrophysics Data System (ADS)

    Chen, Luogeng; Wang, Yanran; Huang, Xiaoming; Hu, Mengyu; Hu, Fang

    2017-10-01

    Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does.

  14. Numerical chromosomal aberrations in Hodgkin's disease detected by in situ hybridisation on routine paraffin sections.

    PubMed Central

    Pringle, J H; Shaw, J A; Gillies, A; Lauder, I

    1997-01-01

    AIMS: To visualise directly numerical chromosomal aberrations and polyploidy in both Hodgkin and Reed Sternberg (HRS) cells and background cells from cases of Hodgkin's disease using in situ hybridisation. METHODS: Non-isotopic DNA in situ hybridisation was applied to interphase cell nuclei of Hodgkin's disease within routine paraffin embedded tissue sections. Two a satellite DNA probes, specific for chromosomes 3 and 12, were used to evaluate the feasibility of this approach. Double labelling with immunocytochemical detection of the CD30 antigen was used to identify HRS cells. Cytogenetic normal diploid and triploid placental tissue served as controls. RESULTS: The eight cases of Hodgkin's disease investigated displayed frequent polysomy, while the majority of background cells showed disomy signals. CONCLUSIONS: Numerical chromosomal aberrations were detected in HRS cells from eight cases of Hodgkin's disease by in situ hybridisation. These data show that in Hodgkin's disease HRS cells frequently display polyploidy compared with background cells and are, therefore, probably the only neoplastic component in this disease. Correlations between polysomy and tumour type or grade could not be made from these data owing to the limited number of cases examined and to problems with interpreting data from truncated nuclei. Images PMID:9306933

  15. Texture orientation-based algorithm for detecting infrared maritime targets.

    PubMed

    Wang, Bin; Dong, Lili; Zhao, Ming; Wu, Houde; Xu, Wenhai

    2015-05-20

    Infrared maritime target detection is a key technology for maritime target searching systems. However, in infrared maritime images (IMIs) taken under complicated sea conditions, background clutters, such as ocean waves, clouds or sea fog, usually have high intensity that can easily overwhelm the brightness of real targets, which is difficult for traditional target detection algorithms to deal with. To mitigate this problem, this paper proposes a novel target detection algorithm based on texture orientation. This algorithm first extracts suspected targets by analyzing the intersubband correlation between horizontal and vertical wavelet subbands of the original IMI on the first scale. Then the self-adaptive wavelet threshold denoising and local singularity analysis of the original IMI is combined to remove false alarms further. Experiments show that compared with traditional algorithms, this algorithm can suppress background clutter much better and realize better single-frame detection for infrared maritime targets. Besides, in order to guarantee accurate target extraction further, the pipeline-filtering algorithm is adopted to eliminate residual false alarms. The high practical value and applicability of this proposed strategy is backed strongly by experimental data acquired under different environmental conditions.

  16. 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

  17. Statistical estimation of ultrasonic propagation path parameters for aberration correction.

    PubMed

    Waag, Robert C; Astheimer, Jeffrey P

    2005-05-01

    Parameters in a linear filter model for ultrasonic propagation are found using statistical estimation. The model uses an inhomogeneous-medium Green's function that is decomposed into a homogeneous-transmission term and a path-dependent aberration term. Power and cross-power spectra of random-medium scattering are estimated over the frequency band of the transmit-receive system by using closely situated scattering volumes. The frequency-domain magnitude of the aberration is obtained from a normalization of the power spectrum. The corresponding phase is reconstructed from cross-power spectra of subaperture signals at adjacent receive positions by a recursion. The subapertures constrain the receive sensitivity pattern to eliminate measurement system phase contributions. The recursion uses a Laplacian-based algorithm to obtain phase from phase differences. Pulse-echo waveforms were acquired from a point reflector and a tissue-like scattering phantom through a tissue-mimicking aberration path from neighboring volumes having essentially the same aberration path. Propagation path aberration parameters calculated from the measurements of random scattering through the aberration phantom agree with corresponding parameters calculated for the same aberrator and array position by using echoes from the point reflector. The results indicate the approach describes, in addition to time shifts, waveform amplitude and shape changes produced by propagation through distributed aberration under realistic conditions.

  18. Fast and robust estimation of ophthalmic wavefront aberrations

    NASA Astrophysics Data System (ADS)

    Dillon, Keith

    2016-12-01

    Rapidly rising levels of myopia, particularly in the developing world, have led to an increased need for inexpensive and automated approaches to optometry. A simple and robust technique is provided for estimating major ophthalmic aberrations using a gradient-based wavefront sensor. The approach is based on the use of numerical calculations to produce diverse combinations of phase components, followed by Fourier transforms to calculate the coefficients. The approach does not utilize phase unwrapping nor iterative solution of inverse problems. This makes the method very fast and tolerant to image artifacts, which do not need to be detected and masked or interpolated as is needed in other techniques. These features make it a promising algorithm on which to base low-cost devices for applications that may have limited access to expert maintenance and operation.

  19. Statistical algorithms improve accuracy of gene fusion detection

    PubMed Central

    Hsieh, Gillian; Bierman, Rob; Szabo, Linda; Lee, Alex Gia; Freeman, Donald E.; Watson, Nathaniel; Sweet-Cordero, E. Alejandro

    2017-01-01

    Abstract Gene fusions are known to play critical roles in tumor pathogenesis. Yet, sensitive and specific algorithms to detect gene fusions in cancer do not currently exist. In this paper, we present a new statistical algorithm, MACHETE (Mismatched Alignment CHimEra Tracking Engine), which achieves highly sensitive and specific detection of gene fusions from RNA-Seq data, including the highest Positive Predictive Value (PPV) compared to the current state-of-the-art, as assessed in simulated data. We show that the best performing published algorithms either find large numbers of fusions in negative control data or suffer from low sensitivity detecting known driving fusions in gold standard settings, such as EWSR1-FLI1. As proof of principle that MACHETE discovers novel gene fusions with high accuracy in vivo, we mined public data to discover and subsequently PCR validate novel gene fusions missed by other algorithms in the ovarian cancer cell line OVCAR3. These results highlight the gains in accuracy achieved by introducing statistical models into fusion detection, and pave the way for unbiased discovery of potentially driving and druggable gene fusions in primary tumors. PMID:28541529

  20. An experimental loop design for the detection of constitutional chromosomal aberrations by array CGH

    PubMed Central

    2009-01-01

    Background Comparative genomic hybridization microarrays for the detection of constitutional chromosomal aberrations is the application of microarray technology coming fastest into routine clinical application. Through genotype-phenotype association, it is also an important technique towards the discovery of disease causing genes and genomewide functional annotation in human. When using a two-channel microarray of genomic DNA probes for array CGH, the basic setup consists in hybridizing a patient against a normal reference sample. Two major disadvantages of this setup are (1) the use of half of the resources to measure a (little informative) reference sample and (2) the possibility that deviating signals are caused by benign copy number variation in the "normal" reference instead of a patient aberration. Instead, we apply an experimental loop design that compares three patients in three hybridizations. Results We develop and compare two statistical methods (linear models of log ratios and mixed models of absolute measurements). In an analysis of 27 patients seen at our genetics center, we observed that the linear models of the log ratios are advantageous over the mixed models of the absolute intensities. Conclusion The loop design and the performance of the statistical analysis contribute to the quick adoption of array CGH as a routine diagnostic tool. They lower the detection limit of mosaicisms and improve the assignment of copy number variation for genetic association studies. PMID:19925645

  1. An experimental loop design for the detection of constitutional chromosomal aberrations by array CGH.

    PubMed

    Allemeersch, Joke; Van Vooren, Steven; Hannes, Femke; De Moor, Bart; Vermeesch, Joris Robert; Moreau, Yves

    2009-11-19

    Comparative genomic hybridization microarrays for the detection of constitutional chromosomal aberrations is the application of microarray technology coming fastest into routine clinical application. Through genotype-phenotype association, it is also an important technique towards the discovery of disease causing genes and genomewide functional annotation in human. When using a two-channel microarray of genomic DNA probes for array CGH, the basic setup consists in hybridizing a patient against a normal reference sample. Two major disadvantages of this setup are (1) the use of half of the resources to measure a (little informative) reference sample and (2) the possibility that deviating signals are caused by benign copy number variation in the "normal" reference instead of a patient aberration. Instead, we apply an experimental loop design that compares three patients in three hybridizations. We develop and compare two statistical methods (linear models of log ratios and mixed models of absolute measurements). In an analysis of 27 patients seen at our genetics center, we observed that the linear models of the log ratios are advantageous over the mixed models of the absolute intensities. The loop design and the performance of the statistical analysis contribute to the quick adoption of array CGH as a routine diagnostic tool. They lower the detection limit of mosaicisms and improve the assignment of copy number variation for genetic association studies.

  2. A novel algorithm for notch detection

    NASA Astrophysics Data System (ADS)

    Acosta, C.; Salazar, D.; Morales, D.

    2013-06-01

    It is common knowledge that DFM guidelines require revisions to design data. These guidelines impose the need for corrections inserted into areas within the design data flow. At times, this requires rather drastic modifications to the data, both during the layer derivation or DRC phase, and especially within the RET phase. For example, OPC. During such data transformations, several polygon geometry changes are introduced, which can substantially increase shot count, geometry complexity, and eventually conversion to mask writer machine formats. In this resulting complex data, it may happen that notches are found that do not significantly contribute to the final manufacturing results, but do in fact contribute to the complexity of the surrounding geometry, and are therefore undesirable. Additionally, there are cases in which the overall figure count can be reduced with minimum impact in the quality of the corrected data, if notches are detected and corrected. Case in point, there are other cases where data quality could be improved if specific valley notches are filled in, or peak notches are cut out. Such cases generally satisfy specific geometrical restrictions in order to be valid candidates for notch correction. Traditional notch detection has been done for rectilinear data (Manhattan-style) and only in axis-parallel directions. The traditional approaches employ dimensional measurement algorithms that measure edge distances along the outside of polygons. These approaches are in general adaptations, and therefore ill-fitted for generalized detection of notches with strange shapes and in strange rotations. This paper covers a novel algorithm developed for the CATS MRCC tool that finds both valley and/or peak notches that are candidates for removal. The algorithm is generalized and invariant to data rotation, so that it can find notches in data rotated in any angle. It includes parameters to control the dimensions of detected notches, as well as algorithm tolerances

  3. Reducing false-positive detections by combining two stage-1 computer-aided mass detection algorithms

    NASA Astrophysics Data System (ADS)

    Bedard, Noah D.; Sampat, Mehul P.; Stokes, Patrick A.; Markey, Mia K.

    2006-03-01

    In this paper we present a strategy for reducing the number of false-positives in computer-aided mass detection. Our approach is to only mark "consensus" detections from among the suspicious sites identified by different "stage-1" detection algorithms. By "stage-1" we mean that each of the Computer-aided Detection (CADe) algorithms is designed to operate with high sensitivity, allowing for a large number of false positives. In this study, two mass detection methods were used: (1) Heath and Bowyer's algorithm based on the average fraction under the minimum filter (AFUM) and (2) a low-threshold bi-lateral subtraction algorithm. The two methods were applied separately to a set of images from the Digital Database for Screening Mammography (DDSM) to obtain paired sets of mass candidates. The consensus mass candidates for each image were identified by a logical "and" operation of the two CADe algorithms so as to eliminate regions of suspicion that were not independently identified by both techniques. It was shown that by combining the evidence from the AFUM filter method with that obtained from bi-lateral subtraction, the same sensitivity could be reached with fewer false-positives per image relative to using the AFUM filter alone.

  4. One algorithm to rule them all? An evaluation and discussion of ten eye movement event-detection algorithms.

    PubMed

    Andersson, Richard; Larsson, Linnea; Holmqvist, Kenneth; Stridh, Martin; Nyström, Marcus

    2017-04-01

    Almost all eye-movement researchers use algorithms to parse raw data and detect distinct types of eye movement events, such as fixations, saccades, and pursuit, and then base their results on these. Surprisingly, these algorithms are rarely evaluated. We evaluated the classifications of ten eye-movement event detection algorithms, on data from an SMI HiSpeed 1250 system, and compared them to manual ratings of two human experts. The evaluation focused on fixations, saccades, and post-saccadic oscillations. The evaluation used both event duration parameters, and sample-by-sample comparisons to rank the algorithms. The resulting event durations varied substantially as a function of what algorithm was used. This evaluation differed from previous evaluations by considering a relatively large set of algorithms, multiple events, and data from both static and dynamic stimuli. The main conclusion is that current detectors of only fixations and saccades work reasonably well for static stimuli, but barely better than chance for dynamic stimuli. Differing results across evaluation methods make it difficult to select one winner for fixation detection. For saccade detection, however, the algorithm by Larsson, Nyström and Stridh (IEEE Transaction on Biomedical Engineering, 60(9):2484-2493,2013) outperforms all algorithms in data from both static and dynamic stimuli. The data also show how improperly selected algorithms applied to dynamic data misestimate fixation and saccade properties.

  5. Rapid metaphase and interphase detection of radiation-induced chromosome aberrations in human lymphocytes by chromosomal suppression in situ hybridization

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

    Cremer, T.; Popp, S.; Emmerich, P.

    1990-01-01

    Chromosomal in situ suppression (CISS)-hybridization of biotinylated phage DNA-library inserts from sorted human chromosomes was used to decorate chromosomes 1 and 7 specifically from pter to qter and to detect structural aberrations of these chromosomes in irradiated human peripheral lymphocytes. In addition, probe pUC1.77 was used to mark the 1q12 subregion in normal and aberrant chromosomes 1. Low LET radiation (60Co-gamma-rays; 1.17 and 1.33 MeV) of lymphocyte cultures was performed with various doses (D = 0, 2, 4, 8 Gy) 5 h after stimulation with phytohaemagglutinin. Irradiated cells were cultivated for an additional 67 h before Colcemid arrested metaphase spreadsmore » were obtained. Aberrations of the specifically stained chromosomes, such as deletions, dicentrics, and rings, were readily scored after in situ hybridization with either the 1q12 specific probe or DNA-library inserts. By the latter approach, translocations of the specifically stained chromosomes could also be reliably assessed. A linear increase of the percentage of specifically stained aberrant chromosomes was observed when plotted as a function of the square of the dose D. A particular advantage of this new approach is provided by the possibility to delineate numerical and structural chromosome aberrations directly in interphase nuclei. These results indicate that cytogenetic monitoring of ionizing radiation may be considerably facilitated by CISS-hybridization.« less

  6. Three-dimensional contour edge detection algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Yizhou; Ong, Sim Heng; Kassim, Ashraf A.; Foong, Kelvin W. C.

    2000-06-01

    This paper presents a novel algorithm for automatically extracting 3D contour edges, which are points of maximum surface curvature in a surface range image. The 3D image data are represented as a surface polygon mesh. The algorithm transforms the range data, obtained by scanning a dental plaster cast, into a 2D gray scale image by linearly converting the z-value of each vertex to a gray value. The Canny operator is applied to the median-filtered image to obtain the edge pixels and their orientations. A vertex in the 3D object corresponding to the detected edge pixel and its neighbors in the direction of the edge gradient are further analyzed with respect to their n-curvatures to extract the real 3D contour edges. This algorithm provides a fast method of reducing and sorting the unwieldy data inherent in the surface mesh representation. It employs powerful 2D algorithms to extract features from the transformed 3D models and refers to the 3D model for further analysis of selected data. This approach substantially reduces the computational burden without losing accuracy. It is also easily extended to detect 3D landmarks and other geometrical features, thus making it applicable to a wide range of applications.

  7. An improved algorithm for wildfire detection

    NASA Astrophysics Data System (ADS)

    Nakau, K.

    2010-12-01

    Satellite information of wild fire location has strong demands from society. Therefore, Understanding such demands is quite important to consider what to improve the wild fire detection algorithm. Interviews and considerations imply that the most important improvements are geographical resolution of the wildfire product and classification of fire; smoldering or flaming. Discussion with fire service agencies are performed with fire service agencies in Alaska and fire service volunteer groups in Indonesia. Alaska Fire Service (AFS) makes 3D-map overlaid by fire location every morning. Then, this 3D-map is examined by leaders of fire service teams to decide their strategy to fighting against wild fire. Especially, firefighters of both agencies seek the best walk path to approach the fire. Because of mountainous landscape, geospatial resolution is quite important for them. For example, walking in bush for 1km, as same as one pixel of fire product, is very tough for firefighters. Also, in case of remote wild fire, fire service agencies utilize satellite information to decide when to have a flight observation to confirm the status; expanding, flaming, smoldering or out. Therefore, it is also quite important to provide the classification of fire; flaming or smoldering. Not only the aspect of disaster management, wildfire emits huge amount of carbon into atmosphere as much as one quarter to one half of CO2 by fuel combustion (IPCC AR4). Reduction of the CO2 emission by human caused wildfire is important. To estimate carbon emission from wildfire, special resolution is quite important. To improve sensitivity of wild fire detection, author adopts radiance based wildfire detection. Different from the existing brightness temperature approach, we can easily consider reflectance of background land coverage. Especially for GCOM-C1/SGLI, band to detect fire with 250m resolution is 1.6μm wavelength. In this band, we have much more sunlight reflection. Therefore, we need to

  8. RUBIC identifies driver genes by detecting recurrent DNA copy number breaks

    PubMed Central

    van Dyk, Ewald; Hoogstraat, Marlous; ten Hoeve, Jelle; Reinders, Marcel J. T.; Wessels, Lodewyk F. A.

    2016-01-01

    The frequent recurrence of copy number aberrations across tumour samples is a reliable hallmark of certain cancer driver genes. However, state-of-the-art algorithms for detecting recurrent aberrations fail to detect several known drivers. In this study, we propose RUBIC, an approach that detects recurrent copy number breaks, rather than recurrently amplified or deleted regions. This change of perspective allows for a simplified approach as recursive peak splitting procedures and repeated re-estimation of the background model are avoided. Furthermore, we control the false discovery rate on the level of called regions, rather than at the probe level, as in competing algorithms. We benchmark RUBIC against GISTIC2 (a state-of-the-art approach) and RAIG (a recently proposed approach) on simulated copy number data and on three SNP6 and NGS copy number data sets from TCGA. We show that RUBIC calls more focal recurrent regions and identifies a much larger fraction of known cancer genes. PMID:27396759

  9. Study of the performance of image restoration under different wavefront aberrations

    NASA Astrophysics Data System (ADS)

    Wang, Xinqiu; Hu, Xinqi

    2016-10-01

    Image restoration is an effective way to improve the quality of images degraded by wave-front aberrations. If the wave-front aberration is too large, the performance of the image restoration will not be good. In this paper, the relationship between the performance of image restoration and the degree of wave-front aberrations is studied. A set of different wave-front aberrations is constructed by Zernike polynomials, and the corresponding PSF under white-light illumination is calculated. A set of blurred images is then obtained through convolution methods. Next we recover the images with the regularized Richardson-Lucy algorithm and use the RMS of the original image and the homologous deblurred image to evaluate the quality of restoration. Consequently, we determine the range of wave-front errors in which the recovered images are acceptable.

  10. An Efficient Conflict Detection Algorithm for Packet Filters

    NASA Astrophysics Data System (ADS)

    Lee, Chun-Liang; Lin, Guan-Yu; Chen, Yaw-Chung

    Packet classification is essential for supporting advanced network services such as firewalls, quality-of-service (QoS), virtual private networks (VPN), and policy-based routing. The rules that routers use to classify packets are called packet filters. If two or more filters overlap, a conflict occurs and leads to ambiguity in packet classification. This study proposes an algorithm that can efficiently detect and resolve filter conflicts using tuple based search. The time complexity of the proposed algorithm is O(nW+s), and the space complexity is O(nW), where n is the number of filters, W is the number of bits in a header field, and s is the number of conflicts. This study uses the synthetic filter databases generated by ClassBench to evaluate the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than existing conflict detection algorithms both in time and space, particularly for databases with large numbers of conflicts.

  11. 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.

  12. A Decision Theoretic Approach to Evaluate Radiation Detection Algorithms

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

    Nobles, Mallory A.; Sego, Landon H.; Cooley, Scott K.

    2013-07-01

    There are a variety of sensor systems deployed at U.S. border crossings and ports of entry that scan for illicit nuclear material. In this work, we develop a framework for comparing the performance of detection algorithms that interpret the output of these scans and determine when secondary screening is needed. We optimize each algorithm to minimize its risk, or expected loss. We measure an algorithm’s risk by considering its performance over a sample, the probability distribution of threat sources, and the consequence of detection errors. While it is common to optimize algorithms by fixing one error rate and minimizing another,more » our framework allows one to simultaneously consider multiple types of detection errors. Our framework is flexible and easily adapted to many different assumptions regarding the probability of a vehicle containing illicit material, and the relative consequences of a false positive and false negative errors. Our methods can therefore inform decision makers of the algorithm family and parameter values which best reduce the threat from illicit nuclear material, given their understanding of the environment at any point in time. To illustrate the applicability of our methods, in this paper, we compare the risk from two families of detection algorithms and discuss the policy implications of our results.« less

  13. Static telescope aberration measurement using lucky imaging techniques

    NASA Astrophysics Data System (ADS)

    López-Marrero, Marcos; Rodríguez-Ramos, Luis Fernando; Marichal-Hernández, José Gil; Rodríguez-Ramos, José Manuel

    2012-07-01

    A procedure has been developed to compute static aberrations once the telescope PSF has been measured with the lucky imaging technique, using a nearby star close to the object of interest as the point source to probe the optical system. This PSF is iteratively turned into a phase map at the pupil using the Gerchberg-Saxton algorithm and then converted to the appropriate actuation information for a deformable mirror having low actuator number but large stroke capability. The main advantage of this procedure is related with the capability of correcting static aberration at the specific pointing direction and without the need of a wavefront sensor.

  14. Automated aberration correction of arbitrary laser modes in high numerical aperture systems.

    PubMed

    Hering, Julian; Waller, Erik H; Von Freymann, Georg

    2016-12-12

    Controlling the point-spread-function in three-dimensional laser lithography is crucial for fabricating structures with highest definition and resolution. In contrast to microscopy, aberrations have to be physically corrected prior to writing, to create well defined doughnut modes, bottlebeams or multi foci modes. We report on a modified Gerchberg-Saxton algorithm for spatial-light-modulator based automated aberration compensation to optimize arbitrary laser-modes in a high numerical aperture system. Using circularly polarized light for the measurement and first-guess initial conditions for amplitude and phase of the pupil function our scalar approach outperforms recent algorithms with vectorial corrections. Besides laser lithography also applications like optical tweezers and microscopy might benefit from the method presented.

  15. Community detection in complex networks by using membrane algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Chuang; Fan, Linan; Liu, Zhou; Dai, Xiang; Xu, Jiamei; Chang, Baoren

    Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

  16. Fast object detection algorithm based on HOG and CNN

    NASA Astrophysics Data System (ADS)

    Lu, Tongwei; Wang, Dandan; Zhang, Yanduo

    2018-04-01

    In the field of computer vision, object classification and object detection are widely used in many fields. The traditional object detection have two main problems:one is that sliding window of the regional selection strategy is high time complexity and have window redundancy. And the other one is that Robustness of the feature is not well. In order to solve those problems, Regional Proposal Network (RPN) is used to select candidate regions instead of selective search algorithm. Compared with traditional algorithms and selective search algorithms, RPN has higher efficiency and accuracy. We combine HOG feature and convolution neural network (CNN) to extract features. And we use SVM to classify. For TorontoNet, our algorithm's mAP is 1.6 percentage points higher. For OxfordNet, our algorithm's mAP is 1.3 percentage higher.

  17. A Space Object Detection Algorithm using Fourier Domain Likelihood Ratio Test

    NASA Astrophysics Data System (ADS)

    Becker, D.; Cain, S.

    Space object detection is of great importance in the highly dependent yet competitive and congested space domain. Detection algorithms employed play a crucial role in fulfilling the detection component in the situational awareness mission to detect, track, characterize and catalog unknown space objects. Many current space detection algorithms use a matched filter or a spatial correlator to make a detection decision at a single pixel point of a spatial image based on the assumption that the data follows a Gaussian distribution. This paper explores the potential for detection performance advantages when operating in the Fourier domain of long exposure images of small and/or dim space objects from ground based telescopes. A binary hypothesis test is developed based on the joint probability distribution function of the image under the hypothesis that an object is present and under the hypothesis that the image only contains background noise. The detection algorithm tests each pixel point of the Fourier transformed images to make the determination if an object is present based on the criteria threshold found in the likelihood ratio test. Using simulated data, the performance of the Fourier domain detection algorithm is compared to the current algorithm used in space situational awareness applications to evaluate its value.

  18. Distributed learning automata-based algorithm for community detection in complex networks

    NASA Astrophysics Data System (ADS)

    Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

    2016-03-01

    Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

  19. Comparison of Aberrations After Standard and Customized Refractive Surgery

    NASA Astrophysics Data System (ADS)

    Fang, L.; He, X.; Wang, Y.

    2013-09-01

    To detect possible differences in residual wavefront aberrations between standard and customized laser refractive surgery based onmathematical modeling, the residual optical aberrations after conventional and customized laser refractive surgery were compared accordingto the ablation profile with transition zone. The results indicated that ablation profile has a significant impact on the residual aberrations.The amount of residual aberrations for conventional correction is higher than that for customized correction. Additionally, the residualaberrations for high myopia eyes are markedly larger than those for moderate myopia eyes. For a 5 mm pupil, the main residual aberrationterm is coma and yet it is spherical aberration for a 7 mm pupil. When the pupil diameter is the same as optical zone or greater, themagnitudes of residual aberrations is obviously larger than that for a smaller pupil. In addition, the magnitudes of the residual fifth orsixth order aberrations are relatively large, especially secondary coma in a 6 mm pupil and secondary spherical aberration in a 7 mm pupil.Therefore, the customized ablation profile may be superior to the conventional correction even though the transition zone and treatmentdecentration are taken into account. However, the customized ablation profile will still induce significant amount of residual aberrations.

  20. Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model.

    PubMed

    Hsieh, Chia-Yeh; Liu, Kai-Chun; Huang, Chih-Ning; Chu, Woei-Chyn; Chan, Chia-Tai

    2017-02-08

    Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences.

  1. Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model

    PubMed Central

    Hsieh, Chia-Yeh; Liu, Kai-Chun; Huang, Chih-Ning; Chu, Woei-Chyn; Chan, Chia-Tai

    2017-01-01

    Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences. PMID:28208694

  2. A Region Tracking-Based Vehicle Detection Algorithm in Nighttime Traffic Scenes

    PubMed Central

    Wang, Jianqiang; Sun, Xiaoyan; Guo, Junbin

    2013-01-01

    The preceding vehicles detection technique in nighttime traffic scenes is an important part of the advanced driver assistance system (ADAS). This paper proposes a region tracking-based vehicle detection algorithm via the image processing technique. First, the brightness of the taillights during nighttime is used as the typical feature, and we use the existing global detection algorithm to detect and pair the taillights. When the vehicle is detected, a time series analysis model is introduced to predict vehicle positions and the possible region (PR) of the vehicle in the next frame. Then, the vehicle is only detected in the PR. This could reduce the detection time and avoid the false pairing between the bright spots in the PR and the bright spots out of the PR. Additionally, we present a thresholds updating method to make the thresholds adaptive. Finally, experimental studies are provided to demonstrate the application and substantiate the superiority of the proposed algorithm. The results show that the proposed algorithm can simultaneously reduce both the false negative detection rate and the false positive detection rate.

  3. 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.

  4. Multi-Objective Community Detection Based on Memetic Algorithm

    PubMed Central

    2015-01-01

    Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

  5. Multi-objective community detection based on memetic algorithm.

    PubMed

    Wu, Peng; Pan, Li

    2015-01-01

    Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

  6. Method for identifying and quantifying nucleic acid sequence aberrations

    DOEpatents

    Lucas, Joe N.; Straume, Tore; Bogen, Kenneth T.

    1998-01-01

    A method for detecting nucleic acid sequence aberrations by detecting nucleic acid sequences having both a first and a second nucleic acid sequence type, the presence of the first and second sequence type on the same nucleic acid sequence indicating the presence of a nucleic acid sequence aberration. The method uses a first hybridization probe which includes a nucleic acid sequence that is complementary to a first sequence type and a first complexing agent capable of attaching to a second complexing agent and a second hybridization probe which includes a nucleic acid sequence that selectively hybridizes to the second nucleic acid sequence type over the first sequence type and includes a detectable marker for detecting the second hybridization probe.

  7. An Automated Energy Detection Algorithm Based on Consecutive Mean Excision

    DTIC Science & Technology

    2018-01-01

    present in the RF spectrum. 15. SUBJECT TERMS RF spectrum, detection threshold algorithm, consecutive mean excision, rank order filter , statistical...Median 4 3.1.9 Rank Order Filter (ROF) 4 3.1.10 Crest Factor (CF) 5 3.2 Statistical Summary 6 4. Algorithm 7 5. Conclusion 8 6. References 9...energy detection algorithm based on morphological filter processing with a semi- disk structure. Adelphi (MD): Army Research Laboratory (US); 2018 Jan

  8. Toward an Objective Enhanced-V Detection Algorithm

    NASA Technical Reports Server (NTRS)

    Brunner, Jason; Feltz, Wayne; Moses, John; Rabin, Robert; Ackerman, Steven

    2007-01-01

    The area of coldest cloud tops above thunderstorms sometimes has a distinct V or U shape. This pattern, often referred to as an "enhanced-V' signature, has been observed to occur during and preceding severe weather in previous studies. This study describes an algorithmic approach to objectively detect enhanced-V features with observations from the Geostationary Operational Environmental Satellite and Low Earth Orbit data. The methodology consists of cross correlation statistics of pixels and thresholds of enhanced-V quantitative parameters. The effectiveness of the enhanced-V detection method will be examined using Geostationary Operational Environmental Satellite, MODerate-resolution Imaging Spectroradiometer, and Advanced Very High Resolution Radiometer image data from case studies in the 2003-2006 seasons. The main goal of this study is to develop an objective enhanced-V detection algorithm for future implementation into operations with future sensors, such as GOES-R.

  9. Improved imaging algorithm for bridge crack detection

    NASA Astrophysics Data System (ADS)

    Lu, Jingxiao; Song, Pingli; Han, Kaihong

    2012-04-01

    This paper present an improved imaging algorithm for bridge crack detection, through optimizing the eight-direction Sobel edge detection operator, making the positioning of edge points more accurate than without the optimization, and effectively reducing the false edges information, so as to facilitate follow-up treatment. In calculating the crack geometry characteristics, we use the method of extracting skeleton on single crack length. In order to calculate crack area, we construct the template of area by making logical bitwise AND operation of the crack image. After experiment, the results show errors of the crack detection method and actual manual measurement are within an acceptable range, meet the needs of engineering applications. This algorithm is high-speed and effective for automated crack measurement, it can provide more valid data for proper planning and appropriate performance of the maintenance and rehabilitation processes of bridge.

  10. Aberration correction for transcranial photoacoustic tomography of primates employing adjunct image data

    NASA Astrophysics Data System (ADS)

    Huang, Chao; Nie, Liming; Schoonover, Robert W.; Guo, Zijian; Schirra, Carsten O.; Anastasio, Mark A.; Wang, Lihong V.

    2012-06-01

    A challenge in photoacoustic tomography (PAT) brain imaging is to compensate for aberrations in the measured photoacoustic data due to their propagation through the skull. By use of information regarding the skull morphology and composition obtained from adjunct x-ray computed tomography image data, we developed a subject-specific imaging model that accounts for such aberrations. A time-reversal-based reconstruction algorithm was employed with this model for image reconstruction. The image reconstruction methodology was evaluated in experimental studies involving phantoms and monkey heads. The results establish that our reconstruction methodology can effectively compensate for skull-induced acoustic aberrations and improve image fidelity in transcranial PAT.

  11. Lining seam elimination algorithm and surface crack detection in concrete tunnel lining

    NASA Astrophysics Data System (ADS)

    Qu, Zhong; Bai, Ling; An, Shi-Quan; Ju, Fang-Rong; Liu, Ling

    2016-11-01

    Due to the particularity of the surface of concrete tunnel lining and the diversity of detection environments such as uneven illumination, smudges, localized rock falls, water leakage, and the inherent seams of the lining structure, existing crack detection algorithms cannot detect real cracks accurately. This paper proposed an algorithm that combines lining seam elimination with the improved percolation detection algorithm based on grid cell analysis for surface crack detection in concrete tunnel lining. First, check the characteristics of pixels within the overlapping grid to remove the background noise and generate the percolation seed map (PSM). Second, cracks are detected based on the PSM by the accelerated percolation algorithm so that the fracture unit areas can be scanned and connected. Finally, the real surface cracks in concrete tunnel lining can be obtained by removing the lining seam and performing percolation denoising. Experimental results show that the proposed algorithm can accurately, quickly, and effectively detect the real surface cracks. Furthermore, it can fill the gap in the existing concrete tunnel lining surface crack detection by removing the lining seam.

  12. A New Pivoting and Iterative Text Detection Algorithm for Biomedical Images

    PubMed Central

    Xu, Songhua; Krauthammer, Michael

    2010-01-01

    There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper’s key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manually labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. In this paper, we demonstrate that a projection histogram-based text detection approach is well suited for text detection in biomedical images, with a performance of F score of .60. The approach performs better than comparable approaches for text detection. Further, we show that the iterative application of the algorithm is boosting overall detection performance. A C++ implementation of our algorithm is freely available through email request for academic use. PMID:20887803

  13. DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D.

    PubMed

    Shuvaev, Sergey A; Lazutkin, Alexander A; Kedrov, Alexander V; Anokhin, Konstantin V; Enikolopov, Grigori N; Koulakov, Alexei A

    2017-01-01

    Current 3D imaging methods, including optical projection tomography, light-sheet microscopy, block-face imaging, and serial two photon tomography enable visualization of large samples of biological tissue. Large volumes of data obtained at high resolution require development of automatic image processing techniques, such as algorithms for automatic cell detection or, more generally, point-like object detection. Current approaches to automated cell detection suffer from difficulties originating from detection of particular cell types, cell populations of different brightness, non-uniformly stained, and overlapping cells. In this study, we present a set of algorithms for robust automatic cell detection in 3D. Our algorithms are suitable for, but not limited to, whole brain regions and individual brain sections. We used watershed procedure to split regional maxima representing overlapping cells. We developed a bootstrap Gaussian fit procedure to evaluate the statistical significance of detected cells. We compared cell detection quality of our algorithm and other software using 42 samples, representing 6 staining and imaging techniques. The results provided by our algorithm matched manual expert quantification with signal-to-noise dependent confidence, including samples with cells of different brightness, non-uniformly stained, and overlapping cells for whole brain regions and individual tissue sections. Our algorithm provided the best cell detection quality among tested free and commercial software.

  14. Automated aberration compensation in high numerical aperture systems for arbitrary laser modes (Conference Presentation)

    NASA Astrophysics Data System (ADS)

    Hering, Julian; Waller, Erik H.; von Freymann, Georg

    2017-02-01

    Since a large number of optical systems and devices are based on differently shaped focal intensity distributions (point-spread-functions, PSF), the PSF's quality is crucial for the application's performance. E.g., optical tweezers, optical potentials for trapping of ultracold atoms as well as stimulated-emission-depletion (STED) based microscopy and lithography rely on precisely controlled intensity distributions. However, especially in high numerical aperture (NA) systems, such complex laser modes are easily distorted by aberrations leading to performance losses. Although different approaches addressing phase retrieval algorithms have been recently presented[1-3], fast and automated aberration compensation for a broad variety of complex shaped PSFs in high NA systems is still missing. Here, we report on a Gerchberg-Saxton[4] based algorithm (GSA) for automated aberration correction of arbitrary PSFs, especially for high NA systems. Deviations between the desired target intensity distribution and the three-dimensionally (3D) scanned experimental focal intensity distribution are used to calculate a correction phase pattern. The target phase distribution plus the correction pattern are displayed on a phase-only spatial-light-modulator (SLM). Focused by a high NA objective, experimental 3D scans of several intensity distributions allow for characterization of the algorithms performance: aberrations are reliably identified and compensated within less than 10 iterations. References 1. B. M. Hanser, M. G. L. Gustafsson, D. A. Agard, and J. W. Sedat, "Phase-retrieved pupil functions in wide-field fluorescence microscopy," J. of Microscopy 216(1), 32-48 (2004). 2. A. Jesacher, A. Schwaighofer, S. Frhapter, C. Maurer, S. Bernet, and M. Ritsch-Marte, "Wavefront correction of spatial light modulators using an optical vortex image," Opt. Express 15(9), 5801-5808 (2007). 3. A. Jesacher and M. J. Booth, "Parallel direct laser writing in three dimensions with spatially dependent

  15. Integral image rendering procedure for aberration correction and size measurement.

    PubMed

    Sommer, Holger; Ihrig, Andreas; Ebenau, Melanie; Flühs, Dirk; Spaan, Bernhard; Eichmann, Marion

    2014-05-20

    The challenge in rendering integral images is to use as much information preserved by the light field as possible to reconstruct a captured scene in a three-dimensional way. We propose a rendering algorithm based on the projection of rays through a detailed simulation of the optical path, considering all the physical properties and locations of the optical elements. The rendered images contain information about the correct size of imaged objects without the need to calibrate the imaging device. Additionally, aberrations of the optical system may be corrected, depending on the setup of the integral imaging device. We show simulation data that illustrates the aberration correction ability and experimental data from our plenoptic camera, which illustrates the capability of our proposed algorithm to measure size and distance. We believe this rendering procedure will be useful in the future for three-dimensional ophthalmic imaging of the human retina.

  16. An Adaptive Immune Genetic Algorithm for Edge Detection

    NASA Astrophysics Data System (ADS)

    Li, Ying; Bai, Bendu; Zhang, Yanning

    An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

  17. A new edge detection algorithm based on Canny idea

    NASA Astrophysics Data System (ADS)

    Feng, Yingke; Zhang, Jinmin; Wang, Siming

    2017-10-01

    The traditional Canny algorithm has poor self-adaptability threshold, and it is more sensitive to noise. In order to overcome these drawbacks, this paper proposed a new edge detection method based on Canny algorithm. Firstly, the media filtering and filtering based on the method of Euclidean distance are adopted to process it; secondly using the Frei-chen algorithm to calculate gradient amplitude; finally, using the Otsu algorithm to calculate partial gradient amplitude operation to get images of thresholds value, then find the average of all thresholds that had been calculated, half of the average is high threshold value, and the half of the high threshold value is low threshold value. Experiment results show that this new method can effectively suppress noise disturbance, keep the edge information, and also improve the edge detection accuracy.

  18. Performance evaluation of spatial compounding in the presence of aberration and adaptive imaging

    NASA Astrophysics Data System (ADS)

    Dahl, Jeremy J.; Guenther, Drake; Trahey, Gregg E.

    2003-05-01

    Spatial compounding has been used for years to reduce speckle in ultrasonic images and to resolve anatomical features hidden behind the grainy appearance of speckle. Adaptive imaging restores image contrast and resolution by compensating for beamforming errors caused by tissue-induced phase errors. Spatial compounding represents a form of incoherent imaging, whereas adaptive imaging attempts to maintain a coherent, diffraction-limited aperture in the presence of aberration. Using a Siemens Antares scanner, we acquired single channel RF data on a commercially available 1-D probe. Individual channel RF data was acquired on a cyst phantom in the presence of a near field electronic phase screen. Simulated data was also acquired for both a 1-D and a custom built 8x96, 1.75-D probe (Tetrad Corp.). The data was compounded using a receive spatial compounding algorithm; a widely used algorithm because it takes advantage of parallel beamforming to avoid reductions in frame rate. Phase correction was also performed by using a least mean squares algorithm to estimate the arrival time errors. We present simulation and experimental data comparing the performance of spatial compounding to phase correction in contrast and resolution tasks. We evaluate spatial compounding and phase correction, and combinations of the two methods, under varying aperture sizes, aperture overlaps, and aberrator strength to examine the optimum configuration and conditions in which spatial compounding will provide a similar or better result than adaptive imaging. We find that, in general, phase correction is hindered at high aberration strengths and spatial frequencies, whereas spatial compounding is helped by these aberrators.

  19. Network Algorithms for Detection of Radiation Sources

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

    Rao, Nageswara S; Brooks, Richard R; Wu, Qishi

    In support of national defense, Domestic Nuclear Detection Office s (DNDO) Intelligent Radiation Sensor Systems (IRSS) program supported the development of networks of radiation counters for detecting, localizing and identifying low-level, hazardous radiation sources. Industry teams developed the first generation of such networks with tens of counters, and demonstrated several of their capabilities in indoor and outdoor characterization tests. Subsequently, these test measurements have been used in algorithm replays using various sub-networks of counters. Test measurements combined with algorithm outputs are used to extract Key Measurements and Benchmark (KMB) datasets. We present two selective analyses of these datasets: (a) amore » notional border monitoring scenario that highlights the benefits of a network of counters compared to individual detectors, and (b) new insights into the Sequential Probability Ratio Test (SPRT) detection method, which lead to its adaptations for improved detection. Using KMB datasets from an outdoor test, we construct a notional border monitoring scenario, wherein twelve 2 *2 NaI detectors are deployed on the periphery of 21*21meter square region. A Cs-137 (175 uCi) source is moved across this region, starting several meters from outside and finally moving away. The measurements from individual counters and the network were processed using replays of a particle filter algorithm developed under IRSS program. The algorithm outputs from KMB datasets clearly illustrate the benefits of combining measurements from all networked counters: the source was detected before it entered the region, during its trajectory inside, and until it moved several meters away. When individual counters are used for detection, the source was detected for much shorter durations, and sometimes was missed in the interior region. The application of SPRT for detecting radiation sources requires choosing the detection threshold, which in turn requires a source

  20. [Monochromatic aberration in accommodation. Dynamic wavefront analysis].

    PubMed

    Fritzsch, M; Dawczynski, J; Jurkutat, S; Vollandt, R; Strobel, J

    2011-06-01

    achieved a sequential analysis of aberrations during accommodation. Significant changes in the lower and higher-order aberrations could be detected. These are additionally varied by the associated pupillary response. Moreover, the synchronicity of wave front reaction in the accommodation process was proven.

  1. Method for identifying and quantifying nucleic acid sequence aberrations

    DOEpatents

    Lucas, J.N.; Straume, T.; Bogen, K.T.

    1998-07-21

    A method is disclosed for detecting nucleic acid sequence aberrations by detecting nucleic acid sequences having both a first and a second nucleic acid sequence type, the presence of the first and second sequence type on the same nucleic acid sequence indicating the presence of a nucleic acid sequence aberration. The method uses a first hybridization probe which includes a nucleic acid sequence that is complementary to a first sequence type and a first complexing agent capable of attaching to a second complexing agent and a second hybridization probe which includes a nucleic acid sequence that selectively hybridizes to the second nucleic acid sequence type over the first sequence type and includes a detectable marker for detecting the second hybridization probe. 11 figs.

  2. A novel line segment detection algorithm based on graph search

    NASA Astrophysics Data System (ADS)

    Zhao, Hong-dan; Liu, Guo-ying; Song, Xu

    2018-02-01

    To overcome the problem of extracting line segment from an image, a method of line segment detection was proposed based on the graph search algorithm. After obtaining the edge detection result of the image, the candidate straight line segments are obtained in four directions. For the candidate straight line segments, their adjacency relationships are depicted by a graph model, based on which the depth-first search algorithm is employed to determine how many adjacent line segments need to be merged. Finally we use the least squares method to fit the detected straight lines. The comparative experimental results verify that the proposed algorithm has achieved better results than the line segment detector (LSD).

  3. Adaptive algorithm of magnetic heading detection

    NASA Astrophysics Data System (ADS)

    Liu, Gong-Xu; Shi, Ling-Feng

    2017-11-01

    Magnetic data obtained from a magnetic sensor usually fluctuate in a certain range, which makes it difficult to estimate the magnetic heading accurately. In fact, magnetic heading information is usually submerged in noise because of all kinds of electromagnetic interference and the diversity of the pedestrian’s motion states. In order to solve this problem, a new adaptive algorithm based on the (typically) right-angled corridors of a building or residential buildings is put forward to process heading information. First, a 3D indoor localization platform is set up based on MPU9250. Then, several groups of data are measured by changing the experimental environment and pedestrian’s motion pace. The raw data from the attached inertial measurement unit are calibrated and arranged into a time-stamped array and written to a data file. Later, the data file is imported into MATLAB for processing and analysis using the proposed adaptive algorithm. Finally, the algorithm is verified by comparison with the existing algorithm. The experimental results show that the algorithm has strong robustness and good fault tolerance, which can detect the heading information accurately and in real-time.

  4. GPU based cloud system for high-performance arrhythmia detection with parallel k-NN algorithm.

    PubMed

    Tae Joon Jun; Hyun Ji Park; Hyuk Yoo; Young-Hak Kim; Daeyoung Kim

    2016-08-01

    In this paper, we propose an GPU based Cloud system for high-performance arrhythmia detection. Pan-Tompkins algorithm is used for QRS detection and we optimized beat classification algorithm with K-Nearest Neighbor (K-NN). To support high performance beat classification on the system, we parallelized beat classification algorithm with CUDA to execute the algorithm on virtualized GPU devices on the Cloud system. MIT-BIH Arrhythmia database is used for validation of the algorithm. The system achieved about 93.5% of detection rate which is comparable to previous researches while our algorithm shows 2.5 times faster execution time compared to CPU only detection algorithm.

  5. Performances of the New Real Time Tsunami Detection Algorithm applied to tide gauges data

    NASA Astrophysics Data System (ADS)

    Chierici, F.; Embriaco, D.; Morucci, S.

    2017-12-01

    Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection (TDA) based on the real-time tide removal and real-time band-pass filtering of seabed pressure time series acquired by Bottom Pressure Recorders. The TDA algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability with respect to the most widely used tsunami detection algorithm, while containing the computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. In this work we present the performance of the TDA algorithm applied to tide gauge data. We have adapted the new tsunami detection algorithm and the Monte Carlo test methodology to tide gauges. Sea level data acquired by coastal tide gauges in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event generated by Tohoku earthquake on March 11th 2011, using data recorded by several tide gauges scattered all over the Pacific area.

  6. A new pivoting and iterative text detection algorithm for biomedical images.

    PubMed

    Xu, Songhua; Krauthammer, Michael

    2010-12-01

    There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper's key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manually labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. We demonstrate that our projection histogram-based text detection approach is well suited for text detection in biomedical images, and that the iterative application of the algorithm boosts performance to an F score of .60. We provide a C++ implementation of our algorithm freely available for academic use. Copyright © 2010 Elsevier Inc. All rights reserved.

  7. A New Pivoting and Iterative Text Detection Algorithm for Biomedical Images

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

    Xu, Songhua; Krauthammer, Prof. Michael

    2010-01-01

    There is interest to expand the reach of literature mining to include the analysis of biomedical images, which often contain a paper's key findings. Examples include recent studies that use Optical Character Recognition (OCR) to extract image text, which is used to boost biomedical image retrieval and classification. Such studies rely on the robust identification of text elements in biomedical images, which is a non-trivial task. In this work, we introduce a new text detection algorithm for biomedical images based on iterative projection histograms. We study the effectiveness of our algorithm by evaluating the performance on a set of manuallymore » labeled random biomedical images, and compare the performance against other state-of-the-art text detection algorithms. We demonstrate that our projection histogram-based text detection approach is well suited for text detection in biomedical images, and that the iterative application of the algorithm boosts performance to an F score of .60. We provide a C++ implementation of our algorithm freely available for academic use.« less

  8. Anomaly detection in hyperspectral imagery: statistics vs. graph-based algorithms

    NASA Astrophysics Data System (ADS)

    Berkson, Emily E.; Messinger, David W.

    2016-05-01

    Anomaly detection (AD) algorithms are frequently applied to hyperspectral imagery, but different algorithms produce different outlier results depending on the image scene content and the assumed background model. This work provides the first comparison of anomaly score distributions between common statistics-based anomaly detection algorithms (RX and subspace-RX) and the graph-based Topological Anomaly Detector (TAD). Anomaly scores in statistical AD algorithms should theoretically approximate a chi-squared distribution; however, this is rarely the case with real hyperspectral imagery. The expected distribution of scores found with graph-based methods remains unclear. We also look for general trends in algorithm performance with varied scene content. Three separate scenes were extracted from the hyperspectral MegaScene image taken over downtown Rochester, NY with the VIS-NIR-SWIR ProSpecTIR instrument. In order of most to least cluttered, we study an urban, suburban, and rural scene. The three AD algorithms were applied to each scene, and the distributions of the most anomalous 5% of pixels were compared. We find that subspace-RX performs better than RX, because the data becomes more normal when the highest variance principal components are removed. We also see that compared to statistical detectors, anomalies detected by TAD are easier to separate from the background. Due to their different underlying assumptions, the statistical and graph-based algorithms highlighted different anomalies within the urban scene. These results will lead to a deeper understanding of these algorithms and their applicability across different types of imagery.

  9. Improved target detection algorithm using Fukunaga-Koontz transform and distance classifier correlation filter

    NASA Astrophysics Data System (ADS)

    Bal, A.; Alam, M. S.; Aslan, M. S.

    2006-05-01

    Often sensor ego-motion or fast target movement causes the target to temporarily go out of the field-of-view leading to reappearing target detection problem in target tracking applications. Since the target goes out of the current frame and reenters at a later frame, the reentering location and variations in rotation, scale, and other 3D orientations of the target are not known thus complicating the detection algorithm has been developed using Fukunaga-Koontz Transform (FKT) and distance classifier correlation filter (DCCF). The detection algorithm uses target and background information, extracted from training samples, to detect possible candidate target images. The detected candidate target images are then introduced into the second algorithm, DCCF, called clutter rejection module, to determine the target coordinates are detected and tracking algorithm is initiated. The performance of the proposed FKT-DCCF based target detection algorithm has been tested using real-world forward looking infrared (FLIR) video sequences.

  10. Detecting microsatellites within genomes: significant variation among algorithms.

    PubMed

    Leclercq, Sébastien; Rivals, Eric; Jarne, Philippe

    2007-04-18

    Microsatellites are short, tandemly-repeated DNA sequences which are widely distributed among genomes. Their structure, role and evolution can be analyzed based on exhaustive extraction from sequenced genomes. Several dedicated algorithms have been developed for this purpose. Here, we compared the detection efficiency of five of them (TRF, Mreps, Sputnik, STAR, and RepeatMasker). Our analysis was first conducted on the human X chromosome, and microsatellite distributions were characterized by microsatellite number, length, and divergence from a pure motif. The algorithms work with user-defined parameters, and we demonstrate that the parameter values chosen can strongly influence microsatellite distributions. The five algorithms were then compared by fixing parameters settings, and the analysis was extended to three other genomes (Saccharomyces cerevisiae, Neurospora crassa and Drosophila melanogaster) spanning a wide range of size and structure. Significant differences for all characteristics of microsatellites were observed among algorithms, but not among genomes, for both perfect and imperfect microsatellites. Striking differences were detected for short microsatellites (below 20 bp), regardless of motif. Since the algorithm used strongly influences empirical distributions, studies analyzing microsatellite evolution based on a comparison between empirical and theoretical size distributions should therefore be considered with caution. We also discuss why a typological definition of microsatellites limits our capacity to capture their genomic distributions.

  11. Photon Counting Using Edge-Detection Algorithm

    NASA Technical Reports Server (NTRS)

    Gin, Jonathan W.; Nguyen, Danh H.; Farr, William H.

    2010-01-01

    New applications such as high-datarate, photon-starved, free-space optical communications require photon counting at flux rates into gigaphoton-per-second regimes coupled with subnanosecond timing accuracy. Current single-photon detectors that are capable of handling such operating conditions are designed in an array format and produce output pulses that span multiple sample times. In order to discern one pulse from another and not to overcount the number of incoming photons, a detection algorithm must be applied to the sampled detector output pulses. As flux rates increase, the ability to implement such a detection algorithm becomes difficult within a digital processor that may reside within a field-programmable gate array (FPGA). Systems have been developed and implemented to both characterize gigahertz bandwidth single-photon detectors, as well as process photon count signals at rates into gigaphotons per second in order to implement communications links at SCPPM (serial concatenated pulse position modulation) encoded data rates exceeding 100 megabits per second with efficiencies greater than two bits per detected photon. A hardware edge-detection algorithm and corresponding signal combining and deserialization hardware were developed to meet these requirements at sample rates up to 10 GHz. The photon discriminator deserializer hardware board accepts four inputs, which allows for the ability to take inputs from a quadphoton counting detector, to support requirements for optical tracking with a reduced number of hardware components. The four inputs are hardware leading-edge detected independently. After leading-edge detection, the resultant samples are ORed together prior to deserialization. The deserialization is performed to reduce the rate at which data is passed to a digital signal processor, perhaps residing within an FPGA. The hardware implements four separate analog inputs that are connected through RF connectors. Each analog input is fed to a high-speed 1

  12. begin{center} MUSIC Algorithms for Rebar Detection

    NASA Astrophysics Data System (ADS)

    Leone, G.; Solimene, R.

    2012-04-01

    In this contribution we consider the problem of detecting and localizing small cross section, with respect to the wavelength, scatterers from their scattered field once a known incident field interrogated the scene where they reside. A pertinent applicative context is rebar detection within concrete pillar. For such a case, scatterers to be detected are represented by rebars themselves or by voids due to their lacking. In both cases, as scatterers have point-like support, a subspace projection method can be conveniently exploited [1]. However, as the field scattered by rebars is stronger than the one due to voids, it is expected that the latter can be difficult to be detected. In order to circumvent this problem, in this contribution we adopt a two-step MUltiple SIgnal Classification (MUSIC) detection algorithm. In particular, the first stage aims at detecting rebars. Once rebar are detected, their positions are exploited to update the Green's function and then a further detection scheme is run to locate voids. However, in this second case, background medium encompasses also the rabars. The analysis is conducted numerically for a simplified two-dimensional scalar scattering geometry. More in detail, as is usual in MUSIC algorithm, a multi-view/multi-static single-frequency configuration is considered [2]. Baratonia, G. Leone, R. Pierri, R. Solimene, "Fault Detection in Grid Scattering by a Time-Reversal MUSIC Approach," Porc. Of ICEAA 2011, Turin, 2011. E. A. Marengo, F. K. Gruber, "Subspace-Based Localization and Inverse Scattering of Multiply Scattering Point Targets," EURASIP Journal on Advances in Signal Processing, 2007, Article ID 17342, 16 pages (2007).

