Science.gov

Sample records for absorption features detected

  1. Detection of a deep 3-microm absorption feature in the spectrum of Amalthea (JV).

    PubMed

    Takato, Naruhisa; Bus, Schelte J; Terada, Hiroshi; Pyo, Tae-Soo; Kobayashi, Naoto

    2004-12-24

    Near-infrared spectra of Jupiter's small inner satellites Amalthea and Thebe are similar to those of D-type asteroids in the 0.8- to 2.5-micrometer wavelength range. A deep absorption feature is detected at 3 micrometers in the spectra of the trailing side of Amalthea, which is similar to that of the non-ice components of Callisto and can be attributed to hydrous minerals. These surface materials cannot be explained if the satellite formed at its present orbit by accreting from a circumjovian nebula. Amalthea and Thebe may be the remnants of Jupiter's inflowing building blocks that formed in the outer part or outside of the circumjovian nebula.

  2. Airborne spectroradiometry: The application of AIS data to detecting subtle mineral absorption features

    NASA Technical Reports Server (NTRS)

    Cocks, T. D.; Green, A. A.

    1986-01-01

    Analysis of Airborne Imaging Spectrometer (AIS) data acquired in Australia has revealed a number of operational problems. Horizontal striping in AIS imagery and spectral distortions due to order overlap were investigated. Horizontal striping, caused by grating position errors can be removed with little or no effect on spectral details. Order overlap remains a problem that seriously compromises identification of subtle mineral absorption features within AIS spectra. A spectrometric model of the AIS was developed to assist in identifying spurious spectral features, and will be used in efforts to restore the spectral integrity of the data.

  3. TOWARD DETECTING THE 2175 A DUST FEATURE ASSOCIATED WITH STRONG HIGH-REDSHIFT Mg II ABSORPTION LINES

    SciTech Connect

    Jiang Peng; Zhou Hongyan; Wang Junxian

    2011-05-10

    We report detections of 39 2175 A dust extinction bump candidates associated with strong Mg II absorption lines at z{approx} 1-1.8 on quasar spectra in Sloan Digital Sky Survey (SDSS) DR3. These strong Mg II absorption line systems are detected among 2951 strong Mg II absorbers with a rest equivalent width W{sub r} {lambda}2796> 1.0 A at 1.0 < z < 1.86, which is part of a full sample of 7421 strong Mg II absorbers compiled by Prochter et al. The redshift range of the absorbers is chosen to allow the 2175 A extinction features to be completely covered withinmore » the SDSS spectrograph operation wavelength range. An upper limit of the background quasar emission redshift at z = 2.1 is set to prevent the Ly{alpha} forest lines from contaminating the sensitive spectral region for the 2175 A bump measurements. The FM90 parameterization is applied to model the optical/UV extinction curve in the rest frame of Mg II absorbers of the 2175 A bump candidates. The simulation technique developed by Jiang et al. is used to derive the statistical significance of the candidate 2175 A bumps. A total of 12 absorbers are detected with 2175 A bumps at a 5{sigma} level of statistical significance, 10 are detected at a 4{sigma} level, and 17 are detected at a 3{sigma} level. Most of the candidate bumps in this work are similar to the relatively weak 2175 A bumps observed in the Large Magellanic Cloud LMC2 supershell rather than the strong ones observed in the Milky Way. This sample has greatly increased the total number of 2175 A extinction bumps measured on SDSS quasar spectra. Follow-up observations may rule out some of the possible false detections and reveal the physical and chemical natures of 2175 A quasar absorbers.« less

  4. Toward Detecting the 2175 Å Dust Feature Associated with Strong High-redshift Mg II Absorption Lines

    NASA Astrophysics Data System (ADS)

    Jiang, Peng; Ge, Jian; Zhou, Hongyan; Wang, Junxian; Wang, Tinggui

    2011-05-01

    We report detections of 39 2175 Å dust extinction bump candidates associated with strong Mg II absorption lines at z~ 1-1.8 on quasar spectra in Sloan Digital Sky Survey (SDSS) DR3. These strong Mg II absorption line systems are detected among 2951 strong Mg II absorbers with a rest equivalent width Wr λ2796> 1.0 Å at 1.0 < z < 1.86, which is part of a full sample of 7421 strong Mg II absorbers compiled by Prochter et al. The redshift range of the absorbers is chosen to allow the 2175 Å extinction features to be completely covered within the SDSS spectrograph operation wavelength range. An upper limit of the background quasar emission redshift at z = 2.1 is set to prevent the Lyα forest lines from contaminating the sensitive spectral region for the 2175 Å bump measurements. The FM90 parameterization is applied to model the optical/UV extinction curve in the rest frame of Mg II absorbers of the 2175 Å bump candidates. The simulation technique developed by Jiang et al. is used to derive the statistical significance of the candidate 2175 Å bumps. A total of 12 absorbers are detected with 2175 Å bumps at a 5σ level of statistical significance, 10 are detected at a 4σ level, and 17 are detected at a 3σ level. Most of the candidate bumps in this work are similar to the relatively weak 2175 Å bumps observed in the Large Magellanic Cloud LMC2 supershell rather than the strong ones observed in the Milky Way. This sample has greatly increased the total number of 2175 Å extinction bumps measured on SDSS quasar spectra. Follow-up observations may rule out some of the possible false detections and reveal the physical and chemical natures of 2175 Å quasar absorbers.

  5. FIRST ULTRAVIOLET REFLECTANCE SPECTRA OF PLUTO AND CHARON BY THE HUBBLE SPACE TELESCOPE COSMIC ORIGINS SPECTROGRAPH: DETECTION OF ABSORPTION FEATURES AND EVIDENCE FOR TEMPORAL CHANGE

    SciTech Connect

    Stern, S. A.; Spencer, J. R.; Shinn, A.