  13. An OMIC biomarker detection algorithm TriVote and its application in methylomic biomarker detection.

    PubMed

    Xu, Cheng; Liu, Jiamei; Yang, Weifeng; Shu, Yayun; Wei, Zhipeng; Zheng, Weiwei; Feng, Xin; Zhou, Fengfeng

    2018-04-01

    Transcriptomic and methylomic patterns represent two major OMIC data sources impacted by both inheritable genetic information and environmental factors, and have been widely used as disease diagnosis and prognosis biomarkers. Modern transcriptomic and methylomic profiling technologies detect the status of tens of thousands or even millions of probing residues in the human genome, and introduce a major computational challenge for the existing feature selection algorithms. This study proposes a three-step feature selection algorithm, TriVote, to detect a subset of transcriptomic or methylomic residues with highly accurate binary classification performance. TriVote outperforms both filter and wrapper feature selection algorithms with both higher classification accuracy and smaller feature number on 17 transcriptomes and two methylomes. Biological functions of the methylome biomarkers detected by TriVote were discussed for their disease associations. An easy-to-use Python package is also released to facilitate the further applications.

  14. Analysis of Community Detection Algorithms for Large Scale Cyber Networks

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

    Mane, Prachita; Shanbhag, Sunanda; Kamath, Tanmayee

    The aim of this project is to use existing community detection algorithms on an IP network dataset to create supernodes within the network. This study compares the performance of different algorithms on the network in terms of running time. The paper begins with an introduction to the concept of clustering and community detection followed by the research question that the team aimed to address. Further the paper describes the graph metrics that were considered in order to shortlist algorithms followed by a brief explanation of each algorithm with respect to the graph metric on which it is based. The nextmore » section in the paper describes the methodology used by the team in order to run the algorithms and determine which algorithm is most efficient with respect to running time. Finally, the last section of the paper includes the results obtained by the team and a conclusion based on those results as well as future work.« less

  15. Subtelomeric multiplex ligation-dependent probe amplification as a supplement for rapid prenatal detection of fetal chromosomal aberrations.

    PubMed

    Chen, Xiangnan; Li, Huanzheng; Mao, Yijian; Xu, Xueqin; Lv, Jiaojiao; Zhou, Lili; Lin, Xiaoling; Tang, Shaohua

    2014-01-01

    Pregnant women with high-risk indications are highly suspected of fetal chromosomal aberrations. To determine whether Multiplex Ligation-dependent Probe Amplification (MLPA) using subtelomeric probe mixes (P036-E2 and P070-B2) is a reliable method for rapid detection of fetal chromosomal aberrations. The subtelomeric MLPA probe mixes were used to evaluate 50 blood samples from healthy individuals. 168 amniocytes and 182 umbilical cord blood samples from high-risk fetuses were analyzed using the same subtelomeric MLPA probe sets. Karyotyping was also performed in all cases of high-risk pregnancies, and single nucleotide polymorphism array analysis was used to confirm submicroscopic and ambiguous results from MLPA/karyotyping. Subtelomeric MLPA analysis of normal samples showed normal result in all cases by use of P036-E2 probe mix, while P070-B2 probe mix gave normal results for all but one case. In one normal control case P070-B2 produced a duplicated signal of probe for 13q34. In the high-risk group, totally 44 chromosomal abnormalities were found by karyotyping and MLPA, including 23 aneuploidies and 21 rearrangements or mosaics. MLPA detected all 23 aneuploidies, 12 rearrangements and 1 mosaic. Importantly, MLPA revealed 4 chromosomal translocations, 2 small supernumerary marker chromosomes (sSMCs), and 3 subtelomeric imbalances that were not well characterized or not detectable by karyotyping. However, MLPA showed negetive results for the remaining 8 rearrangements or mosaics, including 3 low mosaic aneuploidies, 1 inherited sSMC, and 4 paracentric inversions. Results suggest that combined use of subtelomeric MLPA and karyotyping may be an alternative method for using karyotype analyses alone in rapid detection of aneuploidies, rearrangements, and sSMCs.

  16. Artifact removal algorithms for stroke detection using a multistatic MIST beamforming algorithm.

    PubMed

    Ricci, E; Di Domenico, S; Cianca, E; Rossi, T

    2015-01-01

    Microwave imaging (MWI) has been recently proved as a promising imaging modality for low-complexity, low-cost and fast brain imaging tools, which could play a fundamental role to efficiently manage emergencies related to stroke and hemorrhages. This paper focuses on the UWB radar imaging approach and in particular on the processing algorithms of the backscattered signals. Assuming the use of the multistatic version of the MIST (Microwave Imaging Space-Time) beamforming algorithm, developed by Hagness et al. for the early detection of breast cancer, the paper proposes and compares two artifact removal algorithms. Artifacts removal is an essential step of any UWB radar imaging system and currently considered artifact removal algorithms have been shown not to be effective in the specific scenario of brain imaging. First of all, the paper proposes modifications of a known artifact removal algorithm. These modifications are shown to be effective to achieve good localization accuracy and lower false positives. However, the main contribution is the proposal of an artifact removal algorithm based on statistical methods, which allows to achieve even better performance but with much lower computational complexity.

  17. A joint swarm intelligence algorithm for multi-user detection in MIMO-OFDM system

    NASA Astrophysics Data System (ADS)

    Hu, Fengye; Du, Dakun; Zhang, Peng; Wang, Zhijun

    2014-11-01

    In the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system, traditional multi-user detection (MUD) algorithms that usually used to suppress multiple access interference are difficult to balance system detection performance and the complexity of the algorithm. To solve this problem, this paper proposes a joint swarm intelligence algorithm called Ant Colony and Particle Swarm Optimisation (AC-PSO) by integrating particle swarm optimisation (PSO) and ant colony optimisation (ACO) algorithms. According to simulation results, it has been shown that, with low computational complexity, the MUD for the MIMO-OFDM system based on AC-PSO algorithm gains comparable MUD performance with maximum likelihood algorithm. Thus, the proposed AC-PSO algorithm provides a satisfactory trade-off between computational complexity and detection performance.

  18. Prefiltering Model for Homology Detection Algorithms on GPU.

    PubMed

    Retamosa, Germán; de Pedro, Luis; González, Ivan; Tamames, Javier

    2016-01-01

    Homology detection has evolved over the time from heavy algorithms based on dynamic programming approaches to lightweight alternatives based on different heuristic models. However, the main problem with these algorithms is that they use complex statistical models, which makes it difficult to achieve a relevant speedup and find exact matches with the original results. Thus, their acceleration is essential. The aim of this article was to prefilter a sequence database. To make this work, we have implemented a groundbreaking heuristic model based on NVIDIA's graphics processing units (GPUs) and multicore processors. Depending on the sensitivity settings, this makes it possible to quickly reduce the sequence database by factors between 50% and 95%, while rejecting no significant sequences. Furthermore, this prefiltering application can be used together with multiple homology detection algorithms as a part of a next-generation sequencing system. Extensive performance and accuracy tests have been carried out in the Spanish National Centre for Biotechnology (NCB). The results show that GPU hardware can accelerate the execution times of former homology detection applications, such as National Centre for Biotechnology Information (NCBI), Basic Local Alignment Search Tool for Proteins (BLASTP), up to a factor of 4.

  19. Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm

    NASA Astrophysics Data System (ADS)

    Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong

    2018-06-01

    The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.

  20. A street rubbish detection algorithm based on Sift and RCNN

    NASA Astrophysics Data System (ADS)

    Yu, XiPeng; Chen, Zhong; Zhang, Shuo; Zhang, Ting

    2018-02-01

    This paper presents a street rubbish detection algorithm based on image registration with Sift feature and RCNN. Firstly, obtain the rubbish region proposal on the real-time street image and set up the CNN convolution neural network trained by the rubbish samples set consists of rubbish and non-rubbish images; Secondly, for every clean street image, obtain the Sift feature and do image registration with the real-time street image to obtain the differential image, the differential image filters a lot of background information, obtain the rubbish region proposal rect where the rubbish may appear on the differential image by the selective search algorithm. Then, the CNN model is used to detect the image pixel data in each of the region proposal on the real-time street image. According to the output vector of the CNN, it is judged whether the rubbish is in the region proposal or not. If it is rubbish, the region proposal on the real-time street image is marked. This algorithm avoids the large number of false detection caused by the detection on the whole image because the CNN is used to identify the image only in the region proposal on the real-time street image that may appear rubbish. Different from the traditional object detection algorithm based on the region proposal, the region proposal is obtained on the differential image not whole real-time street image, and the number of the invalid region proposal is greatly reduced. The algorithm has the high mean average precision (mAP).

  1. Minimum change in spherical aberration that can be perceived

    PubMed Central

    Manzanera, Silvestre; Artal, Pablo

    2016-01-01

    It is important to know the visual sensitivity to optical blur from both a basic science perspective and a practical point of view. Of particular interest is the sensitivity to blur induced by spherical aberration because it is being used to increase depth of focus as a component of a presbyopic solution. Using a flicker detection-based procedure implemented on an adaptive optics visual simulator, we measured the spherical aberration thresholds that produce just-noticeable differences in perceived image quality. The thresholds were measured for positive and negative values of spherical aberration, for best focus and + 0.5 D and + 1.0 D of defocus. At best focus, the SA thresholds were 0.20 ± 0.01 µm and −0.17 ± 0.03 µm for positive and negative spherical aberration respectively (referred to a 6-mm pupil). These experimental values may be useful in setting spherical aberration permissible levels in different ophthalmic techniques. PMID:27699113

  2. Adaptive compensation of aberrations in ultrafast 3D microscopy using a deformable mirror

    NASA Astrophysics Data System (ADS)

    Sherman, Leah R.; Albert, O.; Schmidt, Christoph F.; Vdovin, Gleb V.; Mourou, Gerard A.; Norris, Theodore B.

    2000-05-01

    3D imaging using a multiphoton scanning confocal microscope is ultimately limited by aberrations of the system. We describe a system to adaptively compensate the aberrations with a deformable mirror. We have increased the transverse scanning range of the microscope by three with compensation of off-axis aberrations.We have also significantly increased the longitudinal scanning depth with compensation of spherical aberrations from the penetration into the sample. Our correction is based on a genetic algorithm that uses second harmonic or two-photon fluorescence signal excited by femtosecond pulses from the sample as the enhancement parameter. This allows us to globally optimize the wavefront without a wavefront measurement. To improve the speed of the optimization we use Zernike polynomials as the basis for correction. Corrections can be stored in a database for look-up with future samples.

  3. The effect of algorithms on copy number variant detection.

    PubMed

    Tsuang, Debby W; Millard, Steven P; Ely, Benjamin; Chi, Peter; Wang, Kenneth; Raskind, Wendy H; Kim, Sulgi; Brkanac, Zoran; Yu, Chang-En

    2010-12-30

    The detection of copy number variants (CNVs) and the results of CNV-disease association studies rely on how CNVs are defined, and because array-based technologies can only infer CNVs, CNV-calling algorithms can produce vastly different findings. Several authors have noted the large-scale variability between CNV-detection methods, as well as the substantial false positive and false negative rates associated with those methods. In this study, we use variations of four common algorithms for CNV detection (PennCNV, QuantiSNP, HMMSeg, and cnvPartition) and two definitions of overlap (any overlap and an overlap of at least 40% of the smaller CNV) to illustrate the effects of varying algorithms and definitions of overlap on CNV discovery. We used a 56 K Illumina genotyping array enriched for CNV regions to generate hybridization intensities and allele frequencies for 48 Caucasian schizophrenia cases and 48 age-, ethnicity-, and gender-matched control subjects. No algorithm found a difference in CNV burden between the two groups. However, the total number of CNVs called ranged from 102 to 3,765 across algorithms. The mean CNV size ranged from 46 kb to 787 kb, and the average number of CNVs per subject ranged from 1 to 39. The number of novel CNVs not previously reported in normal subjects ranged from 0 to 212. Motivated by the availability of multiple publicly available genome-wide SNP arrays, investigators are conducting numerous analyses to identify putative additional CNVs in complex genetic disorders. However, the number of CNVs identified in array-based studies, and whether these CNVs are novel or valid, will depend on the algorithm(s) used. Thus, given the variety of methods used, there will be many false positives and false negatives. Both guidelines for the identification of CNVs inferred from high-density arrays and the establishment of a gold standard for validation of CNVs are needed.

  4. Comparison of algorithms for automatic border detection of melanoma in dermoscopy images

    NASA Astrophysics Data System (ADS)

    Srinivasa Raghavan, Sowmya; Kaur, Ravneet; LeAnder, Robert

    2016-09-01

    Melanoma is one of the most rapidly accelerating cancers in the world [1]. Early diagnosis is critical to an effective cure. We propose a new algorithm for more accurately detecting melanoma borders in dermoscopy images. Proper border detection requires eliminating occlusions like hair and bubbles by processing the original image. The preprocessing step involves transforming the RGB image to the CIE L*u*v* color space, in order to decouple brightness from color information, then increasing contrast, using contrast-limited adaptive histogram equalization (CLAHE), followed by artifacts removal using a Gaussian filter. After preprocessing, the Chen-Vese technique segments the preprocessed images to create a lesion mask which undergoes a morphological closing operation. Next, the largest central blob in the lesion is detected, after which, the blob is dilated to generate an image output mask. Finally, the automatically-generated mask is compared to the manual mask by calculating the XOR error [3]. Our border detection algorithm was developed using training and test sets of 30 and 20 images, respectively. This detection method was compared to the SRM method [4] by calculating the average XOR error for each of the two algorithms. Average error for test images was 0.10, using the new algorithm, and 0.99, using SRM method. In comparing the average error values produced by the two algorithms, it is evident that the average XOR error for our technique is lower than the SRM method, thereby implying that the new algorithm detects borders of melanomas more accurately than the SRM algorithm.

  5. Detecting microsatellites within genomes: significant variation among algorithms

    PubMed Central

    Leclercq, Sébastien; Rivals, Eric; Jarne, Philippe

    2007-01-01

    Background Microsatellites are short, tandemly-repeated DNA sequences which are widely distributed among genomes. Their structure, role and evolution can be analyzed based on exhaustive extraction from sequenced genomes. Several dedicated algorithms have been developed for this purpose. Here, we compared the detection efficiency of five of them (TRF, Mreps, Sputnik, STAR, and RepeatMasker). Results Our analysis was first conducted on the human X chromosome, and microsatellite distributions were characterized by microsatellite number, length, and divergence from a pure motif. The algorithms work with user-defined parameters, and we demonstrate that the parameter values chosen can strongly influence microsatellite distributions. The five algorithms were then compared by fixing parameters settings, and the analysis was extended to three other genomes (Saccharomyces cerevisiae, Neurospora crassa and Drosophila melanogaster) spanning a wide range of size and structure. Significant differences for all characteristics of microsatellites were observed among algorithms, but not among genomes, for both perfect and imperfect microsatellites. Striking differences were detected for short microsatellites (below 20 bp), regardless of motif. Conclusion Since the algorithm used strongly influences empirical distributions, studies analyzing microsatellite evolution based on a comparison between empirical and theoretical size distributions should therefore be considered with caution. We also discuss why a typological definition of microsatellites limits our capacity to capture their genomic distributions. PMID:17442102

  6. Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings.

    PubMed

    Baldassano, Steven N; Brinkmann, Benjamin H; Ung, Hoameng; Blevins, Tyler; Conrad, Erin C; Leyde, Kent; Cook, Mark J; Khambhati, Ankit N; Wagenaar, Joost B; Worrell, Gregory A; Litt, Brian

    2017-06-01

    There exist significant clinical and basic research needs for accurate, automated seizure detection algorithms. These algorithms have translational potential in responsive neurostimulation devices and in automatic parsing of continuous intracranial electroencephalography data. An important barrier to developing accurate, validated algorithms for seizure detection is limited access to high-quality, expertly annotated seizure data from prolonged recordings. To overcome this, we hosted a kaggle.com competition to crowdsource the development of seizure detection algorithms using intracranial electroencephalography from canines and humans with epilepsy. The top three performing algorithms from the contest were then validated on out-of-sample patient data including standard clinical data and continuous ambulatory human data obtained over several years using the implantable NeuroVista seizure advisory system. Two hundred teams of data scientists from all over the world participated in the kaggle.com competition. The top performing teams submitted highly accurate algorithms with consistent performance in the out-of-sample validation study. The performance of these seizure detection algorithms, achieved using freely available code and data, sets a new reproducible benchmark for personalized seizure detection. We have also shared a 'plug and play' pipeline to allow other researchers to easily use these algorithms on their own datasets. The success of this competition demonstrates how sharing code and high quality data results in the creation of powerful translational tools with significant potential to impact patient care. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  7. Artificial neural network for the determination of Hubble Space Telescope aberration from stellar images

    NASA Technical Reports Server (NTRS)

    Barrett, Todd K.; Sandler, David G.

    1993-01-01

    An artificial-neural-network method, first developed for the measurement and control of atmospheric phase distortion, using stellar images, was used to estimate the optical aberration of the Hubble Space Telescope. A total of 26 estimates of distortion was obtained from 23 stellar images acquired at several secondary-mirror axial positions. The results were expressed as coefficients of eight orthogonal Zernike polynomials: focus through third-order spherical. For all modes other than spherical the measured aberration was small. The average spherical aberration of the estimates was -0.299 micron rms, which is in good agreement with predictions obtained when iterative phase-retrieval algorithms were used.

  8. Algorithm for detection the QRS complexes based on support vector machine

    NASA Astrophysics Data System (ADS)

    Van, G. V.; Podmasteryev, K. V.

    2017-11-01

    The efficiency of computer ECG analysis depends on the accurate detection of QRS-complexes. This paper presents an algorithm for QRS complex detection based of support vector machine (SVM). The proposed algorithm is evaluated on annotated standard databases such as MIT-BIH Arrhythmia database. The QRS detector obtained a sensitivity Se = 98.32% and specificity Sp = 95.46% for MIT-BIH Arrhythmia database. This algorithm can be used as the basis for the software to diagnose electrical activity of the heart.

  9. A Comparative Analysis of Community Detection Algorithms on Artificial Networks

    PubMed Central

    Yang, Zhao; Algesheimer, René; Tessone, Claudio J.

    2016-01-01

    Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms’ computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm’s predicting power and the effective computing time. PMID:27476470

  10. CpG islands: algorithms and applications in methylation studies.

    PubMed

    Zhao, Zhongming; Han, Leng

    2009-05-15

    Methylation occurs frequently at 5'-cytosine of the CpG dinucleotides in vertebrate genomes; however, this epigenetic feature is rarely observed in CpG islands (CGIs) or CpG clusters in the promoter regions of genes. Aberrant methylation of the promoter-associated CGIs might influence gene expression and cause carcinogenesis. Because of the functional importance, multiple algorithms have been available for identifying CGIs in a genome or a sequence. They can be categorized into the traditional algorithms (e.g., Gardiner-Garden and Frommer (1987), Takai and Jones (2002), and CpGPRoD (2002)) or statistical property based algorithms (CpGcluster (2006) and CG cluster (2007)). We reviewed the features of these algorithms and evaluated their performance on identifying functional CGIs using genome-wide methylation data. Moreover, identification of CGIs is an initial step in many recent studies for predicting methylation status as well as in the design of methylation detection platforms. We reviewed the benchmarks and features used in these studies.

  11. Detection of Cheating by Decimation Algorithm

    NASA Astrophysics Data System (ADS)

    Yamanaka, Shogo; Ohzeki, Masayuki; Decelle, Aurélien

    2015-02-01

    We expand the item response theory to study the case of "cheating students" for a set of exams, trying to detect them by applying a greedy algorithm of inference. This extended model is closely related to the Boltzmann machine learning. In this paper we aim to infer the correct biases and interactions of our model by considering a relatively small number of sets of training data. Nevertheless, the greedy algorithm that we employed in the present study exhibits good performance with a few number of training data. The key point is the sparseness of the interactions in our problem in the context of the Boltzmann machine learning: the existence of cheating students is expected to be very rare (possibly even in real world). We compare a standard approach to infer the sparse interactions in the Boltzmann machine learning to our greedy algorithm and we find the latter to be superior in several aspects.

  12. Gear Tooth Wear Detection Algorithm

    NASA Technical Reports Server (NTRS)

    Delgado, Irebert R.

    2015-01-01

    Vibration-based condition indicators continue to be developed for Health Usage Monitoring of rotorcraft gearboxes. Testing performed at NASA Glenn Research Center have shown correlations between specific condition indicators and specific types of gear wear. To speed up the detection and analysis of gear teeth, an image detection program based on the Viola-Jones algorithm was trained to automatically detect spiral bevel gear wear pitting. The detector was tested using a training set of gear wear pictures and a blind set of gear wear pictures. The detector accuracy for the training set was 75 percent while the accuracy for the blind set was 15 percent. Further improvements on the accuracy of the detector are required but preliminary results have shown its ability to automatically detect gear tooth wear. The trained detector would be used to quickly evaluate a set of gear or pinion pictures for pits, spalls, or abrasive wear. The results could then be used to correlate with vibration or oil debris data. In general, the program could be retrained to detect features of interest from pictures of a component taken over a period of time.

  13. Clever eye algorithm for target detection of remote sensing imagery

    NASA Astrophysics Data System (ADS)

    Geng, Xiurui; Ji, Luyan; Sun, Kang

    2016-04-01

    Target detection algorithms for hyperspectral remote sensing imagery, such as the two most commonly used remote sensing detection algorithms, the constrained energy minimization (CEM) and matched filter (MF), can usually be attributed to the inner product between a weight filter (or detector) and a pixel vector. CEM and MF have the same expression except that MF requires data centralization first. However, this difference leads to a difference in the target detection results. That is to say, the selection of the data origin could directly affect the performance of the detector. Therefore, does there exist another data origin other than the zero and mean-vector points for a better target detection performance? This is a very meaningful issue in the field of target detection, but it has not been paid enough attention yet. In this study, we propose a novel objective function by introducing the data origin as another variable, and the solution of the function is corresponding to the data origin with the minimal output energy. The process of finding the optimal solution can be vividly regarded as a clever eye automatically searching the best observing position and direction in the feature space, which corresponds to the largest separation between the target and background. Therefore, this new algorithm is referred to as the clever eye algorithm (CE). Based on the Sherman-Morrison formula and the gradient ascent method, CE could derive the optimal target detection result in terms of energy. Experiments with both synthetic and real hyperspectral data have verified the effectiveness of our method.

  14. Simplified projection technique to correct geometric and chromatic lens aberrations using plenoptic imaging.

    PubMed

    Dallaire, Xavier; Thibault, Simon

    2017-04-01

    Plenoptic imaging has been used in the past decade mainly for 3D reconstruction or digital refocusing. It was also shown that this technology has potential for correcting monochromatic aberrations in a standard optical system. In this paper, we present an algorithm for reconstructing images using a projection technique while correcting defects present in it that can apply to chromatic aberrations and wide-angle optical systems. We show that the impact of noise on the reconstruction procedure is minimal. Trade-offs between the sampling of the optical system needed for characterization and image quality are presented. Examples are shown for aberrations in a classic optical system and for chromatic aberrations. The technique is also applied to a wide-angle full field of view of 140° (FFOV 140°) optical system. This technique could be used in order to further simplify or minimize optical systems.

  15. A Contextual Fire Detection Algorithm for Simulated HJ-1B Imagery.

    PubMed

    Qian, Yonggang; Yan, Guangjian; Duan, Sibo; Kong, Xiangsheng

    2009-01-01

    The HJ-1B satellite, which was launched on September 6, 2008, is one of the small ones placed in the constellation for disaster prediction and monitoring. HJ-1B imagery was simulated in this paper, which contains fires of various sizes and temperatures in a wide range of terrestrial biomes and climates, including RED, NIR, MIR and TIR channels. Based on the MODIS version 4 contextual algorithm and the characteristics of HJ-1B sensor, a contextual fire detection algorithm was proposed and tested using simulated HJ-1B data. It was evaluated by the probability of fire detection and false alarm as functions of fire temperature and fire area. Results indicate that when the simulated fire area is larger than 45 m(2) and the simulated fire temperature is larger than 800 K, the algorithm has a higher probability of detection. But if the simulated fire area is smaller than 10 m(2), only when the simulated fire temperature is larger than 900 K, may the fire be detected. For fire areas about 100 m(2), the proposed algorithm has a higher detection probability than that of the MODIS product. Finally, the omission and commission error were evaluated which are important factors to affect the performance of this algorithm. It has been demonstrated that HJ-1B satellite data are much sensitive to smaller and cooler fires than MODIS or AVHRR data and the improved capabilities of HJ-1B data will offer a fine opportunity for the fire detection.

  16. A Contextual Fire Detection Algorithm for Simulated HJ-1B Imagery

    PubMed Central

    Qian, Yonggang; Yan, Guangjian; Duan, Sibo; Kong, Xiangsheng

    2009-01-01

    The HJ-1B satellite, which was launched on September 6, 2008, is one of the small ones placed in the constellation for disaster prediction and monitoring. HJ-1B imagery was simulated in this paper, which contains fires of various sizes and temperatures in a wide range of terrestrial biomes and climates, including RED, NIR, MIR and TIR channels. Based on the MODIS version 4 contextual algorithm and the characteristics of HJ-1B sensor, a contextual fire detection algorithm was proposed and tested using simulated HJ-1B data. It was evaluated by the probability of fire detection and false alarm as functions of fire temperature and fire area. Results indicate that when the simulated fire area is larger than 45 m2 and the simulated fire temperature is larger than 800 K, the algorithm has a higher probability of detection. But if the simulated fire area is smaller than 10 m2, only when the simulated fire temperature is larger than 900 K, may the fire be detected. For fire areas about 100 m2, the proposed algorithm has a higher detection probability than that of the MODIS product. Finally, the omission and commission error were evaluated which are important factors to affect the performance of this algorithm. It has been demonstrated that HJ-1B satellite data are much sensitive to smaller and cooler fires than MODIS or AVHRR data and the improved capabilities of HJ-1B data will offer a fine opportunity for the fire detection. PMID:22399950

  17. An Algorithm to Detect the Retinal Region of Interest

    NASA Astrophysics Data System (ADS)

    Şehirli, E.; Turan, M. K.; Demiral, E.

    2017-11-01

    Retina is one of the important layers of the eyes, which includes sensitive cells to colour and light and nerve fibers. Retina can be displayed by using some medical devices such as fundus camera, ophthalmoscope. Hence, some lesions like microaneurysm, haemorrhage, exudate with many diseases of the eye can be detected by looking at the images taken by devices. In computer vision and biomedical areas, studies to detect lesions of the eyes automatically have been done for a long time. In order to make automated detections, the concept of ROI may be utilized. ROI which stands for region of interest generally serves the purpose of focusing on particular targets. The main concentration of this paper is the algorithm to automatically detect retinal region of interest belonging to different retinal images on a software application. The algorithm consists of three stages such as pre-processing stage, detecting ROI on processed images and overlapping between input image and obtained ROI of the image.

  18. A novel adaptive, real-time algorithm to detect gait events from wearable sensors.

    PubMed

    Chia Bejarano, Noelia; Ambrosini, Emilia; Pedrocchi, Alessandra; Ferrigno, Giancarlo; Monticone, Marco; Ferrante, Simona

    2015-05-01

    A real-time, adaptive algorithm based on two inertial and magnetic sensors placed on the shanks was developed for gait-event detection. For each leg, the algorithm detected the Initial Contact (IC), as the minimum of the flexion/extension angle, and the End Contact (EC) and the Mid-Swing (MS), as minimum and maximum of the angular velocity, respectively. The algorithm consisted of calibration, real-time detection, and step-by-step update. Data collected from 22 healthy subjects (21 to 85 years) walking at three self-selected speeds were used to validate the algorithm against the GaitRite system. Comparable levels of accuracy and significantly lower detection delays were achieved with respect to other published methods. The algorithm robustness was tested on ten healthy subjects performing sudden speed changes and on ten stroke subjects (43 to 89 years). For healthy subjects, F1-scores of 1 and mean detection delays lower than 14 ms were obtained. For stroke subjects, F1-scores of 0.998 and 0.944 were obtained for IC and EC, respectively, with mean detection delays always below 31 ms. The algorithm accurately detected gait events in real time from a heterogeneous dataset of gait patterns and paves the way for the design of closed-loop controllers for customized gait trainings and/or assistive devices.

  19. NASA airborne radar wind shear detection algorithm and the detection of wet microbursts in the vicinity of Orlando, Florida

    NASA Technical Reports Server (NTRS)

    Britt, Charles L.; Bracalente, Emedio M.

    1992-01-01

    The algorithms used in the NASA experimental wind shear radar system for detection, characterization, and determination of windshear hazard are discussed. The performance of the algorithms in the detection of wet microbursts near Orlando is presented. Various suggested algorithms that are currently being evaluated using the flight test results from Denver and Orlando are reviewed.

  20. Corner detection and sorting method based on improved Harris algorithm in camera calibration

    NASA Astrophysics Data System (ADS)

    Xiao, Ying; Wang, Yonghong; Dan, Xizuo; Huang, Anqi; Hu, Yue; Yang, Lianxiang

    2016-11-01

    In traditional Harris corner detection algorithm, the appropriate threshold which is used to eliminate false corners is selected manually. In order to detect corners automatically, an improved algorithm which combines Harris and circular boundary theory of corners is proposed in this paper. After detecting accurate corner coordinates by using Harris algorithm and Forstner algorithm, false corners within chessboard pattern of the calibration plate can be eliminated automatically by using circular boundary theory. Moreover, a corner sorting method based on an improved calibration plate is proposed to eliminate false background corners and sort remaining corners in order. Experiment results show that the proposed algorithms can eliminate all false corners and sort remaining corners correctly and automatically.

  1. A collision detection algorithm for telerobotic arms

    NASA Technical Reports Server (NTRS)

    Tran, Doan Minh; Bartholomew, Maureen Obrien

    1991-01-01

    The telerobotic manipulator's collision detection algorithm is described. Its applied structural model of the world environment and template representation of objects is evaluated. Functional issues that are required for the manipulator to operate in a more complex and realistic environment are discussed.

  2. Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia

    NASA Technical Reports Server (NTRS)

    Park, Sang Seo; Kim, Jhoon; Lee, Jaehwa; Lee, Sukjo; Kim, Jeong Soo; Chang, Lim Seok; Ou, Steve

    2014-01-01

    A new dust detection algorithm is developed by combining the results of multiple dust detectionmethods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust detection methods. However, BTDmethods have limitations in identifying the offset values of the BTDto discriminate clear-sky areas. The current algorithm overcomes the disadvantages of previous dust detection methods by considering the Brightness Temperature Ratio (BTR) values of the dual wavelength channels with 30-day composite, the optical properties of the dust particles, the variability of surface properties, and the cloud contamination. Therefore, the current algorithm shows improvements in detecting the dust loaded region over land during daytime. Finally, the confidence index of the current dust algorithm is shown in 10 × 10 pixels of the MODIS observations. From January to June, 2006, the results of the current algorithm are within 64 to 81% of those found using the fine mode fraction (FMF) and aerosol index (AI) from the MODIS and Ozone Monitoring Instrument (OMI). The agreement between the results of the current algorithm and the OMI AI over the non-polluted land also ranges from 60 to 67% to avoid errors due to the anthropogenic aerosol. In addition, the developed algorithm shows statistically significant results at four AErosol RObotic NETwork (AERONET) sites in East Asia.

  3. Characterising a holographic modal phase mask for the detection of ocular aberrations

    NASA Astrophysics Data System (ADS)

    Corbett, A. D.; Leyva, D. Gil; Diaz-Santana, L.; Wilkinson, T. D.; Zhong, J. J.

    2005-12-01

    The accurate measurement of the double-pass ocular wave front has been shown to have a broad range of applications from LASIK surgery to adaptively corrected retinal imaging. The ocular wave front can be accurately described by a small number of Zernike circle polynomials. The modal wave front sensor was first proposed by Neil et al. and allows the coefficients of the individual Zernike modes to be measured directly. Typically the aberrations measured with the modal sensor are smaller than those seen in the ocular wave front. In this work, we investigated a technique for adapting a modal phase mask for the sensing of the ocular wave front. This involved extending the dynamic range of the sensor by increasing the pinhole size to 2.4mm and optimising the mask bias to 0.75λ. This was found to decrease the RMS error by up to a factor of three for eye-like aberrations with amplitudes up to 0.2μm. For aberrations taken from a sample of real-eye measurements a 20% decrease in the RMS error was observed.

  4. Algorithms for Autonomous Plume Detection on Outer Planet Satellites

    NASA Astrophysics Data System (ADS)

    Lin, Y.; Bunte, M. K.; Saripalli, S.; Greeley, R.

    2011-12-01

    We investigate techniques for automated detection of geophysical events (i.e., volcanic plumes) from spacecraft images. The algorithms presented here have not been previously applied to detection of transient events on outer planet satellites. We apply Scale Invariant Feature Transform (SIFT) to raw images of Io and Enceladus from the Voyager, Galileo, Cassini, and New Horizons missions. SIFT produces distinct interest points in every image; feature descriptors are reasonably invariant to changes in illumination, image noise, rotation, scaling, and small changes in viewpoint. We classified these descriptors as plumes using the k-nearest neighbor (KNN) algorithm. In KNN, an object is classified by its similarity to examples in a training set of images based on user defined thresholds. Using the complete database of Io images and a selection of Enceladus images where 1-3 plumes were manually detected in each image, we successfully detected 74% of plumes in Galileo and New Horizons images, 95% in Voyager images, and 93% in Cassini images. Preliminary tests yielded some false positive detections; further iterations will improve performance. In images where detections fail, plumes are less than 9 pixels in size or are lost in image glare. We compared the appearance of plumes and illuminated mountain slopes to determine the potential for feature classification. We successfully differentiated features. An advantage over other methods is the ability to detect plumes in non-limb views where they appear in the shadowed part of the surface; improvements will enable detection against the illuminated background surface where gradient changes would otherwise preclude detection. This detection method has potential applications to future outer planet missions for sustained plume monitoring campaigns and onboard automated prioritization of all spacecraft data. The complementary nature of this method is such that it could be used in conjunction with edge detection algorithms to

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

    NASA Astrophysics Data System (ADS)

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

    2018-06-01

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

  6. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

    PubMed Central

    Goldstein, Markus; Uchida, Seiichi

    2016-01-01

    Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks. PMID:27093601

  7. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data.

    PubMed

    Goldstein, Markus; Uchida, Seiichi

    2016-01-01

    Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of the dataset into account. This challenge is known as unsupervised anomaly detection and is addressed in many practical applications, for example in network intrusion detection, fraud detection as well as in the life science and medical domain. Dozens of algorithms have been proposed in this area, but unfortunately the research community still lacks a comparative universal evaluation as well as common publicly available datasets. These shortcomings are addressed in this study, where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains. By publishing the source code and the datasets, this paper aims to be a new well-funded basis for unsupervised anomaly detection research. Additionally, this evaluation reveals the strengths and weaknesses of the different approaches for the first time. Besides the anomaly detection performance, computational effort, the impact of parameter settings as well as the global/local anomaly detection behavior is outlined. As a conclusion, we give an advise on algorithm selection for typical real-world tasks.

  8. Fast intersection detection algorithm for PC-based robot off-line programming

    NASA Astrophysics Data System (ADS)

    Fedrowitz, Christian H.

    1994-11-01

    This paper presents a method for fast and reliable collision detection in complex production cells. The algorithm is part of the PC-based robot off-line programming system of the University of Siegen (Ropsus). The method is based on a solid model which is managed by a simplified constructive solid geometry model (CSG-model). The collision detection problem is divided in two steps. In the first step the complexity of the problem is reduced in linear time. In the second step the remaining solids are tested for intersection. For this the Simplex algorithm, which is known from linear optimization, is used. It computes a point which is common to two convex polyhedra. The polyhedra intersect, if such a point exists. Regarding the simplified geometrical model of Ropsus the algorithm runs also in linear time. In conjunction with the first step a resultant collision detection algorithm is found which requires linear time in all. Moreover it computes the resultant intersection polyhedron using the dual transformation.

  9. Determination of aberration center of Ronchigram for automated aberration correctors in scanning transmission electron microscopy.

    PubMed

    Sannomiya, Takumi; Sawada, Hidetaka; Nakamichi, Tomohiro; Hosokawa, Fumio; Nakamura, Yoshio; Tanishiro, Yasumasa; Takayanagi, Kunio

    2013-12-01

    A generic method to determine the aberration center is established, which can be utilized for aberration calculation and axis alignment for aberration corrected electron microscopes. In this method, decentering induced secondary aberrations from inherent primary aberrations are minimized to find the appropriate axis center. The fitness function to find the optimal decentering vector for the axis was defined as a sum of decentering induced secondary aberrations with properly distributed weight values according to the aberration order. Since the appropriate decentering vector is determined from the aberration values calculated at an arbitrary center axis, only one aberration measurement is in principle required to find the center, resulting in /very fast center search. This approach was tested for the Ronchigram based aberration calculation method for aberration corrected scanning transmission electron microscopy. Both in simulation and in experiments, the center search was confirmed to work well although the convergence to find the best axis becomes slower with larger primary aberrations. Such aberration center determination is expected to fully automatize the aberration correction procedures, which used to require pre-alignment of experienced users. This approach is also applicable to automated aperture positioning. Copyright © 2013 Elsevier B.V. All rights reserved.

  10. Accuracy of Person-Fit Statistics: A Monte Carlo Study of the Influence of Aberrance Rates

    ERIC Educational Resources Information Center

    St-Onge, Christina; Valois, Pierre; Abdous, Belkacem; Germain, Stephane

    2011-01-01

    Using a Monte Carlo experimental design, this research examined the relationship between answer patterns' aberrance rates and person-fit statistics (PFS) accuracy. It was observed that as the aberrance rate increased, the detection rates of PFS also increased until, in some situations, a peak was reached and then the detection rates of PFS…

  11. Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications

    NASA Astrophysics Data System (ADS)

    Zhu, Zhe

    2017-08-01

    The free and open access to all archived Landsat images in 2008 has completely changed the way of using Landsat data. Many novel change detection algorithms based on Landsat time series have been developed We present a comprehensive review of four important aspects of change detection studies based on Landsat time series, including frequencies, preprocessing, algorithms, and applications. We observed the trend that the more recent the study, the higher the frequency of Landsat time series used. We reviewed a series of image preprocessing steps, including atmospheric correction, cloud and cloud shadow detection, and composite/fusion/metrics techniques. We divided all change detection algorithms into six categories, including thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression. Within each category, six major characteristics of different algorithms, such as frequency, change index, univariate/multivariate, online/offline, abrupt/gradual change, and sub-pixel/pixel/spatial were analyzed. Moreover, some of the widely-used change detection algorithms were also discussed. Finally, we reviewed different change detection applications by dividing these applications into two categories, change target and change agent detection.

  12. Multitarget detection algorithm for automotive FMCW radar

    NASA Astrophysics Data System (ADS)

    Hyun, Eugin; Oh, Woo-Jin; Lee, Jong-Hun

    2012-06-01

    Today, 77 GHz FMCW (Frequency Modulation Continuous Wave) radar has strong advantages of range and velocity detection for automotive applications. However, FMCW radar brings out ghost targets and missed targets in multi-target situations. In this paper, in order to resolve these limitations, we propose an effective pairing algorithm, which consists of two steps. In the proposed method, a waveform with different slopes in two periods is used. In the 1st pairing processing, all combinations of range and velocity are obtained in each of two wave periods. In the 2nd pairing step, using the results of the 1st pairing processing, fine range and velocity are detected. In that case, we propose the range-velocity windowing technique in order to compensate for the non-ideal beat-frequency characteristic that arises due to the non-linearity of the RF module. Based on experimental results, the performance of the proposed algorithm is improved compared with that of the typical method.

  13. Advances in algorithm fusion for automated sea mine detection and classification

    NASA Astrophysics Data System (ADS)

    Dobeck, Gerald J.; Cobb, J. Tory

    2002-11-01

    Along with other sensors, the Navy uses high-resolution sonar to detect and classify sea mines in mine-hunting operations. Scientists and engineers have devoted substantial effort to the development of automated detection and classification (D/C) algorithms for these high-resolution systems. Several factors spurred these efforts, including: (1) aids for operators to reduce work overload; (2) more optimal use of all available data; and (3) the introduction of unmanned minehunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and manmade clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while maintaining a very high probability of mine detection and classification (PdPc). In recent years, the benefits of fusing the outputs of multiple D/C algorithms (Algorithm Fusion) have been studied. To date, the results have been remarkable, including reliable robustness to new environments. In this paper a brief history of existing Algorithm Fusion technology and some techniques recently used to improve performance are presented. An exploration of new developments is presented in conclusion.

  14. Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder.

    PubMed

    Jeppesen, J; Beniczky, S; Fuglsang Frederiksen, A; Sidenius, P; Johansen, P

    2017-07-01

    Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG seems promising for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. We used the portable CE marked ePatch® heart monitor to record the ECG of 14 patients, who were enrolled in the videoEEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients were used as training set of data for the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Qand S-peaks can create in the tachogram, which causes error in short-term HRVanalysis. The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979%) and positive predictive value (P+ = 99.976%), which was comparable with a previously published QRS-detection algorithm for the ePatch® ECG device, when testing the same dataset. The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy, creating the possibility for real-time seizure detection for these patients.

  15. Modified artificial fish school algorithm for free space optical communication with sensor-less adaptive optics system

    NASA Astrophysics Data System (ADS)

    Cao, Jingtai; Zhao, Xiaohui; Li, Zhaokun; Liu, Wei; Gu, Haijun

    2017-11-01

    The performance of free space optical (FSO) communication system is limited by atmospheric turbulent extremely. Adaptive optics (AO) is the significant method to overcome the atmosphere disturbance. Especially, for the strong scintillation effect, the sensor-less AO system plays a major role for compensation. In this paper, a modified artificial fish school (MAFS) algorithm is proposed to compensate the aberrations in the sensor-less AO system. Both the static and dynamic aberrations compensations are analyzed and the performance of FSO communication before and after aberrations compensations is compared. In addition, MAFS algorithm is compared with artificial fish school (AFS) algorithm, stochastic parallel gradient descent (SPGD) algorithm and simulated annealing (SA) algorithm. It is shown that the MAFS algorithm has a higher convergence speed than SPGD algorithm and SA algorithm, and reaches the better convergence value than AFS algorithm, SPGD algorithm and SA algorithm. The sensor-less AO system with MAFS algorithm effectively increases the coupling efficiency at the receiving terminal with fewer numbers of iterations. In conclusion, the MAFS algorithm has great significance for sensor-less AO system to compensate atmospheric turbulence in FSO communication system.

  16. An ant colony based algorithm for overlapping community detection in complex networks

    NASA Astrophysics Data System (ADS)

    Zhou, Xu; Liu, Yanheng; Zhang, Jindong; Liu, Tuming; Zhang, Di

    2015-06-01

    Community detection is of great importance to understand the structures and functions of networks. Overlap is a significant feature of networks and overlapping community detection has attracted an increasing attention. Many algorithms have been presented to detect overlapping communities. In this paper, we present an ant colony based overlapping community detection algorithm which mainly includes ants' location initialization, ants' movement and post processing phases. An ants' location initialization strategy is designed to identify initial location of ants and initialize label list stored in each node. During the ants' movement phase, the entire ants move according to the transition probability matrix, and a new heuristic information computation approach is redefined to measure similarity between two nodes. Every node keeps a label list through the cooperation made by ants until a termination criterion is reached. A post processing phase is executed on the label list to get final overlapping community structure naturally. We illustrate the capability of our algorithm by making experiments on both synthetic networks and real world networks. The results demonstrate that our algorithm will have better performance in finding overlapping communities and overlapping nodes in synthetic datasets and real world datasets comparing with state-of-the-art algorithms.

  17. Radiation anomaly detection algorithms for field-acquired gamma energy spectra

    NASA Astrophysics Data System (ADS)

    Mukhopadhyay, Sanjoy; Maurer, Richard; Wolff, Ron; Guss, Paul; Mitchell, Stephen

    2015-08-01

    The Remote Sensing Laboratory (RSL) is developing a tactical, networked radiation detection system that will be agile, reconfigurable, and capable of rapid threat assessment with high degree of fidelity and certainty. Our design is driven by the needs of users such as law enforcement personnel who must make decisions by evaluating threat signatures in urban settings. The most efficient tool available to identify the nature of the threat object is real-time gamma spectroscopic analysis, as it is fast and has a very low probability of producing false positive alarm conditions. Urban radiological searches are inherently challenged by the rapid and large spatial variation of background gamma radiation, the presence of benign radioactive materials in terms of the normally occurring radioactive materials (NORM), and shielded and/or masked threat sources. Multiple spectral anomaly detection algorithms have been developed by national laboratories and commercial vendors. For example, the Gamma Detector Response and Analysis Software (GADRAS) a one-dimensional deterministic radiation transport software capable of calculating gamma ray spectra using physics-based detector response functions was developed at Sandia National Laboratories. The nuisance-rejection spectral comparison ratio anomaly detection algorithm (or NSCRAD), developed at Pacific Northwest National Laboratory, uses spectral comparison ratios to detect deviation from benign medical and NORM radiation source and can work in spite of strong presence of NORM and or medical sources. RSL has developed its own wavelet-based gamma energy spectral anomaly detection algorithm called WAVRAD. Test results and relative merits of these different algorithms will be discussed and demonstrated.

  18. Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls

    PubMed Central

    Bagalà, Fabio; Becker, Clemens; Cappello, Angelo; Chiari, Lorenzo; Aminian, Kamiar; Hausdorff, Jeffrey M.; Zijlstra, Wiebren; Klenk, Jochen

    2012-01-01

    Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean±std) 83.0%±30.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0%±27.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design

  19. Comparison of human observer and algorithmic target detection in nonurban forward-looking infrared imagery

    NASA Astrophysics Data System (ADS)

    Weber, Bruce A.

    2005-07-01

    We have performed an experiment that compares the performance of human observers with that of a robust algorithm for the detection of targets in difficult, nonurban forward-looking infrared imagery. Our purpose was to benchmark the comparison and document performance differences for future algorithm improvement. The scale-insensitive detection algorithm, used as a benchmark by the Night Vision Electronic Sensors Directorate for algorithm evaluation, employed a combination of contrastlike features to locate targets. Detection receiver operating characteristic curves and observer-confidence analyses were used to compare human and algorithmic responses and to gain insight into differences. The test database contained ground targets, in natural clutter, whose detectability, as judged by human observers, ranged from easy to very difficult. In general, as compared with human observers, the algorithm detected most of the same targets, but correlated confidence with correct detections poorly and produced many more false alarms at any useful level of performance. Though characterizing human performance was not the intent of this study, results suggest that previous observational experience was not a strong predictor of human performance, and that combining individual human observations by majority vote significantly reduced false-alarm rates.

  20. The algorithm for automatic detection of the calibration object

    NASA Astrophysics Data System (ADS)

    Artem, Kruglov; Irina, Ugfeld

    2017-06-01

    The problem of the automatic image calibration is considered in this paper. The most challenging task of the automatic calibration is a proper detection of the calibration object. The solving of this problem required the appliance of the methods and algorithms of the digital image processing, such as morphology, filtering, edge detection, shape approximation. The step-by-step process of the development of the algorithm and its adopting to the specific conditions of the log cuts in the image's background is presented. Testing of the automatic calibration module was carrying out under the conditions of the production process of the logging enterprise. Through the tests the average possibility of the automatic isolating of the calibration object is 86.1% in the absence of the type 1 errors. The algorithm was implemented in the automatic calibration module within the mobile software for the log deck volume measurement.

  1. A fuzzy clustering algorithm to detect planar and quadric shapes

    NASA Technical Reports Server (NTRS)

    Krishnapuram, Raghu; Frigui, Hichem; Nasraoui, Olfa

    1992-01-01

    In this paper, we introduce a new fuzzy clustering algorithm to detect an unknown number of planar and quadric shapes in noisy data. The proposed algorithm is computationally and implementationally simple, and it overcomes many of the drawbacks of the existing algorithms that have been proposed for similar tasks. Since the clustering is performed in the original image space, and since no features need to be computed, this approach is particularly suited for sparse data. The algorithm may also be used in pattern recognition applications.

  2. A bioinspired collision detection algorithm for VLSI implementation

    NASA Astrophysics Data System (ADS)

    Cuadri, J.; Linan, G.; Stafford, R.; Keil, M. S.; Roca, E.

    2005-06-01

    In this paper a bioinspired algorithm for collision detection is proposed, based on previous models of the locust (Locusta migratoria) visual system reported by F.C. Rind and her group, in the University of Newcastle-upon-Tyne. The algorithm is suitable for VLSI implementation in standard CMOS technologies as a system-on-chip for automotive applications. The working principle of the algorithm is to process a video stream that represents the current scenario, and to fire an alarm whenever an object approaches on a collision course. Moreover, it establishes a scale of warning states, from no danger to collision alarm, depending on the activity detected in the current scenario. In the worst case, the minimum time before collision at which the model fires the collision alarm is 40 msec (1 frame before, at 25 frames per second). Since the average time to successfully fire an airbag system is 2 msec, even in the worst case, this algorithm would be very helpful to more efficiently arm the airbag system, or even take some kind of collision avoidance countermeasures. Furthermore, two additional modules have been included: a "Topological Feature Estimator" and an "Attention Focusing Algorithm". The former takes into account the shape of the approaching object to decide whether it is a person, a road line or a car. This helps to take more adequate countermeasures and to filter false alarms. The latter centres the processing power into the most active zones of the input frame, thus saving memory and processing time resources.

  3. Linear segmentation algorithm for detecting layer boundary with lidar.

    PubMed

    Mao, Feiyue; Gong, Wei; Logan, Timothy

    2013-11-04

    The automatic detection of aerosol- and cloud-layer boundary (base and top) is important in atmospheric lidar data processing, because the boundary information is not only useful for environment and climate studies, but can also be used as input for further data processing. Previous methods have demonstrated limitations in defining the base and top, window-size setting, and have neglected the in-layer attenuation. To overcome these limitations, we present a new layer detection scheme for up-looking lidars based on linear segmentation with a reasonable threshold setting, boundary selecting, and false positive removing strategies. Preliminary results from both real and simulated data show that this algorithm cannot only detect the layer-base as accurate as the simple multi-scale method, but can also detect the layer-top more accurately than that of the simple multi-scale method. Our algorithm can be directly applied to uncalibrated data without requiring any additional measurements or window size selections.

  4. 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.

  5. 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

  6. Algorithms for the detection of chewing behavior in dietary monitoring applications

    NASA Astrophysics Data System (ADS)

    Schmalz, Mark S.; Helal, Abdelsalam; Mendez-Vasquez, Andres

    2009-08-01

    The detection of food consumption is key to the implementation of successful behavior modification in support of dietary monitoring and therapy, for example, during the course of controlling obesity, diabetes, or cardiovascular disease. Since the vast majority of humans consume food via mastication (chewing), we have designed an algorithm that automatically detects chewing behaviors in surveillance video of a person eating. Our algorithm first detects the mouth region, then computes the spatiotemporal frequency spectrum of a small perioral region (including the mouth). Spectral data are analyzed to determine the presence of periodic motion that characterizes chewing. A classifier is then applied to discriminate different types of chewing behaviors. Our algorithm was tested on seven volunteers, whose behaviors included chewing with mouth open, chewing with mouth closed, talking, static face presentation (control case), and moving face presentation. Early test results show that the chewing behaviors induce a temporal frequency peak at 0.5Hz to 2.5Hz, which is readily detected using a distance-based classifier. Computational cost is analyzed for implementation on embedded processing nodes, for example, in a healthcare sensor network. Complexity analysis emphasizes the relationship between the work and space estimates of the algorithm, and its estimated error. It is shown that chewing detection is possible within a computationally efficient, accurate, and subject-independent framework.

  7. Low power multi-camera system and algorithms for automated threat detection

    NASA Astrophysics Data System (ADS)

    Huber, David J.; Khosla, Deepak; Chen, Yang; Van Buer, Darrel J.; Martin, Kevin

    2013-05-01

    A key to any robust automated surveillance system is continuous, wide field-of-view sensor coverage and high accuracy target detection algorithms. Newer systems typically employ an array of multiple fixed cameras that provide individual data streams, each of which is managed by its own processor. This array can continuously capture the entire field of view, but collecting all the data and back-end detection algorithm consumes additional power and increases the size, weight, and power (SWaP) of the package. This is often unacceptable, as many potential surveillance applications have strict system SWaP requirements. This paper describes a wide field-of-view video system that employs multiple fixed cameras and exhibits low SWaP without compromising the target detection rate. We cycle through the sensors, fetch a fixed number of frames, and process them through a modified target detection algorithm. During this time, the other sensors remain powered-down, which reduces the required hardware and power consumption of the system. We show that the resulting gaps in coverage and irregular frame rate do not affect the detection accuracy of the underlying algorithms. This reduces the power of an N-camera system by up to approximately N-fold compared to the baseline normal operation. This work was applied to Phase 2 of DARPA Cognitive Technology Threat Warning System (CT2WS) program and used during field testing.

  8. Wearable physiological sensors and real-time algorithms for detection of acute mountain sickness.

    PubMed

    Muza, Stephen R

    2018-03-01

    This is a minireview of potential wearable physiological sensors and algorithms (process and equations) for detection of acute mountain sickness (AMS). Given the emerging status of this effort, the focus of the review is on the current clinical assessment of AMS, known risk factors (environmental, demographic, and physiological), and current understanding of AMS pathophysiology. Studies that have examined a range of physiological variables to develop AMS prediction and/or detection algorithms are reviewed to provide insight and potential technological roadmaps for future development of real-time physiological sensors and algorithms to detect AMS. Given the lack of signs and nonspecific symptoms associated with AMS, development of wearable physiological sensors and embedded algorithms to predict in the near term or detect established AMS will be challenging. Prior work using [Formula: see text], HR, or HRv has not provided the sensitivity and specificity for useful application to predict or detect AMS. Rather than using spot checks as most prior studies have, wearable systems that continuously measure SpO 2 and HR are commercially available. Employing other statistical modeling approaches such as general linear and logistic mixed models or time series analysis to these continuously measured variables is the most promising approach for developing algorithms that are sensitive and specific for physiological prediction or detection of AMS.

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

    PubMed Central

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

    2016-01-01

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

  10. Stride search: A general algorithm for storm detection in high resolution climate data

    DOE PAGES

    Bosler, Peter Andrew; Roesler, Erika Louise; Taylor, Mark A.; ...

    2015-09-08

    This article discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. Two specific implementations of the spatial search algorithm are compared. The commonly used grid point search algorithm is reviewed, and a new algorithm called Stride Search is introduced. Stride Search is designed to work at all latitudes, while grid point searches may fail in polar regions. Results from the two algorithms are compared for the application of tropicalmore » cyclone detection, and shown to produce similar results for the same set of storm identification criteria. The time required for both algorithms to search the same data set is compared. Furthermore, Stride Search's ability to search extreme latitudes is demonstrated for the case of polar low detection.« less

  11. Deep learning algorithms for detecting explosive hazards in ground penetrating radar data

    NASA Astrophysics Data System (ADS)

    Besaw, Lance E.; Stimac, Philip J.

    2014-05-01

    Buried explosive hazards (BEHs) have been, and continue to be, one of the most deadly threats in modern conflicts. Current handheld sensors rely on a highly trained operator for them to be effective in detecting BEHs. New algorithms are needed to reduce the burden on the operator and improve the performance of handheld BEH detectors. Traditional anomaly detection and discrimination algorithms use "hand-engineered" feature extraction techniques to characterize and classify threats. In this work we use a Deep Belief Network (DBN) to transcend the traditional approaches of BEH detection (e.g., principal component analysis and real-time novelty detection techniques). DBNs are pretrained using an unsupervised learning algorithm to generate compressed representations of unlabeled input data and form feature detectors. They are then fine-tuned using a supervised learning algorithm to form a predictive model. Using ground penetrating radar (GPR) data collected by a robotic cart swinging a handheld detector, our research demonstrates that relatively small DBNs can learn to model GPR background signals and detect BEHs with an acceptable false alarm rate (FAR). In this work, our DBNs achieved 91% probability of detection (Pd) with 1.4 false alarms per square meter when evaluated on anti-tank and anti-personnel targets at temperate and arid test sites. This research demonstrates that DBNs are a viable approach to detect and classify BEHs.

  12. Overlapped Fourier coding for optical aberration removal

    PubMed Central

    Horstmeyer, Roarke; Ou, Xiaoze; Chung, Jaebum; Zheng, Guoan; Yang, Changhuei

    2014-01-01

    We present an imaging procedure that simultaneously optimizes a camera’s resolution and retrieves a sample’s phase over a sequence of snapshots. The technique, termed overlapped Fourier coding (OFC), first digitally pans a small aperture across a camera’s pupil plane with a spatial light modulator. At each aperture location, a unique image is acquired. The OFC algorithm then fuses these low-resolution images into a full-resolution estimate of the complex optical field incident upon the detector. Simultaneously, the algorithm utilizes redundancies within the acquired dataset to computationally estimate and remove unknown optical aberrations and system misalignments via simulated annealing. The result is an imaging system that can computationally overcome its optical imperfections to offer enhanced resolution, at the expense of taking multiple snapshots over time. PMID:25321982

  13. A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features.

    PubMed

    Amudha, P; Karthik, S; Sivakumari, S

    2015-01-01

    Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.

  14. A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features

    PubMed Central

    Amudha, P.; Karthik, S.; Sivakumari, S.

    2015-01-01

    Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different. PMID:26221625

  15. Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography.

    PubMed

    Liu, Li; Gao, Simon S; Bailey, Steven T; Huang, David; Li, Dengwang; Jia, Yali

    2015-09-01

    Optical coherence tomography angiography has recently been used to visualize choroidal neovascularization (CNV) in participants with age-related macular degeneration. Identification and quantification of CNV area is important clinically for disease assessment. An automated algorithm for CNV area detection is presented in this article. It relies on denoising and a saliency detection model to overcome issues such as projection artifacts and the heterogeneity of CNV. Qualitative and quantitative evaluations were performed on scans of 7 participants. Results from the algorithm agreed well with manual delineation of CNV area.

  16. Advanced Oil Spill Detection Algorithms For Satellite Based Maritime Environment Monitoring

    NASA Astrophysics Data System (ADS)

    Radius, Andrea; Azevedo, Rui; Sapage, Tania; Carmo, Paulo

    2013-12-01

    During the last years, the increasing pollution occurrence and the alarming deterioration of the environmental health conditions of the sea, lead to the need of global monitoring capabilities, namely for marine environment management in terms of oil spill detection and indication of the suspected polluter. The sensitivity of Synthetic Aperture Radar (SAR) to the different phenomena on the sea, especially for oil spill and vessel detection, makes it a key instrument for global pollution monitoring. The SAR performances in maritime pollution monitoring are being operationally explored by a set of service providers on behalf of the European Maritime Safety Agency (EMSA), which has launched in 2007 the CleanSeaNet (CSN) project - a pan-European satellite based oil monitoring service. EDISOFT, which is from the beginning a service provider for CSN, is continuously investing in R&D activities that will ultimately lead to better algorithms and better performance on oil spill detection from SAR imagery. This strategy is being pursued through EDISOFT participation in the FP7 EC Sea-U project and in the Automatic Oil Spill Detection (AOSD) ESA project. The Sea-U project has the aim to improve the current state of oil spill detection algorithms, through the informative content maximization obtained with data fusion, the exploitation of different type of data/ sensors and the development of advanced image processing, segmentation and classification techniques. The AOSD project is closely related to the operational segment, because it is focused on the automation of the oil spill detection processing chain, integrating auxiliary data, like wind information, together with image and geometry analysis techniques. The synergy between these different objectives (R&D versus operational) allowed EDISOFT to develop oil spill detection software, that combines the operational automatic aspect, obtained through dedicated integration of the processing chain in the existing open source NEST

  17. Aircraft target detection algorithm based on high resolution spaceborne SAR imagery

    NASA Astrophysics Data System (ADS)

    Zhang, Hui; Hao, Mengxi; Zhang, Cong; Su, Xiaojing

    2018-03-01

    In this paper, an image classification algorithm for airport area is proposed, which based on the statistical features of synthetic aperture radar (SAR) images and the spatial information of pixels. The algorithm combines Gamma mixture model and MRF. The algorithm using Gamma mixture model to obtain the initial classification result. Pixel space correlation based on the classification results are optimized by the MRF technique. Additionally, morphology methods are employed to extract airport (ROI) region where the suspected aircraft target samples are clarified to reduce the false alarm and increase the detection performance. Finally, this paper presents the plane target detection, which have been verified by simulation test.

  18. Algorithms of Crescent Structure Detection in Human Biological Fluid Facies

    NASA Astrophysics Data System (ADS)

    Krasheninnikov, V. R.; Malenova, O. E.; Yashina, A. S.

    2017-05-01

    One of the effective methods of early medical diagnosis is based on the image analysis of human biological fluids. In the process of fluid crystallization there appear characteristic patterns (markers) in the resulting layer (facies). Each marker is a highly probable sign of some pathology even at an early stage of a disease development. When mass health examination is carried out, it is necessary to analyze a large number of images. That is why, the problem of algorithm and software development for automated processing of images is rather urgent nowadays. This paper presents algorithms to detect a crescent structures in images of blood serum and cervical mucus facies. Such a marker indicates the symptoms of ischemic disease. The algorithm presented detects this marker with high probability when the probability of false alarm is low.

  19. Novel trace chemical detection algorithms: a comparative study

    NASA Astrophysics Data System (ADS)

    Raz, Gil; Murphy, Cara; Georgan, Chelsea; Greenwood, Ross; Prasanth, R. K.; Myers, Travis; Goyal, Anish; Kelley, David; Wood, Derek; Kotidis, Petros

    2017-05-01

    Algorithms for standoff detection and estimation of trace chemicals in hyperspectral images in the IR band are a key component for a variety of applications relevant to law-enforcement and the intelligence communities. Performance of these methods is impacted by the spectral signature variability due to presence of contaminants, surface roughness, nonlinear dependence on abundances as well as operational limitations on the compute platforms. In this work we provide a comparative performance and complexity analysis of several classes of algorithms as a function of noise levels, error distribution, scene complexity, and spatial degrees of freedom. The algorithm classes we analyze and test include adaptive cosine estimator (ACE and modifications to it), compressive/sparse methods, Bayesian estimation, and machine learning. We explicitly call out the conditions under which each algorithm class is optimal or near optimal as well as their built-in limitations and failure modes.

  20. Identification of random nucleic acid sequence aberrations using dual capture probes which hybridize to different chromosome regions

    DOEpatents

    Lucas, Joe N.; Straume, Tore; Bogen, Kenneth T.

    1998-01-01

    A method is provided for detecting nucleic acid sequence aberrations using two immobilization steps. According to the method, a nucleic acid sequence aberration is detected by detecting nucleic acid sequences having both a first nucleic acid sequence type (e.g., from a first chromosome) and a second nucleic acid sequence type (e.g., from a second chromosome), the presence of the first and the second nucleic acid sequence type on the same nucleic acid sequence indicating the presence of a nucleic acid sequence aberration. In the method, immobilization of a first hybridization probe is used to isolate a first set of nucleic acids in the sample which contain the first nucleic acid sequence type. Immobilization of a second hybridization probe is then used to isolate a second set of nucleic acids from within the first set of nucleic acids which contain the second nucleic acid sequence type. The second set of nucleic acids are then detected, their presence indicating the presence of a nucleic acid sequence aberration.

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

    NASA Astrophysics Data System (ADS)

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

    2018-01-01

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

  2. Enhancement of Fast Face Detection Algorithm Based on a Cascade of Decision Trees

    NASA Astrophysics Data System (ADS)

    Khryashchev, V. V.; Lebedev, A. A.; Priorov, A. L.

    2017-05-01

    Face detection algorithm based on a cascade of ensembles of decision trees (CEDT) is presented. The new approach allows detecting faces other than the front position through the use of multiple classifiers. Each classifier is trained for a specific range of angles of the rotation head. The results showed a high rate of productivity for CEDT on images with standard size. The algorithm increases the area under the ROC-curve of 13% compared to a standard Viola-Jones face detection algorithm. Final realization of given algorithm consist of 5 different cascades for frontal/non-frontal faces. One more thing which we take from the simulation results is a low computational complexity of CEDT algorithm in comparison with standard Viola-Jones approach. This could prove important in the embedded system and mobile device industries because it can reduce the cost of hardware and make battery life longer.

  3. ASPeak: an abundance sensitive peak detection algorithm for RIP-Seq.

    PubMed

    Kucukural, Alper; Özadam, Hakan; Singh, Guramrit; Moore, Melissa J; Cenik, Can

    2013-10-01

    Unlike DNA, RNA abundances can vary over several orders of magnitude. Thus, identification of RNA-protein binding sites from high-throughput sequencing data presents unique challenges. Although peak identification in ChIP-Seq data has been extensively explored, there are few bioinformatics tools tailored for peak calling on analogous datasets for RNA-binding proteins. Here we describe ASPeak (abundance sensitive peak detection algorithm), an implementation of an algorithm that we previously applied to detect peaks in exon junction complex RNA immunoprecipitation in tandem experiments. Our peak detection algorithm yields stringent and robust target sets enabling sensitive motif finding and downstream functional analyses. ASPeak is implemented in Perl as a complete pipeline that takes bedGraph files as input. ASPeak implementation is freely available at https://sourceforge.net/projects/as-peak under the GNU General Public License. ASPeak can be run on a personal computer, yet is designed to be easily parallelizable. ASPeak can also run on high performance computing clusters providing efficient speedup. The documentation and user manual can be obtained from http://master.dl.sourceforge.net/project/as-peak/manual.pdf.

  4. Optimal spiral phase modulation in Gerchberg-Saxton algorithm for wavefront reconstruction and correction

    NASA Astrophysics Data System (ADS)

    Baránek, M.; Běhal, J.; Bouchal, Z.

    2018-01-01

    In the phase retrieval applications, the Gerchberg-Saxton (GS) algorithm is widely used for the simplicity of implementation. This iterative process can advantageously be deployed in the combination with a spatial light modulator (SLM) enabling simultaneous correction of optical aberrations. As recently demonstrated, the accuracy and efficiency of the aberration correction using the GS algorithm can be significantly enhanced by a vortex image spot used as the target intensity pattern in the iterative process. Here we present an optimization of the spiral phase modulation incorporated into the GS algorithm.

  5. Algorithms and data structures for automated change detection and classification of sidescan sonar imagery

    NASA Astrophysics Data System (ADS)

    Gendron, Marlin Lee

    During Mine Warfare (MIW) operations, MIW analysts perform change detection by visually comparing historical sidescan sonar imagery (SSI) collected by a sidescan sonar with recently collected SSI in an attempt to identify objects (which might be explosive mines) placed at sea since the last time the area was surveyed. This dissertation presents a data structure and three algorithms, developed by the author, that are part of an automated change detection and classification (ACDC) system. MIW analysts at the Naval Oceanographic Office, to reduce the amount of time to perform change detection, are currently using ACDC. The dissertation introductory chapter gives background information on change detection, ACDC, and describes how SSI is produced from raw sonar data. Chapter 2 presents the author's Geospatial Bitmap (GB) data structure, which is capable of storing information geographically and is utilized by the three algorithms. This chapter shows that a GB data structure used in a polygon-smoothing algorithm ran between 1.3--48.4x faster than a sparse matrix data structure. Chapter 3 describes the GB clustering algorithm, which is the author's repeatable, order-independent method for clustering. Results from tests performed in this chapter show that the time to cluster a set of points is not affected by the distribution or the order of the points. In Chapter 4, the author presents his real-time computer-aided detection (CAD) algorithm that automatically detects mine-like objects on the seafloor in SSI. The author ran his GB-based CAD algorithm on real SSI data, and results of these tests indicate that his real-time CAD algorithm performs comparably to or better than other non-real-time CAD algorithms. The author presents his computer-aided search (CAS) algorithm in Chapter 5. CAS helps MIW analysts locate mine-like features that are geospatially close to previously detected features. A comparison between the CAS and a great circle distance algorithm shows that the

  6. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

    PubMed

    Sun, Rui; Cheng, Qi; Wang, Guanyu; Ochieng, Washington Yotto

    2017-09-29

    The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

  7. An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform

    DTIC Science & Technology

    2018-01-01

    ARL-TR-8270 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological Filter...Automated Energy Detection Algorithm Based on Morphological Filter Processing with a Modified Watershed Transform by Kwok F Tom Sensors and Electron...1 October 2016–30 September 2017 4. TITLE AND SUBTITLE An Automated Energy Detection Algorithm Based on Morphological Filter Processing with a

  8. Chromosomal aberrations in 2000 couples of Indian ethnicity with reproductive failure.

    PubMed

    Gada Saxena, S; Desai, K; Shewale, L; Ranjan, P; Saranath, D

    2012-08-01

    Constitutional chromosomal aberrations contribute to infertility and repeated miscarriage leading to reproductive failure in couples. These aberrations may show no obvious clinical manifestations and remain undetected across multiple generations. However, infertility or recurrent spontaneous pregnancy loss, and/or genotypic/phenotypic aberrations may be manifested in the progeny during gametogenesis. The current study was a retrospective analysis to examine the chromosomal aberrations and prevalence in 2000 couples of Indian ethnicity with reproductive failure. Cytogenetic analysis via conventional G-band karyotyping analysis was carried out on phytohaemagglutinin stimulated peripheral blood lymphocytes, cultured in RPMI1640 medium. The chromosomes were enumerated as per International System for Human Cytogenetic Nomenclature at 500-550 band resolution, and recorded in the screening sheets. Chromosomal aberrations were detected in a total of 110 (2.78%) couples, with structural chromosomal aberrations in 88 cases including reciprocal translocations in 56 cases, Robertsonian translocations in 16 cases, inversions in eight cases, deletions in three cases, derivative chromosomes in five cases and numerical chromosome aberrations in 23 cases. The study emphasizes the importance of cytogenetic work up in both the partners associated with a history of reproductive failure. Genetic counselling with an option of prenatal diagnosis should be offered to couples with chromosomal aberrations. Copyright © 2012 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

  9. Evaluation of hybrids algorithms for mass detection in digitalized mammograms

    NASA Astrophysics Data System (ADS)

    Cordero, José; Garzón Reyes, Johnson

    2011-01-01

    The breast cancer remains being a significant public health problem, the early detection of the lesions can increase the success possibilities of the medical treatments. The mammography is an image modality effective to early diagnosis of abnormalities, where the medical image is obtained of the mammary gland with X-rays of low radiation, this allows detect a tumor or circumscribed mass between two to three years before that it was clinically palpable, and is the only method that until now achieved reducing the mortality by breast cancer. In this paper three hybrids algorithms for circumscribed mass detection on digitalized mammograms are evaluated. In the first stage correspond to a review of the enhancement and segmentation techniques used in the processing of the mammographic images. After a shape filtering was applied to the resulting regions. By mean of a Bayesian filter the survivors regions were processed, where the characteristics vector for the classifier was constructed with few measurements. Later, the implemented algorithms were evaluated by ROC curves, where 40 images were taken for the test, 20 normal images and 20 images with circumscribed lesions. Finally, the advantages and disadvantages in the correct detection of a lesion of every algorithm are discussed.

  10. Vision-based vehicle detection and tracking algorithm design

    NASA Astrophysics Data System (ADS)

    Hwang, Junyeon; Huh, Kunsoo; Lee, Donghwi

    2009-12-01

    The vision-based vehicle detection in front of an ego-vehicle is regarded as promising for driver assistance as well as for autonomous vehicle guidance. The feasibility of vehicle detection in a passenger car requires accurate and robust sensing performance. A multivehicle detection system based on stereo vision has been developed for better accuracy and robustness. This system utilizes morphological filter, feature detector, template matching, and epipolar constraint techniques in order to detect the corresponding pairs of vehicles. After the initial detection, the system executes the tracking algorithm for the vehicles. The proposed system can detect front vehicles such as the leading vehicle and side-lane vehicles. The position parameters of the vehicles located in front are obtained based on the detection information. The proposed vehicle detection system is implemented on a passenger car, and its performance is verified experimentally.

  11. An Automated Energy Detection Algorithm Based on Morphological and Statistical Processing Techniques

    DTIC Science & Technology

    2018-01-09

    ARL-TR-8272 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Morphological and...is no longer needed. Do not return it to the originator. ARL-TR-8272 ● JAN 2018 US Army Research Laboratory An Automated Energy ...4. TITLE AND SUBTITLE An Automated Energy Detection Algorithm Based on Morphological and Statistical Processing Techniques 5a. CONTRACT NUMBER

  12. Road detection in SAR images using a tensor voting algorithm

    NASA Astrophysics Data System (ADS)

    Shen, Dajiang; Hu, Chun; Yang, Bing; Tian, Jinwen; Liu, Jian

    2007-11-01

    In this paper, the problem of the detection of road networks in Synthetic Aperture Radar (SAR) images is addressed. Most of the previous methods extract the road by detecting lines and network reconstruction. Traditional algorithms such as MRFs, GA, Level Set, used in the progress of reconstruction are iterative. The tensor voting methodology we proposed is non-iterative, and non-sensitive to initialization. Furthermore, the only free parameter is the size of the neighborhood, related to the scale. The algorithm we present is verified to be effective when it's applied to the road extraction using the real Radarsat Image.

  13. Review of Peak Detection Algorithms in Liquid-Chromatography-Mass Spectrometry

    PubMed Central

    Zhang, Jianqiu; Gonzalez, Elias; Hestilow, Travis; Haskins, William; Huang, Yufei

    2009-01-01

    In this review, we will discuss peak detection in Liquid-Chromatography-Mass Spectrometry (LC/MS) from a signal processing perspective. A brief introduction to LC/MS is followed by a description of the major processing steps in LC/MS. Specifically, the problem of peak detection is formulated and various peak detection algorithms are described and compared. PMID:20190954

  14. Aberrant Gene Expression in Humans

    PubMed Central

    Yang, Ence; Ji, Guoli; Brinkmeyer-Langford, Candice L.; Cai, James J.

    2015-01-01

    Gene expression as an intermediate molecular phenotype has been a focus of research interest. In particular, studies of expression quantitative trait loci (eQTL) have offered promise for understanding gene regulation through the discovery of genetic variants that explain variation in gene expression levels. Existing eQTL methods are designed for assessing the effects of common variants, but not rare variants. Here, we address the problem by establishing a novel analytical framework for evaluating the effects of rare or private variants on gene expression. Our method starts from the identification of outlier individuals that show markedly different gene expression from the majority of a population, and then reveals the contributions of private SNPs to the aberrant gene expression in these outliers. Using population-scale mRNA sequencing data, we identify outlier individuals using a multivariate approach. We find that outlier individuals are more readily detected with respect to gene sets that include genes involved in cellular regulation and signal transduction, and less likely to be detected with respect to the gene sets with genes involved in metabolic pathways and other fundamental molecular functions. Analysis of polymorphic data suggests that private SNPs of outlier individuals are enriched in the enhancer and promoter regions of corresponding aberrantly-expressed genes, suggesting a specific regulatory role of private SNPs, while the commonly-occurring regulatory genetic variants (i.e., eQTL SNPs) show little evidence of involvement. Additional data suggest that non-genetic factors may also underlie aberrant gene expression. Taken together, our findings advance a novel viewpoint relevant to situations wherein common eQTLs fail to predict gene expression when heritable, rare inter-individual variation exists. The analytical framework we describe, taking into consideration the reality of differential phenotypic robustness, may be valuable for investigating

  15. DNA motifs associated with aberrant CpG island methylation.

    PubMed

    Feltus, F Alex; Lee, Eva K; Costello, Joseph F; Plass, Christoph; Vertino, Paula M

    2006-05-01

    Epigenetic silencing involving the aberrant methylation of promoter region CpG islands is widely recognized as a tumor suppressor silencing mechanism in cancer. However, the molecular pathways underlying aberrant DNA methylation remain elusive. Recently we showed that, on a genome-wide level, CpG island loci differ in their intrinsic susceptibility to aberrant methylation and that this susceptibility can be predicted based on underlying sequence context. These data suggest that there are sequence/structural features that contribute to the protection from or susceptibility to aberrant methylation. Here we use motif elicitation coupled with classification techniques to identify DNA sequence motifs that selectively define methylation-prone or methylation-resistant CpG islands. Motifs common to 28 methylation-prone or 47 methylation-resistant CpG island-containing genomic fragments were determined using the MEME and MAST algorithms (). The five most discriminatory motifs derived from methylation-prone sequences were found to be associated with CpG islands in general and were nonrandomly distributed throughout the genome. In contrast, the eight most discriminatory motifs derived from the methylation-resistant CpG islands were randomly distributed throughout the genome. Interestingly, this latter group tended to associate with Alu and other repetitive sequences. Used together, the frequency of occurrence of these motifs successfully discriminated methylation-prone and methylation-resistant CpG island groups with an accuracy of 87% after 10-fold cross-validation. The motifs identified here are candidate methylation-targeting or methylation-protection DNA sequences.

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

  17. Moving Object Detection Using a Parallax Shift Vector Algorithm

    NASA Astrophysics Data System (ADS)

    Gural, Peter S.; Otto, Paul R.; Tedesco, Edward F.

    2018-07-01

    There are various algorithms currently in use to detect asteroids from ground-based observatories, but they are generally restricted to linear or mildly curved movement of the target object across the field of view. Space-based sensors in high inclination, low Earth orbits can induce significant parallax in a collected sequence of images, especially for objects at the typical distances of asteroids in the inner solar system. This results in a highly nonlinear motion pattern of the asteroid across the sensor, which requires a more sophisticated search pattern for detection processing. Both the classical pattern matching used in ground-based asteroid search and the more sensitive matched filtering and synthetic tracking techniques, can be adapted to account for highly complex parallax motion. A new shift vector generation methodology is discussed along with its impacts on commonly used detection algorithms, processing load, and responsiveness to asteroid track reporting. The matched filter, template generator, and pattern matcher source code for the software described herein are available via GitHub.

  18. Breadth-First Search-Based Single-Phase Algorithms for Bridge Detection in Wireless Sensor Networks

    PubMed Central

    Akram, Vahid Khalilpour; Dagdeviren, Orhan

    2013-01-01

    Wireless sensor networks (WSNs) are promising technologies for exploring harsh environments, such as oceans, wild forests, volcanic regions and outer space. Since sensor nodes may have limited transmission range, application packets may be transmitted by multi-hop communication. Thus, connectivity is a very important issue. A bridge is a critical edge whose removal breaks the connectivity of the network. Hence, it is crucial to detect bridges and take preventions. Since sensor nodes are battery-powered, services running on nodes should consume low energy. In this paper, we propose energy-efficient and distributed bridge detection algorithms for WSNs. Our algorithms run single phase and they are integrated with the Breadth-First Search (BFS) algorithm, which is a popular routing algorithm. Our first algorithm is an extended version of Milic's algorithm, which is designed to reduce the message length. Our second algorithm is novel and uses ancestral knowledge to detect bridges. We explain the operation of the algorithms, analyze their proof of correctness, message, time, space and computational complexities. To evaluate practical importance, we provide testbed experiments and extensive simulations. We show that our proposed algorithms provide less resource consumption, and the energy savings of our algorithms are up by 5.5-times. PMID:23845930

  19. High Precision Edge Detection Algorithm for Mechanical Parts

    NASA Astrophysics Data System (ADS)

    Duan, Zhenyun; Wang, Ning; Fu, Jingshun; Zhao, Wenhui; Duan, Boqiang; Zhao, Jungui

    2018-04-01

    High precision and high efficiency measurement is becoming an imperative requirement for a lot of mechanical parts. So in this study, a subpixel-level edge detection algorithm based on the Gaussian integral model is proposed. For this purpose, the step edge normal section line Gaussian integral model of the backlight image is constructed, combined with the point spread function and the single step model. Then gray value of discrete points on the normal section line of pixel edge is calculated by surface interpolation, and the coordinate as well as gray information affected by noise is fitted in accordance with the Gaussian integral model. Therefore, a precise location of a subpixel edge was determined by searching the mean point. Finally, a gear tooth was measured by M&M3525 gear measurement center to verify the proposed algorithm. The theoretical analysis and experimental results show that the local edge fluctuation is reduced effectively by the proposed method in comparison with the existing subpixel edge detection algorithms. The subpixel edge location accuracy and computation speed are improved. And the maximum error of gear tooth profile total deviation is 1.9 μm compared with measurement result with gear measurement center. It indicates that the method has high reliability to meet the requirement of high precision measurement.

  20. Method for Expressing Clinical and Statistical Significance of Ocular and Corneal Wavefront Error Aberrations

    PubMed Central

    Smolek, Michael K.

    2011-01-01

    Purpose The significance of ocular or corneal aberrations may be subject to misinterpretation whenever eyes with different pupil sizes or the application of different Zernike expansion orders are compared. A method is shown that uses simple mathematical interpolation techniques based on normal data to rapidly determine the clinical significance of aberrations, without concern for pupil and expansion order. Methods Corneal topography (Tomey, Inc.; Nagoya, Japan) from 30 normal corneas was collected and the corneal wavefront error analyzed by Zernike polynomial decomposition into specific aberration types for pupil diameters of 3, 5, 7, and 10 mm and Zernike expansion orders of 6, 8, 10 and 12. Using this 4×4 matrix of pupil sizes and fitting orders, best-fitting 3-dimensional functions were determined for the mean and standard deviation of the RMS error for specific aberrations. The functions were encoded into software to determine the significance of data acquired from non-normal cases. Results The best-fitting functions for 6 types of aberrations were determined: defocus, astigmatism, prism, coma, spherical aberration, and all higher-order aberrations. A clinical screening method of color-coding the significance of aberrations in normal, postoperative LASIK, and keratoconus cases having different pupil sizes and different expansion orders is demonstrated. Conclusions A method to calibrate wavefront aberrometry devices by using a standard sample of normal cases was devised. This method could be potentially useful in clinical studies involving patients with uncontrolled pupil sizes or in studies that compare data from aberrometers that use different Zernike fitting-order algorithms. PMID:22157570

  1. EEG seizure detection and prediction algorithms: a survey

    NASA Astrophysics Data System (ADS)

    Alotaiby, Turkey N.; Alshebeili, Saleh A.; Alshawi, Tariq; Ahmad, Ishtiaq; Abd El-Samie, Fathi E.

    2014-12-01

    Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.

  2. Multispectral fluorescence image algorithms for detection of frass on mature tomatoes

    USDA-ARS?s Scientific Manuscript database

    A multispectral algorithm derived from hyperspectral line-scan fluorescence imaging under violet LED excitation was developed for the detection of frass contamination on mature tomatoes. The algorithm utilized the fluorescence intensities at five wavebands, 515 nm, 640 nm, 664 nm, 690 nm, and 724 nm...

  3. Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks

    NASA Astrophysics Data System (ADS)

    Ghasemian, Amir; Zhang, Pan; Clauset, Aaron; Moore, Cristopher; Peel, Leto

    2016-07-01

    The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information.

  4. Modulation transfer function of a fish-eye lens based on the sixth-order wave aberration theory.

    PubMed

    Jia, Han; Lu, Lijun; Cao, Yiqing

    2018-01-10

    A calculation program of the modulation transfer function (MTF) of a fish-eye lens is developed with the autocorrelation method, in which the sixth-order wave aberration theory of ultra-wide-angle optical systems is used to simulate the wave aberration distribution at the exit pupil of the optical systems. The autocorrelation integral is processed with the Gauss-Legendre integral, and the magnification chromatic aberration is discussed to calculate polychromatic MTF. The MTF calculation results of a given example are then compared with those previously obtained based on the fourth-order wave aberration theory of plane-symmetrical optical systems and with those from the Zemax program. The study shows that MTF based on the sixth-order wave aberration theory has satisfactory calculation accuracy even for a fish-eye lens with a large acceptance aperture. And the impacts of different types of aberrations on the MTF of a fish-eye lens are analyzed. Finally, we apply the self-adaptive and normalized real-coded genetic algorithm and the MTF developed in the paper to optimize the Nikon F/2.8 fish-eye lens; consequently, the optimized system shows better MTF performances than those of the original design.

  5. Real-time implementation of a multispectral mine target detection algorithm

    NASA Astrophysics Data System (ADS)

    Samson, Joseph W.; Witter, Lester J.; Kenton, Arthur C.; Holloway, John H., Jr.

    2003-09-01

    Spatial-spectral anomaly detection (the "RX Algorithm") has been exploited on the USMC's Coastal Battlefield Reconnaissance and Analysis (COBRA) Advanced Technology Demonstration (ATD) and several associated technology base studies, and has been found to be a useful method for the automated detection of surface-emplaced antitank land mines in airborne multispectral imagery. RX is a complex image processing algorithm that involves the direct spatial convolution of a target/background mask template over each multispectral image, coupled with a spatially variant background spectral covariance matrix estimation and inversion. The RX throughput on the ATD was about 38X real time using a single Sun UltraSparc system. A goal to demonstrate RX in real-time was begun in FY01. We now report the development and demonstration of a Field Programmable Gate Array (FPGA) solution that achieves a real-time implementation of the RX algorithm at video rates using COBRA ATD data. The approach uses an Annapolis Microsystems Firebird PMC card containing a Xilinx XCV2000E FPGA with over 2,500,000 logic gates and 18MBytes of memory. A prototype system was configured using a Tek Microsystems VME board with dual-PowerPC G4 processors and two PMC slots. The RX algorithm was translated from its C programming implementation into the VHDL language and synthesized into gates that were loaded into the FPGA. The VHDL/synthesizer approach allows key RX parameters to be quickly changed and a new implementation automatically generated. Reprogramming the FPGA is done rapidly and in-circuit. Implementation of the RX algorithm in a single FPGA is a major first step toward achieving real-time land mine detection.

  6. An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm.

    PubMed

    Qin, Qin; Li, Jianqing; Yue, Yinggao; Liu, Chengyu

    2017-01-01

    R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.

  7. An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

    PubMed Central

    Qin, Qin

    2017-01-01

    R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method. PMID:29104745

  8. ICPD-a new peak detection algorithm for LC/MS.

    PubMed

    Zhang, Jianqiu; Haskins, William

    2010-12-01

    The identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research. Increasing evidence has shown that biomarkers are often low abundance proteins. However, LC/MS systems are subject to considerable noise and sample variability, whose statistical characteristics are still elusive, making computational identification of low abundance proteins extremely challenging. As a result, the inability of identifying low abundance proteins in a proteomic study is the main bottleneck in protein biomarker discovery. In this paper, we propose a new peak detection method called Information Combining Peak Detection (ICPD ) for high resolution LC/MS. In LC/MS, peptides elute during a certain time period and as a result, peptide isotope patterns are registered in multiple MS scans. The key feature of the new algorithm is that the observed isotope patterns registered in multiple scans are combined together for estimating the likelihood of the peptide existence. An isotope pattern matching score based on the likelihood probability is provided and utilized for peak detection. The performance of the new algorithm is evaluated based on protein standards with 48 known proteins. The evaluation shows better peak detection accuracy for low abundance proteins than other LC/MS peak detection methods.

  9. ICPD-A New Peak Detection Algorithm for LC/MS

    PubMed Central

    2010-01-01

    Background The identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research. Increasing evidence has shown that biomarkers are often low abundance proteins. However, LC/MS systems are subject to considerable noise and sample variability, whose statistical characteristics are still elusive, making computational identification of low abundance proteins extremely challenging. As a result, the inability of identifying low abundance proteins in a proteomic study is the main bottleneck in protein biomarker discovery. Results In this paper, we propose a new peak detection method called Information Combining Peak Detection (ICPD ) for high resolution LC/MS. In LC/MS, peptides elute during a certain time period and as a result, peptide isotope patterns are registered in multiple MS scans. The key feature of the new algorithm is that the observed isotope patterns registered in multiple scans are combined together for estimating the likelihood of the peptide existence. An isotope pattern matching score based on the likelihood probability is provided and utilized for peak detection. Conclusions The performance of the new algorithm is evaluated based on protein standards with 48 known proteins. The evaluation shows better peak detection accuracy for low abundance proteins than other LC/MS peak detection methods. PMID:21143790

  10. Identification of random nucleic acid sequence aberrations using dual capture probes which hybridize to different chromosome regions

    DOEpatents

    Lucas, J.N.; Straume, T.; Bogen, K.T.

    1998-03-24

    A method is provided for detecting nucleic acid sequence aberrations using two immobilization steps. According to the method, a nucleic acid sequence aberration is detected by detecting nucleic acid sequences having both a first nucleic acid sequence type (e.g., from a first chromosome) and a second nucleic acid sequence type (e.g., from a second chromosome), the presence of the first and the second nucleic acid sequence type on the same nucleic acid sequence indicating the presence of a nucleic acid sequence aberration. In the method, immobilization of a first hybridization probe is used to isolate a first set of nucleic acids in the sample which contain the first nucleic acid sequence type. Immobilization of a second hybridization probe is then used to isolate a second set of nucleic acids from within the first set of nucleic acids which contain the second nucleic acid sequence type. The second set of nucleic acids are then detected, their presence indicating the presence of a nucleic acid sequence aberration. 14 figs.

  11. Network intrusion detection by the coevolutionary immune algorithm of artificial immune systems with clonal selection

    NASA Astrophysics Data System (ADS)

    Salamatova, T.; Zhukov, V.

    2017-02-01

    The paper presents the application of the artificial immune systems apparatus as a heuristic method of network intrusion detection for algorithmic provision of intrusion detection systems. The coevolutionary immune algorithm of artificial immune systems with clonal selection was elaborated. In testing different datasets the empirical results of evaluation of the algorithm effectiveness were achieved. To identify the degree of efficiency the algorithm was compared with analogs. The fundamental rules based of solutions generated by this algorithm are described in the article.

  12. MSeq-CNV: accurate detection of Copy Number Variation from Sequencing of Multiple samples.

    PubMed

    Malekpour, Seyed Amir; Pezeshk, Hamid; Sadeghi, Mehdi

    2018-03-05

    Currently a few tools are capable of detecting genome-wide Copy Number Variations (CNVs) based on sequencing of multiple samples. Although aberrations in mate pair insertion sizes provide additional hints for the CNV detection based on multiple samples, the majority of the current tools rely only on the depth of coverage. Here, we propose a new algorithm (MSeq-CNV) which allows detecting common CNVs across multiple samples. MSeq-CNV applies a mixture density for modeling aberrations in depth of coverage and abnormalities in the mate pair insertion sizes. Each component in this mixture density applies a Binomial distribution for modeling the number of mate pairs with aberration in the insertion size and also a Poisson distribution for emitting the read counts, in each genomic position. MSeq-CNV is applied on simulated data and also on real data of six HapMap individuals with high-coverage sequencing, in 1000 Genomes Project. These individuals include a CEU trio of European ancestry and a YRI trio of Nigerian ethnicity. Ancestry of these individuals is studied by clustering the identified CNVs. MSeq-CNV is also applied for detecting CNVs in two samples with low-coverage sequencing in 1000 Genomes Project and six samples form the Simons Genome Diversity Project.

  13. Enhancing Time-Series Detection Algorithms for Automated Biosurveillance

    PubMed Central

    Burkom, Howard; Xing, Jian; English, Roseanne; Bloom, Steven; Cox, Kenneth; Pavlin, Julie A.

    2009-01-01

    BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity for detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits from 308 Department of Defense (DoD) facilities and 340 hospital emergency departments (EDs). At a constant alert rate of 1%, sensitivity was improved for both datasets by using a minimum standard deviation (SD) of 1.0, a 14–28 day baseline duration for calculating mean and SD, and an adjustment for total clinic visits as a surrogate denominator. Stratifying baseline days into weekdays versus weekends to account for day-of-week effects increased sensitivity for the DoD data but not for the ED data. These enhanced methods may increase sensitivity without increasing the alert rate and may improve the ability to detect outbreaks by using automated surveillance system data. PMID:19331728

  14. Simple Common Plane contact detection algorithm for FE/FD methods

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

    Vorobiev, O

    2006-07-19

    Common-plane (CP) algorithm is widely used in Discrete Element Method (DEM) to model contact forces between interacting particles or blocks. A new simple contact detection algorithm is proposed to model contacts in FE/FD methods which is similar to the CP algorithm. The CP is defined as a plane separating interacting faces of FE/FD mesh instead of blocks or particles in the original CP method. The method does not require iterations. It is very robust and easy to implement both in 2D and 3D case.

  15. Fault Detection of Aircraft System with Random Forest Algorithm and Similarity Measure

    PubMed Central

    Park, Wookje; Jung, Sikhang

    2014-01-01

    Research on fault detection algorithm was developed with the similarity measure and random forest algorithm. The organized algorithm was applied to unmanned aircraft vehicle (UAV) that was readied by us. Similarity measure was designed by the help of distance information, and its usefulness was also verified by proof. Fault decision was carried out by calculation of weighted similarity measure. Twelve available coefficients among healthy and faulty status data group were used to determine the decision. Similarity measure weighting was done and obtained through random forest algorithm (RFA); RF provides data priority. In order to get a fast response of decision, a limited number of coefficients was also considered. Relation of detection rate and amount of feature data were analyzed and illustrated. By repeated trial of similarity calculation, useful data amount was obtained. PMID:25057508

  16. Evaluation of chronic lymphocytic leukemia by oligonucleotide-based microarray analysis uncovers novel aberrations not detected by FISH or cytogenetic analysis

    PubMed Central

    2011-01-01

    Background Cytogenetic evaluation is a key component of the diagnosis and prognosis of chronic lymphocytic leukemia (CLL). We performed oligonucleotide-based comparative genomic hybridization microarray analysis on 34 samples with CLL and known abnormal karyotypes previously determined by cytogenetics and/or fluorescence in situ hybridization (FISH). Results Using a custom designed microarray that targets >1800 genes involved in hematologic disease and other malignancies, we identified additional cryptic aberrations and novel findings in 59% of cases. These included gains and losses of genes associated with cell cycle regulation, apoptosis and susceptibility loci on 3p21.31, 5q35.2q35.3, 10q23.31q23.33, 11q22.3, and 22q11.23. Conclusions Our results show that microarray analysis will detect known aberrations, including microscopic and cryptic alterations. In addition, novel genomic changes will be uncovered that may become important prognostic predictors or treatment targets for CLL in the future. PMID:22087757

  17. A false-alarm aware methodology to develop robust and efficient multi-scale infrared small target detection algorithm

    NASA Astrophysics Data System (ADS)

    Moradi, Saed; Moallem, Payman; Sabahi, Mohamad Farzan

    2018-03-01

    False alarm rate and detection rate are still two contradictory metrics for infrared small target detection in an infrared search and track system (IRST), despite the development of new detection algorithms. In certain circumstances, not detecting true targets is more tolerable than detecting false items as true targets. Hence, considering background clutter and detector noise as the sources of the false alarm in an IRST system, in this paper, a false alarm aware methodology is presented to reduce false alarm rate while the detection rate remains undegraded. To this end, advantages and disadvantages of each detection algorithm are investigated and the sources of the false alarms are determined. Two target detection algorithms having independent false alarm sources are chosen in a way that the disadvantages of the one algorithm can be compensated by the advantages of the other one. In this work, multi-scale average absolute gray difference (AAGD) and Laplacian of point spread function (LoPSF) are utilized as the cornerstones of the desired algorithm of the proposed methodology. After presenting a conceptual model for the desired algorithm, it is implemented through the most straightforward mechanism. The desired algorithm effectively suppresses background clutter and eliminates detector noise. Also, since the input images are processed through just four different scales, the desired algorithm has good capability for real-time implementation. Simulation results in term of signal to clutter ratio and background suppression factor on real and simulated images prove the effectiveness and the performance of the proposed methodology. Since the desired algorithm was developed based on independent false alarm sources, our proposed methodology is expandable to any pair of detection algorithms which have different false alarm sources.

  18. Aberration-free superresolution imaging via binary speckle pattern encoding and processing

    NASA Astrophysics Data System (ADS)

    Ben-Eliezer, Eyal; Marom, Emanuel

    2007-04-01

    We present an approach that provides superresolution beyond the classical limit as well as image restoration in the presence of aberrations; in particular, the ability to obtain superresolution while extending the depth of field (DOF) simultaneously is tested experimentally. It is based on an approach, recently proposed, shown to increase the resolution significantly for in-focus images by speckle encoding and decoding. In our approach, an object multiplied by a fine binary speckle pattern may be located anywhere along an extended DOF region. Since the exact magnification is not known in the presence of defocus aberration, the acquired low-resolution image is electronically processed via a parallel-branch decoding scheme, where in each branch the image is multiplied by the same high-resolution synchronized time-varying binary speckle but with different magnification. Finally, a hard-decision algorithm chooses the branch that provides the highest-resolution output image, thus achieving insensitivity to aberrations as well as DOF variations. Simulation as well as experimental results are presented, exhibiting significant resolution improvement factors.

  19. Bio-Inspired Distributed Decision Algorithms for Anomaly Detection

    DTIC Science & Technology

    2017-03-01

    TERMS DIAMoND, Local Anomaly Detector, Total Impact Estimation, Threat Level Estimator 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU...21 4.2 Performance of the DIAMoND Algorithm as a DNS-Server Level Attack Detection and Mitigation...with 6 Nodes ........................................................................................ 13 8 Hierarchical 2- Level Topology

  20. Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis: Preprint

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

    Zappala, D.; Tavner, P.; Crabtree, C.

    2013-01-01

    Improving the availability of wind turbines (WT) is critical to minimize the cost of wind energy, especially for offshore installations. As gearbox downtime has a significant impact on WT availabilities, the development of reliable and cost-effective gearbox condition monitoring systems (CMS) is of great concern to the wind industry. Timely detection and diagnosis of developing gear defects within a gearbox is an essential part of minimizing unplanned downtime of wind turbines. Monitoring signals from WT gearboxes are highly non-stationary as turbine load and speed vary continuously with time. Time-consuming and costly manual handling of large amounts of monitoring data representmore » one of the main limitations of most current CMSs, so automated algorithms are required. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. The algorithm allowed the assessment of gear fault severity by tracking progressive tooth gear damage during variable speed and load operating conditions of the test rig. Results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into WT CMSs, this algorithm can automate data interpretation reducing the quantity of information that WT operators must handle.« less

  1. Assessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module

    NASA Technical Reports Server (NTRS)

    Gay, Robert S.

    2011-01-01

    Orion Crew Module (CM) touchdown detection is critical to activating the post-landing sequence that safe?s the Reaction Control Jets (RCS), ensures that the vehicle remains upright, and establishes communication with recovery forces. In order to accommodate safe landing of an unmanned vehicle or incapacitated crew, an onboard automated detection system is required. An Orion-specific touchdown detection algorithm was developed and evaluated to differentiate landing events from in-flight events. The proposed method will be used to initiate post-landing cutting of the parachute riser lines, to prevent CM rollover, and to terminate RCS jet firing prior to submersion. The RCS jets continue to fire until touchdown to maintain proper CM orientation with respect to the flight path and to limit impact loads, but have potentially hazardous consequences if submerged while firing. The time available after impact to cut risers and initiate the CM Up-righting System (CMUS) is measured in minutes, whereas the time from touchdown to RCS jet submersion is a function of descent velocity, sea state conditions, and is often less than one second. Evaluation of the detection algorithms was performed for in-flight events (e.g. descent under chutes) using hi-fidelity rigid body analyses in the Decelerator Systems Simulation (DSS), whereas water impacts were simulated using a rigid finite element model of the Orion CM in LS-DYNA. Two touchdown detection algorithms were evaluated with various thresholds: Acceleration magnitude spike detection, and Accumulated velocity changed (over a given time window) spike detection. Data for both detection methods is acquired from an onboard Inertial Measurement Unit (IMU) sensor. The detection algorithms were tested with analytically generated in-flight and landing IMU data simulations. The acceleration spike detection proved to be faster while maintaining desired safety margin. Time to RCS jet submersion was predicted analytically across a series of

  2. Efficient estimation and large-scale evaluation of lateral chromatic aberration for digital image forensics

    NASA Astrophysics Data System (ADS)

    Gloe, Thomas; Borowka, Karsten; Winkler, Antje

    2010-01-01

    The analysis of lateral chromatic aberration forms another ingredient for a well equipped toolbox of an image forensic investigator. Previous work proposed its application to forgery detection1 and image source identification.2 This paper takes a closer look on the current state-of-the-art method to analyse lateral chromatic aberration and presents a new approach to estimate lateral chromatic aberration in a runtime-efficient way. Employing a set of 11 different camera models including 43 devices, the characteristic of lateral chromatic aberration is investigated in a large-scale. The reported results point to general difficulties that have to be considered in real world investigations.

  3. Genomic aberrations in borderline ovarian tumors

    PubMed Central

    2010-01-01

    Background According to the scientific literature, less than 30 borderline ovarian tumors have been karyotyped and less than 100 analyzed for genomic imbalances by CGH. Methods We report a series of borderline ovarian tumors (n = 23) analyzed by G-banding and karyotyping as well as high resolution CGH; in addition, the tumors were analyzed for microsatellite stability status and by FISH for possible 6q deletion. Results All informative tumors were microsatellite stable and none had a deletion in 6q27. All cases with an abnormal karyotype had simple chromosomal aberrations with +7 and +12 as the most common. In three tumors with single structural rearrangements, a common breakpoint in 3q13 was detected. The major copy number changes detected in the borderline tumors were gains from chromosome arms 2q, 6q, 8q, 9p, and 13q and losses from 1p, 12q, 14q, 15q, 16p, 17p, 17q, 19p, 19q, and 22q. The series included five pairs of bilateral tumors and, in two of these pairs, informative data were obtained as to their clonal relationship. In both pairs, similarities were found between the tumors from the right and left side, strongly indicating that bilaterality had occurred via a metastatic process. The bilateral tumors as a group showed more aberrations than did the unilateral ones, consistent with the view that bilaterality is a sign of more advanced disease. Conclusion Because some of the imbalances found in borderline ovarian tumors seem to be similar to imbalances already known from the more extensively studied overt ovarian carcinomas, we speculate that the subset of borderline tumors with detectable imbalances or karyotypic aberrations may contain a smaller subset of tumors with a tendency to develop a more malignant phenotype. The group of borderline tumors with no imbalances would, in this line of thinking, have less or no propensity for clonal evolution and development to full-blown carcinomas. PMID:20184781

  4. An epileptic seizures detection algorithm based on the empirical mode decomposition of EEG.

    PubMed

    Orosco, Lorena; Laciar, Eric; Correa, Agustina Garces; Torres, Abel; Graffigna, Juan P

    2009-01-01

    Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates the energy of each IMF and performs the detection based on an energy threshold and a minimum duration decision. The algorithm was tested in 9 invasive EEG records provided and validated by the Epilepsy Center of the University Hospital of Freiburg. In 90 segments analyzed (39 with epileptic seizures) the sensitivity and specificity obtained with the method were of 56.41% and 75.86% respectively. It could be concluded that EMD is a promissory method for epileptic seizure detection in EEG records.

  5. Structural and numerical chromosome aberration inducers in liver micronucleus test in rats with partial hepatectomy.

    PubMed

    Itoh, Satoru; Hattori, Chiharu; Nagata, Mayumi; Sanbuissho, Atsushi

    2012-08-30

    The liver micronucleus test is an important method to detect pro-mutagens such as active metabolites not reaching bone marrow due to their short lifespan. We have already reported that dosing of the test compound after partial hepatectomy (PH) is essential to detect genotoxicity of numerical chromosome aberration inducers in mice [Mutat. Res. 632 (2007) 89-98]. In naive animals, the proportion of binucleated cells in rats is less than half of that in mice, which suggests a species difference in the response to chromosome aberration inducers. In the present study, we investigated the responses to structural and numerical chromosome aberration inducers in the rat liver micronucleus test. Two structural chromosome aberretion inducers (diethylnitrosamine and 1,2-dimethylhydrazine) and two numerical chromosome aberration inducers (colchicine and carbendazim) were used in the present study. PH was performed a day before or after the dosing of the test compound in 8-week old male F344 rats and hepatocytes were isolated 4 days after the PH. As a result, diethylnitrosamine and 1,2-dimethylhydrazine, structural chromosome aberration inducers, exhibited significant increase in the incidence of micronucleated hepatocyte (MNH) when given either before and after PH. Colchicine and carbendazim, numerical chromosome aberration inducers, did not result in any toxicologically significant increase in MNH frequency when given before PH, while they exhibited MNH induction when given after PH. It is confirmed that dosing after PH is essential in order to detect genotoxicity of numerical chromosome aberration inducers in rats as well as in mice. Regarding the species difference, a different temporal response to colchicine was identified. Colchicine increased the incidence of MNH 4 days after PH in rats, although such induction in mice was observed 8-10 days after PH. Copyright © 2012 Elsevier B.V. All rights reserved.

  6. Stride search: A general algorithm for storm detection in high-resolution climate data

    DOE PAGES

    Bosler, Peter A.; Roesler, Erika L.; Taylor, Mark A.; ...

    2016-04-13

    This study discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. Two specific implementations of the spatial search algorithm are compared: the commonly used grid point search algorithm is reviewed, and a new algorithm called Stride Search is introduced. The Stride Search algorithm is defined independently of the spatial discretization associated with a particular data set. Results from the two algorithms are compared for the application of tropical cyclonemore » detection, and shown to produce similar results for the same set of storm identification criteria. Differences between the two algorithms arise for some storms due to their different definition of search regions in physical space. The physical space associated with each Stride Search region is constant, regardless of data resolution or latitude, and Stride Search is therefore capable of searching all regions of the globe in the same manner. Stride Search's ability to search high latitudes is demonstrated for the case of polar low detection. Wall clock time required for Stride Search is shown to be smaller than a grid point search of the same data, and the relative speed up associated with Stride Search increases as resolution increases.« less

  7. Competitive evaluation of failure detection algorithms for strapdown redundant inertial instruments

    NASA Technical Reports Server (NTRS)

    Wilcox, J. C.

    1973-01-01

    Algorithms for failure detection, isolation, and correction of redundant inertial instruments in the strapdown dodecahedron configuration are competitively evaluated in a digital computer simulation that subjects them to identical environments. Their performance is compared in terms of orientation and inertial velocity errors and in terms of missed and false alarms. The algorithms appear in the simulation program in modular form, so that they may be readily extracted for use elsewhere. The simulation program and its inputs and outputs are described. The algorithms, along with an eight algorithm that was not simulated, also compared analytically to show the relationships among them.

  8. A Study of Lane Detection Algorithm for Personal Vehicle

    NASA Astrophysics Data System (ADS)

    Kobayashi, Kazuyuki; Watanabe, Kajiro; Ohkubo, Tomoyuki; Kurihara, Yosuke

    By the word “Personal vehicle”, we mean a simple and lightweight vehicle expected to emerge as personal ground transportation devices. The motorcycle, electric wheelchair, motor-powered bicycle, etc. are examples of the personal vehicle and have been developed as the useful for transportation for a personal use. Recently, a new types of intelligent personal vehicle called the Segway has been developed which is controlled and stabilized by using on-board intelligent multiple sensors. The demand for needs for such personal vehicles are increasing, 1) to enhance human mobility, 2) to support mobility for elderly person, 3) reduction of environmental burdens. Since rapidly growing personal vehicles' market, a number of accidents caused by human error is also increasing. The accidents are caused by it's drive ability. To enhance or support drive ability as well as to prevent accidents, intelligent assistance is necessary. One of most important elemental functions for personal vehicle is robust lane detection. In this paper, we develop a robust lane detection method for personal vehicle at outdoor environments. The proposed lane detection method employing a 360 degree omni directional camera and unique robust image processing algorithm. In order to detect lanes, combination of template matching technique and Hough transform are employed. The validity of proposed lane detection algorithm is confirmed by actual developed vehicle at various type of sunshined outdoor conditions.

  9. Performance improvement of multi-class detection using greedy algorithm for Viola-Jones cascade selection

    NASA Astrophysics Data System (ADS)

    Tereshin, Alexander A.; Usilin, Sergey A.; Arlazarov, Vladimir V.

    2018-04-01

    This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.

  10. Target detection using the background model from the topological anomaly detection algorithm

    NASA Astrophysics Data System (ADS)

    Dorado Munoz, Leidy P.; Messinger, David W.; Ziemann, Amanda K.

    2013-05-01

    The Topological Anomaly Detection (TAD) algorithm has been used as an anomaly detector in hyperspectral and multispectral images. TAD is an algorithm based on graph theory that constructs a topological model of the background in a scene, and computes an anomalousness ranking for all of the pixels in the image with respect to the background in order to identify pixels with uncommon or strange spectral signatures. The pixels that are modeled as background are clustered into groups or connected components, which could be representative of spectral signatures of materials present in the background. Therefore, the idea of using the background components given by TAD in target detection is explored in this paper. In this way, these connected components are characterized in three different approaches, where the mean signature and endmembers for each component are calculated and used as background basis vectors in Orthogonal Subspace Projection (OSP) and Adaptive Subspace Detector (ASD). Likewise, the covariance matrix of those connected components is estimated and used in detectors: Constrained Energy Minimization (CEM) and Adaptive Coherence Estimator (ACE). The performance of these approaches and the different detectors is compared with a global approach, where the background characterization is derived directly from the image. Experiments and results using self-test data set provided as part of the RIT blind test target detection project are shown.

  11. Seeing Earth's Orbit in the Stars: Parallax and Aberration

    NASA Astrophysics Data System (ADS)

    Timberlake, Todd K.