    We have observed the mid-UV spectra of both Pluto and its large satellite, Charon, at two rotational epochs using the Hubble Space Telescope (HST) Cosmic Origins Spectrograph (COS) in 2010. These are the first HST/COS measurements of Pluto and Charon. Here we describe the observations and our reduction of them, and present the albedo spectra, average mid-UV albedos, and albedo slopes we derive from these data. These data reveal evidence for a strong absorption feature in the mid-UV spectrum of Pluto; evidence for temporal change in Pluto's spectrum since the 1990s is reported, and indirect evidence for a near-UV spectralmore » absorption on Charon is also reported.« less

  6. Detection of bacterial growth by gas absorption.

    PubMed

    Waters, J R

    1992-05-01

    When 24 different aerobic organisms were grown in a shaken culture, all were found to first absorb gas from the headspace. In a rudimentary medium, such as tryptic soy broth, 16 of the 24 organisms did not produce gas following the initial gas absorption. We have developed a simple, noninvasive method for detecting both gas absorption and production in multiple culture vials. The time to positivity was compared with that obtained by the BACTEC 460 blood culture system. For nearly all of these organisms, there was no difference. For some of those organisms that did not produce gas, e.g. Staphylococcus epidermidis, Moraxella osloensis, and Neisseria meningitidis, detection by gas absorption was a few hours faster. Gas absorption appears to be a promising technique for a new automated blood culture system because of its simplicity and because medium without special additives can be used to detect organisms that do not produce gas.

  7. The Mysterious 6565 Å Absorption Feature of the Galactic Halo

    NASA Astrophysics Data System (ADS)

    Sethi, Shiv K.; Shchekinov, Yuri; Nath, Biman B.

    2017-12-01

    We consider various possible scenarios to explain the recent observation of what has been called a broad Hα absorption in our Galactic halo, with peak optical depth τ ≃ 0.01 and equivalent width W≃ 0.17 \\mathringA . We show that the absorbed feature cannot arise from the circumgalactic and ISM Hα absorption. As the observed absorption feature is quite broad ({{Δ }}λ ≃ 30 \\mathringA ), we also consider CNO lines that lie close to Hα as possible alternatives to explain the feature. We show that such lines could also not account for the observed feature. Instead, we suggest that it could arise from diffuse interstellar bands (DIBs) carriers or polyaromatic hydrocarbons (PAHs) absorption. While we identify several such lines close to the Hα transition, we are unable to determine the molecule responsible for the observed feature, partly because of selection effects that prevent us from identifying DIBs/PAHs features close to Hα using local observations. Deep integration of a few extragalactic sources with high spectral resolution might allow us to distinguish between different possible explanations.

  8. Infrared differential absorption for atmospheric pollutant detection

    NASA Technical Reports Server (NTRS)

    Byer, R. L.

    1974-01-01

    Progress made in the generation of tunable infrared radiation and its application to remote pollutant detection by the differential absorption method are summarized. It is recognized that future remote pollutant measurements depended critically on the availability of high energy tunable transmitters. Futhermore, due to eye safety requirements, the transmitted frequency must lie in the 1.4 micron to 13 micron infrared spectral range.

  9. On the nature of absorption features toward nearby stars

    NASA Astrophysics Data System (ADS)

    Kohl, S.; Czesla, S.; Schmitt, J. H. M. M.

    2016-06-01

    Context. Diffuse interstellar absorption bands (DIBs) of largely unknown chemical origin are regularly observed primarily in distant early-type stars. More recently, detections in nearby late-type stars have also been claimed. These stars' spectra are dominated by stellar absorption lines. Specifically, strong interstellar atomic and DIB absorption has been reported in τ Boo. Aims: We test these claims by studying the strength of interstellar absorption in high-resolution TIGRE spectra of the nearby stars τ Boo, HD 33608, and α CrB. Methods: We focus our analysis on a strong DIB located at 5780.61 Å and on the absorption of interstellar Na. First, we carry out a differential analysis by comparing the spectra of the highly similar F-stars, τ Boo and HD 33608, whose light, however, samples different lines of sight. To obtain absolute values for the DIB absorption, we compare the observed spectra of τ Boo, HD 33608, and α CrB to PHOENIX models and carry out basic spectral modeling based on Voigt line profiles. Results: The intercomparison between τ Boo and HD 33608 reveals that the difference in the line depth is 6.85 ± 1.48 mÅ at the DIB location which is, however, unlikely to be caused by DIB absorption. The comparison between PHOENIX models and observed spectra yields an upper limit of 34.0 ± 0.3 mÅ for any additional interstellar absorption in τ Boo; similar results are obtained for HD 33608 and α CrB. For all objects we derive unrealistically large values for the radial velocity of any presumed interstellar clouds. In τ Boo we find Na D absorption with an equivalent width of 0.65 ± 0.07 mÅ and 2.3 ± 0.1 mÅ in the D2 and D1 lines. For the other Na, absorption of the same magnitude could only be detected in the D2 line. Our comparisons between model and data show that the interstellar absorption toward τ Boo is not abnormally high. Conclusions: We find no significant DIB absorption in any of our target stars. Any differences between modeled and