    2013-11-01

    During the 17th century the idea of an orbiting and rotating Earth became increasingly popular, but opponents of this view continued to point out that the theory had observable consequences that had never, in fact, been observed.1 Why, for instance, had astronomers failed to detect the annual parallax of the stars that must occur if Earth orbits the Sun? To address this problem, astronomers of the 17th and 18th centuries sought to measure the annual parallax of stars using telescopes. None of them succeeded. Annual stellar parallax was not successfully measured until 1838, when Friedrich Bessel detected the parallax of the star 61 Cygni.2 But the early failures to detect annual stellar parallax led to the discovery of a new (and entirely unexpected) phenomenon: the aberration of starlight. This paper recounts the story of the discovery of stellar aberration. It is accompanied by a set of activities and computer simulations that allow students to explore this fascinating historical episode and learn important lessons about the nature of science.3

  12. MEMS-based sensing and algorithm development for fall detection and gait analysis

    NASA Astrophysics Data System (ADS)

    Gupta, Piyush; Ramirez, Gabriel; Lie, Donald Y. C.; Dallas, Tim; Banister, Ron E.; Dentino, Andrew

    2010-02-01

    Falls by the elderly are highly detrimental to health, frequently resulting in injury, high medical costs, and even death. Using a MEMS-based sensing system, algorithms are being developed for detecting falls and monitoring the gait of elderly and disabled persons. In this study, wireless sensors utilize Zigbee protocols were incorporated into planar shoe insoles and a waist mounted device. The insole contains four sensors to measure pressure applied by the foot. A MEMS based tri-axial accelerometer is embedded in the insert and a second one is utilized by the waist mounted device. The primary fall detection algorithm is derived from the waist accelerometer. The differential acceleration is calculated from samples received in 1.5s time intervals. This differential acceleration provides the quantification via an energy index. From this index one may ascertain different gait and identify fall events. Once a pre-determined index threshold is exceeded, the algorithm will classify an event as a fall or a stumble. The secondary algorithm is derived from frequency analysis techniques. The analysis consists of wavelet transforms conducted on the waist accelerometer data. The insole pressure data is then used to underline discrepancies in the transforms, providing more accurate data for classifying gait and/or detecting falls. The range of the transform amplitude in the fourth iteration of a Daubechies-6 transform was found sufficient to detect and classify fall events.

  13. Vision-based algorithms for near-host object detection and multilane sensing

    NASA Astrophysics Data System (ADS)

    Kenue, Surender K.

    1995-01-01

    Vision-based sensing can be used for lane sensing, adaptive cruise control, collision warning, and driver performance monitoring functions of intelligent vehicles. Current computer vision algorithms are not robust for handling multiple vehicles in highway scenarios. Several new algorithms are proposed for multi-lane sensing, near-host object detection, vehicle cut-in situations, and specifying regions of interest for object tracking. These algorithms were tested successfully on more than 6000 images taken from real-highway scenes under different daytime lighting conditions.

  14. A novel fast phase correlation algorithm for peak wavelength detection of Fiber Bragg Grating sensors.

    PubMed

    Lamberti, A; Vanlanduit, S; De Pauw, B; Berghmans, F

    2014-03-24

    Fiber Bragg Gratings (FBGs) can be used as sensors for strain, temperature and pressure measurements. For this purpose, the ability to determine the Bragg peak wavelength with adequate wavelength resolution and accuracy is essential. However, conventional peak detection techniques, such as the maximum detection algorithm, can yield inaccurate and imprecise results, especially when the Signal to Noise Ratio (SNR) and the wavelength resolution are poor. Other techniques, such as the cross-correlation demodulation algorithm are more precise and accurate but require a considerable higher computational effort. To overcome these problems, we developed a novel fast phase correlation (FPC) peak detection algorithm, which computes the wavelength shift in the reflected spectrum of a FBG sensor. This paper analyzes the performance of the FPC algorithm for different values of the SNR and wavelength resolution. Using simulations and experiments, we compared the FPC with the maximum detection and cross-correlation algorithms. The FPC method demonstrated a detection precision and accuracy comparable with those of cross-correlation demodulation and considerably higher than those obtained with the maximum detection technique. Additionally, FPC showed to be about 50 times faster than the cross-correlation. It is therefore a promising tool for future implementation in real-time systems or in embedded hardware intended for FBG sensor interrogation.

  15. A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data.

    PubMed

    Gregoire, John M; Dale, Darren; van Dover, R Bruce

    2011-01-01

    Peak detection is ubiquitous in the analysis of spectral data. While many noise-filtering algorithms and peak identification algorithms have been developed, recent work [P. Du, W. Kibbe, and S. Lin, Bioinformatics 22, 2059 (2006); A. Wee, D. Grayden, Y. Zhu, K. Petkovic-Duran, and D. Smith, Electrophoresis 29, 4215 (2008)] has demonstrated that both of these tasks are efficiently performed through analysis of the wavelet transform of the data. In this paper, we present a wavelet-based peak detection algorithm with user-defined parameters that can be readily applied to the application of any spectral data. Particular attention is given to the algorithm's resolution of overlapping peaks. The algorithm is implemented for the analysis of powder diffraction data, and successful detection of Bragg peaks is demonstrated for both low signal-to-noise data from theta-theta diffraction of nanoparticles and combinatorial x-ray diffraction data from a composition spread thin film. These datasets have different types of background signals which are effectively removed in the wavelet-based method, and the results demonstrate that the algorithm provides a robust method for automated peak detection.

  16. CLASSICAL AREAS OF PHENOMENOLOGY: Correcting dynamic residual aberrations of conformal optical systems using AO technology

    NASA Astrophysics Data System (ADS)

    Li, Yan; Li, Lin; Huang, Yi-Fan; Du, Bao-Lin

    2009-07-01

    This paper analyses the dynamic residual aberrations of a conformal optical system and introduces adaptive optics (AO) correction technology to this system. The image sharpening AO system is chosen as the correction scheme. Communication between MATLAB and Code V is established via ActiveX technique in computer simulation. The SPGD algorithm is operated at seven zoom positions to calculate the optimized surface shape of the deformable mirror. After comparison of performance of the corrected system with the baseline system, AO technology is proved to be a good way of correcting the dynamic residual aberration in conformal optical design.

  17. A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors

    PubMed Central

    Acevedo-Avila, Ricardo; Gonzalez-Mendoza, Miguel; Garcia-Garcia, Andres

    2016-01-01

    Blob detection is a common task in vision-based applications. Most existing algorithms are aimed at execution on general purpose computers; while very few can be adapted to the computing restrictions present in embedded platforms. This paper focuses on the design of an algorithm capable of real-time blob detection that minimizes system memory consumption. The proposed algorithm detects objects in one image scan; it is based on a linked-list data structure tree used to label blobs depending on their shape and node information. An example application showing the results of a blob detection co-processor has been built on a low-powered field programmable gate array hardware as a step towards developing a smart video surveillance system. The detection method is intended for general purpose application. As such, several test cases focused on character recognition are also examined. The results obtained present a fair trade-off between accuracy and memory requirements; and prove the validity of the proposed approach for real-time implementation on resource-constrained computing platforms. PMID:27240382

  18. A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors.

    PubMed

    Acevedo-Avila, Ricardo; Gonzalez-Mendoza, Miguel; Garcia-Garcia, Andres

    2016-05-28

    Blob detection is a common task in vision-based applications. Most existing algorithms are aimed at execution on general purpose computers; while very few can be adapted to the computing restrictions present in embedded platforms. This paper focuses on the design of an algorithm capable of real-time blob detection that minimizes system memory consumption. The proposed algorithm detects objects in one image scan; it is based on a linked-list data structure tree used to label blobs depending on their shape and node information. An example application showing the results of a blob detection co-processor has been built on a low-powered field programmable gate array hardware as a step towards developing a smart video surveillance system. The detection method is intended for general purpose application. As such, several test cases focused on character recognition are also examined. The results obtained present a fair trade-off between accuracy and memory requirements; and prove the validity of the proposed approach for real-time implementation on resource-constrained computing platforms.

  19. Ridge-branch-based blood vessel detection algorithm for multimodal retinal images

    NASA Astrophysics Data System (ADS)

    Li, Y.; Hutchings, N.; Knighton, R. W.; Gregori, G.; Lujan, B. J.; Flanagan, J. G.

    2009-02-01

    Automatic detection of retinal blood vessels is important to medical diagnoses and imaging. With the development of imaging technologies, various modals of retinal images are available. Few of currently published algorithms are applied to multimodal retinal images. Besides, the performance of algorithms with pathologies is expected to be improved. The purpose of this paper is to propose an automatic Ridge-Branch-Based (RBB) detection algorithm of blood vessel centerlines and blood vessels for multimodal retinal images (color fundus photographs, fluorescein angiograms, fundus autofluorescence images, SLO fundus images and OCT fundus images, for example). Ridges, which can be considered as centerlines of vessel-like patterns, are first extracted. The method uses the connective branching information of image ridges: if ridge pixels are connected, they are more likely to be in the same class, vessel ridge pixels or non-vessel ridge pixels. Thanks to the good distinguishing ability of the designed "Segment-Based Ridge Features", the classifier and its parameters can be easily adapted to multimodal retinal images without ground truth training. We present thorough experimental results on SLO images, color fundus photograph database and other multimodal retinal images, as well as comparison between other published algorithms. Results showed that the RBB algorithm achieved a good performance.

  20. Simple method based on intensity measurements for characterization of aberrations from micro-optical components.

    PubMed

    Perrin, Stephane; Baranski, Maciej; Froehly, Luc; Albero, Jorge; Passilly, Nicolas; Gorecki, Christophe

    2015-11-01

    We report a simple method, based on intensity measurements, for the characterization of the wavefront and aberrations produced by micro-optical focusing elements. This method employs the setup presented earlier in [Opt. Express 22, 13202 (2014)] for measurements of the 3D point spread function, on which a basic phase-retrieval algorithm is applied. This combination allows for retrieval of the wavefront generated by the micro-optical element and, in addition, quantification of the optical aberrations through the wavefront decomposition with Zernike polynomials. The optical setup requires only an in-motion imaging system. The technique, adapted for the optimization of micro-optical component fabrication, is demonstrated by characterizing a planoconvex microlens.

  1. SU-G-IeP4-09: Method of Human Eye Aberration Measurement Using Plenoptic Camera Over Large Field of View

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

    Lv, Yang; Wang, Ruixing; Ma, Haotong

    Purpose: The measurement based on Shack-Hartmann wave-front sensor(WFS), obtaining both the high and low order wave-front aberrations simultaneously and accurately, has been applied in the detection of human eyes aberration in recent years. However, Its application is limited by the small field of view (FOV), slight eye movement leads the optical bacon image exceeds the lenslet array which result in uncertain detection error. To overcome difficulties of precise eye location, the capacity of detecting eye wave-front aberration over FOV much larger than simply a single conjugate Hartmann WFS accurately and simultaneously is demanded. Methods: Plenoptic camera’s lenslet array subdivides themore » aperture light-field in spatial frequency domain, capture the 4-D light-field information. Data recorded by plenoptic cameras can be used to extract the wave-front phases associated to the eyes aberration. The corresponding theoretical model and simulation system is built up in this article to discuss wave-front measurement performance when utilizing plenoptic camera as wave-front sensor. Results: The simulation results indicate that the plenoptic wave-front method can obtain both the high and low order eyes wave-front aberration with the same accuracy as conventional system in single visual angle detectionand over FOV much larger than simply a single conjugate Hartmann systems. Meanwhile, simulation results show that detection of eye aberrations wave-front in different visual angle can be achieved effectively and simultaneously by plenoptic method, by both point and extended optical beacon from the eye. Conclusion: Plenoptic wave-front method possesses the feasibility in eye aberrations wave-front detection. With larger FOV, the method can effectively reduce the detection error brought by imprecise eye location and simplify the eye aberrations wave-front detection system comparing with which based on Shack-Hartmann WFS. Unique advantage of the plenoptic method lies in

  2. A methodology for evaluating detection performance of ultrasonic array imaging algorithms for coarse-grained materials.

    PubMed

    Van Pamel, Anton; Brett, Colin R; Lowe, Michael J S

    2014-12-01

    Improving the ultrasound inspection capability for coarse-grained metals remains of longstanding interest and is expected to become increasingly important for next-generation electricity power plants. Conventional ultrasonic A-, B-, and C-scans have been found to suffer from strong background noise caused by grain scattering, which can severely limit the detection of defects. However, in recent years, array probes and full matrix capture (FMC) imaging algorithms have unlocked exciting possibilities for improvements. To improve and compare these algorithms, we must rely on robust methodologies to quantify their performance. This article proposes such a methodology to evaluate the detection performance of imaging algorithms. For illustration, the methodology is applied to some example data using three FMC imaging algorithms; total focusing method (TFM), phase-coherent imaging (PCI), and decomposition of the time-reversal operator with multiple scattering filter (DORT MSF). However, it is important to note that this is solely to illustrate the methodology; this article does not attempt the broader investigation of different cases that would be needed to compare the performance of these algorithms in general. The methodology considers the statistics of detection, presenting the detection performance as probability of detection (POD) and probability of false alarm (PFA). A test sample of coarse-grained nickel super alloy, manufactured to represent materials used for future power plant components and containing some simple artificial defects, is used to illustrate the method on the candidate algorithms. The data are captured in pulse-echo mode using 64-element array probes at center frequencies of 1 and 5 MHz. In this particular case, it turns out that all three algorithms are shown to perform very similarly when comparing their flaw detection capabilities.

  3. Mask-induced aberration in EUV lithography

    NASA Astrophysics Data System (ADS)

    Nakajima, Yumi; Sato, Takashi; Inanami, Ryoichi; Nakasugi, Tetsuro; Higashiki, Tatsuhiko

    2009-04-01

    We estimated aberrations using Zernike sensitivity analysis. We found the difference of the tolerated aberration with line direction for illumination. The tolerated aberration of perpendicular line for illumination is much smaller than that of parallel line. We consider this difference to be attributable to the mask 3D effect. We call it mask-induced aberration. In the case of the perpendicular line for illumination, there was a difference in CD between right line and left line without aberration. In this report, we discuss the possibility of pattern formation in NA 0.25 generation EUV lithography tool. In perpendicular pattern for EUV light, the dominant part of aberration is mask-induced aberration. In EUV lithography, pattern correction based on the mask topography effect will be more important.

  4. A novel through-wall respiration detection algorithm using UWB radar.

    PubMed

    Li, Xin; Qiao, Dengyu; Li, Ye; Dai, Huhe

    2013-01-01

    Through-wall respiration detection using Ultra-wideband (UWB) impulse radar can be applied to the post-disaster rescue, e.g., searching living persons trapped in ruined buildings after an earthquake. Since strong interference signals always exist in the real-life scenarios, such as static clutter, noise, etc., while the respiratory signal is very weak, the signal to noise and clutter ratio (SNCR) is quite low. Therefore, through-wall respiration detection using UWB impulse radar under low SNCR is a challenging work in the research field of searching survivors after disaster. In this paper, an improved UWB respiratory signal model is built up based on an even power of cosine function for the first time. This model is used to reveal the harmonic structure of respiratory signal, based on which a novel high-performance respiration detection algorithm is proposed. This novel algorithm is assessed by experimental verification and simulation and shows about a 1.5dB improvement of SNR and SNCR.

  5. Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering

    NASA Astrophysics Data System (ADS)

    Kataoka, Shun; Kobayashi, Takuto; Yasuda, Muneki; Tanaka, Kazuyuki

    2016-11-01

    We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data. Suppose we have the network structure together with the vertex attribute data, that is, the information assigned to each vertex associated with the community to which it belongs. The problem addressed this paper is the detection of the community structure from the information of both the network structure and the vertex attribute data. Our approach is based on the Bayesian approach that models the posterior probability distribution of the community labels. The detection of the community structure in our method is achieved by using belief propagation and an EM algorithm. We numerically verified the performance of our method using computer-generated networks and real-world networks.

  6. CHAMP: a locally adaptive unmixing-based hyperspectral anomaly detection algorithm

    NASA Astrophysics Data System (ADS)

    Crist, Eric P.; Thelen, Brian J.; Carrara, David A.

    1998-10-01

    Anomaly detection offers a means by which to identify potentially important objects in a scene without prior knowledge of their spectral signatures. As such, this approach is less sensitive to variations in target class composition, atmospheric and illumination conditions, and sensor gain settings than would be a spectral matched filter or similar algorithm. The best existing anomaly detectors generally fall into one of two categories: those based on local Gaussian statistics, and those based on linear mixing moles. Unmixing-based approaches better represent the real distribution of data in a scene, but are typically derived and applied on a global or scene-wide basis. Locally adaptive approaches allow detection of more subtle anomalies by accommodating the spatial non-homogeneity of background classes in a typical scene, but provide a poorer representation of the true underlying background distribution. The CHAMP algorithm combines the best attributes of both approaches, applying a linear-mixing model approach in a spatially adaptive manner. The algorithm itself, and teste results on simulated and actual hyperspectral image data, are presented in this paper.

  7. Algorithm for lung cancer detection based on PET/CT images

    NASA Astrophysics Data System (ADS)

    Saita, Shinsuke; Ishimatsu, Keita; Kubo, Mitsuru; Kawata, Yoshiki; Niki, Noboru; Ohtsuka, Hideki; Nishitani, Hiromu; Ohmatsu, Hironobu; Eguchi, Kenji; Kaneko, Masahiro; Moriyama, Noriyuki

    2009-02-01

    The five year survival rate of the lung cancer is low with about twenty-five percent. In addition it is an obstinate lung cancer wherein three out of four people die within five years. Then, the early stage detection and treatment of the lung cancer are important. Recently, we can obtain CT and PET image at the same time because PET/CT device has been developed. PET/CT is possible for a highly accurate cancer diagnosis because it analyzes quantitative shape information from CT image and FDG distribution from PET image. However, neither benign-malignant classification nor staging intended for lung cancer have been established still enough by using PET/CT images. In this study, we detect lung nodules based on internal organs extracted from CT image, and we also develop algorithm which classifies benignmalignant and metastatic or non metastatic lung cancer using lung structure and FDG distribution(one and two hour after administering FDG). We apply the algorithm to 59 PET/CT images (malignant 43 cases [Ad:31, Sq:9, sm:3], benign 16 cases) and show the effectiveness of this algorithm.

  8. Detection of cracks in shafts with the Approximated Entropy algorithm

    NASA Astrophysics Data System (ADS)

    Sampaio, Diego Luchesi; Nicoletti, Rodrigo

    2016-05-01

    The Approximate Entropy is a statistical calculus used primarily in the fields of Medicine, Biology, and Telecommunication for classifying and identifying complex signal data. In this work, an Approximate Entropy algorithm is used to detect cracks in a rotating shaft. The signals of the cracked shaft are obtained from numerical simulations of a de Laval rotor with breathing cracks modelled by the Fracture Mechanics. In this case, one analysed the vertical displacements of the rotor during run-up transients. The results show the feasibility of detecting cracks from 5% depth, irrespective of the unbalance of the rotating system and crack orientation in the shaft. The results also show that the algorithm can differentiate the occurrence of crack only, misalignment only, and crack + misalignment in the system. However, the algorithm is sensitive to intrinsic parameters p (number of data points in a sample vector) and f (fraction of the standard deviation that defines the minimum distance between two sample vectors), and good results are only obtained by appropriately choosing their values according to the sampling rate of the signal.

  9. Runway Safety Monitor Algorithm for Runway Incursion Detection and Alerting

    NASA Technical Reports Server (NTRS)

    Green, David F., Jr.; Jones, Denise R. (Technical Monitor)

    2002-01-01

    The Runway Safety Monitor (RSM) is an algorithm for runway incursion detection and alerting that was developed in support of NASA's Runway Incursion Prevention System (RIPS) research conducted under the NASA Aviation Safety Program's Synthetic Vision System element. The RSM algorithm provides pilots with enhanced situational awareness and warnings of runway incursions in sufficient time to take evasive action and avoid accidents during landings, takeoffs, or taxiing on the runway. The RSM currently runs as a component of the NASA Integrated Display System, an experimental avionics software system for terminal area and surface operations. However, the RSM algorithm can be implemented as a separate program to run on any aircraft with traffic data link capability. The report documents the RSM software and describes in detail how RSM performs runway incursion detection and alerting functions for NASA RIPS. The report also describes the RIPS flight tests conducted at the Dallas-Ft Worth International Airport (DFW) during September and October of 2000, and the RSM performance results and lessons learned from those flight tests.

  10. Diversity in Detection Algorithms for Atmospheric Rivers: A Community Effort to Understand the Consequences

    NASA Astrophysics Data System (ADS)

    Shields, C. A.; Ullrich, P. A.; Rutz, J. J.; Wehner, M. F.; Ralph, M.; Ruby, L.

    2017-12-01

    Atmospheric rivers (ARs) are long, narrow filamentary structures that transport large amounts of moisture in the lower layers of the atmosphere, typically from subtropical regions to mid-latitudes. ARs play an important role in regional hydroclimate by supplying significant amounts of precipitation that can alleviate drought, or in extreme cases, produce dangerous floods. Accurately detecting, or tracking, ARs is important not only for weather forecasting, but is also necessary to understand how these events may change under global warming. Detection algorithms are used on both regional and global scales, and most accurately, using high resolution datasets, or model output. Different detection algorithms can produce different answers. Detection algorithms found in the current literature fall broadly into two categories: "time-stitching", where the AR is tracked with a Lagrangian approach through time and space; and "counting", where ARs are identified for a single point in time for a single location. Counting routines can be further subdivided into algorithms that use absolute thresholds with specific geometry, to algorithms that use relative thresholds, to algorithms based on statistics, to pattern recognition and machine learning techniques. With such a large diversity in detection code, differences in AR tracking and "counts" can vary widely from technique to technique. Uncertainty increases for future climate scenarios, where the difference between relative and absolute thresholding produce vastly different counts, simply due to the moister background state in a warmer world. In an effort to quantify the uncertainty associated with tracking algorithms, the AR detection community has come together to participate in ARTMIP, the Atmospheric River Tracking Method Intercomparison Project. Each participant will provide AR metrics to the greater group by applying their code to a common reanalysis dataset. MERRA2 data was chosen for both temporal and spatial resolution

  11. A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data.

    PubMed

    Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

    2017-06-01

    Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

  12. An Automated Energy Detection Algorithm Based on Kurtosis-Histogram Excision

    DTIC Science & Technology

    2018-01-01

    ARL-TR-8269 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection Algorithm Based on Kurtosis-Histogram Excision...needed. Do not return it to the originator. ARL-TR-8269 ● JAN 2018 US Army Research Laboratory An Automated Energy Detection...collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources

  13. An exact computational method for performance analysis of sequential test algorithms for detecting network intrusions

    NASA Astrophysics Data System (ADS)

    Chen, Xinjia; Lacy, Fred; Carriere, Patrick

    2015-05-01

    Sequential test algorithms are playing increasingly important roles for quick detecting network intrusions such as portscanners. In view of the fact that such algorithms are usually analyzed based on intuitive approximation or asymptotic analysis, we develop an exact computational method for the performance analysis of such algorithms. Our method can be used to calculate the probability of false alarm and average detection time up to arbitrarily pre-specified accuracy.

  14. Development of a novel constellation based landmark detection algorithm

    NASA Astrophysics Data System (ADS)

    Ghayoor, Ali; Vaidya, Jatin G.; Johnson, Hans J.

    2013-03-01

    Anatomical landmarks such as the anterior commissure (AC) and posterior commissure (PC) are commonly used by researchers for co-registration of images. In this paper, we present a novel, automated approach for landmark detection that combines morphometric constraining and statistical shape models to provide accurate estimation of landmark points. This method is made robust to large rotations in initial head orientation by extracting extra information of the eye centers using a radial Hough transform and exploiting the centroid of head mass (CM) using a novel estimation approach. To evaluate the effectiveness of this method, the algorithm is trained on a set of 20 images with manually selected landmarks, and a test dataset is used to compare the automatically detected against the manually detected landmark locations of the AC, PC, midbrain-pons junction (MPJ), and fourth ventricle notch (VN4). The results show that the proposed method is accurate as the average error between the automatically and manually labeled landmark points is less than 1 mm. Also, the algorithm is highly robust as it was successfully run on a large dataset that included different kinds of images with various orientation, spacing, and origin.

  15. Endobronchial angiofibroma in the aberrant tracheal bronchus presenting as spontaneous pneumomediastinum.

    PubMed

    Kim, Kyung Soo; Moon, Young Kyu; Jeon, Hyun Woo; Park, Chan Beom; Ahn, Myeong Im; Lee, Kyo Young; Park, Jae Kil

    2015-07-22

    Spontaneous pneumomediastinum is a self-limiting benign disease but abnormal bronchial lesions can be rarely found incidentally, and in selected cases will require surgical resection. A 38-year-old man presented with a spontaneous pneumomediastinum. Chest computed tomography revealed an incidental linear endobronchial tumour in the aberrant tracheal bronchus. The tumour was removed surgically and diagnosed with a rare benign tumour of endobronchial angiofibroma. We report a rare case of endobronchial angiofibroma in the aberrant tracheal bronchus which was detected during the evaluation of a spontaneous pneumomediastinum.

  16. A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time-Frequency Spectral Features.

    PubMed

    Dao, Duy; Salehizadeh, S M A; Noh, Yeonsik; Chong, Jo Woon; Cho, Chae Ho; McManus, Dave; Darling, Chad E; Mendelson, Yitzhak; Chon, Ki H

    2017-09-01

    Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study, we present a novel approach, "TifMA," based on using the time-frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data. The term "nonusable" refers to segments of PPG data from which the HR signal cannot be recovered accurately. Two sequential classification procedures were included in the TifMA algorithm. The first classifier distinguishes between MNA-corrupted and MNA-free PPG data. Once a segment of data is deemed MNA-corrupted, the next classifier determines whether the HR can be recovered from the corrupted segment or not. A support vector machine (SVM) classifier was used to build a decision boundary for the first classification task using data segments from a training dataset. Features from time-frequency spectra of PPG were extracted to build the detection model. Five datasets were considered for evaluating TifMA performance: (1) and (2) were laboratory-controlled PPG recordings from forehead and finger pulse oximeter sensors with subjects making random movements, (3) and (4) were actual patient PPG recordings from UMass Memorial Medical Center with random free movements and (5) was a laboratory-controlled PPG recording dataset measured at the forehead while the subjects ran on a treadmill. The first dataset was used to analyze the noise sensitivity of the algorithm. Datasets 2-4 were used to evaluate the MNA detection phase of the algorithm. The results from the first phase of the algorithm (MNA detection) were compared to results from three existing MNA detection algorithms: the Hjorth, kurtosis-Shannon entropy, and time-domain variability-SVM approaches. This last is an approach

  17. Bladed wheels damage detection through Non-Harmonic Fourier Analysis improved algorithm

    NASA Astrophysics Data System (ADS)

    Neri, P.

    2017-05-01

    Recent papers introduced the Non-Harmonic Fourier Analysis for bladed wheels damage detection. This technique showed its potential in estimating the frequency of sinusoidal signals even when the acquisition time is short with respect to the vibration period, provided that some hypothesis are fulfilled. Anyway, previously proposed algorithms showed severe limitations in cracks detection at their early stage. The present paper proposes an improved algorithm which allows to detect a blade vibration frequency shift due to a crack whose size is really small compared to the blade width. Such a technique could be implemented for condition-based maintenance, allowing to use non-contact methods for vibration measurements. A stator-fixed laser sensor could monitor all the blades as they pass in front of the spot, giving precious information about the wheel health. This configuration determines an acquisition time for each blade which become shorter as the machine rotational speed increases. In this situation, traditional Discrete Fourier Transform analysis results in poor frequency resolution, being not suitable for small frequency shift detection. Non-Harmonic Fourier Analysis instead showed high reliability in vibration frequency estimation even with data samples collected in a short time range. A description of the improved algorithm is provided in the paper, along with a comparison with the previous one. Finally, a validation of the method is presented, based on finite element simulations results.

  18. Validation of an automated seizure detection algorithm for term neonates

    PubMed Central

    Mathieson, Sean R.; Stevenson, Nathan J.; Low, Evonne; Marnane, William P.; Rennie, Janet M.; Temko, Andrey; Lightbody, Gordon; Boylan, Geraldine B.

    2016-01-01

    Objective The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres. Methods EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed. Results Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6–75.0%, with false detection (FD) rates of 0.04–0.36 FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen’s Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures. Conclusion The SDA achieved promising performance and warrants further testing in a live clinical evaluation. Significance The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens. PMID:26055336

  19. Virtual-Lattice Based Intrusion Detection Algorithm over Actuator-Assisted Underwater Wireless Sensor Networks

    PubMed Central

    Yan, Jing; Li, Xiaolei; Luo, Xiaoyuan; Guan, Xinping

    2017-01-01

    Due to the lack of a physical line of defense, intrusion detection becomes one of the key issues in applications of underwater wireless sensor networks (UWSNs), especially when the confidentiality has prime importance. However, the resource-constrained property of UWSNs such as sparse deployment and energy constraint makes intrusion detection a challenging issue. This paper considers a virtual-lattice-based approach to the intrusion detection problem in UWSNs. Different from most existing works, the UWSNs consist of two kinds of nodes, i.e., sensor nodes (SNs), which cannot move autonomously, and actuator nodes (ANs), which can move autonomously according to the performance requirement. With the cooperation of SNs and ANs, the intruder detection probability is defined. Then, a virtual lattice-based monitor (VLM) algorithm is proposed to detect the intruder. In order to reduce the redundancy of communication links and improve detection probability, an optimal and coordinative lattice-based monitor patrolling (OCLMP) algorithm is further provided for UWSNs, wherein an equal price search strategy is given for ANs to find the shortest patrolling path. Under VLM and OCLMP algorithms, the detection probabilities are calculated, while the topology connectivity can be guaranteed. Finally, simulation results are presented to show that the proposed method in this paper can improve the detection accuracy and save the energy consumption compared with the conventional methods. PMID:28531127

  20. Modification of YAPE keypoint detection algorithm for wide local contrast range images

    NASA Astrophysics Data System (ADS)

    Lukoyanov, A.; Nikolaev, D.; Konovalenko, I.

    2018-04-01

    Keypoint detection is an important tool of image analysis, and among many contemporary keypoint detection algorithms YAPE is known for its computational performance, allowing its use in mobile and embedded systems. One of its shortcomings is high sensitivity to local contrast which leads to high detection density in high-contrast areas while missing detections in low-contrast ones. In this work we study the contrast sensitivity of YAPE and propose a modification which compensates for this property on images with wide local contrast range (Yet Another Contrast-Invariant Point Extractor, YACIPE). As a model example, we considered the traffic sign recognition problem, where some signs are well-lighted, whereas others are in shadows and thus have low contrast. We show that the number of traffic signs on the image of which has not been detected any keypoints is 40% less for the proposed modification compared to the original algorithm.

  1. LPA-CBD an improved label propagation algorithm based on community belonging degree for community detection

    NASA Astrophysics Data System (ADS)

    Gui, Chun; Zhang, Ruisheng; Zhao, Zhili; Wei, Jiaxuan; Hu, Rongjing

    In order to deal with stochasticity in center node selection and instability in community detection of label propagation algorithm, this paper proposes an improved label propagation algorithm named label propagation algorithm based on community belonging degree (LPA-CBD) that employs community belonging degree to determine the number and the center of community. The general process of LPA-CBD is that the initial community is identified by the nodes with the maximum degree, and then it is optimized or expanded by community belonging degree. After getting the rough structure of network community, the remaining nodes are labeled by using label propagation algorithm. The experimental results on 10 real-world networks and three synthetic networks show that LPA-CBD achieves reasonable community number, better algorithm accuracy and higher modularity compared with other four prominent algorithms. Moreover, the proposed algorithm not only has lower algorithm complexity and higher community detection quality, but also improves the stability of the original label propagation algorithm.

  2. Label propagation algorithm for community detection based on node importance and label influence

    NASA Astrophysics Data System (ADS)

    Zhang, Xian-Kun; Ren, Jing; Song, Chen; Jia, Jia; Zhang, Qian

    2017-09-01

    Recently, the detection of high-quality community has become a hot spot in the research of social network. Label propagation algorithm (LPA) has been widely concerned since it has the advantages of linear time complexity and is unnecessary to define objective function and the number of community in advance. However, LPA has the shortcomings of uncertainty and randomness in the label propagation process, which affects the accuracy and stability of the community. For large-scale social network, this paper proposes a novel label propagation algorithm for community detection based on node importance and label influence (LPA_NI). The experiments with comparative algorithms on real-world networks and synthetic networks have shown that LPA_NI can significantly improve the quality of community detection and shorten the iteration period. Also, it has better accuracy and stability in the case of similar complexity.

  3. Development of a fire detection algorithm for the COMS (Communication Ocean and Meteorological Satellite)

    NASA Astrophysics Data System (ADS)

    Kim, Goo; Kim, Dae Sun; Lee, Yang-Won

    2013-10-01

    The forest fires do much damage to our life in ecological and economic aspects. South Korea is probably more liable to suffer from the forest fire because mountain area occupies more than half of land in South Korea. They have recently launched the COMS(Communication Ocean and Meteorological Satellite) which is a geostationary satellite. In this paper, we developed forest fire detection algorithm using COMS data. Generally, forest fire detection algorithm uses characteristics of 4 and 11 micrometer brightness temperature. Our algorithm additionally uses LST(Land Surface Temperature). We confirmed the result of our fire detection algorithm using statistical data of Korea Forest Service and ASTER(Advanced Spaceborne Thermal Emission and Reflection Radiometer) images. We used the data in South Korea On April 1 and 2, 2011 because there are small and big forest fires at that time. The detection rate was 80% in terms of the frequency of the forest fires and was 99% in terms of the damaged area. Considering the number of COMS's channels and its low resolution, this result is a remarkable outcome. To provide users with the result of our algorithm, we developed a smartphone application for users JSP(Java Server Page). This application can work regardless of the smartphone's operating system. This study can be unsuitable for other areas and days because we used just two days data. To improve the accuracy of our algorithm, we need analysis using long-term data as future work.

  4. Sequential structural damage diagnosis algorithm using a change point detection method

    NASA Astrophysics Data System (ADS)

    Noh, H.; Rajagopal, R.; Kiremidjian, A. S.

    2013-11-01

    This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method. The general change point detection method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori, unless we are looking for a known specific type of damage. Therefore, we introduce an additional algorithm that estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using a set of experimental data collected from a four-story steel special moment-resisting frame and multiple sets of simulated data. Various features of different dimensions have been explored, and the algorithm was able to identify damage, particularly when it uses multidimensional damage sensitive features and lower false alarm rates, with a known post-damage feature distribution. For unknown feature distribution cases, the post-damage distribution was consistently estimated and the detection delays were only a few time steps longer than the delays from the general method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

  5. Cloud detection algorithm comparison and validation for operational Landsat data products

    USGS Publications Warehouse

    Foga, Steven Curtis; Scaramuzza, Pat; Guo, Song; Zhu, Zhe; Dilley, Ronald; Beckmann, Tim; Schmidt, Gail L.; Dwyer, John L.; Hughes, MJ; Laue, Brady

    2017-01-01

    Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate

  6. Algorithms for detecting and predicting influenza outbreaks: metanarrative review of prospective evaluations

    PubMed Central

    Spreco, A; Timpka, T

    2016-01-01

    Objectives Reliable monitoring of influenza seasons and pandemic outbreaks is essential for response planning, but compilations of reports on detection and prediction algorithm performance in influenza control practice are largely missing. The aim of this study is to perform a metanarrative review of prospective evaluations of influenza outbreak detection and prediction algorithms restricted settings where authentic surveillance data have been used. Design The study was performed as a metanarrative review. An electronic literature search was performed, papers selected and qualitative and semiquantitative content analyses were conducted. For data extraction and interpretations, researcher triangulation was used for quality assurance. Results Eight prospective evaluations were found that used authentic surveillance data: three studies evaluating detection and five studies evaluating prediction. The methodological perspectives and experiences from the evaluations were found to have been reported in narrative formats representing biodefence informatics and health policy research, respectively. The biodefence informatics narrative having an emphasis on verification of technically and mathematically sound algorithms constituted a large part of the reporting. Four evaluations were reported as health policy research narratives, thus formulated in a manner that allows the results to qualify as policy evidence. Conclusions Awareness of the narrative format in which results are reported is essential when interpreting algorithm evaluations from an infectious disease control practice perspective. PMID:27154479

  7. A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

    PubMed Central

    Thounaojam, Dalton Meitei; Khelchandra, Thongam; Singh, Kh. Manglem; Roy, Sudipta

    2016-01-01

    This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500

  8. The Electrooculogram and a New Blink Detection Algorithm

    DTIC Science & Technology

    2015-10-30

    applications, and physiological monitoring has proven quite helpful with this assessment. One such physiological signal , the electrooculogram ( EOG ...significantly improve performance. One such physiological signal , the electrooculogram ( EOG ), can provide blink rate and blink duration measures. Blink...that such variability substantiates the need for blink detection algorithms, using the EOG signal , that are robust to noise, artifacts, and intra- and

  9. Localization of tumors in various organs, using edge detection algorithms

    NASA Astrophysics Data System (ADS)

    López Vélez, Felipe

    2015-09-01

    The edge of an image is a set of points organized in a curved line, where in each of these points the brightness of the image changes abruptly, or has discontinuities, in order to find these edges there will be five different mathematical methods to be used and later on compared with its peers, this is with the aim of finding which of the methods is the one that can find the edges of any given image. In this paper these five methods will be used for medical purposes in order to find which one is capable of finding the edges of a scanned image more accurately than the others. The problem consists in analyzing the following two biomedicals images. One of them represents a brain tumor and the other one a liver tumor. These images will be analyzed with the help of the five methods described and the results will be compared in order to determine the best method to be used. It was decided to use different algorithms of edge detection in order to obtain the results shown below; Bessel algorithm, Morse algorithm, Hermite algorithm, Weibull algorithm and Sobel algorithm. After analyzing the appliance of each of the methods to both images it's impossible to determine the most accurate method for tumor detection due to the fact that in each case the best method changed, i.e., for the brain tumor image it can be noticed that the Morse method was the best at finding the edges of the image but for the liver tumor image it was the Hermite method. Making further observations it is found that Hermite and Morse have for these two cases the lowest standard deviations, concluding that these two are the most accurate method to find the edges in analysis of biomedical images.

  10. Genetic algorithm in the structural design of Cooke triplet lenses

    NASA Astrophysics Data System (ADS)

    Hazra, Lakshminarayan; Banerjee, Saswatee

    1999-08-01

    This paper is in tune with our efforts to develop a systematic method for multicomponent lens design. Our aim is to find a suitable starting point in the final configuration space, so that popular local search methods like damped least squares (DLS) may directly lead to a useful solution. For 'ab initio' design problems, a thin lens layout specifying the powers of the individual components and the intercomponent separations are worked out analytically. Requirements of central aberration targets for the individual components in order to satisfy the prespecified primary aberration targets for the overall system are then determined by nonlinear optimization. The next step involves structural design of the individual components by optimization techniques. This general method may be adapted for the design of triplets and their derivatives. However, for the thin lens design of a Cooke triplet composed of three airspaced singlets, the two steps of optimization mentioned above may be combined into a single optimization procedure. The optimum configuration for each of the single set, catering to the required Gaussian specification and primary aberration targets for the Cooke triplet, are determined by an application of genetic algorithm (GA). Our implementation of this algorithm is based on simulations of some complex tools of natural evolution, like selection, crossover and mutation. Our version of GA may or may not converge to a unique optimum, depending on some of the algorithm specific parameter values. With our algorithm, practically useful solutions are always available, although convergence to a global optimum can not be guaranteed. This is perfectly in keeping with our need to allow 'floating' of aberration targets in the subproblem level. Some numerical results dealing with our preliminary investigations on this problem are presented.

  11. The VLBA Extragalactic Proper Motion Catalog and a Measurement of the Secular Aberration Drift

    NASA Astrophysics Data System (ADS)

    Truebenbach, Alexandra E.; Darling, Jeremy

    2017-11-01

    We present a catalog of extragalactic proper motions created using archival VLBI data and our own VLBA astrometry. The catalog contains 713 proper motions, with average uncertainties of ˜24 μas yr-1, including 40 new or improved proper motion measurements using relative astrometry with the VLBA. The observations were conducted in the X-band and yielded positions with uncertainties of ˜70 μas. We add 10 new redshifts using spectroscopic observations taken at Apache Point Observatory and Gemini North. With the VLBA Extragalactic Proper Motion Catalog, we detect the secular aberration drift—the apparent motion of extragalactic objects caused by the solar system’s acceleration around the Galactic center—at a 6.3σ significance. We model the aberration drift as a spheroidal dipole, with the square root of the power equal to 4.89 ± 0.77 μas yr-1, an amplitude of 1.69 ± 0.27 μas yr-1, and an apex at (275\\buildrel{\\circ}\\over{.} 2+/- 10\\buildrel{\\circ}\\over{.} 0, -29\\buildrel{\\circ}\\over{.} 4+/- 8\\buildrel{\\circ}\\over{.} 8). Our dipole model detects the aberration drift at a higher significance than some previous studies, but at a lower amplitude than expected or previously measured. The full aberration drift may be partially removed by the no-net-rotation constraint used when measuring archival extragalactic radio source positions. Like the cosmic microwave background dipole, which is induced by the observer’s motion, the aberration drift signal should be subtracted from extragalactic proper motions in order to detect cosmological proper motions, including the Hubble expansion, long-period stochastic gravitational waves, and the collapse of large-scale structure.

  12. 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.

  13. Hidden Markov Model-Based CNV Detection Algorithms for Illumina Genotyping Microarrays.

    PubMed

    Seiser, Eric L; Innocenti, Federico

    2014-01-01

    Somatic alterations in DNA copy number have been well studied in numerous malignancies, yet the role of germline DNA copy number variation in cancer is still emerging. Genotyping microarrays generate allele-specific signal intensities to determine genotype, but may also be used to infer DNA copy number using additional computational approaches. Numerous tools have been developed to analyze Illumina genotype microarray data for copy number variant (CNV) discovery, although commonly utilized algorithms freely available to the public employ approaches based upon the use of hidden Markov models (HMMs). QuantiSNP, PennCNV, and GenoCN utilize HMMs with six copy number states but vary in how transition and emission probabilities are calculated. Performance of these CNV detection algorithms has been shown to be variable between both genotyping platforms and data sets, although HMM approaches generally outperform other current methods. Low sensitivity is prevalent with HMM-based algorithms, suggesting the need for continued improvement in CNV detection methodologies.

  14. [Tachycardia detection in implantable cardioverter-defibrillators by Sorin/LivaNova : Algorithms, pearls and pitfalls].

    PubMed

    Kolb, Christof; Ocklenburg, Rolf

    2016-09-01

    For physicians involved in the treatment of patients with implantable cardioverter-defibrillators (ICDs) the knowledge of tachycardia detection algorithms is of paramount importance. This knowledge is essential for adequate device selection during de-novo implantation, ICD replacement, and for troubleshooting during follow-up. This review describes tachycardia detection algorithms incorporated in ICDs by Sorin/LivaNova and analyses their strengths and weaknesses.

  15. A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data

    NASA Astrophysics Data System (ADS)

    Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

    2017-06-01

    Objective. Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. Approach. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. Main results. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. Significance. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

  16. Automated seismic detection of landslides at regional scales: a Random Forest based detection algorithm

    NASA Astrophysics Data System (ADS)

    Hibert, C.; Michéa, D.; Provost, F.; Malet, J. P.; Geertsema, M.

    2017-12-01

    Detection of landslide occurrences and measurement of their dynamics properties during run-out is a high research priority but a logistical and technical challenge. Seismology has started to help in several important ways. Taking advantage of the densification of global, regional and local networks of broadband seismic stations, recent advances now permit the seismic detection and location of landslides in near-real-time. This seismic detection could potentially greatly increase the spatio-temporal resolution at which we study landslides triggering, which is critical to better understand the influence of external forcings such as rainfalls and earthquakes. However, detecting automatically seismic signals generated by landslides still represents a challenge, especially for events with small mass. The low signal-to-noise ratio classically observed for landslide-generated seismic signals and the difficulty to discriminate these signals from those generated by regional earthquakes or anthropogenic and natural noises are some of the obstacles that have to be circumvented. We present a new method for automatically constructing instrumental landslide catalogues from continuous seismic data. We developed a robust and versatile solution, which can be implemented in any context where a seismic detection of landslides or other mass movements is relevant. The method is based on a spectral detection of the seismic signals and the identification of the sources with a Random Forest machine learning algorithm. The spectral detection allows detecting signals with low signal-to-noise ratio, while the Random Forest algorithm achieve a high rate of positive identification of the seismic signals generated by landslides and other seismic sources. The processing chain is implemented to work in a High Performance Computers centre which permits to explore years of continuous seismic data rapidly. We present here the preliminary results of the application of this processing chain for years

  17. Identification of Copy Number Aberrations in Breast Cancer Subtypes Using Persistence Topology

    PubMed Central

    Arsuaga, Javier; Borrman, Tyler; Cavalcante, Raymond; Gonzalez, Georgina; Park, Catherine

    2015-01-01

    DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Identification of tumor-driving CNAs (driver CNAs) however remains a challenging task, because they are frequently hidden by CNAs that are the product of random events that take place during tumor evolution. Experimental detection of CNAs is commonly accomplished through array comparative genomic hybridization (aCGH) assays followed by supervised and/or unsupervised statistical methods that combine the segmented profiles of all patients to identify driver CNAs. Here, we extend a previously-presented supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a two-dimensional (2D) point cloud with each aCGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph. This representation of the data permits segmenting the data at different resolutions and identifying CNAs by interrogating the topological properties of these simplicial complexes. We tested our approach on a published dataset with the goal of identifying specific breast cancer CNAs associated with specific molecular subtypes. Identification of CNAs associated with each subtype was performed by analyzing each subtype separately from the others and by taking the rest of the subtypes as the control. Our results found a new amplification in 11q at the location of the progesterone receptor in the Luminal A subtype. Aberrations in the Luminal B subtype were found only upon removal of the basal-like subtype from the control set. Under those conditions, all regions found in the original publication, except for 17q, were confirmed; all aberrations, except those in chromosome arms 8q and 12q were confirmed in the basal-like subtype. These two chromosome arms, however, were detected only upon removal of three patients

  18. Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

    NASA Astrophysics Data System (ADS)

    Lee, Sanggyun; Kim, Hyun-cheol; Im, Jungho

    2018-05-01

    We propose a waveform mixture algorithm to detect leads from CryoSat-2 data, which is novel and different from the existing threshold-based lead detection methods. The waveform mixture algorithm adopts the concept of spectral mixture analysis, which is widely used in the field of hyperspectral image analysis. This lead detection method was evaluated with high-resolution (250 m) MODIS images and showed comparable and promising performance in detecting leads when compared to the previous methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters (i.e., stack standard deviation, stack skewness, stack kurtosis, pulse peakiness, and backscatter σ0), as it directly uses L1B waveform data, unlike the existing threshold-based methods. Monthly lead fraction maps were produced by the waveform mixture algorithm, which shows interannual variability of recent sea ice cover during 2011-2016, excluding the summer season (i.e., June to September). We also compared the lead fraction maps to other lead fraction maps generated from previously published data sets, resulting in similar spatiotemporal patterns.

  19. Machine Learning Algorithms for Automated Satellite Snow and Sea Ice Detection

    NASA Astrophysics Data System (ADS)

    Bonev, George

    The continuous mapping of snow and ice cover, particularly in the arctic and poles, are critical to understanding the earth and atmospheric science. Much of the world's sea ice and snow covers the most inhospitable places, making measurements from satellite-based remote sensors essential. Despite the wealth of data from these instruments many challenges remain. For instance, remote sensing instruments reside on-board different satellites and observe the earth at different portions of the electromagnetic spectrum with different spatial footprints. Integrating and fusing this information to make estimates of the surface is a subject of active research. In response to these challenges, this dissertation will present two algorithms that utilize methods from statistics and machine learning, with the goal of improving on the quality and accuracy of current snow and sea ice detection products. The first algorithm aims at implementing snow detection using optical/infrared instrument data. The novelty in this approach is that the classifier is trained using ground station measurements of snow depth that are collocated with the reflectance observed at the satellite. Several classification methods are compared using this training data to identify the one yielding the highest accuracy and optimal space/time complexity. The algorithm is then evaluated against the current operational NASA snow product and it is found that it produces comparable and in some cases superior accuracy results. The second algorithm presents a fully automated approach to sea ice detection that integrates data obtained from passive microwave and optical/infrared satellite instruments. For a particular region of interest the algorithm generates sea ice maps of each individual satellite overpass and then aggregates them to a daily composite level, maximizing the amount of high resolution information available. The algorithm is evaluated at both, the individual satellite overpass level, and at the daily

  20. An Automated Cloud-edge Detection Algorithm Using Cloud Physics and Radar Data

    NASA Technical Reports Server (NTRS)

    Ward, Jennifer G.; Merceret, Francis J.; Grainger, Cedric A.

    2003-01-01

    An automated cloud edge detection algorithm was developed and extensively tested. The algorithm uses in-situ cloud physics data measured by a research aircraft coupled with ground-based weather radar measurements to determine whether the aircraft is in or out of cloud. Cloud edges are determined when the in/out state changes, subject to a hysteresis constraint. The hysteresis constraint prevents isolated transient cloud puffs or data dropouts from being identified as cloud boundaries. The algorithm was verified by detailed manual examination of the data set in comparison to the results from application of the automated algorithm.

  1. Detection of spontaneous vesicle release at individual synapses using multiple wavelets in a CWT-based algorithm.

    PubMed

    Sokoll, Stefan; Tönnies, Klaus; Heine, Martin

    2012-01-01

    In this paper we present an algorithm for the detection of spontaneous activity at individual synapses in microscopy images. By employing the optical marker pHluorin, we are able to visualize synaptic vesicle release with a spatial resolution in the nm range in a non-invasive manner. We compute individual synaptic signals from automatically segmented regions of interest and detect peaks that represent synaptic activity using a continuous wavelet transform based algorithm. As opposed to standard peak detection algorithms, we employ multiple wavelets to match all relevant features of the peak. We evaluate our multiple wavelet algorithm (MWA) on real data and assess the performance on synthetic data over a wide range of signal-to-noise ratios.

  2. Software Piracy Detection Model Using Ant Colony Optimization Algorithm

    NASA Astrophysics Data System (ADS)

    Astiqah Omar, Nor; Zakuan, Zeti Zuryani Mohd; Saian, Rizauddin

    2017-06-01

    Internet enables information to be accessible anytime and anywhere. This scenario creates an environment whereby information can be easily copied. Easy access to the internet is one of the factors which contribute towards piracy in Malaysia as well as the rest of the world. According to a survey conducted by Compliance Gap BSA Global Software Survey in 2013 on software piracy, found out that 43 percent of the software installed on PCs around the world was not properly licensed, the commercial value of the unlicensed installations worldwide was reported to be 62.7 billion. Piracy can happen anywhere including universities. Malaysia as well as other countries in the world is faced with issues of piracy committed by the students in universities. Piracy in universities concern about acts of stealing intellectual property. It can be in the form of software piracy, music piracy, movies piracy and piracy of intellectual materials such as books, articles and journals. This scenario affected the owner of intellectual property as their property is in jeopardy. This study has developed a classification model for detecting software piracy. The model was developed using a swarm intelligence algorithm called the Ant Colony Optimization algorithm. The data for training was collected by a study conducted in Universiti Teknologi MARA (Perlis). Experimental results show that the model detection accuracy rate is better as compared to J48 algorithm.

  3. Maximum likelihood phase-retrieval algorithm: applications.

    PubMed

    Nahrstedt, D A; Southwell, W H

    1984-12-01

    The maximum likelihood estimator approach is shown to be effective in determining the wave front aberration in systems involving laser and flow field diagnostics and optical testing. The robustness of the algorithm enables convergence even in cases of severe wave front error and real, nonsymmetrical, obscured amplitude distributions.

  4. Automatic low-order aberration compensator for solid-state slab lasers

    NASA Astrophysics Data System (ADS)

    Yu, Xin; Dong, Lizhi; Lai, Boheng; Yang, Ping; Kong, Qingfeng; Yang, Kangjian; Liu, Yong; Tang, Guomao; Xu, Bing

    2016-09-01

    Slab geometry is a promising architecture for power scaling of solid-state lasers. By propagating the laser beams along zigzag path in the gain medium, the thermal effects can be well compensated. However, in the non-zigzag direction, the thermal effects are not compensated. Among the overall aberrations in the slab lasers, the major contributors are two low-order aberrations: astigmatism and defocus, which can range up to over 100 microns (peak to valley), leading to detracted beam quality. Another problem with slab lasers is that the output beams are generally in a rectangular aperture with high aspect ratio (normally 1:10), where square beams are favorable for many applications. In order to solve these problems, we propose an automatic low-order aberration compensation system. This system is composed of three lenses fixed on a motorized rail, one is a spherical lens and the others are cylindrical lenses. Astigmatism and defocus can be compensated by merely adjusting the distances between the lenses. Two wave-front sensors are employed in this compensation system, one is used for detecting the initial parameters of the beams, and the other one is used for detecting the remaining aberrations after correction. The adjustments of the three lenses are directly calculated based on beam parameters using ray tracing method. The initial size of the beam is 3.2mm by 26mm, and peak to valley(PV) value of the wave-front is 33.07λ(λ=1064nm). After correction, the dimension becomes 40mm by 40mm, and peak to valley (PV) value of the wave-front is less than 2 microns.

  5. Intrusion-aware alert validation algorithm for cooperative distributed intrusion detection schemes of wireless sensor networks.

    PubMed

    Shaikh, Riaz Ahmed; Jameel, Hassan; d'Auriol, Brian J; Lee, Heejo; Lee, Sungyoung; Song, Young-Jae

    2009-01-01

    Existing anomaly and intrusion detection schemes of wireless sensor networks have mainly focused on the detection of intrusions. Once the intrusion is detected, an alerts or claims will be generated. However, any unidentified malicious nodes in the network could send faulty anomaly and intrusion claims about the legitimate nodes to the other nodes. Verifying the validity of such claims is a critical and challenging issue that is not considered in the existing cooperative-based distributed anomaly and intrusion detection schemes of wireless sensor networks. In this paper, we propose a validation algorithm that addresses this problem. This algorithm utilizes the concept of intrusion-aware reliability that helps to provide adequate reliability at a modest communication cost. In this paper, we also provide a security resiliency analysis of the proposed intrusion-aware alert validation algorithm.

  6. An algorithm for power line detection and warning based on a millimeter-wave radar video.

    PubMed

    Ma, Qirong; Goshi, Darren S; Shih, Yi-Chi; Sun, Ming-Ting

    2011-12-01

    Power-line-strike accident is a major safety threat for low-flying aircrafts such as helicopters, thus an automatic warning system to power lines is highly desirable. In this paper we propose an algorithm for detecting power lines from radar videos from an active millimeter-wave sensor. Hough Transform is employed to detect candidate lines. The major challenge is that the radar videos are very noisy due to ground return. The noise points could fall on the same line which results in signal peaks after Hough Transform similar to the actual cable lines. To differentiate the cable lines from the noise lines, we train a Support Vector Machine to perform the classification. We exploit the Bragg pattern, which is due to the diffraction of electromagnetic wave on the periodic surface of power lines. We propose a set of features to represent the Bragg pattern for the classifier. We also propose a slice-processing algorithm which supports parallel processing, and improves the detection of cables in a cluttered background. Lastly, an adaptive algorithm is proposed to integrate the detection results from individual frames into a reliable video detection decision, in which temporal correlation of the cable pattern across frames is used to make the detection more robust. Extensive experiments with real-world data validated the effectiveness of our cable detection algorithm. © 2011 IEEE

  7. Integration of a Self-Coherence Algorithm into DISAT for Forced Oscillation Detection

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

    Follum, James D.; Tuffner, Francis K.; Amidan, Brett G.

    2015-03-03

    With the increasing number of phasor measurement units on the power system, behaviors typically not observable on the power system are becoming more apparent. Oscillatory behavior on the power system, notably forced oscillations, are one such behavior. However, the large amounts of data coming from the PMUs makes manually detecting and locating these oscillations difficult. To automate portions of the process, an oscillation detection routine was coded into the Data Integrity and Situational Awareness Tool (DISAT) framework. Integration into the DISAT framework allows forced oscillations to be detected and information about the event provided to operational engineers. The oscillation detectionmore » algorithm integrates with the data handling and atypical data detecting capabilities of DISAT, building off of a standard library of functions. This report details that integration with information on the algorithm, some implementation issues, and some sample results from the western United States’ power grid.« less

  8. Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy.

    PubMed

    Scotland, G S; McNamee, P; Fleming, A D; Goatman, K A; Philip, S; Prescott, G J; Sharp, P F; Williams, G J; Wykes, W; Leese, G P; Olson, J A

    2010-06-01

    To assess the cost-effectiveness of an improved automated grading algorithm for diabetic retinopathy against a previously described algorithm, and in comparison with manual grading. Efficacy of the alternative algorithms was assessed using a reference graded set of images from three screening centres in Scotland (1253 cases with observable/referable retinopathy and 6333 individuals with mild or no retinopathy). Screening outcomes and grading and diagnosis costs were modelled for a cohort of 180 000 people, with prevalence of referable retinopathy at 4%. Algorithm (b), which combines image quality assessment with detection algorithms for microaneurysms (MA), blot haemorrhages and exudates, was compared with a simpler algorithm (a) (using image quality assessment and MA/dot haemorrhage (DH) detection), and the current practice of manual grading. Compared with algorithm (a), algorithm (b) would identify an additional 113 cases of referable retinopathy for an incremental cost of pound 68 per additional case. Compared with manual grading, automated grading would be expected to identify between 54 and 123 fewer referable cases, for a grading cost saving between pound 3834 and pound 1727 per case missed. Extrapolation modelling over a 20-year time horizon suggests manual grading would cost between pound 25,676 and pound 267,115 per additional quality adjusted life year gained. Algorithm (b) is more cost-effective than the algorithm based on quality assessment and MA/DH detection. With respect to the value of introducing automated detection systems into screening programmes, automated grading operates within the recommended national standards in Scotland and is likely to be considered a cost-effective alternative to manual disease/no disease grading.

  9. Track-Before-Detect Algorithm for Faint Moving Objects based on Random Sampling and Consensus

    NASA Astrophysics Data System (ADS)

    Dao, P.; Rast, R.; Schlaegel, W.; Schmidt, V.; Dentamaro, A.

    2014-09-01

    There are many algorithms developed for tracking and detecting faint moving objects in congested backgrounds. One obvious application is detection of targets in images where each pixel corresponds to the received power in a particular location. In our application, a visible imager operated in stare mode observes geostationary objects as fixed, stars as moving and non-geostationary objects as drifting in the field of view. We would like to achieve high sensitivity detection of the drifters. The ability to improve SNR with track-before-detect (TBD) processing, where target information is collected and collated before the detection decision is made, allows respectable performance against dim moving objects. Generally, a TBD algorithm consists of a pre-processing stage that highlights potential targets and a temporal filtering stage. However, the algorithms that have been successfully demonstrated, e.g. Viterbi-based and Bayesian-based, demand formidable processing power and memory. We propose an algorithm that exploits the quasi constant velocity of objects, the predictability of the stellar clutter and the intrinsically low false alarm rate of detecting signature candidates in 3-D, based on an iterative method called "RANdom SAmple Consensus” and one that can run real-time on a typical PC. The technique is tailored for searching objects with small telescopes in stare mode. Our RANSAC-MT (Moving Target) algorithm estimates parameters of a mathematical model (e.g., linear motion) from a set of observed data which contains a significant number of outliers while identifying inliers. In the pre-processing phase, candidate blobs were selected based on morphology and an intensity threshold that would normally generate unacceptable level of false alarms. The RANSAC sampling rejects candidates that conform to the predictable motion of the stars. Data collected with a 17 inch telescope by AFRL/RH and a COTS lens/EM-CCD sensor by the AFRL/RD Satellite Assessment Center is

  10. Influenza detection and prediction algorithms: comparative accuracy trial in Östergötland county, Sweden, 2008-2012.

    PubMed

    Spreco, A; Eriksson, O; Dahlström, Ö; Timpka, T

    2017-07-01

    Methods for the detection of influenza epidemics and prediction of their progress have seldom been comparatively evaluated using prospective designs. This study aimed to perform a prospective comparative trial of algorithms for the detection and prediction of increased local influenza activity. Data on clinical influenza diagnoses recorded by physicians and syndromic data from a telenursing service were used. Five detection and three prediction algorithms previously evaluated in public health settings were calibrated and then evaluated over 3 years. When applied on diagnostic data, only detection using the Serfling regression method and prediction using the non-adaptive log-linear regression method showed acceptable performances during winter influenza seasons. For the syndromic data, none of the detection algorithms displayed a satisfactory performance, while non-adaptive log-linear regression was the best performing prediction method. We conclude that evidence was found for that available algorithms for influenza detection and prediction display satisfactory performance when applied on local diagnostic data during winter influenza seasons. When applied on local syndromic data, the evaluated algorithms did not display consistent performance. Further evaluations and research on combination of methods of these types in public health information infrastructures for 'nowcasting' (integrated detection and prediction) of influenza activity are warranted.

  11. Complex chromosome aberrations persist in individuals many years after occupational exposure to densely ionizing radiation: an mFISH study.

    PubMed

    Hande, M Prakash; Azizova, Tamara V; Burak, Ludmilla E; Khokhryakov, Valentin F; Geard, Charles R; Brenner, David J

    2005-09-01

    Long-lived, sensitive, and specific biomarkers of particular mutagenic agents are much sought after and potentially have broad applications in the fields of cancer biology, epidemiology, and prevention. Many clastogens induce a spectrum of chromosome aberrations, and some of them can be exploited as biomarkers of exposure. Densely ionizing radiation, for example, alpha particle radiation (from radon or plutonium) and neutron radiation, preferentially induces complex chromosome aberrations, which can be detected by the 24-color multifluor fluorescence in situ hybridization (mFISH) technique. We report the detection and quantification of stable complex chromosome aberrations in lymphocytes of healthy former nuclear-weapons workers, who were exposed many years ago to plutonium, gamma rays, or both, at the Mayak weapons complex in Russia. We analyzed peripheral-blood lymphocytes from these individuals for the presence of persistent complex chromosome aberrations. A significantly elevated frequency of complex chromosome translocations was detected in the highly exposed plutonium workers but not in the group exposed only to high doses of gamma radiation. No such differences were found for simple chromosomal aberrations. The results suggest that stable complex chromosomal translocations represent a long-lived, quantitative, low-background biomarker of densely ionizing radiation for human populations exposed many years ago. (c) 2005 Wiley-Liss, Inc.

  12. A novel time-domain signal processing algorithm for real time ventricular fibrillation detection

    NASA Astrophysics Data System (ADS)

    Monte, G. E.; Scarone, N. C.; Liscovsky, P. O.; Rotter S/N, P.

    2011-12-01

    This paper presents an application of a novel algorithm for real time detection of ECG pathologies, especially ventricular fibrillation. It is based on segmentation and labeling process of an oversampled signal. After this treatment, analyzing sequence of segments, global signal behaviours are obtained in the same way like a human being does. The entire process can be seen as a morphological filtering after a smart data sampling. The algorithm does not require any ECG digital signal pre-processing, and the computational cost is low, so it can be embedded into the sensors for wearable and permanent applications. The proposed algorithms could be the input signal description to expert systems or to artificial intelligence software in order to detect other pathologies.

  13. Minimal time change detection algorithm for reconfigurable control system and application to aerospace

    NASA Technical Reports Server (NTRS)

    Kim, Sungwan

    1994-01-01

    System parameters should be tracked on-line to build a reconfigurable control system even though there exists an abrupt change. For this purpose, a new performance index that we are studying is the speed of adaptation- how quickly does the system determine that a change has occurred? In this paper, a new, robust algorithm that is optimized to minimize the time delay in detecting a change for fixed false alarm probability is proposed. Simulation results for the aircraft lateral motion with a known or unknown change in control gain matrices, in the presence of doublet input, indicate that the algorithm works fairly well. One of its distinguishing properties is that detection delay of this algorithm is superior to that of Whiteness Test.

  14. From DNA Copy Number to Gene Expression: Local aberrations, Trisomies and Monosomies

    NASA Astrophysics Data System (ADS)

    Shay, Tal

    The goal of my PhD research was to study the effect of DNA copy number changes on gene expression. DNA copy number aberrations may be local, encompassing several genes, or on the level of an entire chromosome, such as trisomy and monosomy. The main dataset I studied was of Glioblastoma, obtained in the framework of a collaboration, but I worked also with public datasets of cancer and Down's Syndrome. The molecular basis of expression changes in Glioblastoma. Glioblastoma is the most common and aggressive type of primary brain tumors in adults. In collaboration with Prof. Hegi (CHUV, Switzerland), we analyzed a rich Glioblastoma dataset including clinical information, DNA copy number (array CGH) and expression profiles. We explored the correlation between DNA copy number and gene expression at the level of chromosomal arms and local genomic aberrations. We detected known amplification and over expression of oncogenes, as well as deletion and down-regulation of tumor suppressor genes. We exploited that information to map alterations of pathways that are known to be disrupted in Glioblastoma, and tried to characterize samples that have no known alteration in any of the studied pathways. Identifying local DNA aberrations of biological significance. Many types of tumors exhibit chromosomal losses or gains and local amplifications and deletions. A region that is aberrant in many tumors, or whose copy number change is stronger, is more likely to be clinically relevant, and not just a by-product of genetic instability. We developed a novel method that defines and prioritizes aberrations by formalizing these intuitions. The method scores each aberration by the fraction of patients harboring it, its length and its amplitude, and assesses the significance of the score by comparing it to a null distribution obtained by permutations. This approach detects genetic locations that are significantly aberrant, generating a 'genomic aberration profile' for each sample. The 'genomic

  15. Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform.

    PubMed

    Cao, Jianfang; Chen, Lichao; Wang, Min; Tian, Yun

    2018-01-01

    The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator's dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data. For the experiments, we constructed datasets of different scales from the Pascal VOC2012 image database. The proposed parallel Otsu-Canny edge detection algorithm performs better than other traditional edge detection algorithms. The parallel approach reduced the running time by approximately 67.2% on a Hadoop cluster architecture consisting of 5 nodes with a dataset of 60,000 images. Overall, our approach system speeds up the system by approximately 3.4 times when processing large-scale datasets, which demonstrates the obvious superiority of our method. The proposed algorithm in this study demonstrates both better edge detection performance and improved time performance.

  16. Energy-based adaptive focusing of waves: application to noninvasive aberration correction of ultrasonic wavefields

    PubMed Central

    Herbert, Eric; Pernot, Mathieu; Montaldo, Gabriel; Fink, Mathias; Tanter, Mickael

    2009-01-01

    An aberration correction method based on the maximization of the wave intensity at the focus of an emitting array is presented. The potential of this new adaptive focusing technique is investigated for ultrasonic focusing in biological tissues. The acoustic intensity is maximized non invasively through the direct measurement or indirect estimation of the beam energy at the focus for a series of spatially coded emissions. For ultrasonic waves, the acoustic energy at the desired focus can be indirectly estimated from the local displacements induced in tissues by the ultrasonic radiation force of the beam. Based on the measurement of these displacements, this method allows the precise estimation of the phase and amplitude aberrations and consequently the correction of aberrations along the beam travel path. The proof of concept is first performed experimentally using a large therapeutic array with strong electronic phase aberrations (up to 2π). Displacements induced by the ultrasonic radiation force at the desired focus are indirectly estimated using the time shift of backscattered echoes recorded on the array. The phase estimation is deduced accurately using a direct inversion algorithm which reduces the standard deviation of the phase distribution from σ = 1.89 before correction to σ = 0.53 following correction. The corrected beam focusing quality is verified using a needle hydrophone. The peak intensity obtained through the aberrator is found to be −7.69 dB below the reference intensity obtained without any aberration. Using the phase correction, a sharp focus is restored through the aberrator with a relative peak intensity of −0.89 dB. The technique is tested experimentally using a linear transmit/receive array through a real aberrating layer. The array is used to automatically correct its beam quality, as it both generates the radiation force with coded excitations and indirectly estimates the acoustic intensity at the focus with speckle tracking. This

  17. A game theoretic algorithm to detect overlapping community structure in networks

    NASA Astrophysics Data System (ADS)

    Zhou, Xu; Zhao, Xiaohui; Liu, Yanheng; Sun, Geng

    2018-04-01

    Community detection can be used as an important technique for product and personalized service recommendation. A game theory based approach to detect overlapping community structure is introduced in this paper. The process of the community formation is converted into a game, when all agents (nodes) cannot improve their own utility, the game process will be terminated. The utility function is composed of a gain and a loss function and we present a new gain function in this paper. In addition, different from choosing action randomly among join, quit and switch for each agent to get new label, two new strategies for each agent to update its label are designed during the game, and the strategies are also evaluated and compared for each agent in order to find its best result. The overlapping community structure is naturally presented when the stop criterion is satisfied. The experimental results demonstrate that the proposed algorithm outperforms other similar algorithms for detecting overlapping communities in networks.

  18. Semi-supervised spectral algorithms for community detection in complex networks based on equivalence of clustering methods

    NASA Astrophysics Data System (ADS)

    Ma, Xiaoke; Wang, Bingbo; Yu, Liang

    2018-01-01

    Community detection is fundamental for revealing the structure-functionality relationship in complex networks, which involves two issues-the quantitative function for community as well as algorithms to discover communities. Despite significant research on either of them, few attempt has been made to establish the connection between the two issues. To attack this problem, a generalized quantification function is proposed for community in weighted networks, which provides a framework that unifies several well-known measures. Then, we prove that the trace optimization of the proposed measure is equivalent with the objective functions of algorithms such as nonnegative matrix factorization, kernel K-means as well as spectral clustering. It serves as the theoretical foundation for designing algorithms for community detection. On the second issue, a semi-supervised spectral clustering algorithm is developed by exploring the equivalence relation via combining the nonnegative matrix factorization and spectral clustering. Different from the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the spectral algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method improves the accuracy of the traditional spectral algorithms in community detection.

  19. Computer simulation of data on chromosome aberrations produced by X rays or alpha particles and detected by fluorescence in situ hybridization.

    PubMed

    Chen, A M; Lucas, J N; Simpson, P J; Griffin, C S; Savage, J R; Brenner, D J; Hlatky, L R; Sachs, R K

    1997-11-01

    With fluorescence in situ hybridization (FISH), many different categories of chromosome aberrations can be recognized-dicentrics, translocations, rings and various complex aberrations such as insertions or three-way interchanges. Relative frequencies for the various aberration categories indicate mechanisms of radiation-induced damage and reflect radiation quality. Data obtained with FISH support a proximity version of the classic random breakage-and-reunion model for the formation of aberrations. A Monte Carlo computer implementation of the model, called the CAS (chromosome aberration simulator), is generalized here to high linear energy transfer (LET) and compared to published data for human cells irradiated with X rays or 238Pu alpha particles. For each kind of radiation, the CAS has two adjustable parameters: the number of interaction sites per cell nucleus and the number of reactive double-strand breaks (DSBs) per gray. Aberration frequencies for various painted chromosomes, of varying lengths, and for 11 different categories of simple or complex aberrations were simulated and compared to the data. The optimal number of interaction sites was found to be approximately 13 for X irradiation and approximately 25 for alpha-particle irradiation. The relative biological effectiveness (RBE) of alpha particles for the induction of reactive DSBs (which are a minority of all DSBs) was found to be approximately 4. The two-parameter CAS model adequately matches data for many different categories of aberrations. It can use data obtained with FISH for any one painting pattern to predict results for any other kind of painting pattern or whole-genome staining, and to estimate a suggested overall numerical damage indicator for chromosome aberration studies, the total misrejoining number.

  20. Intrusion-Aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks

    PubMed Central

    Shaikh, Riaz Ahmed; Jameel, Hassan; d’Auriol, Brian J.; Lee, Heejo; Lee, Sungyoung; Song, Young-Jae

    2009-01-01

    Existing anomaly and intrusion detection schemes of wireless sensor networks have mainly focused on the detection of intrusions. Once the intrusion is detected, an alerts or claims will be generated. However, any unidentified malicious nodes in the network could send faulty anomaly and intrusion claims about the legitimate nodes to the other nodes. Verifying the validity of such claims is a critical and challenging issue that is not considered in the existing cooperative-based distributed anomaly and intrusion detection schemes of wireless sensor networks. In this paper, we propose a validation algorithm that addresses this problem. This algorithm utilizes the concept of intrusion-aware reliability that helps to provide adequate reliability at a modest communication cost. In this paper, we also provide a security resiliency analysis of the proposed intrusion-aware alert validation algorithm. PMID:22454568

  1. Genomic copy number analysis of a spectrum of blue nevi identifies recurrent aberrations of entire chromosomal arms in melanoma ex blue nevus.

    PubMed

    Chan, May P; Andea, Aleodor A; Harms, Paul W; Durham, Alison B; Patel, Rajiv M; Wang, Min; Robichaud, Patrick; Fisher, Gary J; Johnson, Timothy M; Fullen, Douglas R

    2016-03-01

    Blue nevi may display significant atypia or undergo malignant transformation. Morphologic diagnosis of this spectrum of lesions is notoriously difficult, and molecular tools are increasingly used to improve diagnostic accuracy. We studied copy number aberrations in a cohort of cellular blue nevi, atypical cellular blue nevi, and melanomas ex blue nevi using Affymetrix's OncoScan platform. Cases with sufficient DNA were analyzed for GNAQ, GNA11, and HRAS mutations. Copy number aberrations were detected in 0 of 5 (0%) cellular blue nevi, 3 of 12 (25%) atypical cellular blue nevi, and 6 of 9 (67%) melanomas ex blue nevi. None of the atypical cellular blue nevi displayed more than one aberration, whereas complex aberrations involving four or more regions were seen exclusively in melanomas ex blue nevi. Gains and losses of entire chromosomal arms were identified in four of five melanomas ex blue nevi with copy number aberrations. In particular, gains of 1q, 4p, 6p, and 8q, and losses of 1p and 4q were each found in at least two melanomas. Whole chromosome aberrations were also common, and represented the sole finding in one atypical cellular blue nevus. When seen in melanomas, however, whole chromosome aberrations were invariably accompanied by partial aberrations of other chromosomes. Three melanomas ex blue nevi harbored aberrations, which were absent or negligible in their precursor components, suggesting progression in tumor biology. Gene mutations involving GNAQ and GNA11 were each detected in two of eight melanomas ex blue nevi. In conclusion, copy number aberrations are more common and often complex in melanomas ex blue nevi compared with cellular and atypical cellular blue nevi. Identification of recurrent gains and losses of entire chromosomal arms in melanomas ex blue nevi suggests that development of new probes targeting these regions may improve detection and risk stratification of these lesions.

  2. A new approach to optic disc detection in human retinal images using the firefly algorithm.

    PubMed

    Rahebi, Javad; Hardalaç, Fırat

    2016-03-01

    There are various methods and algorithms to detect the optic discs in retinal images. In recent years, much attention has been given to the utilization of the intelligent algorithms. In this paper, we present a new automated method of optic disc detection in human retinal images using the firefly algorithm. The firefly intelligent algorithm is an emerging intelligent algorithm that was inspired by the social behavior of fireflies. The population in this algorithm includes the fireflies, each of which has a specific rate of lighting or fitness. In this method, the insects are compared two by two, and the less attractive insects can be observed to move toward the more attractive insects. Finally, one of the insects is selected as the most attractive, and this insect presents the optimum response to the problem in question. Here, we used the light intensity of the pixels of the retinal image pixels instead of firefly lightings. The movement of these insects due to local fluctuations produces different light intensity values in the images. Because the optic disc is the brightest area in the retinal images, all of the insects move toward brightest area and thus specify the location of the optic disc in the image. The results of implementation show that proposed algorithm could acquire an accuracy rate of 100 % in DRIVE dataset, 95 % in STARE dataset, and 94.38 % in DiaRetDB1 dataset. The results of implementation reveal high capability and accuracy of proposed algorithm in the detection of the optic disc from retinal images. Also, recorded required time for the detection of the optic disc in these images is 2.13 s for DRIVE dataset, 2.81 s for STARE dataset, and 3.52 s for DiaRetDB1 dataset accordingly. These time values are average value.

  3. Correlations between corneal and total wavefront aberrations

    NASA Astrophysics Data System (ADS)

    Mrochen, Michael; Jankov, Mirko; Bueeler, Michael; Seiler, Theo

    2002-06-01

    Purpose: Corneal topography data expressed as corneal aberrations are frequently used to report corneal laser surgery results. However, the optical image quality at the retina depends on all optical elements of the eye such as the human lens. Thus, the aim of this study was to investigate the correlations between the corneal and total wavefront aberrations and to discuss the importance of corneal aberrations for representing corneal laser surgery results. Methods: Thirty three eyes of 22 myopic subjects were measured with a corneal topography system and a Tschernig-type wavefront analyzer after the pupils were dilated to at least 6 mm in diameter. All measurements were centered with respect to the line of sight. Corneal and total wavefront aberrations were calculated up to the 6th Zernike order in the same reference plane. Results: Statistically significant correlations (p < 0.05) between the corneal and total wavefront aberrations were found for the astigmatism (C3,C5) and all 3rd Zernike order coefficients such as coma (C7,C8). No statistically significant correlations were found for all 4th to 6th order Zernike coefficients except for the 5th order horizontal coma C18 (p equals 0.003). On average, all Zernike coefficients for the corneal aberrations were found to be larger compared to Zernike coefficients for the total wavefront aberrations. Conclusions: Corneal aberrations are only of limited use for representing the optical quality of the human eye after corneal laser surgery. This is due to the lack of correlation between corneal and total wavefront aberrations in most of the higher order aberrations. Besides this, the data present in this study yield towards an aberration balancing between corneal aberrations and the optical elements within the eye that reduces the aberration from the cornea by a certain degree. Consequently, ideal customized ablations have to take both, corneal and total wavefront aberrations, into consideration.

  4. Development and testing of incident detection algorithms. Vol. 2, research methodology and detailed results.

    DOT National Transportation Integrated Search

    1976-04-01

    The development and testing of incident detection algorithms was based on Los Angeles and Minneapolis freeway surveillance data. Algorithms considered were based on times series and pattern recognition techniques. Attention was given to the effects o...

  5. Improving staff response to seizures on the epilepsy monitoring unit with online EEG seizure detection algorithms.

    PubMed

    Rommens, Nicole; Geertsema, Evelien; Jansen Holleboom, Lisanne; Cox, Fieke; Visser, Gerhard

    2018-05-11

    User safety and the quality of diagnostics on the epilepsy monitoring unit (EMU) depend on reaction to seizures. Online seizure detection might improve this. While good sensitivity and specificity is reported, the added value above staff response is unclear. We ascertained the added value of two electroencephalograph (EEG) seizure detection algorithms in terms of additional detected seizures or faster detection time. EEG-video seizure recordings of people admitted to an EMU over one year were included, with a maximum of two seizures per subject. All recordings were retrospectively analyzed using Encevis EpiScan and BESA Epilepsy. Detection sensitivity and latency of the algorithms were compared to staff responses. False positive rates were estimated on 30 uninterrupted recordings (roughly 24 h per subject) of consecutive subjects admitted to the EMU. EEG-video recordings used included 188 seizures. The response rate of staff was 67%, of Encevis 67%, and of BESA Epilepsy 65%. Of the 62 seizures missed by staff, 66% were recognized by Encevis and 39% by BESA Epilepsy. The median latency was 31 s (staff), 10 s (Encevis), and 14 s (BESA Epilepsy). After correcting for walking time from the observation room to the subject, both algorithms detected faster than staff in 65% of detected seizures. The full recordings included 617 h of EEG. Encevis had a median false positive rate of 4.9 per 24 h and BESA Epilepsy of 2.1 per 24 h. EEG-video seizure detection algorithms may improve reaction to seizures by improving the total number of seizures detected and the speed of detection. The false positive rate is feasible for use in a clinical situation. Implementation of these algorithms might result in faster diagnostic testing and better observation during seizures. Copyright © 2018. Published by Elsevier Inc.

  6. A new comparison of hyperspectral anomaly detection algorithms for real-time applications

    NASA Astrophysics Data System (ADS)

    Díaz, María.; López, Sebastián.; Sarmiento, Roberto

    2016-10-01

    Due to the high spectral resolution that remotely sensed hyperspectral images provide, there has been an increasing interest in anomaly detection. The aim of anomaly detection is to stand over pixels whose spectral signature differs significantly from the background spectra. Basically, anomaly detectors mark pixels with a certain score, considering as anomalies those whose scores are higher than a threshold. Receiver Operating Characteristic (ROC) curves have been widely used as an assessment measure in order to compare the performance of different algorithms. ROC curves are graphical plots which illustrate the trade- off between false positive and true positive rates. However, they are limited in order to make deep comparisons due to the fact that they discard relevant factors required in real-time applications such as run times, costs of misclassification and the competence to mark anomalies with high scores. This last fact is fundamental in anomaly detection in order to distinguish them easily from the background without any posterior processing. An extensive set of simulations have been made using different anomaly detection algorithms, comparing their performances and efficiencies using several extra metrics in order to complement ROC curves analysis. Results support our proposal and demonstrate that ROC curves do not provide a good visualization of detection performances for themselves. Moreover, a figure of merit has been proposed in this paper which encompasses in a single global metric all the measures yielded for the proposed additional metrics. Therefore, this figure, named Detection Efficiency (DE), takes into account several crucial types of performance assessment that ROC curves do not consider. Results demonstrate that algorithms with the best detection performances according to ROC curves do not have the highest DE values. Consequently, the recommendation of using extra measures to properly evaluate performances have been supported and justified by

  7. OF TRYPANOSOMATIDS. ENDOTRANSFORMATIONS AND ABERRATIONS].

    PubMed

    Frolov, A O; Malysheva, M N; Kostygov, A Yu

    2016-01-01

    Endotransformations and aberrations of the life cycle in the evolutionary history of trypanosomatids (Kinetoplastea: Trypanosomatidae) are analyzed. We treat the term "endotransformations" as evolutionarily fixed changes of phases and/or developmental stages of parasites. By contrast, we treat aberrations as evolutionary unstable, periodically arising deformations of developmental phases of trypanosomatids, never leading to life cycle changes. Various examples of life cycle endotransformations and aberrations in representatives of the family Trypanosomatidae are discussed.

  8. Testing of Lagrange multiplier damped least-squares control algorithm for woofer-tweeter adaptive optics

    PubMed Central

    Zou, Weiyao; Burns, Stephen A.

    2012-01-01

    A Lagrange multiplier-based damped least-squares control algorithm for woofer-tweeter (W-T) dual deformable-mirror (DM) adaptive optics (AO) is tested with a breadboard system. We show that the algorithm can complementarily command the two DMs to correct wavefront aberrations within a single optimization process: the woofer DM correcting the high-stroke, low-order aberrations, and the tweeter DM correcting the low-stroke, high-order aberrations. The optimal damping factor for a DM is found to be the median of the eigenvalue spectrum of the influence matrix of that DM. Wavefront control accuracy is maximized with the optimized control parameters. For the breadboard system, the residual wavefront error can be controlled to the precision of 0.03 μm in root mean square. The W-T dual-DM AO has applications in both ophthalmology and astronomy. PMID:22441462

  9. Mathematical detection of aortic valve opening (B point) in impedance cardiography: A comparison of three popular algorithms.

    PubMed

    Árbol, Javier Rodríguez; Perakakis, Pandelis; Garrido, Alba; Mata, José Luis; Fernández-Santaella, M Carmen; Vila, Jaime

    2017-03-01

    The preejection period (PEP) is an index of left ventricle contractility widely used in psychophysiological research. Its computation requires detecting the moment when the aortic valve opens, which coincides with the B point in the first derivative of impedance cardiogram (ICG). Although this operation has been traditionally made via visual inspection, several algorithms based on derivative calculations have been developed to enable an automatic performance of the task. However, despite their popularity, data about their empirical validation are not always available. The present study analyzes the performance in the estimation of the aortic valve opening of three popular algorithms, by comparing their performance with the visual detection of the B point made by two independent scorers. Algorithm 1 is based on the first derivative of the ICG, Algorithm 2 on the second derivative, and Algorithm 3 on the third derivative. Algorithm 3 showed the highest accuracy rate (78.77%), followed by Algorithm 1 (24.57%) and Algorithm 2 (13.82%). In the automatic computation of PEP, Algorithm 2 resulted in significantly more missed cycles (48.57%) than Algorithm 1 (6.3%) and Algorithm 3 (3.5%). Algorithm 2 also estimated a significantly lower average PEP (70 ms), compared with the values obtained by Algorithm 1 (119 ms) and Algorithm 3 (113 ms). Our findings indicate that the algorithm based on the third derivative of the ICG performs significantly better. Nevertheless, a visual inspection of the signal proves indispensable, and this article provides a novel visual guide to facilitate the manual detection of the B point. © 2016 Society for Psychophysiological Research.

  10. Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on the Hadoop Platform

    PubMed Central

    Wang, Min; Tian, Yun

    2018-01-01

    The Canny operator is widely used to detect edges in images. However, as the size of the image dataset increases, the edge detection performance of the Canny operator decreases and its runtime becomes excessive. To improve the runtime and edge detection performance of the Canny operator, in this paper, we propose a parallel design and implementation for an Otsu-optimized Canny operator using a MapReduce parallel programming model that runs on the Hadoop platform. The Otsu algorithm is used to optimize the Canny operator's dual threshold and improve the edge detection performance, while the MapReduce parallel programming model facilitates parallel processing for the Canny operator to solve the processing speed and communication cost problems that occur when the Canny edge detection algorithm is applied to big data. For the experiments, we constructed datasets of different scales from the Pascal VOC2012 image database. The proposed parallel Otsu-Canny edge detection algorithm performs better than other traditional edge detection algorithms. The parallel approach reduced the running time by approximately 67.2% on a Hadoop cluster architecture consisting of 5 nodes with a dataset of 60,000 images. Overall, our approach system speeds up the system by approximately 3.4 times when processing large-scale datasets, which demonstrates the obvious superiority of our method. The proposed algorithm in this study demonstrates both better edge detection performance and improved time performance. PMID:29861711

  11. An effective detection algorithm for region duplication forgery in digital images

    NASA Astrophysics Data System (ADS)

    Yavuz, Fatih; Bal, Abdullah; Cukur, Huseyin

    2016-04-01

    Powerful image editing tools are very common and easy to use these days. This situation may cause some forgeries by adding or removing some information on the digital images. In order to detect these types of forgeries such as region duplication, we present an effective algorithm based on fixed-size block computation and discrete wavelet transform (DWT). In this approach, the original image is divided into fixed-size blocks, and then wavelet transform is applied for dimension reduction. Each block is processed by Fourier Transform and represented by circle regions. Four features are extracted from each block. Finally, the feature vectors are lexicographically sorted, and duplicated image blocks are detected according to comparison metric results. The experimental results show that the proposed algorithm presents computational efficiency due to fixed-size circle block architecture.

  12. How Small Can Impact Craters Be Detected at Large Scale by Automated Algorithms?

    NASA Astrophysics Data System (ADS)

    Bandeira, L.; Machado, M.; Pina, P.; Marques, J. S.

    2013-12-01

    The last decade has seen a widespread publication of crater detection algorithms (CDA) with increasing detection performances. The adaptive nature of some of the algorithms [1] has permitting their use in the construction or update of global catalogues for Mars and the Moon. Nevertheless, the smallest craters detected in these situations by CDA have 10 pixels in diameter (or about 2 km in MOC-WA images) [2] or can go down to 16 pixels or 200 m in HRSC imagery [3]. The availability of Martian images with metric (HRSC and CTX) and centimetric (HiRISE) resolutions is permitting to unveil craters not perceived before, thus automated approaches seem a natural way of detecting the myriad of these structures. In this study we present the efforts, based on our previous algorithms [2-3] and new training strategies, to push the automated detection of craters to a dimensional threshold as close as possible to the detail that can be perceived on the images, something that has not been addressed yet in a systematic way. The approach is based on the selection of candidate regions of the images (portions that contain crescent highlight and shadow shapes indicating a possible presence of a crater) using mathematical morphology operators (connected operators of different sizes) and on the extraction of texture features (Haar-like) and classification by Adaboost, into crater and non-crater. This is a supervised approach, meaning that a training phase, in which manually labelled samples are provided, is necessary so the classifier can learn what crater and non-crater structures are. The algorithm is intensively tested in Martian HiRISE images, from different locations on the planet, in order to cover the largest surface types from the geological point view (different ages and crater densities) and also from the imaging or textural perspective (different degrees of smoothness/roughness). The quality of the detections obtained is clearly dependent on the dimension of the craters

  13. Unsupervised algorithms for intrusion detection and identification in wireless ad hoc sensor networks

    NASA Astrophysics Data System (ADS)

    Hortos, William S.

    2009-05-01

    In previous work by the author, parameters across network protocol layers were selected as features in supervised algorithms that detect and identify certain intrusion attacks on wireless ad hoc sensor networks (WSNs) carrying multisensor data. The algorithms improved the residual performance of the intrusion prevention measures provided by any dynamic key-management schemes and trust models implemented among network nodes. The approach of this paper does not train algorithms on the signature of known attack traffic, but, instead, the approach is based on unsupervised anomaly detection techniques that learn the signature of normal network traffic. Unsupervised learning does not require the data to be labeled or to be purely of one type, i.e., normal or attack traffic. The approach can be augmented to add any security attributes and quantified trust levels, established during data exchanges among nodes, to the set of cross-layer features from the WSN protocols. A two-stage framework is introduced for the security algorithms to overcome the problems of input size and resource constraints. The first stage is an unsupervised clustering algorithm which reduces the payload of network data packets to a tractable size. The second stage is a traditional anomaly detection algorithm based on a variation of support vector machines (SVMs), whose efficiency is improved by the availability of data in the packet payload. In the first stage, selected algorithms are adapted to WSN platforms to meet system requirements for simple parallel distributed computation, distributed storage and data robustness. A set of mobile software agents, acting like an ant colony in securing the WSN, are distributed at the nodes to implement the algorithms. The agents move among the layers involved in the network response to the intrusions at each active node and trustworthy neighborhood, collecting parametric values and executing assigned decision tasks. This minimizes the need to move large amounts

  14. Firefly Algorithm in detection of TEC seismo-ionospheric anomalies

    NASA Astrophysics Data System (ADS)

    Akhoondzadeh, Mehdi

    2015-07-01

    Anomaly detection in time series of different earthquake precursors is an essential introduction to create an early warning system with an allowable uncertainty. Since these time series are more often non linear, complex and massive, therefore the applied predictor method should be able to detect the discord patterns from a large data in a short time. This study acknowledges Firefly Algorithm (FA) as a simple and robust predictor to detect the TEC (Total Electron Content) seismo-ionospheric anomalies around the time of the some powerful earthquakes including Chile (27 February 2010), Varzeghan (11 August 2012) and Saravan (16 April 2013). Outstanding anomalies were observed 7 and 5 days before the Chile and Varzeghan earthquakes, respectively and also 3 and 8 days prior to the Saravan earthquake.

  15. The Automated Assessment of Postural Stability: Balance Detection Algorithm.

    PubMed

    Napoli, Alessandro; Glass, Stephen M; Tucker, Carole; Obeid, Iyad

    2017-12-01

    Impaired balance is a common indicator of mild traumatic brain injury, concussion and musculoskeletal injury. Given the clinical relevance of such injuries, especially in military settings, it is paramount to develop more accurate and reliable on-field evaluation tools. This work presents the design and implementation of the automated assessment of postural stability (AAPS) system, for on-field evaluations following concussion. The AAPS is a computer system, based on inexpensive off-the-shelf components and custom software, that aims to automatically and reliably evaluate balance deficits, by replicating a known on-field clinical test, namely, the Balance Error Scoring System (BESS). The AAPS main innovation is its balance error detection algorithm that has been designed to acquire data from a Microsoft Kinect ® sensor and convert them into clinically-relevant BESS scores, using the same detection criteria defined by the original BESS test. In order to assess the AAPS balance evaluation capability, a total of 15 healthy subjects (7 male, 8 female) were required to perform the BESS test, while simultaneously being tracked by a Kinect 2.0 sensor and a professional-grade motion capture system (Qualisys AB, Gothenburg, Sweden). High definition videos with BESS trials were scored off-line by three experienced observers for reference scores. AAPS performance was assessed by comparing the AAPS automated scores to those derived by three experienced observers. Our results show that the AAPS error detection algorithm presented here can accurately and precisely detect balance deficits with performance levels that are comparable to those of experienced medical personnel. Specifically, agreement levels between the AAPS algorithm and the human average BESS scores ranging between 87.9% (single-leg on foam) and 99.8% (double-leg on firm ground) were detected. Moreover, statistically significant differences in balance scores were not detected by an ANOVA test with alpha equal to 0

  16. Monochromatic ocular wave aberrations in young monkeys

    PubMed Central

    Ramamirtham, Ramkumar; Kee, Chea-su; Hung, Li-Fang; Qiao-Grider, Ying; Roorda, Austin; Smith, Earl L.

    2006-01-01

    High-order monochromatic aberrations could potentially influence vision-dependent refractive development in a variety of ways. As a first step in understanding the effects of wave aberration on refractive development, we characterized the maturational changes that take place in the high-order aberrations of infant rhesus monkey eyes. Specifically, we compared the monochromatic wave aberrations of infant and adolescent animals and measured the longitudinal changes in the high-order aberrations of infant monkeys during the early period when emmetropization takes place. Our main findings were that (1) adolescent monkey eyes have excellent optical quality, exhibiting total RMS errors that were slightly better than those for adult human eyes that have the same numerical aperture and (2) shortly after birth, infant rhesus monkeys exhibited relatively larger magnitudes of high-order aberrations predominately spherical aberration, coma, and trefoil, which decreased rapidly to assume adolescent values by about 200 days of age. The results demonstrate that rhesus monkey eyes are a good model for studying the contribution of individual ocular components to the eye’s overall aberration structure, the mechanisms responsible for the improvements in optical quality that occur during early ocular development, and the effects of high-order aberrations on ocular growth and emmetropization. PMID:16750549

  17. Damage diagnosis algorithm using a sequential change point detection method with an unknown distribution for damage

    NASA Astrophysics Data System (ADS)

    Noh, Hae Young; Rajagopal, Ram; Kiremidjian, Anne S.

    2012-04-01

    This paper introduces a damage diagnosis algorithm for civil structures that uses a sequential change point detection method for the cases where the post-damage feature distribution is unknown a priori. This algorithm extracts features from structural vibration data using time-series analysis and then declares damage using the change point detection method. The change point detection method asymptotically minimizes detection delay for a given false alarm rate. The conventional method uses the known pre- and post-damage feature distributions to perform a sequential hypothesis test. In practice, however, the post-damage distribution is unlikely to be known a priori. Therefore, our algorithm estimates and updates this distribution as data are collected using the maximum likelihood and the Bayesian methods. We also applied an approximate method to reduce the computation load and memory requirement associated with the estimation. The algorithm is validated using multiple sets of simulated data and a set of experimental data collected from a four-story steel special moment-resisting frame. Our algorithm was able to estimate the post-damage distribution consistently and resulted in detection delays only a few seconds longer than the delays from the conventional method that assumes we know the post-damage feature distribution. We confirmed that the Bayesian method is particularly efficient in declaring damage with minimal memory requirement, but the maximum likelihood method provides an insightful heuristic approach.

  18. Robust and unobtrusive algorithm based on position independence for step detection

    NASA Astrophysics Data System (ADS)

    Qiu, KeCheng; Li, MengYang; Luo, YiHan

    2018-04-01

    Running is becoming one of the most popular exercises among the people, monitoring steps can help users better understand their running process and improve exercise efficiency. In this paper, we design and implement a robust and unobtrusive algorithm based on position independence for step detection under real environment. It applies Butterworth filter to suppress high frequency interference and then employs the projection based on mathematics to transform system to solve the problem of unknown position of smartphone. Finally, using sliding window to suppress the false peak. The algorithm was tested for eight participants on the Android 7.0 platform. In our experiments, the results show that the proposed algorithm can achieve desired effect in spite of device pose.

  19. Spatial-time-state fusion algorithm for defect detection through eddy current pulsed thermography

    NASA Astrophysics Data System (ADS)

    Xiao, Xiang; Gao, Bin; Woo, Wai Lok; Tian, Gui Yun; Xiao, Xiao Ting

    2018-05-01

    Eddy Current Pulsed Thermography (ECPT) has received extensive attention due to its high sensitive of detectability on surface and subsurface cracks. However, it remains as a difficult challenge in unsupervised detection as to identify defects without knowing any prior knowledge. This paper presents a spatial-time-state features fusion algorithm to obtain fully profile of the defects by directional scanning. The proposed method is intended to conduct features extraction by using independent component analysis (ICA) and automatic features selection embedding genetic algorithm. Finally, the optimal feature of each step is fused to obtain defects reconstruction by applying common orthogonal basis extraction (COBE) method. Experiments have been conducted to validate the study and verify the efficacy of the proposed method on blind defect detection.

  20. Aberrant lymphoid antigen expression in acute myeloid leukemia in Saudi Arabia.

    PubMed

    El-Sissy, Azza H; El-Mashari, May A; Bassuni, Wafaa Y; El-Swaayed, Aziza F

    2006-09-01

    Immunophenotyping improves both accuracy and reproducibility of acute leukemia classification and is considered particularly useful for identifying aberrant lineage association of acute leukemia, biphenotypic and bilineal acute leukemia, as well as monitoring minimal residual disease. Some immunophenotypes correlate with cytogenetic abnormalities and prognosis. Is to determine aberrant lymphoid antigen expression in Saudi acute myeloid leukemia (AML), correlate them with FAB subtypes, evaluate early surface markers CD7 and CD56, and to investigate the role of cytoplasmic CD79a (a B cell marker that is assigned a high score of 2.0 in the WHO classification). Thirty four newly diagnosed AML cases were included in this study, 47% showed aberrant lymphoid antigen expression. CD9 was the most frequently expressed lymphoid antigen (29.4%) followed by CD7 & CD19 (11.8%), CD4 (8.8%) and CD22 (2.9%). CD9 was expressed in 3/6 (50%) of M3 cases, CD7 was expressed in 11.8% and was mostly confined to FAB M1 and M2 and associated with immature antigens CD34, HLA-DR and TdT. CD56 was expressed in 7/34 (20.6%) cases, three of these cases (42.9%) belonged to the monocytic group. CD56 was also detected in 2 cases with 11q23 rearrangement. CD56 was expressed in 2/7 (28.6%) M2 cases, and was associated with t (8;21) (q22;q22) together with CD19. Co-expression of CD56 and CD7 was detected in 2.9% of the cases. CD79a was expressed in one case together with CD19, diagnosed as acute biphenotypic leukemia, and was associated with t(8;21) (q22;q22). Minimal residual disease in AML is very difficult to trace, detection of aberrant expression of lymphoid antigens will make it easier. The high score given to CD79a by EGIL is questionable based on cytogenetic classification.

  1. Iteration of ultrasound aberration correction methods

    NASA Astrophysics Data System (ADS)

    Maasoey, Svein-Erik; Angelsen, Bjoern; Varslot, Trond

    2004-05-01

    Aberration in ultrasound medical imaging is usually modeled by time-delay and amplitude variations concentrated on the transmitting/receiving array. This filter process is here denoted a TDA filter. The TDA filter is an approximation to the physical aberration process, which occurs over an extended part of the human body wall. Estimation of the TDA filter, and performing correction on transmit and receive, has proven difficult. It has yet to be shown that this method works adequately for severe aberration. Estimation of the TDA filter can be iterated by retransmitting a corrected signal and re-estimate until a convergence criterion is fulfilled (adaptive imaging). Two methods for estimating time-delay and amplitude variations in receive signals from random scatterers have been developed. One method correlates each element signal with a reference signal. The other method use eigenvalue decomposition of the receive cross-spectrum matrix, based upon a receive energy-maximizing criterion. Simulations of iterating aberration correction with a TDA filter have been investigated to study its convergence properties. A weak and strong human-body wall model generated aberration. Both emulated the human abdominal wall. Results after iteration improve aberration correction substantially, and both estimation methods converge, even for the case of strong aberration.

  2. Optical Algorithm for Cloud Shadow Detection Over Water

    DTIC Science & Technology

    2013-02-01

    REPORT DATE (DD-MM-YYYY) 05-02-2013 2. REPORT TYPE Journal Article 3. DATES COVERED (From ■ To) 4. TITLE AND SUBTITLE Optical Algorithm for Cloud...particularly over humid tropical regions. Throughout the year, about two-thirds of the Earth’s surface is always covered by clouds [1]. The problem...V. Khlopenkov and A. P. Trishchenko, "SPARC: New cloud, snow , cloud shadow detection scheme for historical I-km AVHHR data over Canada," / Atmos

  3. A novel track-before-detect algorithm based on optimal nonlinear filtering for detecting and tracking infrared dim target

    NASA Astrophysics Data System (ADS)

    Tian, Yuexin; Gao, Kun; Liu, Ying; Han, Lu

    2015-08-01

    Aiming at the nonlinear and non-Gaussian features of the real infrared scenes, an optimal nonlinear filtering based algorithm for the infrared dim target tracking-before-detecting application is proposed. It uses the nonlinear theory to construct the state and observation models and uses the spectral separation scheme based Wiener chaos expansion method to resolve the stochastic differential equation of the constructed models. In order to improve computation efficiency, the most time-consuming operations independent of observation data are processed on the fore observation stage. The other observation data related rapid computations are implemented subsequently. Simulation results show that the algorithm possesses excellent detection performance and is more suitable for real-time processing.

  4. Algorithms for detection of objects in image sequences captured from an airborne imaging system

    NASA Technical Reports Server (NTRS)

    Kasturi, Rangachar; Camps, Octavia; Tang, Yuan-Liang; Devadiga, Sadashiva; Gandhi, Tarak

    1995-01-01

    This research was initiated as a part of the effort at the NASA Ames Research Center to design a computer vision based system that can enhance the safety of navigation by aiding the pilots in detecting various obstacles on the runway during critical section of the flight such as a landing maneuver. The primary goal is the development of algorithms for detection of moving objects from a sequence of images obtained from an on-board video camera. Image regions corresponding to the independently moving objects are segmented from the background by applying constraint filtering on the optical flow computed from the initial few frames of the sequence. These detected regions are tracked over subsequent frames using a model based tracking algorithm. Position and velocity of the moving objects in the world coordinate is estimated using an extended Kalman filter. The algorithms are tested using the NASA line image sequence with six static trucks and a simulated moving truck and experimental results are described. Various limitations of the currently implemented version of the above algorithm are identified and possible solutions to build a practical working system are investigated.

  5. Chromosomal aberrations and deoxyribonucleic acid single-strand breaks in adipose-derived stem cells during long-term expansion in vitro.

    PubMed

    Froelich, Katrin; Mickler, Johannes; Steusloff, Gudrun; Technau, Antje; Ramos Tirado, Mario; Scherzed, Agmal; Hackenberg, Stephan; Radeloff, Andreas; Hagen, Rudolf; Kleinsasser, Norbert

    2013-07-01

    Adipose-derived stem cells (ASCs) are a promising mesenchymal cell source for tissue engineering approaches. To obtain an adequate cell amount, in vitro expansion of the cells may be required in some cases. To monitor potential contraindications for therapeutic applications in humans, DNA strand breaks and chromosomal aberrations in ASCs during in vitro expansion were examined. After isolation of ASC from human lipoaspirates of seven patients, in vitro expansion over 10 passages was performed. Cells from passages 1, 2, 3, 5 and 10 were used for the alkaline single-cell microgel electrophoresis (comet) assay to detect DNA single-strand breaks and alkali labile as well as incomplete excision repair sites. Chromosomal changes were examined by means of the chromosomal aberration test. During in vitro expansion, ASC showed no DNA single-strand breaks in the comet assay. With the chromosomal aberration test, however, a significant increase in chromosomal aberrations were detected. The study showed that although no DNA fragmentation could be determined, the safety of ASC cannot be ensured with respect to chromosome stability during in vitro expansion. Thus, reliable analyses for detecting ASC populations, which accumulate chromosomal aberrations or even undergo malignant transformation during extensive in vitro expansion, must be implemented as part of the safety evaluation of these cells for stem cell-based therapy. Copyright © 2013 International Society for Cellular Therapy. Published by Elsevier Inc. All rights reserved.

  6. Effect of met-enkephalin on chromosomal aberrations in the lymphocytes of the peripheral blood of patients with multiple sclerosis.

    PubMed

    Rakanović-Todić, Maida; Burnazović-Ristić, Lejla; Ibrulj, Slavka; Mulbegović, Nedžad

    2014-05-01

    Endogenious opiod met-enkephalin throughout previous research manifested cytoprotective and anti-inflammatory effects. Previous research suggests that met-enkephalin has cytogenetic effects. Reducement in the frequency of structural chromosome aberrations as well as a suppressive effect on lymphocyte cell cycle is found. It also reduces apoptosis in the blood samples of the patients with immune-mediated diseases. Met-enkephalin exerts immunomodulatory properties and induces stabilization of the clinical condition in patients with multiple Sclerosis (MS). The goal of the present research was to evaluate met-enkephalin in vitro effects on the number and type of chromosome aberrations in the peripheral blood lymphocytes of patients with MS. Our research detected disappearance of ring chromosomes and chromosome fragmentations in the cultures of the peripheral blood lymphocytes treated with met-enkephalin (1.2 μg/mL). However, this research did not detect any significant effects of met-enkephalin on the reduction of structural chromosome aberrations and disappearance of dicentric chromosomes. Chromosomes with the greatest percent of inclusion in chromosome aberrations were noted as: chromosome 1, chromosome 2 and chromosome 9. Additionally, we confirmed chromosome 14 as the most frequently included in translocations. Furthermore, met-enkephalin effects on the increase of the numerical aberrations in both concentrations applied were detected. Those findings should be interpreted cautiously and more research in this field should be conducted.

  7. An Efficient Moving Target Detection Algorithm Based on Sparsity-Aware Spectrum Estimation

    PubMed Central

    Shen, Mingwei; Wang, Jie; Wu, Di; Zhu, Daiyin

    2014-01-01

    In this paper, an efficient direct data domain space-time adaptive processing (STAP) algorithm for moving targets detection is proposed, which is achieved based on the distinct spectrum features of clutter and target signals in the angle-Doppler domain. To reduce the computational complexity, the high-resolution angle-Doppler spectrum is obtained by finding the sparsest coefficients in the angle domain using the reduced-dimension data within each Doppler bin. Moreover, we will then present a knowledge-aided block-size detection algorithm that can discriminate between the moving targets and the clutter based on the extracted spectrum features. The feasibility and effectiveness of the proposed method are validated through both numerical simulations and raw data processing results. PMID:25222035

  8. Correcting the wavefront aberration of membrane mirror based on liquid crystal spatial light modulator

    NASA Astrophysics Data System (ADS)

    Yang, Bin; Wei, Yin; Chen, Xinhua; Tang, Minxue

    2014-11-01

    Membrane mirror with flexible polymer film substrate is a new-concept ultra lightweight mirror for space applications. Compared with traditional mirrors, membrane mirror has the advantages of lightweight, folding and deployable, low cost and etc. Due to the surface shape of flexible membrane mirror is easy to deviate from the design surface shape, it will bring wavefront aberration to the optical system. In order to solve this problem, a method of membrane mirror wavefront aberration correction based on the liquid crystal spatial light modulator (LCSLM) will be studied in this paper. The wavefront aberration correction principle of LCSLM is described and the phase modulation property of a LCSLM is measured and analyzed firstly. Then the membrane mirror wavefront aberration correction system is designed and established according to the optical properties of a membrane mirror. The LCSLM and a Hartmann-Shack sensor are used as a wavefront corrector and a wavefront detector, respectively. The detected wavefront aberration is calculated and converted into voltage value on LCSLM for the mirror wavefront aberration correction by programming in Matlab. When in experiment, the wavefront aberration of a glass plane mirror with a diameter of 70 mm is measured and corrected for verifying the feasibility of the experiment system and the correctness of the program. The PV value and RMS value of distorted wavefront are reduced and near diffraction limited optical performance is achieved. On this basis, the wavefront aberration of the aperture center Φ25 mm in a membrane mirror with a diameter of 200 mm is corrected and the errors are analyzed. It provides a means of correcting the wavefront aberration of membrane mirror.

  9. Genome-wide identification of significant aberrations in cancer genome.

    PubMed

    Yuan, Xiguo; Yu, Guoqiang; Hou, Xuchu; Shih, Ie-Ming; Clarke, Robert; Zhang, Junying; Hoffman, Eric P; Wang, Roger R; Zhang, Zhen; Wang, Yue

    2012-07-27

    Somatic Copy Number Alterations (CNAs) in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC), a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1) exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2) performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3) iteratively detecting Significant Copy Number Aberrations (SCAs) and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme. We test and compare the performance of SAIC against four peer methods (GISTIC, STAC, KC-SMART, CMDS) on a large number of simulation datasets. Experimental results show that SAIC outperforms peer methods in terms of larger area under the Receiver Operating Characteristics curve and increased detection power. We then apply SAIC to analyze structural genomic aberrations acquired in four real cancer genome-wide copy number data sets (ovarian cancer, metastatic prostate cancer, lung adenocarcinoma, glioblastoma). When compared with previously reported results, SAIC successfully identifies most SCAs known to be of biological significance and associated with oncogenes (e.g., KRAS, CCNE1, and MYC) or tumor suppressor genes (e.g., CDKN2A/B). Furthermore, SAIC identifies a number of novel SCAs in these copy number data that encompass tumor related genes and may warrant further studies. Supported by a well-grounded theoretical framework, SAIC has been developed and used to identify SCAs in various cancer copy number data sets, providing useful information to study the landscape of cancer genomes. Open-source and platform-independent SAIC software is

  10. Performance analysis of a fault inferring nonlinear detection system algorithm with integrated avionics flight data

    NASA Technical Reports Server (NTRS)

    Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

    1985-01-01

    This paper presents the performance analysis results of a fault inferring nonlinear detection system (FINDS) using integrated avionics sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. First, an overview of the FINDS algorithm structure is given. Then, aircraft state estimate time histories and statistics for the flight data sensors are discussed. This is followed by an explanation of modifications made to the detection and decision functions in FINDS to improve false alarm and failure detection performance. Next, the failure detection and false alarm performance of the FINDS algorithm are analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minutes of flight data. Results indicate that the detection speed, failure level estimation, and false alarm performance show a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed is faster for filter measurement sensors such as MLS than for filter input sensors such as flight control accelerometers. Finally, the progress in modifications of the FINDS algorithm design to accommodate flight computer constraints is discussed.