  10. SPATIALLY RESOLVED HCN ABSORPTION FEATURES IN THE CIRCUMNUCLEAR REGION OF NGC 1052

    SciTech Connect

    Sawada-Satoh, Satoko; Roh, Duk-Gyoo; Oh, Se-Jin

    We present the first VLBI detection of HCN molecular absorption in the nearby active galactic nucleus NGC 1052. Utilizing the 1 mas resolution achieved by the Korean VLBI Network, we have spatially resolved the HCN absorption against a double-sided nuclear jet structure. Two velocity features of HCN absorption are detected significantly at the radial velocity of 1656 and 1719 km s{sup −1}, redshifted by 149 and 212 km s{sup −1} with respect to the systemic velocity of the galaxy. The column density of the HCN molecule is estimated to be 10{sup 15}–10{sup 16} cm{sup −2}, assuming an excitation temperature ofmore » 100–230 K. The absorption features show high optical depth localized on the receding jet side, where the free–free absorption occurred due to the circumnuclear torus. The size of the foreground absorbing molecular gas is estimated to be on approximately one-parsec scales, which agrees well with the approximate size of the circumnuclear torus. HCN absorbing gas is likely to be several clumps smaller than 0.1 pc inside the circumnuclear torus. The redshifted velocities of the HCN absorption features imply that HCN absorbing gas traces ongoing infall motion inside the circumnuclear torus onto the central engine.« less

  11. Spatially Resolved HCN Absorption Features in the Circumnuclear Region of NGC 1052

    NASA Astrophysics Data System (ADS)

    Sawada-Satoh, Satoko; Roh, Duk-Gyoo; Oh, Se-Jin; Lee, Sang-Sung; Byun, Do-Young; Kameno, Seiji; Yeom, Jae-Hwan; Jung, Dong-Kyu; Kim, Hyo-Ryoung; Hwang, Ju-Yeon

    2016-10-01

    We present the first VLBI detection of HCN molecular absorption in the nearby active galactic nucleus NGC 1052. Utilizing the 1 mas resolution achieved by the Korean VLBI Network, we have spatially resolved the HCN absorption against a double-sided nuclear jet structure. Two velocity features of HCN absorption are detected significantly at the radial velocity of 1656 and 1719 km s-1, redshifted by 149 and 212 km s-1 with respect to the systemic velocity of the galaxy. The column density of the HCN molecule is estimated to be 1015-1016 cm-2, assuming an excitation temperature of 100-230 K. The absorption features show high optical depth localized on the receding jet side, where the free-free absorption occurred due to the circumnuclear torus. The size of the foreground absorbing molecular gas is estimated to be on approximately one-parsec scales, which agrees well with the approximate size of the circumnuclear torus. HCN absorbing gas is likely to be several clumps smaller than 0.1 pc inside the circumnuclear torus. The redshifted velocities of the HCN absorption features imply that HCN absorbing gas traces ongoing infall motion inside the circumnuclear torus onto the central engine.

  12. Permeation absorption sampler with multiple detection

    DOEpatents

    Zaromb, Solomon

    1990-01-01

    A system for detecting analytes in air or aqueous systems includes a permeation absorption preconcentrator sampler for the analytes and analyte detectors. The preconcentrator has an inner fluid-permeable container into which a charge of analyte-sorbing liquid is intermittently injected, and a fluid-impermeable outer container. The sample is passed through the outer container and around the inner container for trapping and preconcentrating the analyte in the sorbing liquid. The analyte can be detected photometrically by injecting with the sorbing material a reagent which reacts with the analyte to produce a characteristic color or fluorescence which is detected by illuminating the contents of the inner container with a light source and measuring the absorbed or emitted light, or by producing a characteristic chemiluminescence which can be detected by a suitable light sensor. The analyte can also be detected amperometrically. Multiple inner containers may be provided into which a plurality of sorbing liquids are respectively introduced for simultaneously detecting different analytes. Baffles may be provided in the outer container. A calibration technique is disclosed.

  13. Exploring the Time Evolution of Cool Metallic Absorption Features in UV Burst Spectra

    NASA Astrophysics Data System (ADS)

    Belmes, K.; Madsen, C. A.; DeLuca, E.

    2017-12-01

    UV bursts are compact brightenings in active regions that appear in UV images. They are identified through three spectroscopic features: (1) broadening and intensification of NUV/FUV emission lines, (2) the presence of optically thin Si IV emission, and (3) the presence of absorption features from cool metallic ions. Properties (2) and (3) imply that bursts exist at transition region temperatures (≥ 80,000 K) but are located in the cooler lower chromosphere ( 5,000 K). Their energetic and dynamical properties remain poorly constrained. Improving our understanding of this phenomena could help us further constrain the energetic and dynamical properties of the chromosphere, as well as give us insight into whether or not UV bursts contribute to chromospheric and/or coronal heating. We analyzed the time evolution of UV bursts using spectral data from the Interface Region Imaging Spectrograph (IRIS). We inspected Si IV 1393.8 Å line profiles for Ni II 1393.3 Å absorption features to look for signs of heating. Weakening of absorption features over time could indicate heating of the cool ions above the burst, implying that thermal energy from the burst could rapidly conduct upward through the chromosphere. To detect the spectral profiles corresponding to bursts, we applied a four-parameter Gaussian fit to every profile in each observation and took cuts in parameter space to isolate the bursts. We then manually reviewed the remaining profiles by looking for a statistically significant appearance of Ni II 1393.3 Å absorption. We quantified these absorption features by normalizing the Si IV 1393.8 Å emission profiles and measuring the maximum fractional extinction in each. Our preliminary results indicate that Ni II 1393.3 Å absorption may undergo a cycle of strengthening and weakening throughout a burst's lifetime. However, further investigation is needed for confirmation. This work is supported by the NSF-REU solar physics program at SAO, grant number AGS-1560313.

  14. Investigations on the 1.7 micron residual absorption feature in the vegetation reflection spectrum

    NASA Technical Reports Server (NTRS)

    Verdebout, J.; Jacquemoud, S.; Andreoli, G.; Hosgood, B.; Sieber, A.

    1993-01-01

    The detection and interpretation of the weak absorption features associated with the biochemical components of vegetation is of great potential interest to a variety of applications ranging from classification to global change studies. This recent subject is also challenging because the spectral signature of the biochemicals is only detectable as a small distortion of the infrared spectrum which is mainly governed by water. Furthermore, the interpretation is complicated by complexity of the molecules (lignin, cellulose, starch, proteins) which contain a large number of different and common chemical bonds. In this paper, we present investigations on the absorption feature centered at 1.7 micron; these were conducted both on AVIRIS data and laboratory reflectance spectra of leaves.

  15. Wavelength calibration of imaging spectrometer using atmospheric absorption features

    NASA Astrophysics Data System (ADS)

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

    2012-11-01

    Imaging spectrometer is a promising remote sensing instrument widely used in many filed, such as hazard forecasting, environmental monitoring and so on. The reliability of the spectral data is the determination to the scientific communities. The wavelength position at the focal plane of the imaging spectrometer will change as the pressure and temperature vary, or the mechanical vibration. It is difficult for the onboard calibration instrument itself to keep the spectrum reference accuracy and it also occupies weight and the volume of the remote sensing platform. Because the spectral images suffer from the atmospheric effects, the carbon oxide, water vapor, oxygen and solar Fraunhofer line, the onboard wavelength calibration can be processed by the spectral images themselves. In this paper, wavelength calibration is based on the modeled and measured atmospheric absorption spectra. The modeled spectra constructed by the atmospheric radiative transfer code. The spectral angle is used to determine the best spectral similarity between the modeled spectra and measured spectra and estimates the wavelength position. The smile shape can be obtained when the matching process across all columns of the data. The present method is successful applied on the Hyperion data. The value of the wavelength shift is obtained by shape matching of oxygen absorption feature and the characteristics are comparable to that of the prelaunch measurements.

  16. Iron K Features in the Quasar E 1821+643: Evidence for Gravitationally Redshifted Absorption?

    NASA Technical Reports Server (NTRS)

    Yaqoob, Tahir; Serlemitsos, Peter

    2005-01-01

    We report a Chandra high-energy grating detection of a narrow, redshifted absorption line superimposed on the red wing of a broad Fe K line in the z = 0.297 quasar E 1821+643. The absorption line is detected at a confidence level, estimated by two different methods, in the range approx. 2 - 3 sigma. Although the detection significance is not high enough to exclude a non-astrophysical origin, accounting for the absorption feature when modeling the X-ray spectrum implies that the Fe-K emission line is broad, and consistent with an origin in a relativistic accretion disk. Ignoring the apparent absorption feature leads to the conclusion that the Fe-K emission line is narrower, and also affects the inferred peak energy of the line (and hence the inferred ionization state of Fe). If the absorption line (at approx. 6.2 keV in the quasar frame) is real, we argue that it could be due to gravitationally redshifted Fe XXV or Fe XXVI resonance absorption within approx. 10 - 20 gravitational radii of the putative central black hole. The absorption line is not detected in earlier ASCA and Chandra low-energy grating observations, but the absorption line is not unequivocally ruled out by these data. The Chandra high-energy grating Fe-K emission line is consistent with an origin predominantly in Fe I-XVII or so. In an ASCA observation eight years earlier, the Fe-K line peaked at approx. 6.6 keV, closer to the energies of He-like Fe triplet lines. Further, in a Chandra low-energy grating observation the Fe-K line profile was double-peaked, one peak corresponding to Fe I-XVII or so, the other peak to Fe XXVI Ly alpha. Such a wide range in ionization state of Fe is not ruled out by the HEG and ASCA data either, and is suggestive of a complex structure for the line-emitter.

  17. Balmer and Metal Absorption Feature Gradients in M32

    NASA Astrophysics Data System (ADS)

    Worthey, Guy

    2004-12-01

    New data sources are used to assess Lick/IDS feature strength gradients inside the half-light radius Re of the compact Local Group elliptical galaxy M32. A Hubble Space Telescope (HST) STIS spectrum seemed to indicate ionized gas and a very young central stellar population. In fact, this conclusion is entirely spurious because of incomplete removal of ion hits. More robust ground-based spectra taken at the MDM Observatory are, in contrast, the most accurate measurements of Lick/IDS indices yet obtained for M32. All but a few (of 24 measured) indices show a statistically significant gradient. The CN indices show a maximum at 4" radius, dropping off both toward the nucleus and away from it. At 2" radius there is a discontinuity in the surface brightness profile, but this feature is not reflected in any spectral feature. Comparing with models, the index gradients indicate a mean age and abundance gradient in the sense that the nucleus is a factor of 2.5 younger and a factor of 0.3 dex more metal-rich than at 1Re. This conclusion is only weakly dependent on which index combinations are used and is robust to high accuracy. Stars near the M32 nucleus have a mean age and heavy element abundance [M/H] of (4.7 Gyr, +0.02), judging from models by Worthey with variable abundance ratios. This result has very small formal random errors, although, of course, there is significant age-metallicity degeneracy along an (age, abundance) line segment from (5.0 Gyr, 0.00) to (4.5 Gyr, +0.05). An abundance pattern of [C/M]=+0.077 (carbon abundance affects CN, C24668, and the bluer Balmer features), [N/M]=-0.13, [Mg/M]=-0.18, [Fe/M]~0.0, and [Na/M]=+0.12 is required to fit the feature data, with a fitting precision of about 0.01 dex (with two caveats: the [Fe/M] guess has about twice this precision because of the relative insensitivity of the Fe5335 feature to iron, and the [Na/M] value may be falsely amplified because of interstellar absorption). Model uncertainties make the accuracies

  18. Sensor feature fusion for detecting buried objects

    SciTech Connect

    Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.