  11. The performance analysis of three-dimensional track-before-detect algorithm based on Fisher-Tippett-Gnedenko theorem

    NASA Astrophysics Data System (ADS)

    Cho, Hoonkyung; Chun, Joohwan; Song, Sungchan

    2016-09-01

    The dim moving target tracking from the infrared image sequence in the presence of high clutter and noise has been recently under intensive investigation. The track-before-detect (TBD) algorithm processing the image sequence over a number of frames before decisions on the target track and existence is known to be especially attractive in very low SNR environments (⩽ 3 dB). In this paper, we shortly present a three-dimensional (3-D) TBD with dynamic programming (TBD-DP) algorithm using multiple IR image sensors. Since traditional two-dimensional TBD algorithm cannot track and detect the along the viewing direction, we use 3-D TBD with multiple sensors and also strictly analyze the detection performance (false alarm and detection probabilities) based on Fisher-Tippett-Gnedenko theorem. The 3-D TBD-DP algorithm which does not require a separate image registration step uses the pixel intensity values jointly read off from multiple image frames to compute the merit function required in the DP process. Therefore, we also establish the relationship between the pixel coordinates of image frame and the reference coordinates.

  12. Airport Traffic Conflict Detection and Resolution Algorithm Evaluation

    NASA Technical Reports Server (NTRS)

    Jones, Denise R.; Chartrand, Ryan C.; Wilson, Sara R.; Commo, Sean A.; Ballard, Kathryn M.; Otero, Sharon D.; Barker, Glover D.

    2016-01-01

    Two conflict detection and resolution (CD&R) algorithms for the terminal maneuvering area (TMA) were evaluated in a fast-time batch simulation study at the National Aeronautics and Space Administration (NASA) Langley Research Center. One CD&R algorithm, developed at NASA, was designed to enhance surface situation awareness and provide cockpit alerts of potential conflicts during runway, taxi, and low altitude air-to-air operations. The second algorithm, Enhanced Traffic Situation Awareness on the Airport Surface with Indications and Alerts (SURF IA), was designed to increase flight crew awareness of the runway environment and facilitate an appropriate and timely response to potential conflict situations. The purpose of the study was to evaluate the performance of the aircraft-based CD&R algorithms during various runway, taxiway, and low altitude scenarios, multiple levels of CD&R system equipage, and various levels of horizontal position accuracy. Algorithm performance was assessed through various metrics including the collision rate, nuisance and missed alert rate, and alert toggling rate. The data suggests that, in general, alert toggling, nuisance and missed alerts, and unnecessary maneuvering occurred more frequently as the position accuracy was reduced. Collision avoidance was more effective when all of the aircraft were equipped with CD&R and maneuvered to avoid a collision after an alert was issued. In order to reduce the number of unwanted (nuisance) alerts when taxiing across a runway, a buffer is needed between the hold line and the alerting zone so alerts are not generated when an aircraft is behind the hold line. All of the results support RTCA horizontal position accuracy requirements for performing a CD&R function to reduce the likelihood and severity of runway incursions and collisions.

  13. Autopiquer - a Robust and Reliable Peak Detection Algorithm for Mass Spectrometry

    NASA Astrophysics Data System (ADS)

    Kilgour, David P. A.; Hughes, Sam; Kilgour, Samantha L.; Mackay, C. Logan; Palmblad, Magnus; Tran, Bao Quoc; Goo, Young Ah; Ernst, Robert K.; Clarke, David J.; Goodlett, David R.

    2017-02-01

    We present a simple algorithm for robust and unsupervised peak detection by determining a noise threshold in isotopically resolved mass spectrometry data. Solving this problem will greatly reduce the subjective and time-consuming manual picking of mass spectral peaks and so will prove beneficial in many research applications. The Autopiquer approach uses autocorrelation to test for the presence of (isotopic) structure in overlapping windows across the spectrum. Within each window, a noise threshold is optimized to remove the most unstructured data, whilst keeping as much of the (isotopic) structure as possible. This algorithm has been successfully demonstrated for both peak detection and spectral compression on data from many different classes of mass spectrometer and for different sample types, and this approach should also be extendible to other types of data that contain regularly spaced discrete peaks.

  14. Autopiquer - a Robust and Reliable Peak Detection Algorithm for Mass Spectrometry.

    PubMed

    Kilgour, David P A; Hughes, Sam; Kilgour, Samantha L; Mackay, C Logan; Palmblad, Magnus; Tran, Bao Quoc; Goo, Young Ah; Ernst, Robert K; Clarke, David J; Goodlett, David R

    2017-02-01

    We present a simple algorithm for robust and unsupervised peak detection by determining a noise threshold in isotopically resolved mass spectrometry data. Solving this problem will greatly reduce the subjective and time-consuming manual picking of mass spectral peaks and so will prove beneficial in many research applications. The Autopiquer approach uses autocorrelation to test for the presence of (isotopic) structure in overlapping windows across the spectrum. Within each window, a noise threshold is optimized to remove the most unstructured data, whilst keeping as much of the (isotopic) structure as possible. This algorithm has been successfully demonstrated for both peak detection and spectral compression on data from many different classes of mass spectrometer and for different sample types, and this approach should also be extendible to other types of data that contain regularly spaced discrete peaks. Graphical Abstract ᅟ.

  15. Algorithm for parametric community detection in networks.

    PubMed

    Bettinelli, Andrea; Hansen, Pierre; Liberti, Leo

    2012-07-01

    Modularity maximization is extensively used to detect communities in complex networks. It has been shown, however, that this method suffers from a resolution limit: Small communities may be undetectable in the presence of larger ones even if they are very dense. To alleviate this defect, various modifications of the modularity function have been proposed as well as multiresolution methods. In this paper we systematically study a simple model (proposed by Pons and Latapy [Theor. Comput. Sci. 412, 892 (2011)] and similar to the parametric model of Reichardt and Bornholdt [Phys. Rev. E 74, 016110 (2006)]) with a single parameter α that balances the fraction of within community edges and the expected fraction of edges according to the configuration model. An exact algorithm is proposed to find optimal solutions for all values of α as well as the corresponding successive intervals of α values for which they are optimal. This algorithm relies upon a routine for exact modularity maximization and is limited to moderate size instances. An agglomerative hierarchical heuristic is therefore proposed to address parametric modularity detection in large networks. At each iteration the smallest value of α for which it is worthwhile to merge two communities of the current partition is found. Then merging is performed and the data are updated accordingly. An implementation is proposed with the same time and space complexity as the well-known Clauset-Newman-Moore (CNM) heuristic [Phys. Rev. E 70, 066111 (2004)]. Experimental results on artificial and real world problems show that (i) communities are detected by both exact and heuristic methods for all values of the parameter α; (ii) the dendrogram summarizing the results of the heuristic method provides a useful tool for substantive analysis, as illustrated particularly on a Les Misérables data set; (iii) the difference between the parametric modularity values given by the exact method and those given by the heuristic is

  16. An adaptive algorithm for the detection of microcalcifications in simulated low-dose mammography.

    PubMed

    Treiber, O; Wanninger, F; Führ, H; Panzer, W; Regulla, D; Winkler, G

    2003-02-21

    This paper uses the task of microcalcification detection as a benchmark problem to assess the potential for dose reduction in x-ray mammography. We present the results of a newly developed algorithm for detection of microcalcifications as a case study for a typical commercial film-screen system (Kodak Min-R 2000/2190). The first part of the paper deals with the simulation of dose reduction for film-screen mammography based on a physical model of the imaging process. Use of a more sensitive film-screen system is expected to result in additional smoothing of the image. We introduce two different models of that behaviour, called moderate and strong smoothing. We then present an adaptive, model-based microcalcification detection algorithm. Comparing detection results with ground-truth images obtained under the supervision of an expert radiologist allows us to establish the soundness of the detection algorithm. We measure the performance on the dose-reduced images in order to assess the loss of information due to dose reduction. It turns out that the smoothing behaviour has a strong influence on detection rates. For moderate smoothing. a dose reduction by 25% has no serious influence on the detection results. whereas a dose reduction by 50% already entails a marked deterioration of the performance. Strong smoothing generally leads to an unacceptable loss of image quality. The test results emphasize the impact of the more sensitive film-screen system and its characteristics on the problem of assessing the potential for dose reduction in film-screen mammography. The general approach presented in the paper can be adapted to fully digital mammography.

  17. An adaptive algorithm for the detection of microcalcifications in simulated low-dose mammography

    NASA Astrophysics Data System (ADS)

    Treiber, O.; Wanninger, F.; Führ, H.; Panzer, W.; Regulla, D.; Winkler, G.

    2003-02-01

    This paper uses the task of microcalcification detection as a benchmark problem to assess the potential for dose reduction in x-ray mammography. We present the results of a newly developed algorithm for detection of microcalcifications as a case study for a typical commercial film-screen system (Kodak Min-R 2000/2190). The first part of the paper deals with the simulation of dose reduction for film-screen mammography based on a physical model of the imaging process. Use of a more sensitive film-screen system is expected to result in additional smoothing of the image. We introduce two different models of that behaviour, called moderate and strong smoothing. We then present an adaptive, model-based microcalcification detection algorithm. Comparing detection results with ground-truth images obtained under the supervision of an expert radiologist allows us to establish the soundness of the detection algorithm. We measure the performance on the dose-reduced images in order to assess the loss of information due to dose reduction. It turns out that the smoothing behaviour has a strong influence on detection rates. For moderate smoothing, a dose reduction by 25% has no serious influence on the detection results, whereas a dose reduction by 50% already entails a marked deterioration of the performance. Strong smoothing generally leads to an unacceptable loss of image quality. The test results emphasize the impact of the more sensitive film-screen system and its characteristics on the problem of assessing the potential for dose reduction in film-screen mammography. The general approach presented in the paper can be adapted to fully digital mammography.

  18. Wavefront aberrations of x-ray dynamical diffraction beams.

    PubMed

    Liao, Keliang; Hong, Youli; Sheng, Weifan

    2014-10-01

    The effects of dynamical diffraction in x-ray diffractive optics with large numerical aperture render the wavefront aberrations difficult to describe using the aberration polynomials, yet knowledge of them plays an important role in a vast variety of scientific problems ranging from optical testing to adaptive optics. Although the diffraction theory of optical aberrations was established decades ago, its application in the area of x-ray dynamical diffraction theory (DDT) is still lacking. Here, we conduct a theoretical study on the aberration properties of x-ray dynamical diffraction beams. By treating the modulus of the complex envelope as the amplitude weight function in the orthogonalization procedure, we generalize the nonrecursive matrix method for the determination of orthonormal aberration polynomials, wherein Zernike DDT and Legendre DDT polynomials are proposed. As an example, we investigate the aberration evolution inside a tilted multilayer Laue lens. The corresponding Legendre DDT polynomials are obtained numerically, which represent balanced aberrations yielding minimum variance of the classical aberrations of an anamorphic optical system. The balancing of classical aberrations and their standard deviations are discussed. We also present the Strehl ratio of the primary and secondary balanced aberrations.

  19. Rapid Change Detection Algorithm for Disaster Management

    NASA Astrophysics Data System (ADS)

    Michel, U.; Thunig, H.; Ehlers, M.; Reinartz, P.

    2012-07-01

    This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of absence of remote sensing know how. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.

  20. Testing of Lagrange multiplier damped least-squares control algorithm for woofer-tweeter adaptive optics.

    PubMed

    Zou, Weiyao; Burns, Stephen A

    2012-03-20

    A Lagrange multiplier-based damped least-squares control algorithm for woofer-tweeter (W-T) dual deformable-mirror (DM) adaptive optics (AO) is tested with a breadboard system. We show that the algorithm can complementarily command the two DMs to correct wavefront aberrations within a single optimization process: the woofer DM correcting the high-stroke, low-order aberrations, and the tweeter DM correcting the low-stroke, high-order aberrations. The optimal damping factor for a DM is found to be the median of the eigenvalue spectrum of the influence matrix of that DM. Wavefront control accuracy is maximized with the optimized control parameters. For the breadboard system, the residual wavefront error can be controlled to the precision of 0.03 μm in root mean square. The W-T dual-DM AO has applications in both ophthalmology and astronomy. © 2012 Optical Society of America

  1. Eliminating chromatic aberration of lens and recognition of thermal images with artificial intelligence applications

    NASA Astrophysics Data System (ADS)

    Fang, Yi-Chin; Wu, Bo-Wen; Lin, Wei-Tang; Jon, Jen-Liung

    2007-11-01

    Resolution and color are two main directions for measuring optical digital image, but it will be a hard work to integral improve the image quality of optical system, because there are many limits such as size, materials and environment of optical system design. Therefore, it is important to let blurred images as aberrations and noises or due to the characteristics of human vision as far distance and small targets to raise the capability of image recognition with artificial intelligence such as genetic algorithm and neural network in the condition that decreasing color aberration of optical system and not to increase complex calculation in the image processes. This study could achieve the goal of integral, economically and effectively to improve recognition and classification in low quality image from optical system and environment.

  2. Development and testing of an algorithm to detect implantable cardioverter-defibrillator lead failure.

    PubMed

    Gunderson, Bruce D; Gillberg, Jeffrey M; Wood, Mark A; Vijayaraman, Pugazhendhi; Shepard, Richard K; Ellenbogen, Kenneth A

    2006-02-01

    Implantable cardioverter-defibrillator (ICD) lead failures often present as inappropriate shock therapy. An algorithm that can reliably discriminate between ventricular tachyarrhythmias and noise due to lead failure may prevent patient discomfort and anxiety and avoid device-induced proarrhythmia by preventing inappropriate ICD shocks. The goal of this analysis was to test an ICD tachycardia detection algorithm that differentiates noise due to lead failure from ventricular tachyarrhythmias. We tested an algorithm that uses a measure of the ventricular intracardiac electrogram baseline to discriminate the sinus rhythm isoelectric line from the right ventricular coil-can (i.e., far-field) electrogram during oversensing of noise caused by a lead failure. The baseline measure was defined as the product of the sum (mV) and standard deviation (mV) of the voltage samples for a 188-ms window centered on each sensed electrogram. If the minimum baseline measure of the last 12 beats was <0.35 mV-mV, then the detected rhythm was considered noise due to a lead failure. The first ICD-detected episode of lead failure and inappropriate detection from 24 ICD patients with a pace/sense lead failure and all ventricular arrhythmias from 56 ICD patients without a lead failure were selected. The stored data were analyzed to determine the sensitivity and specificity of the algorithm to detect lead failures. The minimum baseline measure for the 24 lead failure episodes (0.28 +/- 0.34 mV-mV) was smaller than the 135 ventricular tachycardia (40.8 +/- 43.0 mV-mV, P <.0001) and 55 ventricular fibrillation episodes (19.1 +/- 22.8 mV-mV, P <.05). A minimum baseline <0.35 mV-mV threshold had a sensitivity of 83% (20/24) with a 100% (190/190) specificity. A baseline measure of the far-field electrogram had a high sensitivity and specificity to detect lead failure noise compared with ventricular tachycardia or fibrillation.

  3. Algorithm for Automated Detection of Edges of Clouds

    NASA Technical Reports Server (NTRS)

    Ward, Jennifer G.; Merceret, Francis J.

    2006-01-01

    An algorithm processes cloud-physics data gathered in situ by an aircraft, along with reflectivity data gathered by ground-based radar, to determine whether the aircraft is inside or outside a cloud at a given time. A cloud edge is deemed to be detected when the in/out state changes, subject to a hysteresis constraint. Such determinations are important in continuing research on relationships among lightning, electric charges in clouds, and decay of electric fields with distance from cloud edges.

  4. On the Definition of Aberration

    NASA Astrophysics Data System (ADS)

    Xu, Minghui; Wang, Guangli

    2014-12-01

    There was a groundbreaking step in the history of astronomy in 1728 when the effect of aberration was discovered by James Bradley (1693-1762). Recently, the solar acceleration, due to the variations in the aberrational effect of extragalactic sources caused by it, has been determined from VLBI observations with an uncertainty of about 0.5 mm{\\cdot}{s^{-1}}{\\cdot}{yr^{-1}} level. As a basic concept in astrometry with a nearly 300-year history, the definition of aberration, however, is still equivocal and discordant in the literature. It has been under continuing debate whether it depends on the relative motion between the observer and the observed source or only on the motion of the observer with respect to the frame of reference. In this paper, we will review the debate and the inconsistency in the definition of the aberration since the last century, and then discuss its definition in detail, which involves the discussions on the planetary aberration, the stellar aberration, the proper motion of an object during the travel time of light from the object to the observer, and the way of selecting the reference frame to express and distinguish the motions of the source and the observer. The aberration is essentially caused by the transformation between coordinate systems, and consequently quantified by the velocity of the observer with respect to the selected reference frame, independent of the motion of the source. Obviously, this nature is totally different from that of the definition given by the IAU WG NFA (Capitaine, 2007) in 2006, which is stated as, ``the apparent angular displacement of the observed position of a celestial object from its geometric position, caused by the finite velocity of light in combination with the motions of the observer and of the observed object.''

  5. Detection and tracking of a moving target using SAR images with the particle filter-based track-before-detect algorithm.

    PubMed

    Gao, Han; Li, Jingwen

    2014-06-19

    A novel approach to detecting and tracking a moving target using synthetic aperture radar (SAR) images is proposed in this paper. Achieved with the particle filter (PF) based track-before-detect (TBD) algorithm, the approach is capable of detecting and tracking the low signal-to-noise ratio (SNR) moving target with SAR systems, which the traditional track-after-detect (TAD) approach is inadequate for. By incorporating the signal model of the SAR moving target into the algorithm, the ambiguity in target azimuth position and radial velocity is resolved while tracking, which leads directly to the true estimation. With the sub-area substituted for the whole area to calculate the likelihood ratio and a pertinent choice of the number of particles, the computational efficiency is improved with little loss in the detection and tracking performance. The feasibility of the approach is validated and the performance is evaluated with Monte Carlo trials. It is demonstrated that the proposed approach is capable to detect and track a moving target with SNR as low as 7 dB, and outperforms the traditional TAD approach when the SNR is below 14 dB.

  6. Detection and Tracking of a Moving Target Using SAR Images with the Particle Filter-Based Track-Before-Detect Algorithm

    PubMed Central

    Gao, Han; Li, Jingwen

    2014-01-01

    A novel approach to detecting and tracking a moving target using synthetic aperture radar (SAR) images is proposed in this paper. Achieved with the particle filter (PF) based track-before-detect (TBD) algorithm, the approach is capable of detecting and tracking the low signal-to-noise ratio (SNR) moving target with SAR systems, which the traditional track-after-detect (TAD) approach is inadequate for. By incorporating the signal model of the SAR moving target into the algorithm, the ambiguity in target azimuth position and radial velocity is resolved while tracking, which leads directly to the true estimation. With the sub-area substituted for the whole area to calculate the likelihood ratio and a pertinent choice of the number of particles, the computational efficiency is improved with little loss in the detection and tracking performance. The feasibility of the approach is validated and the performance is evaluated with Monte Carlo trials. It is demonstrated that the proposed approach is capable to detect and track a moving target with SNR as low as 7 dB, and outperforms the traditional TAD approach when the SNR is below 14 dB. PMID:24949640

  7. Canine urothelial carcinoma: genomically aberrant and comparatively relevant

    PubMed Central

    Shapiro, S. G.; Raghunath, S.; Williams, C.; Motsinger-Reif, A. A.; Cullen, J. M.; Liu, T.; Albertson, D.; Ruvolo, M.; Lucas, A. Bergstrom; Jin, J.; Knapp, D. W.; Schiffman, J. D.

    2015-01-01

    Urothelial carcinoma (UC), also referred to as transitional cell carcinoma (TCC), is the most common bladder malignancy in both human and canine populations. In human UC, numerous studies have demonstrated the prevalence of chromosomal imbalances. Although the histopathology of the disease is similar in both species, studies evaluating the genomic profile of canine UC are lacking, limiting the discovery of key comparative molecular markers associated with driving UC pathogenesis. In the present study, we evaluated 31 primary canine UC biopsies by oligonucleotide array comparative genomic hybridization (oaCGH). Results highlighted the presence of three highly recurrent numerical aberrations: gain of dog chromosome (CFA) 13 and 36 and loss of CFA 19. Regional gains of CFA 13 and 36 were present in 97% and 84% of cases, respectively, and losses on CFA 19 were present in 77% of cases. Fluorescence in situ hybridization (FISH), using targeted bacterial artificial chromosome (BAC) clones and custom Agilent SureFISH probes, was performed to detect and quantify these regions in paraffin-embedded biopsy sections and urine-derived urothelial cells. The data indicate that these three aberrations are potentially diagnostic of UC. Comparison of our canine oaCGH data with that of 285 human cases identified a series of shared copy number aberrations. Using an informatics approach to interrogate the frequency of copy number aberrations across both species, we identified those that had the highest joint probability of association with UC. The most significant joint region contained the gene PABPC1, which should be considered further for its role in UC progression. In addition, cross-species filtering of genome-wide copy number data highlighted several genes as high-profile candidates for further analysis, including CDKN2A, S100A8/9, and LRP1B. We propose that these common aberrations are indicative of an evolutionarily conserved mechanism of pathogenesis and harbor genes key to

  8. Canine urothelial carcinoma: genomically aberrant and comparatively relevant.

    PubMed

    Shapiro, S G; Raghunath, S; Williams, C; Motsinger-Reif, A A; Cullen, J M; Liu, T; Albertson, D; Ruvolo, M; Bergstrom Lucas, A; Jin, J; Knapp, D W; Schiffman, J D; Breen, M

    2015-06-01

    Urothelial carcinoma (UC), also referred to as transitional cell carcinoma (TCC), is the most common bladder malignancy in both human and canine populations. In human UC, numerous studies have demonstrated the prevalence of chromosomal imbalances. Although the histopathology of the disease is similar in both species, studies evaluating the genomic profile of canine UC are lacking, limiting the discovery of key comparative molecular markers associated with driving UC pathogenesis. In the present study, we evaluated 31 primary canine UC biopsies by oligonucleotide array comparative genomic hybridization (oaCGH). Results highlighted the presence of three highly recurrent numerical aberrations: gain of dog chromosome (CFA) 13 and 36 and loss of CFA 19. Regional gains of CFA 13 and 36 were present in 97 % and 84 % of cases, respectively, and losses on CFA 19 were present in 77 % of cases. Fluorescence in situ hybridization (FISH), using targeted bacterial artificial chromosome (BAC) clones and custom Agilent SureFISH probes, was performed to detect and quantify these regions in paraffin-embedded biopsy sections and urine-derived urothelial cells. The data indicate that these three aberrations are potentially diagnostic of UC. Comparison of our canine oaCGH data with that of 285 human cases identified a series of shared copy number aberrations. Using an informatics approach to interrogate the frequency of copy number aberrations across both species, we identified those that had the highest joint probability of association with UC. The most significant joint region contained the gene PABPC1, which should be considered further for its role in UC progression. In addition, cross-species filtering of genome-wide copy number data highlighted several genes as high-profile candidates for further analysis, including CDKN2A, S100A8/9, and LRP1B. We propose that these common aberrations are indicative of an evolutionarily conserved mechanism of pathogenesis and harbor genes

  9. Analysis of the moderate resolution imaging spectroradiometer contextual algorithm for small fire detection

    Treesearch

    W. Wang; J.J. Qu; X. Hao; Y. Liu

    2009-01-01

    In the southeastern United States, most wildland fires are of low intensity. Asubstantial number of these fires cannot be detected by the MODIS contextual algorithm. Toimprove the accuracy of fire detection for this region, the remote-sensed characteristics ofthese fires have to be systematically...

  10. Minimizing camera-eye optical aberrations during the 3D reconstruction of retinal structures

    NASA Astrophysics Data System (ADS)

    Aldana-Iuit, Javier; Martinez-Perez, M. Elena; Espinosa-Romero, Arturo; Diaz-Uribe, Rufino

    2010-05-01

    3D reconstruction of blood vessels is a powerful visualization tool for physicians, since it allows them to refer to qualitative representation of their subject of study. In this paper we propose a 3D reconstruction method of retinal vessels from fundus images. The reconstruction method propose herein uses images of the same retinal structure in epipolar geometry. Images are preprocessed by RISA system for segmenting blood vessels and obtaining feature points for correspondences. The correspondence points process is solved using correlation. The LMedS analysis and Graph Transformation Matching algorithm are used for outliers suppression. Camera projection matrices are computed with the normalized eight point algorithm. Finally, we retrieve 3D position of the retinal tree points by linear triangulation. In order to increase the power of visualization, 3D tree skeletons are represented by surfaces via generalized cylinders whose radius correspond to morphological measurements obtained by RISA. In this paper the complete calibration process including the fundus camera and the optical properties of the eye, the so called camera-eye system is proposed. On one hand, the internal parameters of the fundus camera are obtained by classical algorithms using a reference pattern. On the other hand, we minimize the undesirable efects of the aberrations induced by the eyeball optical system assuming that contact enlarging lens corrects astigmatism, spherical and coma aberrations are reduced changing the aperture size and eye refractive errors are suppressed adjusting camera focus during image acquisition. Evaluation of two self-calibration proposals and results of 3D blood vessel surface reconstruction are presented.

  11. Plagiarism Detection Algorithm for Source Code in Computer Science Education

    ERIC Educational Resources Information Center

    Liu, Xin; Xu, Chan; Ouyang, Boyu

    2015-01-01

    Nowadays, computer programming is getting more necessary in the course of program design in college education. However, the trick of plagiarizing plus a little modification exists among some students' home works. It's not easy for teachers to judge if there's plagiarizing in source code or not. Traditional detection algorithms cannot fit this…

  12. Simulation of Automatic Incidents Detection Algorithm on the Transport Network

    ERIC Educational Resources Information Center

    Nikolaev, Andrey B.; Sapego, Yuliya S.; Jakubovich, Anatolij N.; Berner, Leonid I.; Ivakhnenko, Andrey M.

    2016-01-01

    Management of traffic incident is a functional part of the whole approach to solving traffic problems in the framework of intelligent transport systems. Development of an effective process of traffic incident management is an important part of the transport system. In this research, it's suggested algorithm based on fuzzy logic to detect traffic…

  13. Effect of monochromatic aberrations on photorefractive patterns

    NASA Astrophysics Data System (ADS)

    Campbell, Melanie C. W.; Bobier, W. R.; Roorda, A.

    1995-08-01

    Photorefractive methods have become popular in the measurement of refractive and accommodative states of infants and children owing to their photographic nature and rapid speed of measurement. As in the case of any method that measures the refractive state of the human eye, monochromatic aberrations will reduce the accuracy of the measurement. Monochromatic aberrations cannot be as easily predicted or controlled as chromatic aberrations during the measurement, and accordingly they will introduce measurement errors. This study defines this error or uncertainty by extending the existing paraxial optical analyses of coaxial and eccentric photorefraction. This new optical analysis predicts that, for the amounts of spherical aberration (SA) reported for the human eye, there will be a significant degree of measurement uncertainty introduced for all photorefractive methods. The dioptric amount of this uncertainty may exceed the maximum amount of SA present in the eye. The calculated effects on photorefractive measurement of a real eye with a mixture of spherical aberration and coma are shown to be significant. The ability, developed here, to predict photorefractive patterns corresponding to different amounts and types of monochromatic aberration may in the future lead to an extension of photorefractive methods to the dual measurement of refractive states and aberrations of individual eyes. aberration, retinal image quality,

  14. Syndromic Algorithms for Detection of Gambiense Human African Trypanosomiasis in South Sudan

    PubMed Central

    Palmer, Jennifer J.; Surur, Elizeous I.; Goch, Garang W.; Mayen, Mangar A.; Lindner, Andreas K.; Pittet, Anne; Kasparian, Serena; Checchi, Francesco; Whitty, Christopher J. M.

    2013-01-01

    Background Active screening by mobile teams is considered the best method for detecting human African trypanosomiasis (HAT) caused by Trypanosoma brucei gambiense but the current funding context in many post-conflict countries limits this approach. As an alternative, non-specialist health care workers (HCWs) in peripheral health facilities could be trained to identify potential cases who need testing based on their symptoms. We explored the predictive value of syndromic referral algorithms to identify symptomatic cases of HAT among a treatment-seeking population in Nimule, South Sudan. Methodology/Principal Findings Symptom data from 462 patients (27 cases) presenting for a HAT test via passive screening over a 7 month period were collected to construct and evaluate over 14,000 four item syndromic algorithms considered simple enough to be used by peripheral HCWs. For comparison, algorithms developed in other settings were also tested on our data, and a panel of expert HAT clinicians were asked to make referral decisions based on the symptom dataset. The best performing algorithms consisted of three core symptoms (sleep problems, neurological problems and weight loss), with or without a history of oedema, cervical adenopathy or proximity to livestock. They had a sensitivity of 88.9–92.6%, a negative predictive value of up to 98.8% and a positive predictive value in this context of 8.4–8.7%. In terms of sensitivity, these out-performed more complex algorithms identified in other studies, as well as the expert panel. The best-performing algorithm is predicted to identify about 9/10 treatment-seeking HAT cases, though only 1/10 patients referred would test positive. Conclusions/Significance In the absence of regular active screening, improving referrals of HAT patients through other means is essential. Systematic use of syndromic algorithms by peripheral HCWs has the potential to increase case detection and would increase their participation in HAT programmes. The

  15. Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm.

    PubMed

    Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

    2016-10-01

    Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.

  16. Towards Real-Time Detection of Gait Events on Different Terrains Using Time-Frequency Analysis and Peak Heuristics Algorithm

    PubMed Central

    Zhou, Hui; Ji, Ning; Samuel, Oluwarotimi Williams; Cao, Yafei; Zhao, Zheyi; Chen, Shixiong; Li, Guanglin

    2016-01-01

    Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO) and heel strike (HS) gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses. PMID:27706086

  17. Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System

    NASA Astrophysics Data System (ADS)

    Nawir, Mukrimah; Amir, Amiza; Lynn, Ong Bi; Yaakob, Naimah; Badlishah Ahmad, R.

    2018-05-01

    The rapid growth of technologies might endanger them to various network attacks due to the nature of data which are frequently exchange their data through Internet and large-scale data that need to be handle. Moreover, network anomaly detection using machine learning faced difficulty when dealing the involvement of dataset where the number of labelled network dataset is very few in public and this caused many researchers keep used the most commonly network dataset (KDDCup99) which is not relevant to employ the machine learning (ML) algorithms for a classification. Several issues regarding these available labelled network datasets are discussed in this paper. The aim of this paper to build a network anomaly detection system using machine learning algorithms that are efficient, effective and fast processing. The finding showed that AODE algorithm is performed well in term of accuracy and processing time for binary classification towards UNSW-NB15 dataset.

  18. A novel ship CFAR detection algorithm based on adaptive parameter enhancement and wake-aided detection in SAR images

    NASA Astrophysics Data System (ADS)

    Meng, Siqi; Ren, Kan; Lu, Dongming; Gu, Guohua; Chen, Qian; Lu, Guojun

    2018-03-01

    Synthetic aperture radar (SAR) is an indispensable and useful method for marine monitoring. With the increase of SAR sensors, high resolution images can be acquired and contain more target structure information, such as more spatial details etc. This paper presents a novel adaptive parameter transform (APT) domain constant false alarm rate (CFAR) to highlight targets. The whole method is based on the APT domain value. Firstly, the image is mapped to the new transform domain by the algorithm. Secondly, the false candidate target pixels are screened out by the CFAR detector to highlight the target ships. Thirdly, the ship pixels are replaced by the homogeneous sea pixels. And then, the enhanced image is processed by Niblack algorithm to obtain the wake binary image. Finally, normalized Hough transform (NHT) is used to detect wakes in the binary image, as a verification of the presence of the ships. Experiments on real SAR images validate that the proposed transform does enhance the target structure and improve the contrast of the image. The algorithm has a good performance in the ship and ship wake detection.

  19. Aberration influence and active compensation on laser mode properties for asymmetric folded resonators

    NASA Astrophysics Data System (ADS)

    Zhang, Xiang; Hu, Zhiqiu; Yang, Wentao; Su, Likun

    2017-09-01

    We demonstrate the influence on mode features with introducing typical intracavity perturbation and results of aberrated wavefront compensation in a folded-type unstable resonator used in high energy lasers. The mode properties and aberration coefficient with intracavity misalignment are achieved by iterative calculation and Zernike polynomial fitting. Experimental results for the relation of intracavity maladjustment and mode characteristics are further obtained in terms of S-H detection and model wavefront reconstruction. It indicates that intracavity phase perturbation has significant influence on out coupling beam properties, and the uniform and symmetry of the mode is rapidly disrupted even by a slight misalignment of the resonator mirrors. Meanwhile, the far-field beam patterns will obviously degrade with increasing the distance between the convex mirror and the phase perturbation position even if the equivalent disturbation is inputted into such the resonator. The closed-loop device for compensating intracavity low order aberration is successfully fabricated. Moreover, Zernike defocus aberration is also effectively controlled by precisely adjusting resonator length, and the beam quality is noticeably improved.

  20. Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms

    PubMed Central

    Liu, Min-Yin; Huang, Adam; Huang, Norden E.

    2017-01-01

    Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz) measured by electroencephalography (EEG) mostly during non-rapid eye movement (NREM) stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1) the lack of common benchmark databases, and (2) the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA), the Strength Pareto Evolutionary Algorithm (SPEA2), to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT), and two Hilbert-Huang transform (HHT) based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737. PMID:28572762

  1. Interactions between pacing and arrhythmia detection algorithms in the dual chamber implantable cardioverter defibrillator.

    PubMed

    Dijkman, B; Wellens, H J

    2001-09-01

    Dual chamber implantable cardioverter defibrillator (ICD) combines the possibility to detect and treat ventricular and atrial arrhythmias with the possibility of modern heart stimulation techniques. Advanced pacing algorithms together with extended arrhythmia detection capabilities can give rise to new types of device-device interactions. Some of the possible interactions are illustrated by four cases documented in four models of dual chamber ICDs. Functioning of new features in dual chamber devices is influenced by the fact that the pacemaker is not a separate device but a part of the ICD system and that both are being used in a patient with arrhythmia. Programming measures are suggested to optimize use of new pacing algorithms while maintaining correct arrhythmia detection.

  2. An Algorithm for Real-Time Pulse Waveform Segmentation and Artifact Detection in Photoplethysmograms.

    PubMed

    Fischer, Christoph; Domer, Benno; Wibmer, Thomas; Penzel, Thomas

    2017-03-01

    Photoplethysmography has been used in a wide range of medical devices for measuring oxygen saturation, cardiac output, assessing autonomic function, and detecting peripheral vascular disease. Artifacts can render the photoplethysmogram (PPG) useless. Thus, algorithms capable of identifying artifacts are critically important. However, the published PPG algorithms are limited in algorithm and study design. Therefore, the authors developed a novel embedded algorithm for real-time pulse waveform (PWF) segmentation and artifact detection based on a contour analysis in the time domain. This paper provides an overview about PWF and artifact classifications, presents the developed PWF analysis, and demonstrates the implementation on a 32-bit ARM core microcontroller. The PWF analysis was validated with data records from 63 subjects acquired in a sleep laboratory, ergometry laboratory, and intensive care unit in equal parts. The output of the algorithm was compared with harmonized experts' annotations of the PPG with a total duration of 31.5 h. The algorithm achieved a beat-to-beat comparison sensitivity of 99.6%, specificity of 90.5%, precision of 98.5%, and accuracy of 98.3%. The interrater agreement expressed as Cohen's kappa coefficient was 0.927 and as F-measure was 0.990. In conclusion, the PWF analysis seems to be a suitable method for PPG signal quality determination, real-time annotation, data compression, and calculation of additional pulse wave metrics such as amplitude, duration, and rise time.

  3. Comparison and optimization of radar-based hail detection algorithms in Slovenia

    NASA Astrophysics Data System (ADS)

    Stržinar, Gregor; Skok, Gregor

    2018-05-01

    Four commonly used radar-based hail detection algorithms are evaluated and optimized in Slovenia. The algorithms are verified against ground observations of hail at manned stations in the period between May and August, from 2002 to 2010. The algorithms are optimized by determining the optimal values of all possible algorithm parameters. A number of different contingency-table-based scores are evaluated with a combination of Critical Success Index and frequency bias proving to be the best choice for optimization. The best performance indexes are given by Waldvogel and the severe hail index, followed by vertically integrated liquid and maximum radar reflectivity. Using the optimal parameter values, a hail frequency climatology map for the whole of Slovenia is produced. The analysis shows that there is a considerable variability of hail occurrence within the Republic of Slovenia. The hail frequency ranges from almost 0 to 1.7 hail days per year with an average value of about 0.7 hail days per year.

  4. Matched-filter algorithm for subpixel spectral detection in hyperspectral image data

    NASA Astrophysics Data System (ADS)

    Borough, Howard C.

    1991-11-01

    Hyperspectral imagery, spatial imagery with associated wavelength data for every pixel, offers a significant potential for improved detection and identification of certain classes of targets. The ability to make spectral identifications of objects which only partially fill a single pixel (due to range or small size) is of considerable interest. Multiband imagery such as Landsat's 5 and 7 band imagery has demonstrated significant utility in the past. Hyperspectral imaging systems with hundreds of spectral bands offer improved performance. To explore the application of differentpixel spectral detection algorithms a synthesized set of hyperspectral image data (hypercubes) was generated utilizing NASA earth resources and other spectral data. The data was modified using LOWTRAN 7 to model the illumination, atmospheric contributions, attenuations and viewing geometry to represent a nadir view from 10,000 ft. altitude. The base hypercube (HC) represented 16 by 21 spatial pixels with 101 wavelength samples from 0.5 to 2.5 micrometers for each pixel. Insertions were made into the base data to provide random location, random pixel percentage, and random material. Fifteen different hypercubes were generated for blind testing of candidate algorithms. An algorithm utilizing a matched filter in the spectral dimension proved surprisingly good yielding 100% detections for pixels filled greater than 40% with a standard camouflage paint, and a 50% probability of detection for pixels filled 20% with the paint, with no false alarms. The false alarm rate as a function of the number of spectral bands in the range from 101 to 12 bands was measured and found to increase from zero to 50% illustrating the value of a large number of spectral bands. This test was on imagery without system noise; the next step is to incorporate typical system noise sources.

  5. A Monochromatic, Aberration-Corrected, Dual-Beam Low Energy Electron Microscope

    PubMed Central

    Mankos, Marian; Shadman, Khashayar

    2013-01-01

    The monochromatic, aberration-corrected, dual-beam low energy electron microscope (MAD-LEEM) is a novel instrument aimed at imaging of nanostructures and surfaces at sub-nanometer resolution that includes a monochromator, aberration corrector and dual beam illumination. The monochromator reduces the energy spread of the illuminating electron beam, which significantly improves spectroscopic and spatial resolution. The aberration corrector utilizes an electron mirror with negative aberrations that can be used to compensate the aberrations of the LEEM objective lens for a range of electron energies. Dual flood illumination eliminates charging generated when a conventional LEEM is used to image insulating specimens. MAD-LEEM is designed for the purpose of imaging biological and insulating specimens, which are difficult to image with conventional LEEM, Low-Voltage SEM, and TEM instruments. The MAD-LEEM instrument can also be used as a general purpose LEEM with significantly improved resolution. The low impact energy of the electrons is critical for avoiding beam damage, as high energy electrons with keV kinetic energies used in SEMs and TEMs cause irreversible change to many specimens, in particular biological materials. A potential application for MAD-LEEM is in DNA sequencing, which demands imaging techniques that enable DNA sequencing at high resolution and speed, and at low cost. The key advantages of the MAD-LEEM approach for this application are the low electron impact energies, the long read lengths, and the absence of heavy-atom DNA labeling. Image contrast simulations of the detectability of individual nucleotides in a DNA strand have been developed in order to refine the optics blur and DNA base contrast requirements for this application. PMID:23582636

  6. A monochromatic, aberration-corrected, dual-beam low energy electron microscope.

    PubMed

    Mankos, Marian; Shadman, Khashayar

    2013-07-01

    The monochromatic, aberration-corrected, dual-beam low energy electron microscope (MAD-LEEM) is a novel instrument aimed at imaging of nanostructures and surfaces at sub-nanometer resolution that includes a monochromator, aberration corrector and dual beam illumination. The monochromator reduces the energy spread of the illuminating electron beam, which significantly improves spectroscopic and spatial resolution. The aberration corrector utilizes an electron mirror with negative aberrations that can be used to compensate the aberrations of the LEEM objective lens for a range of electron energies. Dual flood illumination eliminates charging generated when a conventional LEEM is used to image insulating specimens. MAD-LEEM is designed for the purpose of imaging biological and insulating specimens, which are difficult to image with conventional LEEM, Low-Voltage SEM, and TEM instruments. The MAD-LEEM instrument can also be used as a general purpose LEEM with significantly improved resolution. The low impact energy of the electrons is critical for avoiding beam damage, as high energy electrons with keV kinetic energies used in SEMs and TEMs cause irreversible change to many specimens, in particular biological materials. A potential application for MAD-LEEM is in DNA sequencing, which demands imaging techniques that enable DNA sequencing at high resolution and speed, and at low cost. The key advantages of the MAD-LEEM approach for this application are the low electron impact energies, the long read lengths, and the absence of heavy-atom DNA labeling. Image contrast simulations of the detectability of individual nucleotides in a DNA strand have been developed in order to refine the optics blur and DNA base contrast requirements for this application. Copyright © 2013 Elsevier B.V. All rights reserved.

  7. Phase retrieval algorithm for JWST Flight and Testbed Telescope

    NASA Astrophysics Data System (ADS)

    Dean, Bruce H.; Aronstein, David L.; Smith, J. Scott; Shiri, Ron; Acton, D. Scott

    2006-06-01

    An image-based wavefront sensing and control algorithm for the James Webb Space Telescope (JWST) is presented. The algorithm heritage is discussed in addition to implications for algorithm performance dictated by NASA's Technology Readiness Level (TRL) 6. The algorithm uses feedback through an adaptive diversity function to avoid the need for phase-unwrapping post-processing steps. Algorithm results are demonstrated using JWST Testbed Telescope (TBT) commissioning data and the accuracy is assessed by comparison with interferometer results on a multi-wave phase aberration. Strategies for minimizing aliasing artifacts in the recovered phase are presented and orthogonal basis functions are implemented for representing wavefronts in irregular hexagonal apertures. Algorithm implementation on a parallel cluster of high-speed digital signal processors (DSPs) is also discussed.

  8. Computer algorithms for automated detection and analysis of local Ca2+ releases in spontaneously beating cardiac pacemaker cells

    PubMed Central

    Kim, Mary S.; Tsutsui, Kenta; Stern, Michael D.; Lakatta, Edward G.; Maltsev, Victor A.

    2017-01-01

    Local Ca2+ Releases (LCRs) are crucial events involved in cardiac pacemaker cell function. However, specific algorithms for automatic LCR detection and analysis have not been developed in live, spontaneously beating pacemaker cells. In the present study we measured LCRs using a high-speed 2D-camera in spontaneously contracting sinoatrial (SA) node cells isolated from rabbit and guinea pig and developed a new algorithm capable of detecting and analyzing the LCRs spatially in two-dimensions, and in time. Our algorithm tracks points along the midline of the contracting cell. It uses these points as a coordinate system for affine transform, producing a transformed image series where the cell does not contract. Action potential-induced Ca2+ transients and LCRs were thereafter isolated from recording noise by applying a series of spatial filters. The LCR birth and death events were detected by a differential (frame-to-frame) sensitivity algorithm applied to each pixel (cell location). An LCR was detected when its signal changes sufficiently quickly within a sufficiently large area. The LCR is considered to have died when its amplitude decays substantially, or when it merges into the rising whole cell Ca2+ transient. Ultimately, our algorithm provides major LCR parameters such as period, signal mass, duration, and propagation path area. As the LCRs propagate within live cells, the algorithm identifies splitting and merging behaviors, indicating the importance of locally propagating Ca2+-induced-Ca2+-release for the fate of LCRs and for generating a powerful ensemble Ca2+ signal. Thus, our new computer algorithms eliminate motion artifacts and detect 2D local spatiotemporal events from recording noise and global signals. While the algorithms were developed to detect LCRs in sinoatrial nodal cells, they have the potential to be used in other applications in biophysics and cell physiology, for example, to detect Ca2+ wavelets (abortive waves), sparks and embers in muscle

  9. Algorithm for Detecting a Bright Spot in an Image

    NASA Technical Reports Server (NTRS)

    2009-01-01

    An algorithm processes the pixel intensities of a digitized image to detect and locate a circular bright spot, the approximate size of which is known in advance. The algorithm is used to find images of the Sun in cameras aboard the Mars Exploration Rovers. (The images are used in estimating orientations of the Rovers relative to the direction to the Sun.) The algorithm can also be adapted to tracking of circular shaped bright targets in other diverse applications. The first step in the algorithm is to calculate a dark-current ramp a correction necessitated by the scheme that governs the readout of pixel charges in the charge-coupled-device camera in the original Mars Exploration Rover application. In this scheme, the fraction of each frame period during which dark current is accumulated in a given pixel (and, hence, the dark-current contribution to the pixel image-intensity reading) is proportional to the pixel row number. For the purpose of the algorithm, the dark-current contribution to the intensity reading from each pixel is assumed to equal the average of intensity readings from all pixels in the same row, and the factor of proportionality is estimated on the basis of this assumption. Then the product of the row number and the factor of proportionality is subtracted from the reading from each pixel to obtain a dark-current-corrected intensity reading. The next step in the algorithm is to determine the best location, within the overall image, for a window of N N pixels (where N is an odd number) large enough to contain the bright spot of interest plus a small margin. (In the original application, the overall image contains 1,024 by 1,024 pixels, the image of the Sun is about 22 pixels in diameter, and N is chosen to be 29.)

  10. Railway obstacle detection algorithm using neural network

    NASA Astrophysics Data System (ADS)

    Yu, Mingyang; Yang, Peng; Wei, Sen

    2018-05-01

    Aiming at the difficulty of detection of obstacle in outdoor railway scene, a data-oriented method based on neural network to obtain image objects is proposed. First, we mark objects of images(such as people, trains, animals) acquired on the Internet. and then use the residual learning units to build Fast R-CNN framework. Then, the neural network is trained to get the target image characteristics by using stochastic gradient descent algorithm. Finally, a well-trained model is used to identify an outdoor railway image. if it includes trains and other objects, it will issue an alert. Experiments show that the correct rate of warning reached 94.85%.

  11. Real-time 3-D contrast-enhanced transcranial ultrasound and aberration correction.

    PubMed

    Ivancevich, Nikolas M; Pinton, Gianmarco F; Nicoletto, Heather A; Bennett, Ellen; Laskowitz, Daniel T; Smith, Stephen W

    2008-09-01

    Contrast-enhanced (CE) transcranial ultrasound (US) and reconstructed 3-D transcranial ultrasound have shown advantages over traditional methods in a variety of cerebrovascular diseases. We present the results from a novel ultrasound technique, namely real-time 3-D contrast-enhanced transcranial ultrasound. Using real-time 3-D (RT3D) ultrasound and microbubble contrast agent, we scanned 17 healthy volunteers via a single temporal window and nine via the suboccipital window and report our detection rates for the major cerebral vessels. In 71% of subjects, both of our observers identified the ipsilateral circle of Willis from the temporal window, and in 59% we imaged the entire circle of Willis. From the suboccipital window, both observers detected the entire vertebrobasilar circulation in 22% of subjects, and in 44%, the basilar artery. After performing phase aberration correction on one subject, we were able to increase the diagnostic value of the scan, detecting a vessel not present in the uncorrected scan. These preliminary results suggest that RT3D CE transcranial US and RT3D CE transcranial US with phase aberration correction have the potential to greatly impact the field of neurosonology.

  12. Real-Time 3D Contrast-Enhanced Transcranial Ultrasound and Aberration Correction

    PubMed Central

    Ivancevich, Nikolas M.; Pinton, Gianmarco F.; Nicoletto, Heather A.; Bennett, Ellen; Laskowitz, Daniel T.; Smith, Stephen W.

    2008-01-01

    Contrast-enhanced (CE) transcranial ultrasound (US) and reconstructed 3D transcranial ultrasound have shown advantages over traditional methods in a variety of cerebrovascular diseases. We present the results from a novel ultrasound technique, namely real-time 3D contrast-enhanced transcranial ultrasound. Using real-time 3D (RT3D) ultrasound and micro-bubble contrast agent, we scanned 17 healthy volunteers via a single temporal window and 9 via the sub-occipital window and report our detection rates for the major cerebral vessels. In 71% of subjects, both of our observers identified the ipsilateral circle of Willis from the temporal window, and in 59% we imaged the entire circle of Willis. From the sub-occipital window, both observers detected the entire vertebrobasilar circulation in 22% of subjects, and in 44% the basilar artery. After performing phase aberration correction on one subject, we were able to increase the diagnostic value of the scan, detecting a vessel not present in the uncorrected scan. These preliminary results suggest that RT3D CE transcranial US and RT3D CE transcranial US with phase aberration correction have the potential to greatly impact the field of neurosonology. PMID:18395321

  13. Imaging characteristics of Zernike and annular polynomial aberrations.

    PubMed

    Mahajan, Virendra N; Díaz, José Antonio

    2013-04-01

    The general equations for the point-spread function (PSF) and optical transfer function (OTF) are given for any pupil shape, and they are applied to optical imaging systems with circular and annular pupils. The symmetry properties of the PSF, the real and imaginary parts of the OTF, and the modulation transfer function (MTF) of a system with a circular pupil aberrated by a Zernike circle polynomial aberration are derived. The interferograms and PSFs are illustrated for some typical polynomial aberrations with a sigma value of one wave, and 3D PSFs and MTFs are shown for 0.1 wave. The Strehl ratio is also calculated for polynomial aberrations with a sigma value of 0.1 wave, and shown to be well estimated from the sigma value. The numerical results are compared with the corresponding results in the literature. Because of the same angular dependence of the corresponding annular and circle polynomial aberrations, the symmetry properties of systems with annular pupils aberrated by an annular polynomial aberration are the same as those for a circular pupil aberrated by a corresponding circle polynomial aberration. They are also illustrated with numerical examples.

  14. Validation of vision-based obstacle detection algorithms for low-altitude helicopter flight

    NASA Technical Reports Server (NTRS)

    Suorsa, Raymond; Sridhar, Banavar

    1991-01-01

    A validation facility being used at the NASA Ames Research Center is described which is aimed at testing vision based obstacle detection and range estimation algorithms suitable for low level helicopter flight. The facility is capable of processing hundreds of frames of calibrated multicamera 6 degree-of-freedom motion image sequencies, generating calibrated multicamera laboratory images using convenient window-based software, and viewing range estimation results from different algorithms along with truth data using powerful window-based visualization software.

  15. Nodal aberration theory applied to freeform surfaces

    NASA Astrophysics Data System (ADS)

    Fuerschbach, Kyle; Rolland, Jannick P.; Thompson, Kevin P.

    2014-12-01

    When new three-dimensional packages are developed for imaging optical systems, the rotational symmetry of the optical system is often broken, changing its imaging behavior and making the optical performance worse. A method to restore the performance is to use freeform optical surfaces that compensate directly the aberrations introduced from tilting and decentering the optical surfaces. In order to effectively optimize the shape of a freeform surface to restore optical functionality, it is helpful to understand the aberration effect the surface may induce. Using nodal aberration theory the aberration fields induced by a freeform surface in an optical system are explored. These theoretical predications are experimentally validated with the design and implementation of an aberration generating telescope.

  16. Corneal spherical aberration in Saudi population

    PubMed Central

    Al-Sayyari, Tarfah M.; Fawzy, Samah M.; Al-Saleh, Ahmed A.

    2014-01-01

    Purpose To find out the mean corneal spherical aberration and its changes with age in Saudi population. Setting AlHokama Eye Specialist Center, Riyadh, Saudi Arabia. Methods Three hundred (300) eyes of 185 Saudi subjects (97 men and 88 women), whose age ranged from 15 to 85 years old, with matched refractive errors, were divided into three groups according to their age, 100 for each. All the subjects were included in measuring the spherical aberration (SA) using pentacam HR (OCULUS, Germany) at the 6-mm optical zone. Results The mean corneal spherical aberration (CSA) of the fourth order (Z40) of the whole groups was 0.252 ± 0.1154 μm. Patients from 15 to 35 years old have root mean square (RMS) of CSA of 0.2068 ± 0.07151 μm, 0.2370 ± 0.08023 μm was the RMS of CSA of the patients from 35 to 50 years old, while those from 50 to 85 years old have a CSA-RMS of 0.31511 ± 0.1503 μm (P < 0.0001). A positive correlation was found between the spherical aberration (Z40) and the progress of age (r = 0.3429, P < 0.0001). The high order aberration (HOA) presented 28.1% of the total corneal aberrations. While the fourth order corneal spherical aberration constituted 57% of the HOA and 16% of the total aberration. The pupil diameter shows a negative correlation with the increase in age (P = 0.0012). Conclusion Our results showed a CSA (Z40) that is varied among the population, comparable to other studies, and significantly correlates to the progress of age. PMID:25278799

  17. Evaluation of a global algorithm for wavefront reconstruction for Shack-Hartmann wave-front sensors and thick fundus reflectors.

    PubMed

    Liu, Tao; Thibos, Larry; Marin, Gildas; Hernandez, Martha

    2014-01-01

    Conventional aberration analysis by a Shack-Hartmann aberrometer is based on the implicit assumption that an injected probe beam reflects from a single fundus layer. In fact, the biological fundus is a thick reflector and therefore conventional analysis may produce errors of unknown magnitude. We developed a novel computational method to investigate this potential failure of conventional analysis. The Shack-Hartmann wavefront sensor was simulated by computer software and used to recover by two methods the known wavefront aberrations expected from a population of normally-aberrated human eyes and bi-layer fundus reflection. The conventional method determines the centroid of each spot in the SH data image, from which wavefront slopes are computed for least-squares fitting with derivatives of Zernike polynomials. The novel 'global' method iteratively adjusted the aberration coefficients derived from conventional centroid analysis until the SH image, when treated as a unitary picture, optimally matched the original data image. Both methods recovered higher order aberrations accurately and precisely, but only the global algorithm correctly recovered the defocus coefficients associated with each layer of fundus reflection. The global algorithm accurately recovered Zernike coefficients for mean defocus and bi-layer separation with maximum error <0.1%. The global algorithm was robust for bi-layer separation up to 2 dioptres for a typical SH wavefront sensor design. For 100 randomly generated test wavefronts with 0.7 D axial separation, the retrieved mean axial separation was 0.70 D with standard deviations (S.D.) of 0.002 D. Sufficient information is contained in SH data images to measure the dioptric thickness of dual-layer fundus reflection. The global algorithm is superior since it successfully recovered the focus value associated with both fundus layers even when their separation was too small to produce clearly separated spots, while the conventional analysis

  18. A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots.

    PubMed

    Lee, Tae-Jae; Yi, Dong-Hoon; Cho, Dong-Il Dan

    2016-03-01

    This paper presents a monocular vision sensor-based obstacle detection algorithm for autonomous robots. Each individual image pixel at the bottom region of interest is labeled as belonging either to an obstacle or the floor. While conventional methods depend on point tracking for geometric cues for obstacle detection, the proposed algorithm uses the inverse perspective mapping (IPM) method. This method is much more advantageous when the camera is not high off the floor, which makes point tracking near the floor difficult. Markov random field-based obstacle segmentation is then performed using the IPM results and a floor appearance model. Next, the shortest distance between the robot and the obstacle is calculated. The algorithm is tested by applying it to 70 datasets, 20 of which include nonobstacle images where considerable changes in floor appearance occur. The obstacle segmentation accuracies and the distance estimation error are quantitatively analyzed. For obstacle datasets, the segmentation precision and the average distance estimation error of the proposed method are 81.4% and 1.6 cm, respectively, whereas those for a conventional method are 57.5% and 9.9 cm, respectively. For nonobstacle datasets, the proposed method gives 0.0% false positive rates, while the conventional method gives 17.6%.