    1993-04-01

    Given multiple registered images of the earth`s surface from dual-band sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised teaming pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in amore » two step process to classify a subimage. Thee first step, referred to as feature selection, determines the features of sub-images which result in the greatest separability among the classes. The second step, image labeling, uses the selected features and the decisions from a pattern classifier to label the regions in the image which are likely to correspond to buried mines. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the sensors add value to the detection system. The most important features from the various sensors are fused using supervised teaming pattern classifiers (including neural networks). We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.« less

  19. The application of feature selection to the development of Gaussian process models for percutaneous absorption.

    PubMed

    Lam, Lun Tak; Sun, Yi; Davey, Neil; Adams, Rod; Prapopoulou, Maria; Brown, Marc B; Moss, Gary P

    2010-06-01

    The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determined that the best GPR models were those that contained log P, melting point and the number of hydrogen bond donor groups as significant descriptors. Further statistical analysis also found that great synergy existed between certain parameters. It suggested that a number of the descriptors employed were effectively interchangeable, thus questioning the use of models where discrete variables are output, usually in the form of an equation. The use of a nonlinear GPR method produced models with significantly improved predictivity, compared with SLN or QSPR models. Feature selection methods were able to provide important mechanistic information. However, it was also shown that significant synergy existed between certain parameters, and as such it

  20. Monocular precrash vehicle detection: features and classifiers.

    PubMed

    Sun, Zehang; Bebis, George; Miller, Ronald

    2006-07-01

    Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by treating the problem of vehicle detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular vehicle detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average detection rate of 10 Hz. Our vehicle detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where vehicles might be present are extracted. This step uses multiscale techniques not only to speed up detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.

  1. Fall Detection Using Smartphone Audio Features.

    PubMed

    Cheffena, Michael

    2016-07-01

    An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC), and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above 98%. The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user.

  2. Detectability of cold streams into high-redshift galaxies by absorption lines

    NASA Astrophysics Data System (ADS)

    Goerdt, Tobias; Dekel, Avishai; Sternberg, Amiel; Gnat, Orly; Ceverino, Daniel

    2012-08-01

    Cold gas streaming along the dark matter filaments of the cosmic web is predicted to be the major source of fuel for disc buildup, violent disc instability and star formation in massive galaxies at high redshift. We investigate to what extent such cold gas is detectable in the extended circumgalactic environment of galaxies via Lyα absorption and selected low-ionization metal absorption lines. We model the expected absorption signatures using high-resolution zoom-in adaptive mesh refinement cosmological simulations. In the post-processing, we distinguish between self-shielded gas and unshielded gas. In the self-shielded gas, which is optically thick to Lyman continuum radiation, we assume pure collisional ionization for species with an ionization potential greater than 13.6 eV. In the optically-thin, unshielded gas, these species are also photoionized by the metagalactic radiation. In addition to absorption of radiation from background quasars, we compute the absorption line profiles of radiation emitted by the galaxy at the centre of the same halo. We predict the strength of the absorption signal for individual galaxies without stacking. We find that the Lyα absorption profiles produced by the streams are consistent with observations of absorption and emission Lyα profiles in high-redshift galaxies. Due to the low metallicities in the streams, and their low covering factors, the metal absorption features are weak and difficult to detect.

  3. Semi-Supervised Geographical Feature Detection

    NASA Astrophysics Data System (ADS)

    Yu, H.; Yu, L.; Kuo, K. S.

    2016-12-01

    Extraction and tracking geographical features is a fundamental requirement in many geoscience fields. However, this operation has become an increasingly challenging task for domain scientists when tackling a large amount of geoscience data. Although domain scientists may have a relatively clear definition of features, it is difficult to capture the presence of features in an accurate and efficient fashion. We propose a semi-supervised approach to address large geographical feature detection. Our approach has two main components. First, we represent a heterogeneous geoscience data in a unified high-dimensional space, which can facilitate us to evaluate the similarity of data points with respect to geolocation, time, and variable values. We characterize the data from these measures, and use a set of hash functions to parameterize the initial knowledge of the data. Second, for any user query, our approach can automatically extract the initial results based on the hash functions. To improve the accuracy of querying, our approach provides a visualization interface to display the querying results and allow users to interactively explore and refine them. The user feedback will be used to enhance our knowledge base in an iterative manner. In our implementation, we use high-performance computing techniques to accelerate the construction of hash functions. Our design facilitates a parallelization scheme for feature detection and extraction, which is a traditionally challenging problem for large-scale data. We evaluate our approach and demonstrate the effectiveness using both synthetic and real world datasets.

  4. Identification of Absorption Features in an Extrasolar Planet Atmosphere

    NASA Astrophysics Data System (ADS)

    Barman, T.

    2007-06-01

    Water absorption is identified in the atmosphere of HD 209458b by comparing models for the planet's transmitted spectrum to recent, multiwavelength, eclipse-depth measurements (from 0.3 to 1 μm) published by Knutson et al. A cloud-free model that includes solar abundances, rainout of condensates, and photoionization of sodium and potassium is in good agreement with the entire set of eclipse-depth measurements from the ultraviolet to near-infrared. Constraints are placed on condensate removal by gravitational settling, the bulk metallicity, and the redistribution of absorbed stellar flux. Comparisons are also made to the Charbonneau et al. sodium measurements.

  5. Using high spectral resolution spectrophotometry to study broad mineral absorption features on Mars

    NASA Technical Reports Server (NTRS)

    Blaney, D. L.; Crisp, D.

    1993-01-01

    Traditionally telescopic measurements of mineralogic absorption features have been made using relatively low to moderate (R=30-300) spectral resolution. Mineralogic absorption features tend to be broad so high resolution spectroscopy (R greater than 10,000) does not provide significant additional compositional information. Low to moderate resolution spectroscopy allows an observer to obtain data over a wide wavelength range (hundreds to thousands of wavenumbers) compared to the several wavenumber intervals that are collected using high resolution spectrometers. However, spectrophotometry at high resolution has major advantages over lower resolution spectroscopy in situations that are applicable to studies of the Martian surface, i.e., at wavelengths where relatively weak surface absorption features and atmospheric gas absorption features both occur.

  6. A search for ultraviolet circumstellar gas absorption features in alpha Piscis Austrinus (Fomalhaut), a possible Beta Pictoris-like system

    NASA Technical Reports Server (NTRS)

    Cheng, K.-P.; Bruhweiler, Fred C.; Kondo, Yoji

    1994-01-01

    Archival high-dispersion International Ultraviolet Explorer (IUE) spectra have been used to search for circumstellar gas absorption features in alpha PsA (A3 V), a nearby (6.7 pc) proto-planetary system candidate. Recent sub-millimeter mapping observations around the region of alpha PsA indicate a spatially resolved dust disk like the one seen around Beta Pic. To determine how closely this putative disk resembles that of Beta Pic, we have searched for signatures of circumstellar gaseous absorption in all the available IUE high-dispersion data of alpha PsA. Examination of co-added IUE spectra shows weak circumstellar absorptions from excited levels in the resonance multiplet of Fe II near 2600 A. We also conclude that the sharp C I feature near 1657 A, previously identified as interstellar absorption toward alpha PsA, likely has a circumstellar origin. However, because the weakness of these absorption features, we will consider the presence of circumstellar gas as tentative and should be verified by using the Goddard High-Resolution Spectrograph aboard the Hubble Space Telescope. No corresponding circumstellar absorption is detected in higher ionization Fe III and Al III. Since the collisionally ionized nonphotospheric Al III resonance absorption seen in Beta Pic is likely formed close to the stellar surface, its absence in the UV spectra of alpha PsA could imply that, in contrast with Beta Pic, there is no active gaseous disk infall onto the central star. In the alpha PsA gaseous disk, if we assume a solar abundance for iron and all the iron is in the form of Fe II, plus a disk temperature of 5000 K, the Fe II UV1 absorption at 2611.8743 A infers a total hydrogen column density along the line of sight through the circumstellar disk of N(H) approximately equals 3.8 x 10(exp 17)/cm.

  7. During air cool process aerosol absorption detection with photothermal interferometry

    NASA Astrophysics Data System (ADS)

    Li, Baosheng; Xu, Limei; Huang, Junling; Ma, Fei; Wang, Yicheng; Li, Zhengqiang

    2014-11-01

    This paper studies the basic principle of laser photothermal interferometry method of aerosol particles absorption coefficient. The photothermal interferometry method with higher accuracy and lower uncertainty can directly measure the absorption coefficient of atmospheric aerosols and not be affected by scattered light. With Jones matrix expression, the math expression of a special polarization interferometer is described. This paper using folded Jamin interferometer, which overcomes the influence of vibration on measuring system. Interference come from light polarization beam with two orthogonal and then combine to one beam, finally aerosol absorption induced refractive index changes can be gotten with four beam of phase orthogonal light. These kinds of styles really improve the stability of system and resolution of the system. Four-channel detections interact with interference fringes, to reduce the light intensity `zero drift' effect on the system. In the laboratory, this device typical aerosol absorption index, it shows that the result completely agrees with actual value. After heated by laser, cool process of air also show the process of aerosol absorption. This kind of instrument will be used to monitor ambient aerosol absorption and suspended particulate matter chemical component. Keywords: Aerosol absorption coefficient; Photothermal interferometry; Suspended particulate matter.

  8. Phyllosilicate absorption features in main-belt and outer-belt asteroid reflectance spectra.

    PubMed

    Vilas, F; Gaffey, M J

    1989-11-10

    Absorption features having depths up to 5% are identified in high-quality, high-resolution reflectance spectra of 16 dark asteroids in the main belt and in the Cybele and Hilda groups. Analogs among the CM2 carbonaceous chondrite meteorites exist for some of these asteroids, suggesting that these absorptions are due to iron oxides in phyllosilicates formed on the asteroidal surfaces by aqueous alteration processes. Spectra of ten additional asteroids, located beyond the outer edge of the main belt, show no discernible absorption features, suggesting that aqueous alteration did not always operate at these heliocentric distances.

  9. Phyllosilicate absorption features in main-belt and outer-belt asteroid reflectance spectra

    NASA Technical Reports Server (NTRS)

    Vilas, Faith; Gaffey, Michael J.

    1989-01-01

    Absorption features having depths up to 5 percent are identified in high-quality, high-resolution reflectance spectra of 16 dark asteroids in the main belt and in the Cybele and Hilda groups. Analogs among the CM2 carbonaceous chondrite meteorites exist for some of these asteroids, suggesting that these absorptions are due to iron oxides in phyllosilicates formed on the asteroidal surfaces by aqueous alteration processes. Spectra of ten additional asteroids, located beyond the outer edge of the main belt, show no discernible absorption features, suggesting that aqueous alteration did not always operate at these heliocentric distances.

  10. Implications of high-velocity interstellar H I absorption features

    NASA Technical Reports Server (NTRS)

    Cowie, L.; York, D. G.; Laurent, C.; Vidal-Madjar, A.

    1979-01-01

    Contributions to the interstellar H I column density at high velocities from immediate postshock gas and from the cooling gas behind a shock are compared. The detection of high-velocity H I in L-epsilon and L-delta for Iota Ori is reported and interpreted as cooling gas behind a shock of 100 km/s velocity. The immediate postshock gas should be observable for shock velocities greater than 200 km/s and permits direct determination of the velocities of adiabatic shocks in the interstellar medium. It is pointed out that interstellar L-alpha and L-beta lines may not have purely Lorentzian profiles if high-velocity H I is a widespread phenomenon.

  11. Oxygen detection using the laser diode absorption technique

    NASA Technical Reports Server (NTRS)

    Disimile, P. J.; Fox, C. W.