  19. A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots

    PubMed Central

    Lee, Tae-Jae; Yi, Dong-Hoon; Cho, Dong-Il “Dan”

    2016-01-01

    This paper presents a monocular vision sensor-based obstacle detection algorithm for autonomous robots. Each individual image pixel at the bottom region of interest is labeled as belonging either to an obstacle or the floor. While conventional methods depend on point tracking for geometric cues for obstacle detection, the proposed algorithm uses the inverse perspective mapping (IPM) method. This method is much more advantageous when the camera is not high off the floor, which makes point tracking near the floor difficult. Markov random field-based obstacle segmentation is then performed using the IPM results and a floor appearance model. Next, the shortest distance between the robot and the obstacle is calculated. The algorithm is tested by applying it to 70 datasets, 20 of which include nonobstacle images where considerable changes in floor appearance occur. The obstacle segmentation accuracies and the distance estimation error are quantitatively analyzed. For obstacle datasets, the segmentation precision and the average distance estimation error of the proposed method are 81.4% and 1.6 cm, respectively, whereas those for a conventional method are 57.5% and 9.9 cm, respectively. For nonobstacle datasets, the proposed method gives 0.0% false positive rates, while the conventional method gives 17.6%. PMID:26938540

  20. Genetic diagnosis of sex chromosome aberrations in horses based on parentage test by microsatellite DNA and analysis of X- and Y-linked markers.

    PubMed

    Kakoi, H; Hirota, K; Gawahara, H; Kurosawa, M; Kuwajima, M

    2005-03-01

    Sex chromosome aberrations are often associated with clinical signs that affect equine health and reproduction. However, abnormal manifestation with sex chromosome aberration usually appears at maturity and potential disorders may be suspected infrequently. A reliable survey at an early stage is therefore required. To detect and characterise sex chromosome aberrations in newborn foals by the parentage test and analysis using X- and Y-linked markers. We conducted a genetic diagnosis combined with a parentage test by microsatellite DNA and analysis of X- and Y-linked genetic markers in newborn light-breed foals (n = 17, 471). The minimum incidence of sex chromosome aberration in horses was estimated in the context of available population data. Eighteen cases with aberrations involving 63,XO, 65,XXY and 65,XXX were found. The XO, XXY (pure 65,XXY and/or mosaics/chimaeras) and XXX were found in 0.15, 0.02 and 0.01% of the population, respectively, based solely on detection of abnormal segregation of a single X chromosome marker, LEX003. Detection at an early age and understanding of the prevalence of sex chromosome aberrations should assist in the diagnosis and managment of horses kept for breeding. Further, the parental origin of the X chromosome of each disorder could be proved by the results of genetic analysis, thereby contributing to cytogenetic characterisation.

  1. Performance of target detection algorithm in compressive sensing miniature ultraspectral imaging compressed sensing system

    NASA Astrophysics Data System (ADS)

    Gedalin, Daniel; Oiknine, Yaniv; August, Isaac; Blumberg, Dan G.; Rotman, Stanley R.; Stern, Adrian

    2017-04-01

    Compressive sensing theory was proposed to deal with the high quantity of measurements demanded by traditional hyperspectral systems. Recently, a compressive spectral imaging technique dubbed compressive sensing miniature ultraspectral imaging (CS-MUSI) was presented. This system uses a voltage controlled liquid crystal device to create multiplexed hyperspectral cubes. We evaluate the utility of the data captured using the CS-MUSI system for the task of target detection. Specifically, we compare the performance of the matched filter target detection algorithm in traditional hyperspectral systems and in CS-MUSI multiplexed hyperspectral cubes. We found that the target detection algorithm performs similarly in both cases, despite the fact that the CS-MUSI data is up to an order of magnitude less than that in conventional hyperspectral cubes. Moreover, the target detection is approximately an order of magnitude faster in CS-MUSI data.

  2. Unsupervised, low latency anomaly detection of algorithmically generated domain names by generative probabilistic modeling.

    PubMed

    Raghuram, Jayaram; Miller, David J; Kesidis, George

    2014-07-01

    We propose a method for detecting anomalous domain names, with focus on algorithmically generated domain names which are frequently associated with malicious activities such as fast flux service networks, particularly for bot networks (or botnets), malware, and phishing. Our method is based on learning a (null hypothesis) probability model based on a large set of domain names that have been white listed by some reliable authority. Since these names are mostly assigned by humans, they are pronounceable, and tend to have a distribution of characters, words, word lengths, and number of words that are typical of some language (mostly English), and often consist of words drawn from a known lexicon. On the other hand, in the present day scenario, algorithmically generated domain names typically have distributions that are quite different from that of human-created domain names. We propose a fully generative model for the probability distribution of benign (white listed) domain names which can be used in an anomaly detection setting for identifying putative algorithmically generated domain names. Unlike other methods, our approach can make detections without considering any additional (latency producing) information sources, often used to detect fast flux activity. Experiments on a publicly available, large data set of domain names associated with fast flux service networks show encouraging results, relative to several baseline methods, with higher detection rates and low false positive rates.

  3. Unsupervised, low latency anomaly detection of algorithmically generated domain names by generative probabilistic modeling

    PubMed Central

    Raghuram, Jayaram; Miller, David J.; Kesidis, George

    2014-01-01

    We propose a method for detecting anomalous domain names, with focus on algorithmically generated domain names which are frequently associated with malicious activities such as fast flux service networks, particularly for bot networks (or botnets), malware, and phishing. Our method is based on learning a (null hypothesis) probability model based on a large set of domain names that have been white listed by some reliable authority. Since these names are mostly assigned by humans, they are pronounceable, and tend to have a distribution of characters, words, word lengths, and number of words that are typical of some language (mostly English), and often consist of words drawn from a known lexicon. On the other hand, in the present day scenario, algorithmically generated domain names typically have distributions that are quite different from that of human-created domain names. We propose a fully generative model for the probability distribution of benign (white listed) domain names which can be used in an anomaly detection setting for identifying putative algorithmically generated domain names. Unlike other methods, our approach can make detections without considering any additional (latency producing) information sources, often used to detect fast flux activity. Experiments on a publicly available, large data set of domain names associated with fast flux service networks show encouraging results, relative to several baseline methods, with higher detection rates and low false positive rates. PMID:25685511

  4. Transcranial phase aberration correction using beam simulations and MR-ARFI

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

    Vyas, Urvi, E-mail: urvi.vyas@gmail.com; Kaye, Elena; Pauly, Kim Butts

    2014-03-15

    Purpose: Transcranial magnetic resonance-guided focused ultrasound surgery is a noninvasive technique for causing selective tissue necrosis. Variations in density, thickness, and shape of the skull cause aberrations in the location and shape of the focal zone. In this paper, the authors propose a hybrid simulation-MR-ARFI technique to achieve aberration correction for transcranial MR-guided focused ultrasound surgery. The technique uses ultrasound beam propagation simulations with MR Acoustic Radiation Force Imaging (MR-ARFI) to correct skull-caused phase aberrations. Methods: Skull-based numerical aberrations were obtained from a MR-guided focused ultrasound patient treatment and were added to all elements of the InSightec conformal bone focusedmore » ultrasound surgery transducer during transmission. In the first experiment, the 1024 aberrations derived from a human skull were condensed into 16 aberrations by averaging over the transducer area of 64 elements. In the second experiment, all 1024 aberrations were applied to the transducer. The aberrated MR-ARFI images were used in the hybrid simulation-MR-ARFI technique to find 16 estimated aberrations. These estimated aberrations were subtracted from the original aberrations to result in the corrected images. Each aberration experiment (16-aberration and 1024-aberration) was repeated three times. Results: The corrected MR-ARFI image was compared to the aberrated image and the ideal image (image with zero aberrations) for each experiment. The hybrid simulation-MR-ARFI technique resulted in an average increase in focal MR-ARFI phase of 44% for the 16-aberration case and 52% for the 1024-aberration case, and recovered 83% and 39% of the ideal MR-ARFI phase for the 16-aberrations and 1024-aberration case, respectively. Conclusions: Using one MR-ARFI image and noa priori information about the applied phase aberrations, the hybrid simulation-MR-ARFI technique improved the maximum MR-ARFI phase of the beam's focus.« less

  5. Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs

    PubMed Central

    Al-Kaff, Abdulla; García, Fernando; Martín, David; De La Escalera, Arturo; Armingol, José María

    2017-01-01

    One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar.For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works. PMID:28481277

  6. Research on Aircraft Target Detection Algorithm Based on Improved Radial Gradient Transformation

    NASA Astrophysics Data System (ADS)

    Zhao, Z. M.; Gao, X. M.; Jiang, D. N.; Zhang, Y. Q.

    2018-04-01

    Aiming at the problem that the target may have different orientation in the unmanned aerial vehicle (UAV) image, the target detection algorithm based on the rotation invariant feature is studied, and this paper proposes a method of RIFF (Rotation-Invariant Fast Features) based on look up table and polar coordinate acceleration to be used for aircraft target detection. The experiment shows that the detection performance of this method is basically equal to the RIFF, and the operation efficiency is greatly improved.

  7. Obtaining the phase in the star test using genetic algorithms

    NASA Astrophysics Data System (ADS)

    Salazar Romero, Marcos A.; Vazquez-Montiel, Sergio; Cornejo-Rodriguez, Alejandro

    2004-10-01

    The star test is conceptually perhaps the most basic and simplest of all methods of testing image-forming optical systems, the irradiance distribution at the image of a point source (such as a star) is give for the Point Spread Function, PSF. The PSF is very sensitive to aberrations. One way to quantify the PSF is measuring the irradiance distribution on the image of the source point. On the other hand, if we know the aberrations introduced by the optical systems and utilizing the diffraction theory then we can calculate the PSF. In this work we propose a method in order to find the wavefront aberrations starting from the PSF, transforming the problem of fitting a polynomial of aberrations in a problem of optimization using Genetic Algorithm. Also, we show that this method is immune to the noise introduced in the register or recording of the image. Results of these methods are shown.

  8. Anti-forensics of chromatic aberration

    NASA Astrophysics Data System (ADS)

    Mayer, Owen; Stamm, Matthew C.

    2015-03-01

    Over the past decade, a number of information forensic techniques have been developed to identify digital image manipulation and falsification. Recent research has shown, however, that an intelligent forger can use anti-forensic countermeasures to disguise their forgeries. In this paper, an anti-forensic technique is proposed to falsify the lateral chromatic aberration present in a digital image. Lateral chromatic aberration corresponds to the relative contraction or expansion between an image's color channels that occurs due to a lens's inability to focus all wavelengths of light on the same point. Previous work has used localized inconsistencies in an image's chromatic aberration to expose cut-and-paste image forgeries. The anti-forensic technique presented in this paper operates by estimating the expected lateral chromatic aberration at an image location, then removing deviations from this estimate caused by tampering or falsification. Experimental results are presented that demonstrate that our anti-forensic technique can be used to effectively disguise evidence of an image forgery.

  9. Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine

    PubMed Central

    Zhou, Jingyu; Tian, Shulin; Yang, Chenglin; Ren, Xuelong

    2014-01-01

    This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. PMID:25610458

  10. A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

    NASA Astrophysics Data System (ADS)

    Shuxin, Li; Zhilong, Zhang; Biao, Li

    2018-01-01

    Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

  11. Automatic Tool Selection in V-bending Processes by Using an Intelligent Collision Detection Algorithm

    NASA Astrophysics Data System (ADS)

    Salem, A. A.

    2017-09-01

    V-bending is widely used to produce the sheet metal components. There are global Changes in the shape of the sheet metal component during progressive bending processes. Accordingly, collisions may be occurred between part and tool during bending. Collision-free is considered one of the feasibility conditions of V-bending process planning which the tool selection is verified by the absence of the collisions. This paper proposes an intelligent collision detection algorithm which has the ability to distinguish between 2D bent parts and the other bent parts. Due to this ability, 2D and 3D collision detection subroutines have been developed in the proposed algorithm. This division of algorithm’s subroutines could reduce the computational operations during collisions detecting.

  12. A Evaluation of Optical Aberrations in Underwater Hologrammetry

    NASA Astrophysics Data System (ADS)

    Kilpatrick, J. M.

    Available from UMI in association with The British Library. An iterative ray-trace procedure is developed in conjunction with semi-analytic expressions for spherical aberration, coma, and astigmatism in the reconstructed holographic images of underwater objects. An exact expression for the astigmatic difference is obtained, based on the geometry of the caustic for refraction. The geometrical characteristics of the aberrated images associated with axial and non-axial field positions are represented by ray intersection diagrams. A third order expression for the wavefront aberration introduced at a planar air/water boundary is given. The associated third order aberration coefficients are used to obtain analytic expressions for the aberrations observed in underwater hologrammetry. The results of the third order treatment are shown to give good agreement with the results obtained by geometrical ray tracing and by direct measurement on the reconstructed real image. The third order aberration coefficients are employed to estimate the limit of resolution in the presence of the aberrations associated with reconstruction in air. In concurrence with practical observations it is found that the estimated resolution is primarily limited by astigmatism. The limitations of the planar window in underwater imaging applications are outlined and various schemes are considered to effect a reduction in the extent of aberration. The analogous problems encountered in underwater photography are examined in order to establish the grounds for a common solution based on a conventional optical corrector. The performance of one such system, the Ivanoff Corrector, is investigated. The spherical aberration associated with axial image formation is evaluated. The equivalence of the third order wavefront aberration introduced at a planar air/water boundary to that introduced upon reconstruction by an appropriate wavelength change is shown to provide a basis for the compensation of aberrations in

  13. Defect-detection algorithm for noncontact acoustic inspection using spectrum entropy

    NASA Astrophysics Data System (ADS)

    Sugimoto, Kazuko; Akamatsu, Ryo; Sugimoto, Tsuneyoshi; Utagawa, Noriyuki; Kuroda, Chitose; Katakura, Kageyoshi

    2015-07-01

    In recent years, the detachment of concrete from bridges or tunnels and the degradation of concrete structures have become serious social problems. The importance of inspection, repair, and updating is recognized in measures against degradation. We have so far studied the noncontact acoustic inspection method using airborne sound and the laser Doppler vibrometer. In this method, depending on the surface state (reflectance, dirt, etc.), the quantity of the light of the returning laser decreases and optical noise resulting from the leakage of light reception arises. Some influencing factors are the stability of the output of the laser Doppler vibrometer, the low reflective characteristic of the measurement surface, the diffused reflection characteristic, measurement distance, and laser irradiation angle. If defect detection depends only on the vibration energy ratio since the frequency characteristic of the optical noise resembles white noise, the detection of optical noise resulting from the leakage of light reception may indicate a defective part. Therefore, in this work, the combination of the vibrational energy ratio and spectrum entropy is used to judge whether a measured point is healthy or defective or an abnormal measurement point. An algorithm that enables more vivid detection of a defective part is proposed. When our technique was applied in an experiment with real concrete structures, the defective part could be extracted more vividly and the validity of our proposed algorithm was confirmed.

  14. Coherence and diffraction limited resolution in microscopic OCT by a unified approach for the correction of dispersion and aberrations

    NASA Astrophysics Data System (ADS)

    Schulz-Hildebrandt, H.; Münter, Michael; Ahrens, M.; Spahr, H.; Hillmann, D.; König, P.; Hüttmann, G.

    2018-03-01

    Optical coherence tomography (OCT) images scattering tissues with 5 to 15 μm resolution. This is usually not sufficient for a distinction of cellular and subcellular structures. Increasing axial and lateral resolution and compensation of artifacts caused by dispersion and aberrations is required to achieve cellular and subcellular resolution. This includes defocus which limit the usable depth of field at high lateral resolution. OCT gives access the phase of the scattered light and hence correction of dispersion and aberrations is possible by numerical algorithms. Here we present a unified dispersion/aberration correction which is based on a polynomial parameterization of the phase error and an optimization of the image quality using Shannon's entropy. For validation, a supercontinuum light sources and a costume-made spectrometer with 400 nm bandwidth were combined with a high NA microscope objective in a setup for tissue and small animal imaging. Using this setup and computation corrections, volumetric imaging at 1.5 μm resolution is possible. Cellular and near cellular resolution is demonstrated in porcine cornea and the drosophila larva, when computational correction of dispersion and aberrations is used. Due to the excellent correction of the used microscope objective, defocus was the main contribution to the aberrations. In addition, higher aberrations caused by the sample itself were successfully corrected. Dispersion and aberrations are closely related artifacts in microscopic OCT imaging. Hence they can be corrected in the same way by optimization of the image quality. This way microscopic resolution is easily achieved in OCT imaging of static biological tissues.

  15. A New Algorithm for Detecting Cloud Height using OMPS/LP Measurements

    NASA Technical Reports Server (NTRS)

    Chen, Zhong; DeLand, Matthew; Bhartia, Pawan K.

    2016-01-01

    The Ozone Mapping and Profiler Suite Limb Profiler (OMPS/LP) ozone product requires the determination of cloud height for each event to establish the lower boundary of the profile for the retrieval algorithm. We have created a revised cloud detection algorithm for LP measurements that uses the spectral dependence of the vertical gradient in radiance between two wavelengths in the visible and near-IR spectral regions. This approach provides better discrimination between clouds and aerosols than results obtained using a single wavelength. Observed LP cloud height values show good agreement with coincident Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements.

  16. Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection

    PubMed Central

    Meng, Jiahui; Zhao, Danfeng; Tian, Hai; Zhang, Liang

    2018-01-01

    In order to improve the performance of non-binary low-density parity check codes (LDPC) hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA) and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes’ (VN) magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF) algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER) of 10−5 over an additive white Gaussian noise (AWGN) channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced. PMID:29342963

  17. Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection.

    PubMed

    Meng, Jiahui; Zhao, Danfeng; Tian, Hai; Zhang, Liang

    2018-01-15

    In order to improve the performance of non-binary low-density parity check codes (LDPC) hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA) and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes' (VN) magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF) algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER) of 10 -5 over an additive white Gaussian noise (AWGN) channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced.

  18. [Fluorescent signal detection of chromatographic chip by algorithms of pyramid connection and Gaussian mixture model].

    PubMed

    Hu, Beibei; Zhang, Xueqing; Chen, Haopeng; Cui, Daxiang

    2011-03-01

    We proposed a new algorithm for automatic identification of fluorescent signal. Based on the features of chromatographic chips, mathematic morphology in RGB color space was used to filter and enhance the images, pyramid connection was used to segment the areas of fluorescent signal, and then the method of Gaussian Mixture Model was used to detect the fluorescent signal. Finally we calculated the average fluorescent intensity in obtained fluorescent areas. Our results show that the algorithm has a good efficacy to segment the fluorescent areas, can detect the fluorescent signal quickly and accurately, and finally realize the quantitative detection of fluorescent signal in chromatographic chip.

  19. A real-time implementation of an advanced sensor failure detection, isolation, and accommodation algorithm

    NASA Technical Reports Server (NTRS)

    Delaat, J. C.; Merrill, W. C.

    1983-01-01

    A sensor failure detection, isolation, and accommodation algorithm was developed which incorporates analytic sensor redundancy through software. This algorithm was implemented in a high level language on a microprocessor based controls computer. Parallel processing and state-of-the-art 16-bit microprocessors are used along with efficient programming practices to achieve real-time operation.

  20. The design and hardware implementation of a low-power real-time seizure detection algorithm

    NASA Astrophysics Data System (ADS)

    Raghunathan, Shriram; Gupta, Sumeet K.; Ward, Matthew P.; Worth, Robert M.; Roy, Kaushik; Irazoqui, Pedro P.

    2009-10-01

    Epilepsy affects more than 1% of the world's population. Responsive neurostimulation is emerging as an alternative therapy for the 30% of the epileptic patient population that does not benefit from pharmacological treatment. Efficient seizure detection algorithms will enable closed-loop epilepsy prostheses by stimulating the epileptogenic focus within an early onset window. Critically, this is expected to reduce neuronal desensitization over time and lead to longer-term device efficacy. This work presents a novel event-based seizure detection algorithm along with a low-power digital circuit implementation. Hippocampal depth-electrode recordings from six kainate-treated rats are used to validate the algorithm and hardware performance in this preliminary study. The design process illustrates crucial trade-offs in translating mathematical models into hardware implementations and validates statistical optimizations made with empirical data analyses on results obtained using a real-time functioning hardware prototype. Using quantitatively predicted thresholds from the depth-electrode recordings, the auto-updating algorithm performs with an average sensitivity and selectivity of 95.3 ± 0.02% and 88.9 ± 0.01% (mean ± SEα = 0.05), respectively, on untrained data with a detection delay of 8.5 s [5.97, 11.04] from electrographic onset. The hardware implementation is shown feasible using CMOS circuits consuming under 350 nW of power from a 250 mV supply voltage from simulations on the MIT 180 nm SOI process.

  1. Neuronal Mechanism for Compensation of Longitudinal Chromatic Aberration-Derived Algorithm.

    PubMed

    Barkan, Yuval; Spitzer, Hedva

    2018-01-01

    The human visual system faces many challenges, among them the need to overcome the imperfections of its optics, which degrade the retinal image. One of the most dominant limitations is longitudinal chromatic aberration (LCA), which causes short wavelengths (blue light) to be focused in front of the retina with consequent blurring of the retinal chromatic image. The perceived visual appearance, however, does not display such chromatic distortions. The intriguing question, therefore, is how the perceived visual appearance of a sharp and clear chromatic image is achieved despite the imperfections of the ocular optics. To address this issue, we propose a neural mechanism and computational model, based on the unique properties of the S -cone pathway. The model suggests that the visual system overcomes LCA through two known properties of the S channel: (1) omitting the contribution of the S channel from the high-spatial resolution pathway (utilizing only the L and M channels). (b) Having large and coextensive receptive fields that correspond to the small bistratified cells. Here, we use computational simulations of our model on real images to show how integrating these two basic principles can provide a significant compensation for LCA. Further support for the proposed neuronal mechanism is given by the ability of the model to predict an enigmatic visual phenomenon of large color shifts as part of the assimilation effect.

  2. Neuronal Mechanism for Compensation of Longitudinal Chromatic Aberration-Derived Algorithm

    PubMed Central

    Barkan, Yuval; Spitzer, Hedva

    2018-01-01

    The human visual system faces many challenges, among them the need to overcome the imperfections of its optics, which degrade the retinal image. One of the most dominant limitations is longitudinal chromatic aberration (LCA), which causes short wavelengths (blue light) to be focused in front of the retina with consequent blurring of the retinal chromatic image. The perceived visual appearance, however, does not display such chromatic distortions. The intriguing question, therefore, is how the perceived visual appearance of a sharp and clear chromatic image is achieved despite the imperfections of the ocular optics. To address this issue, we propose a neural mechanism and computational model, based on the unique properties of the S-cone pathway. The model suggests that the visual system overcomes LCA through two known properties of the S channel: (1) omitting the contribution of the S channel from the high-spatial resolution pathway (utilizing only the L and M channels). (b) Having large and coextensive receptive fields that correspond to the small bistratified cells. Here, we use computational simulations of our model on real images to show how integrating these two basic principles can provide a significant compensation for LCA. Further support for the proposed neuronal mechanism is given by the ability of the model to predict an enigmatic visual phenomenon of large color shifts as part of the assimilation effect. PMID:29527525

  3. A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast-enhanced ultrasound.

    PubMed

    Gatos, Ilias; Tsantis, Stavros; Spiliopoulos, Stavros; Skouroliakou, Aikaterini; Theotokas, Ioannis; Zoumpoulis, Pavlos; Hazle, John D; Kagadis, George C

    2015-07-01

    Detect and classify focal liver lesions (FLLs) from contrast-enhanced ultrasound (CEUS) imaging by means of an automated quantification algorithm. The proposed algorithm employs a sophisticated segmentation method to detect and contour focal lesions from 52 CEUS video sequences (30 benign and 22 malignant). Lesion detection involves wavelet transform zero crossings utilization as an initialization step to the Markov random field model toward the lesion contour extraction. After FLL detection across frames, time intensity curve (TIC) is computed which provides the contrast agents' behavior at all vascular phases with respect to adjacent parenchyma for each patient. From each TIC, eight features were automatically calculated and employed into the support vector machines (SVMs) classification algorithm in the design of the image analysis model. With regard to FLLs detection accuracy, all lesions detected had an average overlap value of 0.89 ± 0.16 with manual segmentations for all CEUS frame-subsets included in the study. Highest classification accuracy from the SVM model was 90.3%, misdiagnosing three benign and two malignant FLLs with sensitivity and specificity values of 93.1% and 86.9%, respectively. The proposed quantification system that employs FLLs detection and classification algorithms may be of value to physicians as a second opinion tool for avoiding unnecessary invasive procedures.

  4. Multi-gene fluorescence in situ hybridization to detect cell cycle gene copy number aberrations in young breast cancer patients

    PubMed Central

    Li, Chunyan; Bai, Jingchao; Hao, Xiaomeng; Zhang, Sheng; Hu, Yunhui; Zhang, Xiaobei; Yuan, Weiping; Hu, Linping; Cheng, Tao; Zetterberg, Anders; Lee, Mong-Hong; Zhang, J

    2014-01-01

    Breast cancer is a disease of cell cycle, and the dysfunction of cell cycle checkpoints plays a vital role in the occurrence and development of breast cancer. We employed multi-gene fluorescence in situ hybridization (M-FISH) to investigate gene copy number aberrations (CNAs) of 4 genes (Rb1, CHEK2, c-Myc, CCND1) that are involved in the regulation of cell cycle, in order to analyze the impact of gene aberrations on prognosis in the young breast cancer patients. Gene copy number aberrations of these 4 genes were more frequently observed in young breast cancer patients when compared with the older group. Further, these CNAs were more frequently seen in Luminal B type, Her2 overexpression, and tiple-negative breast cancer (TNBC) type in young breast cancer patients. The variations of CCND1, Rb1, and CHEK2 were significantly correlated with poor survival in the young breast cancer patient group, while the amplification of c-Myc was not obviously correlated with poor survival in young breast cancer patients. Thus, gene copy number aberrations (CNAs) of cell cycle-regulated genes can serve as an important tool for prognosis in young breast cancer patients. PMID:24621502

  5. Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images

    NASA Astrophysics Data System (ADS)

    Amrute, Junedh M.; Athanasiou, Lambros S.; Rikhtegar, Farhad; de la Torre Hernández, José M.; Camarero, Tamara García; Edelman, Elazer R.

    2018-03-01

    Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging-they are relatively invisible via angiography-and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images.

  6. Aberrated laser beams in terms of Zernike polynomials

    NASA Astrophysics Data System (ADS)

    Alda, Javier; Alonso, Jose; Bernabeu, Eusebio

    1996-11-01

    The characterization of light beams has devoted a lot of attention in the past decade. Several formalisms have been presented to treat the problem of parameter invariance and characterization in the propagation of light beam along ideal, ABCD, optical systems. The hard and soft apertured optical systems have been treated too. Also some aberrations have been analyzed, but it has not appeared a formalism able to treat the problem as a whole. In this contribution we use a classical approach to describe the problem of aberrated, and therefore apertured, light beams. The wavefront aberration is included in a pure phase term expanded in terms of the Zernike polynomials. Then, we can use the relation between the lower order Zernike polynomia and the Seidel or third order aberrations. We analyze the astigmatism, the spherical aberration and the coma, and we show how higher order aberrations can be taken into account. We have calculated the divergence, and the radius of curvature of such aberrated beams and the influence of these aberrations in the quality of the light beam. Some numerical simulations have been done to illustrate the method.

  7. Structural centrosome aberrations promote non-cell-autonomous invasiveness.

    PubMed

    Ganier, Olivier; Schnerch, Dominik; Oertle, Philipp; Lim, Roderick Yh; Plodinec, Marija; Nigg, Erich A

    2018-05-02

    Centrosomes are the main microtubule-organizing centers of animal cells. Although centrosome aberrations are common in tumors, their consequences remain subject to debate. Here, we studied the impact of structural centrosome aberrations, induced by deregulated expression of ninein-like protein (NLP), on epithelial spheres grown in Matrigel matrices. We demonstrate that NLP-induced structural centrosome aberrations trigger the escape ("budding") of living cells from epithelia. Remarkably, all cells disseminating into the matrix were undergoing mitosis. This invasive behavior reflects a novel mechanism that depends on the acquisition of two distinct properties. First, NLP-induced centrosome aberrations trigger a re-organization of the cytoskeleton, which stabilizes microtubules and weakens E-cadherin junctions during mitosis. Second, atomic force microscopy reveals that cells harboring these centrosome aberrations display increased stiffness. As a consequence, mitotic cells are pushed out of mosaic epithelia, particularly if they lack centrosome aberrations. We conclude that centrosome aberrations can trigger cell dissemination through a novel, non-cell-autonomous mechanism, raising the prospect that centrosome aberrations contribute to the dissemination of metastatic cells harboring normal centrosomes. © 2018 The Authors. Published under the terms of the CC BY NC ND 4.0 license.

  8. Whole eye wavefront aberrations in Mexican male subjects.

    PubMed

    Cantú, Roberto; Rosales, Marco A; Tepichín, Eduardo; Curioca, Andrée; Montes, Victor; Bonilla, Julio

    2004-01-01

    To analyze the characteristics, incidence, and appearance of wavefront aberrations in undilated, normal, unoperated eyes. Eighty-eight eyes of 44 healthy male Mexican subjects (mean age 25.32 years, range 18 to 36 yr) were divided into three groups based on uncorrected visual acuity of greater than or equal to 20/20, 20/30, or 20/40. UCVA measurements were obtained using an Acuity Max computer screen chart. Wavefront aberrations were measured with the Nidek OPD-Scan ARK 10000, Ver. 1.11b. All measurements were carried out at the same center by the same technician during a single session, following manufacturer instructions. Background illumination was 3 Lux. Wavefront aberration measurements for each group were statistically analyzed using StatView; an average eye was characterized and the resulting aberrations were simulated using MATLAB. We obtained wavefront aberration maps for the 20/20 undilated normal unoperated eyes for total, low, and high order aberration coefficients. Wavefront maps for right eyes were practically the same as those for left eyes. Higher aberrations did not contribute substantially to total wavefront analysis. Average aberrations of this "normal eye" will be used as criteria to decide the necessity of wavefront-guided ablation in our facilities. We will focus on the nearly zero average of high order aberrations in this normal whole eye as a reference to be matched.

  9. Accommodation to wavefront vergence and chromatic aberration.

    PubMed

    Wang, Yinan; Kruger, Philip B; Li, James S; Lin, Peter L; Stark, Lawrence R

    2011-05-01

    Longitudinal chromatic aberration (LCA) provides a cue to accommodation with small pupils. However, large pupils increase monochromatic aberrations, which may obscure chromatic blur. In this study, we examined the effect of pupil size and LCA on accommodation. Accommodation was recorded by infrared optometer while observers (nine normal trichromats) viewed a sinusoidally moving Maltese cross target in a Badal stimulus system. There were two illumination conditions: white (3000 K; 20 cd/m) and monochromatic (550 nm with 10 nm bandwidth; 20 cd/m) and two artificial pupil conditions (3 and 5.7 mm). Separately, static measurements of wavefront aberration were made with the eye accommodating to targets between 0 and 4 D (COAS, Wavefront Sciences). Large individual differences in accommodation to wavefront vergence and to LCA are a hallmark of accommodation. LCA continues to provide a signal at large pupil sizes despite higher levels of monochromatic aberrations. Monochromatic aberrations may defend against chromatic blur at high spatial frequencies, but accommodation responds best to optical vergence and to LCA at 3 c/deg where blur from higher order aberrations is less.

  10. Aberrant expression of the PHF14 gene in biliary tract cancer cells

    PubMed Central

    AKAZAWA, TAKAKO; YASUI, KOHICHIROH; GEN, YASUYUKI; YAMADA, NOBUHISA; TOMIE, AKIRA; DOHI, OSAMU; MITSUYOSHI, HIRONORI; YAGI, NOBUAKI; ITOH, YOSHITO; NAITO, YUJI; YOSHIKAWA, TOSHIKAZU

    2013-01-01

    DNA copy number aberrations in human biliary tract cancer (BTC) cell lines were investigated using a high-density oligonucleotide microarray. A novel homozygous deletion was detected at chromosomal region 7p21.3 in the OZ cell line. Further validation experiments using genomic PCR revealed a homozygous deletion of a single gene, plant homeodomain (PHD) finger protein 14 (PHF14). No PHF14 mRNA or protein expression was detected, thus demonstrating the absence of PHF14 expression in the OZ cell line. Although the PHD finger protein is considered to be involved in chromatin-mediated transcriptional regulation, little is known about the function of PHF14 in cancer. The present study observed that the knock down of PHF14 using small interfering RNA (siRNA) enhanced the growth of the BTC cells. These observations suggest that aberrant PHF14 expression may have a role in the tumorigenesis of BTC. PMID:23833654

  11. Depth measurements through controlled aberrations of projected patterns.

    PubMed

    Birch, Gabriel C; Tyo, J Scott; Schwiegerling, Jim

    2012-03-12

    Three-dimensional displays have become increasingly present in consumer markets. However, the ability to capture three-dimensional images in space confined environments and without major modifications to current cameras is uncommon. Our goal is to create a simple modification to a conventional camera that allows for three dimensional reconstruction. We require such an imaging system have imaging and illumination paths coincident. Furthermore, we require that any three-dimensional modification to a camera also permits full resolution 2D image capture.Here we present a method of extracting depth information with a single camera and aberrated projected pattern. A commercial digital camera is used in conjunction with a projector system with astigmatic focus to capture images of a scene. By using an astigmatic projected pattern we can create two different focus depths for horizontal and vertical features of a projected pattern, thereby encoding depth. By designing an aberrated projected pattern, we are able to exploit this differential focus in post-processing designed to exploit the projected pattern and optical system. We are able to correlate the distance of an object at a particular transverse position from the camera to ratios of particular wavelet coefficients.We present our information regarding construction, calibration, and images produced by this system. The nature of linking a projected pattern design and image processing algorithms will be discussed.

  12. A hyperspectral imagery anomaly detection algorithm based on local three-dimensional orthogonal subspace projection

    NASA Astrophysics Data System (ADS)

    Zhang, Xing; Wen, Gongjian

    2015-10-01

    Anomaly detection (AD) becomes increasingly important in hyperspectral imagery analysis with many practical applications. Local orthogonal subspace projection (LOSP) detector is a popular anomaly detector which exploits local endmembers/eigenvectors around the pixel under test (PUT) to construct background subspace. However, this subspace only takes advantage of the spectral information, but the spatial correlat ion of the background clutter is neglected, which leads to the anomaly detection result sensitive to the accuracy of the estimated subspace. In this paper, a local three dimensional orthogonal subspace projection (3D-LOSP) algorithm is proposed. Firstly, under the jointly use of both spectral and spatial information, three directional background subspaces are created along the image height direction, the image width direction and the spectral direction, respectively. Then, the three corresponding orthogonal subspaces are calculated. After that, each vector along three direction of the local cube is projected onto the corresponding orthogonal subspace. Finally, a composite score is given through the three direction operators. In 3D-LOSP, the anomalies are redefined as the target not only spectrally different to the background, but also spatially distinct. Thanks to the addition of the spatial information, the robustness of the anomaly detection result has been improved greatly by the proposed 3D-LOSP algorithm. It is noteworthy that the proposed algorithm is an expansion of LOSP and this ideology can inspire many other spectral-based anomaly detection methods. Experiments with real hyperspectral images have proved the stability of the detection result.

  13. Improving Correlation Algorithms to Detect and Characterize Smaller Magnitude Induced Seismicity Swarms

    NASA Astrophysics Data System (ADS)

    Skoumal, R.; Brudzinski, M.; Currie, B.

    2015-12-01

    Induced seismic sequences often occur as swarms that can include thousands of small (< M 2) earthquakes. While the identification of this microseismicity would invariably aid in the characterization and modeling of induced sequences, traditional earthquake detection techniques often provide incomplete catalogs, even when local networks are deployed. Because induced sequences often include scores of micro-earthquakes that prelude larger magnitude events, the identification of these small magnitude events would be crucial for the early identification of induced sequences. By taking advantage of the repeating, swarm-like nature of induced seismicity, a more robust catalog can be created using complementary correlation algorithms in near real-time without the reliance on traditional earthquake detection and association routines. Since traditional earthquake catalog methodologies using regional networks have a relatively high detection threshold (M 2+), we have sought to develop correlation routines that can detect smaller magnitude sequences. While short-term/long-term amplitude average detection algorithms requires significant signal-to-noise ratio at multiple stations for confident identification, a correlation detector is capable of identifying earthquakes with high confidence using just a single station. The result is an embarrassingly parallel task that can be employed for a network to be used as an early warning system for potentially induced seismicity while also better characterizing tectonic sequences beyond what traditional methods allow.

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

    PubMed

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

    2011-04-01

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

  15. Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

    PubMed Central

    Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

    2014-01-01

    Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms. PMID:24723806

  16. Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

    PubMed

    Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

    2014-01-01

    Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

  17. Shadow Detection from Very High Resoluton Satellite Image Using Grabcut Segmentation and Ratio-Band Algorithms

    NASA Astrophysics Data System (ADS)

    Kadhim, N. M. S. M.; Mourshed, M.; Bray, M. T.

    2015-03-01

    Very-High-Resolution (VHR) satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the-art shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour), the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates significant performance of

  18. Fast iterative censoring CFAR algorithm for ship detection from SAR images

    NASA Astrophysics Data System (ADS)

    Gu, Dandan; Yue, Hui; Zhang, Yuan; Gao, Pengcheng

    2017-11-01

    Ship detection is one of the essential techniques for ship recognition from synthetic aperture radar (SAR) images. This paper presents a fast iterative detection procedure to eliminate the influence of target returns on the estimation of local sea clutter distributions for constant false alarm rate (CFAR) detectors. A fast block detector is first employed to extract potential target sub-images; and then, an iterative censoring CFAR algorithm is used to detect ship candidates from each target blocks adaptively and efficiently, where parallel detection is available, and statistical parameters of G0 distribution fitting local sea clutter well can be quickly estimated based on an integral image operator. Experimental results of TerraSAR-X images demonstrate the effectiveness of the proposed technique.

  19. Sixth-order wave aberration theory of ultrawide-angle optical systems.

    PubMed

    Lu, Lijun; Cao, Yiqing

    2017-10-20

    In this paper, we develop sixth-order wave aberration theory of ultrawide-angle optical systems like fisheye lenses. Based on the concept and approach to develop wave aberration theory of plane-symmetric optical systems, we first derive the sixth-order intrinsic wave aberrations and the fifth-order ray aberrations; second, we present a method to calculate the pupil aberration of such kind of optical systems to develop the extrinsic aberrations; third, the relation of aperture-ray coordinates between adjacent optical surfaces is fitted with the second-order polynomial to improve the calculation accuracy of the wave aberrations of a fisheye lens with a large acceptance aperture. Finally, the resultant aberration expressions are applied to calculate the aberrations of two design examples of fisheye lenses; the calculation results are compared with the ray-tracing ones with Zemax software to validate the aberration expressions.

  20. Model-based wavefront sensorless adaptive optics system for large aberrations and extended objects.

    PubMed

    Yang, Huizhen; Soloviev, Oleg; Verhaegen, Michel

    2015-09-21

    A model-based wavefront sensorless (WFSless) adaptive optics (AO) system with a 61-element deformable mirror is simulated to correct the imaging of a turbulence-degraded extended object. A fast closed-loop control algorithm, which is based on the linear relation between the mean square of the aberration gradients and the second moment of the image intensity distribution, is used to generate the control signals for the actuators of the deformable mirror (DM). The restoration capability and the convergence rate of the AO system are investigated with different turbulence strength wave-front aberrations. Simulation results show the model-based WFSless AO system can restore those images degraded by different turbulence strengths successfully and obtain the correction very close to the achievable capability of the given DM. Compared with the ideal correction of 61-element DM, the averaged relative error of RMS value is 6%. The convergence rate of AO system is independent of the turbulence strength and only depends on the number of actuators of DM.

  1. The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model

    NASA Astrophysics Data System (ADS)

    Di, Nur Faraidah Muhammad; Satari, Siti Zanariah

    2017-05-01

    Outlier detection in linear data sets has been done vigorously but only a small amount of work has been done for outlier detection in circular data. In this study, we proposed multiple outliers detection in circular regression models based on the clustering algorithm. Clustering technique basically utilizes distance measure to define distance between various data points. Here, we introduce the similarity distance based on Euclidean distance for circular model and obtain a cluster tree using the single linkage clustering algorithm. Then, a stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height is proposed. We classify the cluster group that exceeds the stopping rule as potential outlier. Our aim is to demonstrate the effectiveness of proposed algorithms with the similarity distances in detecting the outliers. It is found that the proposed methods are performed well and applicable for circular regression model.

  2. A Novel Walking Detection and Step Counting Algorithm Using Unconstrained Smartphones.

    PubMed

    Kang, Xiaomin; Huang, Baoqi; Qi, Guodong

    2018-01-19

    Recently, with the development of artificial intelligence technologies and the popularity of mobile devices, walking detection and step counting have gained much attention since they play an important role in the fields of equipment positioning, saving energy, behavior recognition, etc. In this paper, a novel algorithm is proposed to simultaneously detect walking motion and count steps through unconstrained smartphones in the sense that the smartphone placement is not only arbitrary but also alterable. On account of the periodicity of the walking motion and sensitivity of gyroscopes, the proposed algorithm extracts the frequency domain features from three-dimensional (3D) angular velocities of a smartphone through FFT (fast Fourier transform) and identifies whether its holder is walking or not irrespective of its placement. Furthermore, the corresponding step frequency is recursively updated to evaluate the step count in real time. Extensive experiments are conducted by involving eight subjects and different walking scenarios in a realistic environment. It is shown that the proposed method achieves the precision of 93.76 % and recall of 93.65 % for walking detection, and its overall performance is significantly better than other well-known methods. Moreover, the accuracy of step counting by the proposed method is 95.74 % , and is better than both of the several well-known counterparts and commercial products.

  3. The combined use of the RST-FIRES algorithm and geostationary satellite data to timely detect fires

    NASA Astrophysics Data System (ADS)

    Filizzola, Carolina; Corrado, Rosita; Marchese, Francesco; Mazzeo, Giuseppe; Paciello, Rossana; Pergola, Nicola; Tramutoli, Valerio

    2017-04-01

    Timely detection of fires may enable a rapid contrast action before they become uncontrolled and wipe out entire forests. Remote sensing, especially based on geostationary satellite data, can be successfully used to this aim. Differently from sensors onboard polar orbiting platforms, instruments on geostationary satellites guarantee a very high temporal resolution (from 30 to 2,5 minutes) which may be usefully employed to carry out a "continuous" monitoring over large areas as well as to timely detect fires at their early stages. Together with adequate satellite data, an appropriate fire detection algorithm should be used. Over the last years, many fire detection algorithms have been just adapted from polar to geostationary sensors and, consequently, the very high temporal resolution of geostationary sensors is not exploited at all in tests for fire identification. In addition, even when specifically designed for geostationary satellite sensors, fire detection algorithms are frequently based on fixed thresholds tests which are generally set up in the most conservative way to avoid false alarm proliferation. The result is a low algorithm sensitivity which generally means that only large and/or extremely intense events are detected. This work describes the Robust Satellite Techniques for FIRES detection and monitoring (RST-FIRES) which is a multi-temporal change-detection technique trying to overcome the above mentioned issues. Its performance in terms of reliability and sensitivity was verified using data acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the Meteosat Second Generation (MSG) geostationary platform. More than 20,000 SEVIRI images, collected during a four-year-collaboration with the Regional Civil Protection Departments and Local Authorities of two Italian regions, were used. About 950 near real-time ground and aerial checks of the RST-FIRES detections were performed. This study also demonstrates the added value of

  4. Textural defect detect using a revised ant colony clustering algorithm

    NASA Astrophysics Data System (ADS)

    Zou, Chao; Xiao, Li; Wang, Bingwen

    2007-11-01

    We propose a totally novel method based on a revised ant colony clustering algorithm (ACCA) to explore the topic of textural defect detection. In this algorithm, our efforts are mainly made on the definition of local irregularity measurement and the implementation of the revised ACCA. The local irregular measurement defined evaluates the local textural inconsistency of each pixel against their mini-environment. In our revised ACCA, the behaviors of each ant are divided into two steps: release pheromone and act. The quantity of pheromone released is proportional to the irregularity measurement; the actions of the ants to act next are chosen independently of each other in a stochastic way according to some evaluated heuristic knowledge. The independency of ants implies the inherent parallel computation architecture of this algorithm. We apply the proposed method in some typical textural images with defects. From the series of pheromone distribution map (PDM), it can be clearly seen that the pheromone distribution approaches the textual defects gradually. By some post-processing, the final distribution of pheromone can demonstrate the shape and area of the defects well.

  5. Automatic building detection based on Purposive FastICA (PFICA) algorithm using monocular high resolution Google Earth images

    NASA Astrophysics Data System (ADS)

    Ghaffarian, Saman; Ghaffarian, Salar

    2014-11-01

    This paper proposes an improved FastICA model named as Purposive FastICA (PFICA) with initializing by a simple color space transformation and a novel masking approach to automatically detect buildings from high resolution Google Earth imagery. ICA and FastICA algorithms are defined as Blind Source Separation (BSS) techniques for unmixing source signals using the reference data sets. In order to overcome the limitations of the ICA and FastICA algorithms and make them purposeful, we developed a novel method involving three main steps: 1-Improving the FastICA algorithm using Moore-Penrose pseudo inverse matrix model, 2-Automated seeding of the PFICA algorithm based on LUV color space and proposed simple rules to split image into three regions; shadow + vegetation, baresoil + roads and buildings, respectively, 3-Masking out the final building detection results from PFICA outputs utilizing the K-means clustering algorithm with two number of clusters and conducting simple morphological operations to remove noises. Evaluation of the results illustrates that buildings detected from dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.6% and 85.5% overall pixel-based and object-based precision performances, respectively.

  6. Simple non-laboratory- and laboratory-based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus.

    PubMed

    Wong, Carlos K H; Siu, Shing-Chung; Wan, Eric Y F; Jiao, Fang-Fang; Yu, Esther Y T; Fung, Colman S C; Wong, Ka-Wai; Leung, Angela Y M; Lam, Cindy L K

    2016-05-01

    The aim of the present study was to develop a simple nomogram that can be used to predict the risk of diabetes mellitus (DM) in the asymptomatic non-diabetic subjects based on non-laboratory- and laboratory-based risk algorithms. Anthropometric data, plasma fasting glucose, full lipid profile, exercise habits, and family history of DM were collected from Chinese non-diabetic subjects aged 18-70 years. Logistic regression analysis was performed on a random sample of 2518 subjects to construct non-laboratory- and laboratory-based risk assessment algorithms for detection of undiagnosed DM; both algorithms were validated on data of the remaining sample (n = 839). The Hosmer-Lemeshow test and area under the receiver operating characteristic (ROC) curve (AUC) were used to assess the calibration and discrimination of the DM risk algorithms. Of 3357 subjects recruited, 271 (8.1%) had undiagnosed DM defined by fasting glucose ≥7.0 mmol/L or 2-h post-load plasma glucose ≥11.1 mmol/L after an oral glucose tolerance test. The non-laboratory-based risk algorithm, with scores ranging from 0 to 33, included age, body mass index, family history of DM, regular exercise, and uncontrolled blood pressure; the laboratory-based risk algorithm, with scores ranging from 0 to 37, added triglyceride level to the risk factors. Both algorithms demonstrated acceptable calibration (Hosmer-Lemeshow test: P = 0.229 and P = 0.483) and discrimination (AUC 0.709 and 0.711) for detection of undiagnosed DM. A simple-to-use nomogram for detecting undiagnosed DM has been developed using validated non-laboratory-based and laboratory-based risk algorithms. © 2015 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd.

  7. Spherical aberrations of human astigmatic corneas.

    PubMed

    Zhao, Huawei; Dai, Guang-Ming; Chen, Li; Weeber, Henk A; Piers, Patricia A

    2011-11-01

    To evaluate whether the average spherical aberration of human astigmatic corneas is statistically equivalent to human nonastigmatic corneas. Spherical aberrations of 445 astigmatic corneas prior to laser vision correction were retrospectively investigated to determine Zernike coefficients for central corneal areas 6 mm in diameter using CTView (Sarver and Associates). Data were divided into groups according to cylinder power (0.01 to 0.25 diopters [D], 0.26 to 0.75 D, 0.76 to 1.06 D, 1.07 to 1.53 D, 1.54 to 2.00 D, and >2.00 D) and according to age by decade. Spherical aberrations were correlated with age and astigmatic power among groups and the entire population. Statistical analyses were conducted, and P<.05 was considered statistically significant. Mean patient age was 42.6±11 years. Astigmatic corneas had an average astigmatic power of 0.78±0.58 D and mean spherical aberration was 0.25±0.13 μm for the entire population and approximately the same (0.27 μm) for individual groups, ranging from 0.23 to 0.29 μm (P>.05 for all tested groups). Mean spherical aberration of astigmatic corneas was not correlated significantly with cylinder power or age (P>.05). Spherical aberrations are similar to those of nonastigmatic corneas, permitting the use of these additional data in the design of aspheric toric intra-ocular lenses. Copyright 2011, SLACK Incorporated.

  8. Active Correction of Aperture Discontinuities-Optimized Stroke Minimization. I. A New Adaptive Interaction Matrix Algorithm

    NASA Astrophysics Data System (ADS)

    Mazoyer, J.; Pueyo, L.; N'Diaye, M.; Fogarty, K.; Zimmerman, N.; Leboulleux, L.; St. Laurent, K. E.; Soummer, R.; Shaklan, S.; Norman, C.

    2018-01-01

    Future searches for bio-markers on habitable exoplanets will rely on telescope instruments that achieve extremely high contrast at small planet-to-star angular separations. Coronagraphy is a promising starlight suppression technique, providing excellent contrast and throughput for off-axis sources on clear apertures. However, the complexity of space- and ground-based telescope apertures goes on increasing over time, owing to the combination of primary mirror segmentation, the secondary mirror, and its support structures. These discontinuities in the telescope aperture limit the coronagraph performance. In this paper, we present ACAD-OSM, a novel active method to correct for the diffractive effects of aperture discontinuities in the final image plane of a coronagraph. Active methods use one or several deformable mirrors that are controlled with an interaction matrix to correct for the aberrations in the pupil. However, they are often limited by the amount of aberrations introduced by aperture discontinuities. This algorithm relies on the recalibration of the interaction matrix during the correction process to overcome this limitation. We first describe the ACAD-OSM technique and compare it to the previous active methods for the correction of aperture discontinuities. We then show its performance in terms of contrast and off-axis throughput for static aperture discontinuities (segmentation, struts) and for some aberrations evolving over the life of the instrument (residual phase aberrations, artifacts in the aperture, misalignments in the coronagraph design). This technique can now obtain the Earth-like planet detection threshold of {10}10 contrast on any given aperture over at least a 10% spectral bandwidth, with several coronagraph designs.

  9. The feasibility test of state-of-the-art face detection algorithms for vehicle occupant detection

    NASA Astrophysics Data System (ADS)

    Makrushin, Andrey; Dittmann, Jana; Vielhauer, Claus; Langnickel, Mirko; Kraetzer, Christian

    2010-01-01

    Vehicle seat occupancy detection systems are designed to prevent the deployment of airbags at unoccupied seats, thus avoiding the considerable cost imposed by the replacement of airbags. Occupancy detection can also improve passenger comfort, e.g. by activating air-conditioning systems. The most promising development perspectives are seen in optical sensing systems which have become cheaper and smaller in recent years. The most plausible way to check the seat occupancy by occupants is the detection of presence and location of heads, or more precisely, faces. This paper compares the detection performances of the three most commonly used and widely available face detection algorithms: Viola- Jones, Kienzle et al. and Nilsson et al. The main objective of this work is to identify whether one of these systems is suitable for use in a vehicle environment with variable and mostly non-uniform illumination conditions, and whether any one face detection system can be sufficient for seat occupancy detection. The evaluation of detection performance is based on a large database comprising 53,928 video frames containing proprietary data collected from 39 persons of both sexes and different ages and body height as well as different objects such as bags and rearward/forward facing child restraint systems.

  10. High order aberration and straylight evaluation after cataract surgery with implantation of an aspheric, aberration correcting monofocal intraocular lens

    PubMed Central

    Kretz, Florian T A; Tandogan, Tamer; Khoramnia, Ramin; Auffarth, Gerd U

    2015-01-01

    AIM To evaluate the quality of vision in respect to high order aberrations and straylight perception after implantation of an aspheric, aberration correcting, monofocal intraocular lens (IOL). METHODS Twenty-one patients (34 eyes) aged 50 to 83y underwent cataract surgery with implantation of an aspheric, aberration correcting IOL (Tecnis ZCB00, Abbott Medical Optics). Three months after surgery they were examined for uncorrected (UDVA) and corrected distance visual acuity (CDVA), contrast sensitivity (CS) under photopic and mesopic conditions with and without glare source, ocular high order aberrations (HOA, Zywave II) and retinal straylight (C-Quant). RESULTS Postoperatively, patients achieved a postoperative CDVA of 0.0 logMAR or better in 97.1% of eyes. Mean values of high order abberations were +0.02±0.27 (primary coma components) and -0.04±0.16 (spherical aberration term). Straylight values of the C-Quant were 1.35±0.44 log which is within normal range of age matched phakic patients. The CS measurements under mesopic and photopic conditions in combination with and without glare did not show any statistical significance in the patient group observed (P≥0.28). CONCLUSION The implantation of an aspherical aberration correcting monofocal IOL after cataract surgery resulted in very low residual higher order aberration (HOA) and normal straylight. PMID:26309872

  11. Accommodation to Wavefront Vergence and Chromatic Aberration

    PubMed Central

    Wang, Yinan; Kruger, Philip B.; Li, James S.; Lin, Peter L.; Stark, Lawrence R.