    1991-01-01

    Accurate measurement of the concentration and flow rate of gaseous oxygen is becoming of greater importance. The detection technique presented is based on the principal of light absorption by the Oxygen A-Band. Oxygen molecules have characteristics which attenuate radiation in the 759-770 nm wavelength range. With an ability to measure changes in the relative light transmission to less than 0.01 percent, a sensitive optical gas detection system was configured. This system is smaller in size and light in weight, has low energy requirements and has a rapid response time. In this research program, the application of temperature tuning laser diodes and their ability to be wavelength shifted to a selected absorption spectral peak has allowed concentrations as low as 1300 ppm to be detected.

  12. Label free detection of phospholipids by infrared absorption spectroscopy

    NASA Astrophysics Data System (ADS)

    Ahmed, Tahsin; Foster, Erick; Vigil, Genevieve; Khan, Aamir A.; Bohn, Paul; Howard, Scott S.

    2014-08-01

    We present our study on compact, label-free dissolved lipid sensing by combining capillary electrophoresis separation in a PDMS microfluidic chip online with mid-infrared (MIR) absorption spectroscopy for biomarker detection. On-chip capillary electrophoresis is used to separate the biomarkers without introducing any extrinsic contrast agent, which reduces both cost and complexity. The label free biomarker detection could be done by interrogating separated biomarkers in the channel by MIR absorption spectroscopy. Phospholipids biomarkers of degenerative neurological, kidney, and bone diseases are detectable using this label free technique. These phospholipids exhibit strong absorption resonances in the MIR and are present in biofluids including urine, blood plasma, and cerebrospinal fluid. MIR spectroscopy of a 12-carbon chain phosphatidic acid (PA) (1,2-dilauroyl-snglycero- 3-phosphate (sodium salt)) dissolved in N-methylformamide, exhibits a strong amide peak near wavenumber 1660 cm-1 (wavelength 6 μm), arising from the phosphate headgroup vibrations within a low-loss window of the solvent. PA has a similar structure to many important phospholipids molecules like phosphatidylcholine (PC), phosphatidylinositol (PI), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), and phosphatidylserine (PS), making it an ideal molecule for initial proof-of-concept studies. This newly proposed detection technique can lead us to minimal sample preparation and is capable of identifying several biomarkers from the same sample simultaneously.

  13. IUE's View of Callisto: Detection of an SO2 Absorption Correlated to Possible Torus Neutral Wind Alterations

    NASA Technical Reports Server (NTRS)

    Lane, Arthur L.; Domingue, Deborah L.

    1997-01-01

    Observations taken with the International Ultraviolet Explorer (IUE) detected a 0.28 micron absorption feature on Callisto's leading and Jupiter-facing hemispheres. This feature is similar to Europa's 0.28 micron feature, however it shows no correlation with magnetospheric ion bombardment. The strongest 0.28 micron signature is seen in the region containing the Valhalla impact. This absorption feature also shows some spatial correlation to possible neutral wind interactions, suggestive of S implantation (rather than S(sub x)) into Callisto's water ice surface, Indications of possible temporal variations (on the 10% level) are seen at other wavelengths between the 1984-1986 and the 1996 observations.

  14. Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm

    NASA Astrophysics Data System (ADS)

    Kokaly, Raymond F.; Skidmore, Andrew K.

    2015-12-01

    Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic Csbnd H bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r2 = 0.79, RMSE = 2.1%, n = 56), and canopy (r2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences

  15. Derivative Analysis of Absorption Features in Hyperspectral Remote Sensing Data of Carbonate Sediments

    DTIC Science & Technology

    2002-12-30

    reflectance of carbonate sediments and application to shallow water benthic habitat classification,” Doctoral Dissertation, University of Miami. Chap.3...resolve overlapping features. A primary application has been to analyze pigment and chemical composition of leaves in order to track physiological...final absorption feature was observed at 630 nm, in a region associated with the biliprotein, phycocyanin [16,17]. As biliproteins are water soluble

  16. Classification by diagnosing all absorption features (CDAF) for the most abundant minerals in airborne hyperspectral images

    NASA Astrophysics Data System (ADS)

    Mobasheri, Mohammad Reza; Ghamary-Asl, Mohsen

    2011-12-01

    Imaging through hyperspectral technology is a powerful tool that can be used to spectrally identify and spatially map materials based on their specific absorption characteristics in electromagnetic spectrum. A robust method called Tetracorder has shown its effectiveness at material identification and mapping, using a set of algorithms within an expert system decision-making framework. In this study, using some stages of Tetracorder, a technique called classification by diagnosing all absorption features (CDAF) is introduced. This technique enables one to assign a class to the most abundant mineral in each pixel with high accuracy. The technique is based on the derivation of information from reflectance spectra of the image. This can be done through extraction of spectral absorption features of any minerals from their respected laboratory-measured reflectance spectra, and comparing it with those extracted from the pixels in the image. The CDAF technique has been executed on the AVIRIS image where the results show an overall accuracy of better than 96%.

  17. Detection of H I absorption in the dwarf galaxy Haro 11

    NASA Astrophysics Data System (ADS)

    MacHattie, Jeremy A.; Irwin, Judith A.; Madden, Suzanne C.; Cormier, Diane; Rémy-Ruyer, Aurélie

    2014-02-01

    We present the results of an analysis of archival 21 cm (H I) data of the blue compact dwarf galaxy Haro 11 (ESO 350-IG038). Observations were obtained at the Very Large Array, and the presence of a compact absorption feature near the optical centre of the galaxy has been detected. The central location of the absorption feature coincides with the centre of the continuum background of the galaxy, as well as with the location of knot B. The absorption feature yields an H I mass in the range of 3-10 × 108 M⊙, corresponding to spin temperatures from 91 K to 200 K, respectively. The absence of H I seen in emission places an upper limit of 1.7 × 109 M⊙ on the mass. To our knowledge this is the first example of a dwarf galaxy that shows H I absorption from its own background continuum. The continuum emission from the galaxy is also used to determine star formation rates, namely 6.85 ± 0.05 M⊙ yr-1 (for a stellar mass range of 5 M⊙ < M < 100 M⊙), or 32.8 ± 0.2 M⊙ yr-1 (for an extended range of 0.1 M⊙ < M < 100 M⊙).