    2011-01-01

    Purpose Longitudinal chromatic aberration (LCA) provides a cue to accommodation with small pupils. However, large pupils increase monochromatic aberrations, which may obscure chromatic blur. In the present study, we examined the effect of pupil size and LCA on accommodation. Methods Accommodation was recorded by infrared optometer while observers (nine normal trichromats) viewed a sinusoidally moving Maltese cross target in a Badal stimulus system. There were two illumination conditions: white (3000 K; 20 cd/m2) and monochromatic (550 nm with 10 nm bandwidth; 20 cd/m2) and two artificial pupil conditions (3 mm and 5.7 mm). Separately, static measurements of wavefront aberration were made with the eye accommodating to targets between 0 and 4 D (COAS, Wavefront Sciences). Results Large individual differences in accommodation to wavefront vergence and to LCA are a hallmark of accommodation. LCA continues to provide a signal at large pupil sizes despite higher levels of monochromatic aberrations. Conclusions Monochromatic aberrations may defend against chromatic blur at high spatial frequencies, but accommodation responds best to optical vergence and to LCA at 3 c/deg where blur from higher order aberrations is less. PMID:21317666

  12. A density based algorithm to detect cavities and holes from planar points

    NASA Astrophysics Data System (ADS)

    Zhu, Jie; Sun, Yizhong; Pang, Yueyong

    2017-12-01

    Delaunay-based shape reconstruction algorithms are widely used in approximating the shape from planar points. However, these algorithms cannot ensure the optimality of varied reconstructed cavity boundaries and hole boundaries. This inadequate reconstruction can be primarily attributed to the lack of efficient mathematic formulation for the two structures (hole and cavity). In this paper, we develop an efficient algorithm for generating cavities and holes from planar points. The algorithm yields the final boundary based on an iterative removal of the Delaunay triangulation. Our algorithm is mainly divided into two steps, namely, rough and refined shape reconstructions. The rough shape reconstruction performed by the algorithm is controlled by a relative parameter. Based on the rough result, the refined shape reconstruction mainly aims to detect holes and pure cavities. Cavity and hole are conceptualized as a structure with a low-density region surrounded by the high-density region. With this structure, cavity and hole are characterized by a mathematic formulation called as compactness of point formed by the length variation of the edges incident to point in Delaunay triangulation. The boundaries of cavity and hole are then found by locating a shape gradient change in compactness of point set. The experimental comparison with other shape reconstruction approaches shows that the proposed algorithm is able to accurately yield the boundaries of cavity and hole with varying point set densities and distributions.

  13. Detection algorithm for glass bottle mouth defect by continuous wavelet transform based on machine vision

    NASA Astrophysics Data System (ADS)

    Qian, Jinfang; Zhang, Changjiang

    2014-11-01

    An efficient algorithm based on continuous wavelet transform combining with pre-knowledge, which can be used to detect the defect of glass bottle mouth, is proposed. Firstly, under the condition of ball integral light source, a perfect glass bottle mouth image is obtained by Japanese Computar camera through the interface of IEEE-1394b. A single threshold method based on gray level histogram is used to obtain the binary image of the glass bottle mouth. In order to efficiently suppress noise, moving average filter is employed to smooth the histogram of original glass bottle mouth image. And then continuous wavelet transform is done to accurately determine the segmentation threshold. Mathematical morphology operations are used to get normal binary bottle mouth mask. A glass bottle to be detected is moving to the detection zone by conveyor belt. Both bottle mouth image and binary image are obtained by above method. The binary image is multiplied with normal bottle mask and a region of interest is got. Four parameters (number of connected regions, coordinate of centroid position, diameter of inner cycle, and area of annular region) can be computed based on the region of interest. Glass bottle mouth detection rules are designed by above four parameters so as to accurately detect and identify the defect conditions of glass bottle. Finally, the glass bottles of Coca-Cola Company are used to verify the proposed algorithm. The experimental results show that the proposed algorithm can accurately detect the defect conditions of the glass bottles and have 98% detecting accuracy.

  14. New Graph Models and Algorithms for Detecting Salient Structures from Cluttered Images

    DTIC Science & Technology

    2010-02-24

    Development of graph models and algorithms to detect boundaries that show certain levels of symmetry, an important geometric property of many...Bookstein. Morphometric tools for landmark data. Cambridge University Press, 1991. [8] F. L. Bookstein. Principal warps: Thin-plate splines and the

  15. Theoretical investigation of aberrations upon ametropic human eyes

    NASA Astrophysics Data System (ADS)

    Tan, Bo; Chen, Ying-Ling; Lewis, J. W. L.; Baker, Kevin

    2003-11-01

    The human eye aberrations are important for visual acuity and ophthalmic diagnostics and surgical procedures. Reported monochromatic aberration data of the normal 20/20 human eyes are scarce. There exist even fewer reports of the relation between ametropic conditions and aberrations. We theoretically investigate the monochromatic and chromatic aberrations of human eyes for refractive errors of -10 to +10 diopters. Schematic human eye models are employed using optical design software for axial, index, and refractive types of ametropia.

  16. Anterior Corneal, Posterior Corneal, and Lenticular Contributions to Ocular Aberrations.

    PubMed

    Atchison, David A; Suheimat, Marwan; Mathur, Ankit; Lister, Lucas J; Rozema, Jos

    2016-10-01

    To determine the corneal surfaces and lens contributions to ocular aberrations. There were 61 healthy participants with ages ranging from 20 to 55 years and refractions -8.25 diopters (D) to +3.25 D. Anterior and posterior corneal topographies were obtained with an Oculus Pentacam, and ocular aberrations were obtained with an iTrace aberrometer. Raytracing through models of corneas provided total corneal and surface component aberrations for 5-mm-diameter pupils. Lenticular contributions were given as differences between ocular and corneal aberrations. Theoretical raytracing investigated influence of object distance on aberrations. Apart from defocus, the highest aberration coefficients were horizontal astigmatism, horizontal coma, and spherical aberration. Most correlations between lenticular and ocular parameters were positive and significant, with compensation of total corneal aberrations by lenticular aberrations for 5/12 coefficients. Anterior corneal aberrations were approximately three times higher than posterior corneal aberrations and usually had opposite signs. Corneal topographic centers were displaced from aberrometer pupil centers by 0.32 ± 0.19 mm nasally and 0.02 ± 0.16 mm inferiorly; disregarding corneal decentration relative to pupil center was significant for oblique astigmatism, horizontal coma, and horizontal trefoil. An object at infinity, rather than at the image in the anterior cornea, gave incorrect aberration estimates of the posterior cornea. Corneal and lenticular aberration magnitudes are similar, and aberrations of the anterior corneal surface are approximately three times those of the posterior surface. Corneal decentration relative to pupil center has significant effects on oblique astigmatism, horizontal coma, and horizontal trefoil. When estimating component aberrations, it is important to use correct object/image conjugates and heights at surfaces.

  17. Research on conflict detection algorithm in 3D visualization environment of urban rail transit line

    NASA Astrophysics Data System (ADS)

    Wang, Li; Xiong, Jing; You, Kuokuo

    2017-03-01

    In this paper, a method of collision detection is introduced, and the theory of three-dimensional modeling of underground buildings and urban rail lines is realized by rapidly extracting the buildings that are in conflict with the track area in the 3D visualization environment. According to the characteristics of the buildings, CSG and B-rep are used to model the buildings based on CSG and B-rep. On the basis of studying the modeling characteristics, this paper proposes to use the AABB level bounding volume method to detect the first conflict and improve the detection efficiency, and then use the triangular rapid intersection detection algorithm to detect the conflict, and finally determine whether the building collides with the track area. Through the algorithm of this paper, we can quickly extract buildings colliding with the influence area of the track line, so as to help the line design, choose the best route and calculate the cost of land acquisition in the three-dimensional visualization environment.

  18. Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network.

    PubMed

    Karayiannis, Nicolaos B; Mukherjee, Amit; Glover, John R; Ktonas, Periklis Y; Frost, James D; Hrachovy, Richard A; Mizrahi, Eli M

    2006-04-01

    This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development.

  19. Enhancing nuclear quadrupole resonance (NQR) signature detection leveraging interference suppression algorithms

    NASA Astrophysics Data System (ADS)

    DeBardelaben, James A.; Miller, Jeremy K.; Myrick, Wilbur L.; Miller, Joel B.; Gilbreath, G. Charmaine; Bajramaj, Blerta

    2012-06-01

    Nuclear quadrupole resonance (NQR) is a radio frequency (RF) magnetic spectroscopic technique that has been shown to detect and identify a wide range of explosive materials containing quadrupolar nuclei. The NQR response signal provides a unique signature of the material of interest. The signal is, however, very weak and can be masked by non-stationary RF interference (RFI) and thermal noise, limiting detection distance. In this paper, we investigate the bounds on the NQR detection range for ammonium nitrate. We leverage a low-cost RFI data acquisition system composed of inexpensive B-field sensing and commercial-off-the-shelf (COTS) software-defined radios (SDR). Using collected data as RFI reference signals, we apply adaptive filtering algorithms to mitigate RFI and enable NQR detection techniques to approach theoretical range bounds in tactical environments.

  20. Clairvoyant fusion: a new methodology for designing robust detection algorithms

    NASA Astrophysics Data System (ADS)

    Schaum, Alan

    2016-10-01

    Many realistic detection problems cannot be solved with simple statistical tests for known alternative probability models. Uncontrollable environmental conditions, imperfect sensors, and other uncertainties transform simple detection problems with likelihood ratio solutions into composite hypothesis (CH) testing problems. Recently many multi- and hyperspectral sensing CH problems have been addressed with a new approach. Clairvoyant fusion (CF) integrates the optimal detectors ("clairvoyants") associated with every unspecified value of the parameters appearing in a detection model. For problems with discrete parameter values, logical rules emerge for combining the decisions of the associated clairvoyants. For many problems with continuous parameters, analytic methods of CF have been found that produce closed-form solutions-or approximations for intractable problems. Here the principals of CF are reviewed and mathematical insights are described that have proven useful in the derivation of solutions. It is also shown how a second-stage fusion procedure can be used to create theoretically superior detection algorithms for ALL discrete parameter problems.

  1. A novel seizure detection algorithm informed by hidden Markov model event states

    NASA Astrophysics Data System (ADS)

    Baldassano, Steven; Wulsin, Drausin; Ung, Hoameng; Blevins, Tyler; Brown, Mesha-Gay; Fox, Emily; Litt, Brian

    2016-06-01

    Objective. Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model. Approach. We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control. Main results. Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h-1). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)). Significance. This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.

  2. Nodal aberration theory for wild-filed asymmetric optical systems

    NASA Astrophysics Data System (ADS)

    Chen, Yang; Cheng, Xuemin; Hao, Qun

    2016-10-01

    Nodal Aberration Theory (NAT) was used to calculate the zero field position in Full Field Display (FFD) for the given aberration term. Aiming at wide-filed non-rotational symmetric decentered optical systems, we have presented the nodal geography behavior of the family of third-order and fifth-order aberrations. Meanwhile, we have calculated the wavefront aberration expressions when one optical element in the system is tilted, which was not at the entrance pupil. By using a three-piece-cellphone lens example in optical design software CodeV, the nodal geography is testified under several situations; and the wavefront aberrations are calculated when the optical element is tilted. The properties of the nodal aberrations are analyzed by using Fringe Zernike coefficients, which are directly related with the wavefront aberration terms and usually obtained by real ray trace and wavefront surface fitting.

  3. A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

    PubMed Central

    Zhu, Bohui; Ding, Yongsheng; Hao, Kuangrong

    2013-01-01

    This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias. This diagnostic system consists of signal processing, feature extraction, and the IEMMC algorithm for clustering of ECG arrhythmias. First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types of arrhythmias by the IEMMC algorithm. Three types of performance evaluation indicators are used to assess the effect of the IEMMC method for ECG arrhythmias, such as sensitivity, specificity, and accuracy. Compared with K-means and iterSVR algorithms, the IEMMC algorithm reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias. PMID:23690875

  4. Shack-Hartmann wavefront sensor with large dynamic range.

    PubMed

    Xia, Mingliang; Li, Chao; Hu, Lifa; Cao, Zhaoliang; Mu, Quanquan; Xuan, Li

    2010-01-01

    A new spot centroid detection algorithm for a Shack-Hartmann wavefront sensor (SHWFS) is experimentally investigated. The algorithm is a kind of dynamic tracking algorithm that tracks and calculates the corresponding spot centroid of the current spot map based on the spot centroid of the previous spot map, according to the strong correlation of the wavefront slope and the centroid of the corresponding spot between temporally adjacent SHWFS measurements. That is, for adjacent measurements, the spot centroid movement will usually fall within some range. Using the algorithm, the dynamic range of an SHWFS can be expanded by a factor of three in the measurement of tilt aberration compared with the conventional algorithm, more than 1.3 times in the measurement of defocus aberration, and more than 2 times in the measurement of the mixture of spherical aberration plus coma aberration. The algorithm is applied in our SHWFS to measure the distorted wavefront of the human eye. The experimental results of the adaptive optics (AO) system for retina imaging are presented to prove its feasibility for highly aberrated eyes.

  5. Wavefront sensorless adaptive optics OCT with the DONE algorithm for in vivo human retinal imaging [Invited].

    PubMed

    Verstraete, Hans R G W; Heisler, Morgan; Ju, Myeong Jin; Wahl, Daniel; Bliek, Laurens; Kalkman, Jeroen; Bonora, Stefano; Jian, Yifan; Verhaegen, Michel; Sarunic, Marinko V

    2017-04-01

    In this report, which is an international collaboration of OCT, adaptive optics, and control research, we demonstrate the Data-based Online Nonlinear Extremum-seeker (DONE) algorithm to guide the image based optimization for wavefront sensorless adaptive optics (WFSL-AO) OCT for in vivo human retinal imaging. The ocular aberrations were corrected using a multi-actuator adaptive lens after linearization of the hysteresis in the piezoelectric actuators. The DONE algorithm succeeded in drastically improving image quality and the OCT signal intensity, up to a factor seven, while achieving a computational time of 1 ms per iteration, making it applicable for many high speed applications. We demonstrate the correction of five aberrations using 70 iterations of the DONE algorithm performed over 2.8 s of continuous volumetric OCT acquisition. Data acquired from an imaging phantom and in vivo from human research volunteers are presented.

  6. Catheter Hydrophone Aberration Correction for Transcranial Histotripsy Treatment of Intracerebral Hemorrhage: Proof-of-Concept.

    PubMed

    Gerhardson, Tyler; Sukovich, Jonathan R; Pandey, Aditya S; Hall, Timothy L; Cain, Charles A; Xu, Zhen

    2017-11-01

    Histotripsy is a minimally invasive ultrasound therapy that has shown rapid liquefaction of blood clots through human skullcaps in an in vitro intracerebral hemorrhage model. However, the efficiency of these treatments can be compromised if the skull-induced aberrations are uncorrected. We have developed a catheter hydrophone which can perform aberration correction (AC) and drain the liquefied clot following histotripsy treatment. Histotripsy pulses were delivered through an excised human skullcap using a 256-element, 500-kHz hemisphere array transducer with a 15-cm focal distance. A custom hydrophone was fabricated using a mm PZT-5h crystal interfaced to a coaxial cable and integrated into a drainage catheter. An AC algorithm was developed to correct the aberrations introduced between histotripsy pulses from each array element. An increase in focal pressure of up to 60% was achieved at the geometric focus and 27%-62% across a range of electronic steering locations. The sagittal and axial -6-dB beam widths decreased from 4.6 to 2.2 mm in the sagittal direction and 8 to 4.4 mm in the axial direction, compared to 1.5 and 3 mm in the absence of aberration. After performing AC, lesions with diameters ranging from 0.24 to 1.35 mm were generated using electronic steering over a mm grid in a tissue-mimicking phantom. An average volume of 4.07 ± 0.91 mL was liquefied and drained after using electronic steering to treat a 4.2-mL spherical volume in in vitro bovine clots through the skullcap.

  7. Aberration corrected STEM by means of diffraction gratings

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

    Linck, Martin; Ercius, Peter A.; Pierce, Jordan S.

    In the past 15 years, the advent of aberration correction technology in electron microscopy has enabled materials analysis on the atomic scale. This is made possible by precise arrangements of multipole electrodes and magnetic solenoids to compensate the aberrations inherent to any focusing element of an electron microscope. In this paper, we describe an alternative method to correct for the spherical aberration of the objective lens in scanning transmission electron microscopy (STEM) using a passive, nanofabricated diffractive optical element. This holographic device is installed in the probe forming aperture of a conventional electron microscope and can be designed to removemore » arbitrarily complex aberrations from the electron's wave front. In this work, we show a proof-of-principle experiment that demonstrates successful correction of the spherical aberration in STEM by means of such a grating corrector (GCOR). Our GCOR enables us to record aberration-corrected high-resolution high-angle annular dark field (HAADF-) STEM images, although yet without advancement in probe current and resolution. Finally, improvements in this technology could provide an economical solution for aberration-corrected high-resolution STEM in certain use scenarios.« less

  8. Aberration corrected STEM by means of diffraction gratings

    DOE PAGES

    Linck, Martin; Ercius, Peter A.; Pierce, Jordan S.; ...

    2017-06-12

    In the past 15 years, the advent of aberration correction technology in electron microscopy has enabled materials analysis on the atomic scale. This is made possible by precise arrangements of multipole electrodes and magnetic solenoids to compensate the aberrations inherent to any focusing element of an electron microscope. In this paper, we describe an alternative method to correct for the spherical aberration of the objective lens in scanning transmission electron microscopy (STEM) using a passive, nanofabricated diffractive optical element. This holographic device is installed in the probe forming aperture of a conventional electron microscope and can be designed to removemore » arbitrarily complex aberrations from the electron's wave front. In this work, we show a proof-of-principle experiment that demonstrates successful correction of the spherical aberration in STEM by means of such a grating corrector (GCOR). Our GCOR enables us to record aberration-corrected high-resolution high-angle annular dark field (HAADF-) STEM images, although yet without advancement in probe current and resolution. Finally, improvements in this technology could provide an economical solution for aberration-corrected high-resolution STEM in certain use scenarios.« less

  9. A simplified Suomi NPP VIIRS dust detection algorithm

    NASA Astrophysics Data System (ADS)

    Yang, Yikun; Sun, Lin; Zhu, Jinshan; Wei, Jing; Su, Qinghua; Sun, Wenxiao; Liu, Fangwei; Shu, Meiyan

    2017-11-01

    Due to the complex characteristics of dust and sparse ground-based monitoring stations, dust monitoring is facing severe challenges, especially in dust storm-prone areas. Aim at constructing a high-precision dust storm detection model, a pixel database, consisted of dusts over a variety of typical feature types such as cloud, vegetation, Gobi and ice/snow, was constructed, and their distributions of reflectance and Brightness Temperatures (BT) were analysed, based on which, a new Simplified Dust Detection Algorithm (SDDA) for the Suomi National Polar-Orbiting Partnership Visible infrared Imaging Radiometer (NPP VIIRS) is proposed. NPP VIIRS images covering the northern China and Mongolian regions, where features serious dust storms, were selected to perform the dust detection experiments. The monitoring results were compared with the true colour composite images, and results showed that most of the dust areas can be accurately detected, except for fragmented thin dusts over bright surfaces. The dust ground-based measurements obtained from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) and the Ozone Monitoring Instrument Aerosol Index (OMI AI) products were selected for comparison purposes. Results showed that the dust monitoring results agreed well in the spatial distribution with OMI AI dust products and the MICAPS ground-measured data with an average high accuracy of 83.10%. The SDDA is relatively robust and can realize automatic monitoring for dust storms.

  10. Aberrant DNA methylation of tumor-related genes in oral rinse: a noninvasive method for detection of oral squamous cell carcinoma.

    PubMed

    Nagata, Satoshi; Hamada, Tomofumi; Yamada, Norishige; Yokoyama, Seiya; Kitamoto, Sho; Kanmura, Yuji; Nomura, Masahiro; Kamikawa, Yoshiaki; Yonezawa, Suguru; Sugihara, Kazumasa

    2012-09-01

    The early detection of oral squamous cell carcinoma (OSCC) is important, and a screening test with high sensitivity and specificity is urgently needed. Therefore, in this study, the authors investigated the methylation status of tumor-related genes with the objective of establishing a noninvasive method for the detection of OSCC. Oral rinse samples were obtained from 34 patients with OSCC and from 24 healthy individuals (controls). The methylation status of 13 genes was determined by using methylation-specific polymerase chain reaction analysis and was quantified using a microchip electrophoresis system. Promoter methylation in each participant was screened by receiver operating characteristic analysis, and the utility of each gene's methylation status, alone and in combination with other genes, was evaluated as a tool for oral cancer detection. Eight of the 13 genes had significantly higher levels of DNA methylation in samples from patients with OSCC than in controls. The genes E-cadherin (ECAD), transmembrane protein with epidermal growth factor-like and 2 follistatin-like domains 2 (TMEFF2), retinoic acid receptor beta (RARβ), and O-6 methylguanine DNA methyltransferase (MGMT) had high sensitivity (>75%) and specificity for the detection of oral cancer. OSCC was detected with 100% sensitivity and 87.5% specificity using a combination of ECAD, TMEFF2, RARβ, and MGMT and with 97.1% sensitivity and 91.7% specificity using a combination of ECAD, TMEFF2, and MGMT. The aberrant methylation of a combination of marker genes present in oral rinse samples was used to detect OSCC with >90% sensitivity and specificity. The detection of methylated marker genes from oral rinse samples has great potential for the noninvasive detection of OSCC. Copyright © 2012 American Cancer Society.

  11. Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of Machine Learning Algorithms.

    PubMed

    Gaur, Pallavi; Chaturvedi, Anoop

    2017-07-22

    The clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes. Along with obtaining clustering pattern and candidate motifs in exosomal miRNAs, this work also elaborates the usefulness of the machine learning algorithms that can be efficiently used and executed on various programming languages/platforms. Data were clustered and sequence candidate motifs were detected successfully. The results were compared and validated with some available web tools such as 'BLASTN' and 'MEME suite'. The machine learning algorithms for aforementioned objectives were applied successfully. This work elaborated utility of machine learning algorithms and language platforms to achieve the tasks of clustering and candidate motif detection in exosomal miRNAs. With the information on mentioned objectives, deeper insight would be gained for analyses of newly discovered miRNAs in exosomes which are considered to be circulating biomarkers. In addition, the execution of machine learning algorithms on various language platforms gives more flexibility to users to try multiple iterations according to their requirements. This approach can be applied to other biological data-mining tasks as well.

  12. Experimental Verification of Bayesian Planet Detection Algorithms with a Shaped Pupil Coronagraph

    NASA Astrophysics Data System (ADS)

    Savransky, D.; Groff, T. D.; Kasdin, N. J.

    2010-10-01

    We evaluate the feasibility of applying Bayesian detection techniques to discovering exoplanets using high contrast laboratory data with simulated planetary signals. Background images are generated at the Princeton High Contrast Imaging Lab (HCIL), with a coronagraphic system utilizing a shaped pupil and two deformable mirrors (DMs) in series. Estimates of the electric field at the science camera are used to correct for quasi-static speckle and produce symmetric high contrast dark regions in the image plane. Planetary signals are added in software, or via a physical star-planet simulator which adds a second off-axis point source before the coronagraph with a beam recombiner, calibrated to a fixed contrast level relative to the source. We produce a variety of images, with varying integration times and simulated planetary brightness. We then apply automated detection algorithms such as matched filtering to attempt to extract the planetary signals. This allows us to evaluate the efficiency of these techniques in detecting planets in a high noise regime and eliminating false positives, as well as to test existing algorithms for calculating the required integration times for these techniques to be applicable.

  13. Comparison of algorithms for blood stain detection applied to forensic hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Yang, Jie; Messinger, David W.; Mathew, Jobin J.; Dube, Roger R.

    2016-05-01

    Blood stains are among the most important types of evidence for forensic investigation. They contain valuable DNA information, and the pattern of the stains can suggest specifics about the nature of the violence that transpired at the scene. Early detection of blood stains is particularly important since the blood reacts physically and chemically with air and materials over time. Accurate identification of blood remnants, including regions that might have been intentionally cleaned, is an important aspect of forensic investigation. Hyperspectral imaging might be a potential method to detect blood stains because it is non-contact and provides substantial spectral information that can be used to identify regions in a scene with trace amounts of blood. The potential complexity of scenes in which such vast violence occurs can be high when the range of scene material types and conditions containing blood stains at a crime scene are considered. Some stains are hard to detect by the unaided eye, especially if a conscious effort to clean the scene has occurred (we refer to these as "latent" blood stains). In this paper we present the initial results of a study of the use of hyperspectral imaging algorithms for blood detection in complex scenes. We describe a hyperspectral imaging system which generates images covering 400 nm - 700 nm visible range with a spectral resolution of 10 nm. Three image sets of 31 wavelength bands were generated using this camera for a simulated indoor crime scene in which blood stains were placed on a T-shirt and walls. To detect blood stains in the scene, Principal Component Analysis (PCA), Subspace Reed Xiaoli Detection (SRXD), and Topological Anomaly Detection (TAD) algorithms were used. Comparison of the three hyperspectral image analysis techniques shows that TAD is most suitable for detecting blood stains and discovering latent blood stains.

  14. mBAND Analysis of Late Chromosome Aberrations in Human Lymphocytes Induced by Gamma Rays and Fe Ions

    NASA Technical Reports Server (NTRS)

    Sunagawa, Mayumi; Zhang, Ye; Yeshitla, Samrawit; Kadhim, Munira; Wilson, Bobby; Wu, Honglu

    2014-01-01

    Chromosomal translocations and inversions are considered stable, and cells containing these types of chromosome aberrations can survive multiple cell divisions. An efficient method to detect an inversion is multi-color banding fluorescent in situ hybridization (mBAND) which allows identification of both inter- and intrachromosome aberrations simultaneously. Post irradiation, chromosome aberrations may also arise after multiple cell divisions as a result of genomic instability. To investigate the stable or late-arising chromosome aberrations induced after radiation exposure, we exposed human lymphocytes to gamma rays and Fe ions ex vivo, and cultured the cells for multiple generations. Chromosome aberrations were analyzed in cells collected at first mitosis and at several time intervals during the culture period post irradiation. With gamma irradiation, about half of the damages observed at first mitosis remained after 7 day- and 14 day- culture, suggesting the transmissibility of damages to the surviving progeny. Detailed analysis of chromosome break ends participating in exchanges revealed a greater fraction of break ends involved in intrachromosome aberrations in the 7- and 14-day samples in comparison to the fraction at first mitosis. In particular, simple inversions were found at 7 and 14 days, but not at the first mitosis, suggesting that some of the aberrations might be formed days post irradiation. In contrast, at the doses that produced similar frequencies of gamma-induced chromosome aberrations as observed at first mitosis, a significantly lower yield of aberrations remained at the same population doublings after Fe ion exposure. At these equitoxic doses, more complex type aberrations were observed for Fe ions, indicating that Fe ion-induced initial chromosome damages are more severe and may lead to cell death. Comparison between low and high doses of Fe ion irradiation in the induction of late damages will also be discussed.

  15. Runway Safety Monitor Algorithm for Single and Crossing Runway Incursion Detection and Alerting

    NASA Technical Reports Server (NTRS)

    Green, David F., Jr.

    2006-01-01

    The Runway Safety Monitor (RSM) is an aircraft based algorithm for runway incursion detection and alerting that was developed in support of NASA's Runway Incursion Prevention System (RIPS) research conducted under the NASA Aviation Safety and Security Program's Synthetic Vision System project. The RSM algorithm provides warnings of runway incursions in sufficient time for pilots to take evasive action and avoid accidents during landings, takeoffs or when taxiing on the runway. The report documents the RSM software and describes in detail how RSM performs runway incursion detection and alerting functions for NASA RIPS. The report also describes the RIPS flight tests conducted at the Reno/Tahoe International Airport (RNO) and the Wallops Flight Facility (WAL) during July and August of 2004, and the RSM performance results and lessons learned from those flight tests.

  16. Aberration correction for charged particle lithography

    NASA Astrophysics Data System (ADS)

    Munro, Eric; Zhu, Xieqing; Rouse, John A.; Liu, Haoning

    2001-12-01

    At present, the throughput of projection-type charge particle lithography systems, such as PREVAIL and SCALPEL, is limited primarily by the combined effects of field curvature in the projection lenses and Coulomb interaction in the particle beam. These are fundamental physical limitations, inherent in charged particle optics, so there seems little scope for significantly improving the design of such systems, using conventional rotationally symmetric electron lenses. This paper explores the possibility of overcoming the field aberrations of round electron lense, by using a novel aberration corrector, proposed by Professor H. Rose of University of Darmstadt, called a hexapole planator. In this scheme, a set of round lenses is first used to simultaneously correct distortion and coma. The hexapole planator is then used to correct the field curvature and astigmatism, and to create a negative spherical aberration. The size of the transfer lenses around the planator can then be adjusted to zero the residual spherical aberration. In a way, an electron optical projection system is obtained that is free of all primary geometrical aberrations. In this paper, the feasibility of this concept has been studied with a computer simulation. The simulations verify that this scheme can indeed work, for both electrostatic and magnetic projection systems. Two design studies have been carried out. The first is for an electrostatic system that could be used for ion beam lithography, and the second is for a magnetic projection system for electron beam lithography. In both cases, designs have been achieved in which all primary third-order geometrical aberrations are totally eliminated.

  17. Aberration Compensation in Aplanatic Solid Immersion Lens Microscopy

    DTIC Science & Technology

    2013-11-08

    model and ray tracing software ( Zemax ) to understand how much aberrations are in the system and how much can be compensated by the DM. Subsequently...aberration. Table 2 shows the Zemax simulation on this particular case. With aberration compensation, the finest resolvable group is at 252 nm

  18. Effect of segmentation algorithms on the performance of computerized detection of lung nodules in CT

    PubMed Central

    Guo, Wei; Li, Qiang

    2014-01-01

    Purpose: The purpose of this study is to reveal how the performance of lung nodule segmentation algorithm impacts the performance of lung nodule detection, and to provide guidelines for choosing an appropriate segmentation algorithm with appropriate parameters in a computer-aided detection (CAD) scheme. Methods: The database consisted of 85 CT scans with 111 nodules of 3 mm or larger in diameter from the standard CT lung nodule database created by the Lung Image Database Consortium. The initial nodule candidates were identified as those with strong response to a selective nodule enhancement filter. A uniform viewpoint reformation technique was applied to a three-dimensional nodule candidate to generate 24 two-dimensional (2D) reformatted images, which would be used to effectively distinguish between true nodules and false positives. Six different algorithms were employed to segment the initial nodule candidates in the 2D reformatted images. Finally, 2D features from the segmented areas in the 24 reformatted images were determined, selected, and classified for removal of false positives. Therefore, there were six similar CAD schemes, in which only the segmentation algorithms were different. The six segmentation algorithms included the fixed thresholding (FT), Otsu thresholding (OTSU), fuzzy C-means (FCM), Gaussian mixture model (GMM), Chan and Vese model (CV), and local binary fitting (LBF). The mean Jaccard index and the mean absolute distance (Dmean) were employed to evaluate the performance of segmentation algorithms, and the number of false positives at a fixed sensitivity was employed to evaluate the performance of the CAD schemes. Results: For the segmentation algorithms of FT, OTSU, FCM, GMM, CV, and LBF, the highest mean Jaccard index between the segmented nodule and the ground truth were 0.601, 0.586, 0.588, 0.563, 0.543, and 0.553, respectively, and the corresponding Dmean were 1.74, 1.80, 2.32, 2.80, 3.48, and 3.18 pixels, respectively. With these

  19. The development of line-scan image recognition algorithms for the detection of frass on mature tomatoes

    USDA-ARS?s Scientific Manuscript database

    In this research, a multispectral algorithm derived from hyperspectral line-scan fluorescence imaging under violet LED excitation was developed for the detection of frass contamination on mature tomatoes. The algorithm utilized the fluorescence intensities at two wavebands, 664 nm and 690 nm, for co...

  20. A computer-aided detection (CAD) system with a 3D algorithm for small acute intracranial hemorrhage

    NASA Astrophysics Data System (ADS)

    Wang, Ximing; Fernandez, James; Deshpande, Ruchi; Lee, Joon K.; Chan, Tao; Liu, Brent

    2012-02-01

    Acute Intracranial hemorrhage (AIH) requires urgent diagnosis in the emergency setting to mitigate eventual sequelae. However, experienced radiologists may not always be available to make a timely diagnosis. This is especially true for small AIH, defined as lesion smaller than 10 mm in size. A computer-aided detection (CAD) system for the detection of small AIH would facilitate timely diagnosis. A previously developed 2D algorithm shows high false positive rates in the evaluation based on LAC/USC cases, due to the limitation of setting up correct coordinate system for the knowledge-based classification system. To achieve a higher sensitivity and specificity, a new 3D algorithm is developed. The algorithm utilizes a top-hat transformation and dynamic threshold map to detect small AIH lesions. Several key structures of brain are detected and are used to set up a 3D anatomical coordinate system. A rule-based classification of the lesion detected is applied based on the anatomical coordinate system. For convenient evaluation in clinical environment, the CAD module is integrated with a stand-alone system. The CAD is evaluated by small AIH cases and matched normal collected in LAC/USC. The result of 3D CAD and the previous 2D CAD has been compared.

  1. Detection of wood failure by image processing method: influence of algorithm, adhesive and wood species

    Treesearch

    Lanying Lin; Sheng He; Feng Fu; Xiping Wang

    2015-01-01

    Wood failure percentage (WFP) is an important index for evaluating the bond strength of plywood. Currently, the method used for detecting WFP is visual inspection, which lacks efficiency. In order to improve it, image processing methods are applied to wood failure detection. The present study used thresholding and K-means clustering algorithms in wood failure detection...

  2. An improved algorithm for small and cool fire detection using MODIS data: A preliminary study in the southeastern United States

    Treesearch

    Wanting Wang; John J. Qu; Xianjun Hao; Yongqiang Liu; William T. Sommers

    2006-01-01

    Traditional fire detection algorithms mainly rely on hot spot detection using thermal infrared (TIR) channels with fixed or contextual thresholds. Three solar reflectance channels (0.65 μm, 0.86 μm, and 2.1 μm) were recently adopted into the MODIS version 4 contextual algorithm to improve the active fire detection. In the southeastern United...

  3. Micro-Scale Genomic DNA Copy Number Aberrations as Another Means of Mutagenesis in Breast Cancer

    PubMed Central

    Chao, Hann-Hsiang; He, Xiaping; Parker, Joel S.; Zhao, Wei; Perou, Charles M.

    2012-01-01

    Introduction In breast cancer, the basal-like subtype has high levels of genomic instability relative to other breast cancer subtypes with many basal-like-specific regions of aberration. There is evidence that this genomic instability extends to smaller scale genomic aberrations, as shown by a previously described micro-deletion event in the PTEN gene in the Basal-like SUM149 breast cancer cell line. Methods We sought to identify if small regions of genomic DNA copy number changes exist by using a high density, gene-centric Comparative Genomic Hybridizations (CGH) array on cell lines and primary tumors. A custom tiling array for CGH (244,000 probes, 200 bp tiling resolution) was created to identify small regions of genomic change, which was focused on previously identified basal-like-specific, and general cancer genes. Tumor genomic DNA from 94 patients and 2 breast cancer cell lines was labeled and hybridized to these arrays. Aberrations were called using SWITCHdna and the smallest 25% of SWITCHdna-defined genomic segments were called micro-aberrations (<64 contiguous probes, ∼ 15 kb). Results Our data showed that primary tumor breast cancer genomes frequently contained many small-scale copy number gains and losses, termed micro-aberrations, most of which are undetectable using typical-density genome-wide aCGH arrays. The basal-like subtype exhibited the highest incidence of these events. These micro-aberrations sometimes altered expression of the involved gene. We confirmed the presence of the PTEN micro-amplification in SUM149 and by mRNA-seq showed that this resulted in loss of expression of all exons downstream of this event. Micro-aberrations disproportionately affected the 5′ regions of the affected genes, including the promoter region, and high frequency of micro-aberrations was associated with poor survival. Conclusion Using a high-probe-density, gene-centric aCGH microarray, we present evidence of small-scale genomic aberrations that can contribute to

  4. Rooting Out Aberrant Behavior in Training.

    ERIC Educational Resources Information Center

    Kokalis, Jerry, Jr.; Paquin, Dave

    1989-01-01

    Discusses aberrant, or disruptive, behavior in an industrial/business, classroom-based, instructor-led training setting. Three examples of aberrant behavior are described, typical case studies are provided for each, and preventive (long-term) and corrective (on-the-spot) strategies for dealing with the problems are discussed. (LRW)

  5. An Adaptive Cultural Algorithm with Improved Quantum-behaved Particle Swarm Optimization for Sonar Image Detection.

    PubMed

    Wang, Xingmei; Hao, Wenqian; Li, Qiming

    2017-12-18

    This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of particles, iterative times and the fitness value of particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved particle swarm optimization algorithm (QPSO). The improved quantum-behaved particle swarm optimization algorithm (IQPSO) can make particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.

  6. Human eyes do not need monochromatic aberrations for dynamic accommodation.

    PubMed

    Bernal-Molina, Paula; Marín-Franch, Iván; Del Águila-Carrasco, Antonio J; Esteve-Taboada, Jose J; López-Gil, Norberto; Kruger, Philip B; Montés-Micó, Robert

    2017-09-01

    To determine if human accommodation uses the eye's own monochromatic aberrations to track dynamic accommodative stimuli. Wavefront aberrations were measured while subjects monocularly viewed a monochromatic Maltese cross moving sinusoidally around 2D of accommodative demand with 1D amplitude at 0.2 Hz. The amplitude and phase (delay) of the accommodation response were compared to the actual vergence of the stimulus to obtain gain and temporal phase, calculated from wavefront aberrations recorded over time during experimental trials. The tested conditions were as follows: Correction of all the subject's aberrations except defocus (C); Correction of all the subject's aberrations except defocus and habitual second-order astigmatism (AS); Correction of all the subject's aberrations except defocus and odd higher-order aberrations (HOAs); Correction of all the subject's aberrations except defocus and even HOAs (E); Natural aberrations of the subject's eye, i.e., the adaptive-optics system only corrected the optical system's aberrations (N); Correction of all the subject's aberrations except defocus and fourth-order spherical aberration (SA). The correction was performed at 20 Hz and each condition was repeated six times in randomised order. Average gain (±2 standard errors of the mean) varied little across conditions; between 0.55 ± 0.06 (SA), and 0.62 ± 0.06 (AS). Average phase (±2 standard errors of the mean) also varied little; between 0.41 ± 0.02 s (E), and 0.47 ± 0.02 s (O). After Bonferroni correction, no statistically significant differences in gain or phase were found in the presence of specific monochromatic aberrations or in their absence. These results show that the eye's monochromatic aberrations are not necessary for accommodation to track dynamic accommodative stimuli. © 2017 The Authors. Ophthalmic and Physiological Optics published by John Wiley & Sons Ltd on behalf of College of Optometrists.

  7. Fault Detection of Bearing Systems through EEMD and Optimization Algorithm

    PubMed Central

    Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

    2017-01-01

    This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. PMID:29143772

  8. A novel dynamical community detection algorithm based on weighting scheme

    NASA Astrophysics Data System (ADS)

    Li, Ju; Yu, Kai; Hu, Ke

    2015-12-01

    Network dynamics plays an important role in analyzing the correlation between the function properties and the topological structure. In this paper, we propose a novel dynamical iteration (DI) algorithm, which incorporates the iterative process of membership vector with weighting scheme, i.e. weighting W and tightness T. These new elements can be used to adjust the link strength and the node compactness for improving the speed and accuracy of community structure detection. To estimate the optimal stop time of iteration, we utilize a new stability measure which is defined as the Markov random walk auto-covariance. We do not need to specify the number of communities in advance. It naturally supports the overlapping communities by associating each node with a membership vector describing the node's involvement in each community. Theoretical analysis and experiments show that the algorithm can uncover communities effectively and efficiently.

  9. Detection and clustering of features in aerial images by neuron network-based algorithm

    NASA Astrophysics Data System (ADS)

    Vozenilek, Vit

    2015-12-01

    The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of general features analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft .NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropriate combination of comprehensive features that describe the colors and selected shapes of individual areas can be useful for image analysis.

  10. Third-rank chromatic aberrations of electron lenses.

    PubMed

    Liu, Zhixiong

    2018-02-01

    In this paper the third-rank chromatic aberration coefficients of round electron lenses are analytically derived and numerically calculated by Mathematica. Furthermore, the numerical results are cross-checked by the differential algebraic (DA) method, which verifies that all the formulas for the third-rank chromatic aberration coefficients are completely correct. It is hoped that this work would be helpful for further chromatic aberration correction in electron microscopy. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. A Fiber Bragg Grating Interrogation System with Self-Adaption Threshold Peak Detection Algorithm.

    PubMed

    Zhang, Weifang; Li, Yingwu; Jin, Bo; Ren, Feifei; Wang, Hongxun; Dai, Wei

    2018-04-08

    A Fiber Bragg Grating (FBG) interrogation system with a self-adaption threshold peak detection algorithm is proposed and experimentally demonstrated in this study. This system is composed of a field programmable gate array (FPGA) and advanced RISC machine (ARM) platform, tunable Fabry-Perot (F-P) filter and optical switch. To improve system resolution, the F-P filter was employed. As this filter is non-linear, this causes the shifting of central wavelengths with the deviation compensated by the parts of the circuit. Time-division multiplexing (TDM) of FBG sensors is achieved by an optical switch, with the system able to realize the combination of 256 FBG sensors. The wavelength scanning speed of 800 Hz can be achieved by a FPGA+ARM platform. In addition, a peak detection algorithm based on a self-adaption threshold is designed and the peak recognition rate is 100%. Experiments with different temperatures were conducted to demonstrate the effectiveness of the system. Four FBG sensors were examined in the thermal chamber without stress. When the temperature changed from 0 °C to 100 °C, the degree of linearity between central wavelengths and temperature was about 0.999 with the temperature sensitivity being 10 pm/°C. The static interrogation precision was able to reach 0.5 pm. Through the comparison of different peak detection algorithms and interrogation approaches, the system was verified to have an optimum comprehensive performance in terms of precision, capacity and speed.

  12. A Fiber Bragg Grating Interrogation System with Self-Adaption Threshold Peak Detection Algorithm

    PubMed Central

    Zhang, Weifang; Li, Yingwu; Jin, Bo; Ren, Feifei

    2018-01-01

    A Fiber Bragg Grating (FBG) interrogation system with a self-adaption threshold peak detection algorithm is proposed and experimentally demonstrated in this study. This system is composed of a field programmable gate array (FPGA) and advanced RISC machine (ARM) platform, tunable Fabry–Perot (F–P) filter and optical switch. To improve system resolution, the F–P filter was employed. As this filter is non-linear, this causes the shifting of central wavelengths with the deviation compensated by the parts of the circuit. Time-division multiplexing (TDM) of FBG sensors is achieved by an optical switch, with the system able to realize the combination of 256 FBG sensors. The wavelength scanning speed of 800 Hz can be achieved by a FPGA+ARM platform. In addition, a peak detection algorithm based on a self-adaption threshold is designed and the peak recognition rate is 100%. Experiments with different temperatures were conducted to demonstrate the effectiveness of the system. Four FBG sensors were examined in the thermal chamber without stress. When the temperature changed from 0 °C to 100 °C, the degree of linearity between central wavelengths and temperature was about 0.999 with the temperature sensitivity being 10 pm/°C. The static interrogation precision was able to reach 0.5 pm. Through the comparison of different peak detection algorithms and interrogation approaches, the system was verified to have an optimum comprehensive performance in terms of precision, capacity and speed. PMID:29642507

  13. Microarray-based genomic profiling reveals novel genomic aberrations in follicular lymphoma which associate with patient survival and gene expression status.

    PubMed

    Schwaenen, Carsten; Viardot, Andreas; Berger, Hilmar; Barth, Thomas F E; Bentink, Stefan; Döhner, Hartmut; Enz, Martina; Feller, Alfred C; Hansmann, Martin-Leo; Hummel, Michael; Kestler, Hans A; Klapper, Wolfram; Kreuz, Markus; Lenze, Dido; Loeffler, Markus; Möller, Peter; Müller-Hermelink, Hans-Konrad; Ott, German; Rosolowski, Maciej; Rosenwald, Andreas; Ruf, Sandra; Siebert, Reiner; Spang, Rainer; Stein, Harald; Truemper, Lorenz; Lichter, Peter; Bentz, Martin; Wessendorf, Swen

    2009-01-01

    Follicular lymphoma (FL) is characterized by a large number of chromosomal aberrations. However, their exact genomic extension and involved target genes remain to be determined. For this purpose, we used array-based intermediate-high resolution genomic profiling in combination with Affymetrix gene expression analysis. Tumor specimens from 128 FL patients were analyzed for the presence of genomic aberrations and the results were correlated to clinical data sets and mRNA expression levels. In 114 (89%) of the 128 analyzed cases, a total of 688 genomic aberrations (384 gains/amplifications and 304 losses) were detected. Frequent genomic aberrations were: -1p36 (18%), +2p15 (24%), -3q (14%), -6q (25%), +7p (19%), +7q (23%), +8q (14%), -9p (16%), -11q (15%), +12q (20%), -13q (11%), -17p (16%), +18p (18%), and +18q (28%). Critical segments of these imbalances were delineated to genomic fragments with a minimum size down to 0.2 Mb. By comparison of these with mRNA gene expression data, putative candidate genes were identified. Moreover, we found that deletions affecting the tumor suppressor gene CDKN2A/B on 9p21 were detected in nontransformed FL grade I-II. For this aberration as well as for -6q25 and -6q26, an association with inferior survival was observed.

  14. Design of infrasound-detection system via adaptive LMSTDE algorithm

    NASA Technical Reports Server (NTRS)

    Khalaf, C. S.; Stoughton, J. W.

    1984-01-01

    A proposed solution to an aviation safety problem is based on passive detection of turbulent weather phenomena through their infrasonic emission. This thesis describes a system design that is adequate for detection and bearing evaluation of infrasounds. An array of four sensors, with the appropriate hardware, is used for the detection part. Bearing evaluation is based on estimates of time delays between sensor outputs. The generalized cross correlation (GCC), as the conventional time-delay estimation (TDE) method, is first reviewed. An adaptive TDE approach, using the least mean square (LMS) algorithm, is then discussed. A comparison between the two techniques is made and the advantages of the adaptive approach are listed. The behavior of the GCC, as a Roth processor, is examined for the anticipated signals. It is shown that the Roth processor has the desired effect of sharpening the peak of the correlation function. It is also shown that the LMSTDE technique is an equivalent implementation of the Roth processor in the time domain. A LMSTDE lead-lag model, with a variable stability coefficient and a convergence criterion, is designed.

  15. Development of an algorithm for automatic detection and rating of squeak and rattle events

    NASA Astrophysics Data System (ADS)

    Chandrika, Unnikrishnan Kuttan; Kim, Jay H.

    2010-10-01

    A new algorithm for automatic detection and rating of squeak and rattle (S&R) events was developed. The algorithm utilizes the perceived transient loudness (PTL) that approximates the human perception of a transient noise. At first, instantaneous specific loudness time histories are calculated over 1-24 bark range by applying the analytic wavelet transform and Zwicker loudness transform to the recorded noise. Transient specific loudness time histories are then obtained by removing estimated contributions of the background noise from instantaneous specific loudness time histories. These transient specific loudness time histories are summed to obtain the transient loudness time history. Finally, the PTL time history is obtained by applying Glasberg and Moore temporal integration to the transient loudness time history. Detection of S&R events utilizes the PTL time history obtained by summing only 18-24 barks components to take advantage of high signal-to-noise ratio in the high frequency range. A S&R event is identified when the value of the PTL time history exceeds the detection threshold pre-determined by a jury test. The maximum value of the PTL time history is used for rating of S&R events. Another jury test showed that the method performs much better if the PTL time history obtained by summing all frequency components is used. Therefore, r ating of S&R events utilizes this modified PTL time history. Two additional jury tests were conducted to validate the developed detection and rating methods. The algorithm developed in this work will enable automatic detection and rating of S&R events with good accuracy and minimum possibility of false alarm.

  16. Optical phase aberration generation using a Liquid Crystal Spatial Light Modulator

    NASA Astrophysics Data System (ADS)

    Wilcox, Christopher C.

    In this dissertation, a Liquid Crystal Spatial Light Modulator is used to simulate optical aberrations in an optical system. Any optical aberration can be simulated through the use of software developed for this project. A new method of simulating atmospheric turbulence is also presented. The Earth's atmosphere is a large, non-linear, non-homogeneous medium that is constantly flowing in a random fashion that affects light as it propagates through it. The Kolmogorov model for atmospheric turbulence is a description of the nature of the wavefront perturbations introduced by the atmosphere and it is one of the most accepted models. It is supported by a variety of experimental measurements and research and is quite widely used in simulations for atmospheric imaging. This model provides a statistical description of how random fluctuations in humidity and temperature affect the refractive index of the atmosphere for imaging through atmospheric turbulence. These refractive index fluctuations in turn affect the propagation of light through the atmosphere. An adaptive optical system can be developed to correct these wavefront perturbations for an optical system. However, prior to deployment, an adaptive optical system requires calibration and full characterization in the laboratory. Creating realistic atmospheric simulations is often expensive and computationally intensive using common techniques. To combat some of these issues often the temporal properties in the simulation are neglected. This dissertation outlines a new method developed for generating atmospheric turbulence and a testbed that simulates its aberrations far more inexpensively and with greater fidelity using a Liquid Crystal Spatial Light Modulator. This system allows the simulation of atmospheric seeing conditions ranging from very poor to very good and different algorithms may be easily employed on the device for comparison. These simulations can be dynamically generated and modified very quickly and

  17. Comparison of Diagnostic Algorithms for Detecting Toxigenic Clostridium difficile in Routine Practice at a Tertiary Referral Hospital in Korea.

    PubMed

    Moon, Hee-Won; Kim, Hyeong Nyeon; Hur, Mina; Shim, Hee Sook; Kim, Heejung; Yun, Yeo-Min

    2016-01-01

    Since every single test has some limitations for detecting toxigenic Clostridium difficile, multistep algorithms are recommended. This study aimed to compare the current, representative diagnostic algorithms for detecting toxigenic C. difficile, using VIDAS C. difficile toxin A&B (toxin ELFA), VIDAS C. difficile GDH (GDH ELFA, bioMérieux, Marcy-l'Etoile, France), and Xpert C. difficile (Cepheid, Sunnyvale, California, USA). In 271 consecutive stool samples, toxigenic culture, toxin ELFA, GDH ELFA, and Xpert C. difficile were performed. We simulated two algorithms: screening by GDH ELFA and confirmation by Xpert C. difficile (GDH + Xpert) and combined algorithm of GDH ELFA, toxin ELFA, and Xpert C. difficile (GDH + Toxin + Xpert). The performance of each assay and algorithm was assessed. The agreement of Xpert C. difficile and two algorithms (GDH + Xpert and GDH+ Toxin + Xpert) with toxigenic culture were strong (Kappa, 0.848, 0.857, and 0.868, respectively). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of algorithms (GDH + Xpert and GDH + Toxin + Xpert) were 96.7%, 95.8%, 85.0%, 98.1%, and 94.5%, 95.8%, 82.3%, 98.5%, respectively. There were no significant differences between Xpert C. difficile and two algorithms in sensitivity, specificity, PPV and NPV. The performances of both algorithms for detecting toxigenic C. difficile were comparable to that of Xpert C. difficile. Either algorithm would be useful in clinical laboratories and can be optimized in the diagnostic workflow of C. difficile depending on costs, test volume, and clinical needs.

  18. The Distribution of Chromosomal Aberrations in Human Cells Predicted by a Generalized Time-Dependent Model of Radiation-Induced Formation of Aberrations

    NASA Technical Reports Server (NTRS)

    Ponomarev, Artem L.; George, K.; Cucinotta, F. A.

    2011-01-01

    New experimental data show how chromosomal aberrations for low- and high-LET radiation are dependent on DSB repair deficiencies in wild-type, AT and NBS cells. We simulated the development of chromosomal aberrations in these cells lines in a stochastic track-structure-dependent model, in which different cells have different kinetics of DSB repair. We updated a previously formulated model of chromosomal aberrations, which was based on a stochastic Monte Carlo approach, to consider the time-dependence of DSB rejoining. The previous version of the model had an assumption that all DSBs would rejoin, and therefore we called it a time-independent model. The chromosomal-aberrations model takes into account the DNA and track structure for low- and high-LET radiations, and provides an explanation and prediction of the statistics of rare and more complex aberrations. We compared the program-simulated kinetics of DSB rejoining to the experimentally-derived bimodal exponential curves of the DSB kinetics. We scored the formation of translocations, dicentrics, acentric and centric rings, deletions, and inversions. The fraction of DSBs participating in aberrations was studied in relation to the rejoining time. Comparisons of simulated dose dependence for simple aberrations to the experimental dose-dependence for HF19, AT and NBS cells will be made.

  19. Harmonic oscillator states in aberration optics

    NASA Technical Reports Server (NTRS)

    Wolf, Kurt Bernardo

    1993-01-01

    The states of the three-dimensional quantum harmonic oscillator classify optical aberrations of axis-symmetric systems due to the isomorphism between the two mathematical structures. Cartesian quanta and angular momentum classifications have their corresponding aberration classifications. The operation of concatenation of optical elements introduces a new operation between harmonic oscillator states.

  20. Cubic spline anchored grid pattern algorithm for high-resolution detection of subsurface cavities by the IR-CAT method

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

    Kassab, A.J.; Pollard, J.E.

    An algorithm is presented for the high-resolution detection of irregular-shaped subsurface cavities within irregular-shaped bodies by the IR-CAT method. The theoretical basis of the algorithm is rooted in the solution of an inverse geometric steady-state heat conduction problem. A Cauchy boundary condition is prescribed at the exposed surface, and the inverse geometric heat conduction problem is formulated by specifying the thermal condition at the inner cavities walls, whose unknown geometries are to be detected. The location of the inner cavities is initially estimated, and the domain boundaries are discretized. Linear boundary elements are used in conjunction with cubic splines formore » high resolution of the cavity walls. An anchored grid pattern (AGP) is established to constrain the cubic spline knots that control the inner cavity geometry to evolve along the AGP at each iterative step. A residual is defined measuring the difference between imposed and computed boundary conditions. A Newton-Raphson method with a Broyden update is used to automate the detection of inner cavity walls. During the iterative procedure, the movement of the inner cavity walls is restricted to physically realistic intermediate solutions. Numerical simulation demonstrates the superior resolution of the cubic spline AGP algorithm over the linear spline-based AGP in the detection of an irregular-shaped cavity. Numerical simulation is also used to test the sensitivity of the linear and cubic spline AGP algorithms by simulating bias and random error in measured surface temperature. The proposed AGP algorithm is shown to satisfactorily detect cavities with these simulated data.« less