  18. The 4.5 micron Sulfate Absorption Feature on Mars and Its Relationship to Formation Environment

    NASA Technical Reports Server (NTRS)

    Blaney, D. L.

    2001-01-01

    The 4.5 micron sulfate absorption feature on Mars is spatially variable. It is a sensitive composition and hydration state and can be used to identify different types of aqueous environments. Additional information is contained in the original extended abstract.

  19. IUE detector saturation and the new 2800 A absorption feature 'discovered' by Karim, Hoyle, and Wickramasinghe

    NASA Astrophysics Data System (ADS)

    Savage, B. D.; Sitko, M. L.

    1984-03-01

    The 2800 A feature of Karim et al. (1983) is shown to be the result of IUE detector saturation effects in overexposed spectra. A properly exposed spectrum and an overexposed one are shown. The latter shows a broad absorption peak at 2800 A while the former does not.

  20. [Application of atomic absorption spectrometry in the engine knock detection].

    PubMed

    Chen, Li-Dan

    2013-02-01

    Because existing human experience diagnosis method and apparatus for auxiliary diagnosis method are difficult to diagnose quickly engine knock. Atomic absorption spectrometry was used to detect the automobile engine knock in in innovative way. After having determined Fe, Al, Cu, Cr and Pb content in the 35 groups of Audi A6 engine oil whose travel course is 2 000 -70 000 kilometers and whose sampling interval is 2 000 kilometers by atomic absorption spectrometry, the database of primary metal content in the same automobile engine at different mileage was established. The research shows that the main metal content fluctuates within a certain range. In practical engineering applications, after the determination of engine oil main metal content and comparison with its database value, it can not only help to diagnose the type and location of engine knock without the disintegration and reduce vehicle maintenance costs and improve the accuracy of engine knock fault diagnosis.

  1. Detection of absorption lines in the spectra of X-ray bursts from X1608-52

    NASA Astrophysics Data System (ADS)

    Nakamura, Norio; Inoue, Hajime; Tanaka, Yasuo

    X-ray bursts from X 1608-52 were observed with the gas scintillation proportional counters on the Tenma satellite. Absorption features were detected in the spectra of three bursts among 17 bursts observed. These absorption features are consistent with a common absorption line at 4.1 keV. The energy and the properties of the absorption lines of the X 1608-52 bursts are very similar to those observed from the X 1636-53 bursts by Waki et al. (1984). Near equality of the absorption-line energies for X 1636-53 and X 1608-52 would imply that mass and radius of the neutron stars in these two systems are very similar to each other.

  2. Significance of ambient conditions in uranium absorption and emission features of laser ablation plasmas

    SciTech Connect

    Skrodzki, P. J.; Shah, N. P.; Taylor, N.

    2016-11-01

    This study employs laser ablation (LA) to investigate mechanisms for U optical signal variation under various environmental conditions during laser absorption spectroscopy (LAS) and optical emission spectroscopy (OES). Potential explored mechanisms for signal quenching related to ambient conditions include plasma chemistry (e.g., uranium oxide formation), ambient gas confinement effects, and other collisional interactions between plasma constituents and the ambient gas. LA-LAS studies show that the persistence of the U ground state population is significantly reduced in the presence of air ambient compared to nitrogen. LA-OES results yield congested spectra from which the U I 356.18 nm transition is prominent andmore » serves as the basis for signal tracking. LA-OES signal and persistence vary negligibly between the test gases (air and N2), unlike the LA-LAS results. The plume hydrodynamic features and plume fundamental properties showed similar results in both air and nitrogen ambient. Investigation of U oxide formation in the laser-produced plasma suggests that low U concentration in a sample hinders consistent detection of UO molecular spectra.« less

  3. Significance of ambient conditions in uranium absorption and emission features of laser ablation plasmas

    SciTech Connect

    Skrodzki, P. J.; Shah, N. P.; Taylor, N.

    2016-10-02

    This study employs laser ablation (LA) to investigate mechanisms for U optical signal variation under various environmental conditions during laser absorption spectroscopy (LAS) and optical emission spectroscopy (OES). Potential mechanisms explored for signal quenching related to ambient conditions include plasma chemistry (e.g., uranium oxide formation), ambient gas confinement effects, and other collisional interactions between plas-ma constituents and the ambient gas. LA-LAS studies show that the persistence of the U ground state population is significantly reduced in the presence of air ambient compared to nitrogen. LA-OES yields congested spectra from which the U I 356.18 nm transition is prominent and servesmore » as the basis for signal tracking. LA-OES signal and per-sistence vary negligibly between the test gases (air and N 2), unlike the LA-LAS results. The plume hydrodynamic features and plume fundamental properties showed similar results in both air and nitrogen ambient. In conclusion, investigation of U oxide formation in the laser-produced plasma suggests that low U concentration in a sample hinders consistent detection of UO molecular spectra.« less

  4. Detecting ultralight bosonic dark matter via absorption in superconductors

    DOE PAGES

    Hochberg, Yonit; Lin, Tongyan; Zurek, Kathryn M.

    2016-07-18

    Superconducting targets have recently been proposed for the direct detection of dark matter as light as a keV, via elastic scattering off conduction electrons in Cooper pairs. Detecting such light dark matter requires sensitivity to energies as small as the superconducting gap of O(meV). Here we show that these same superconducting devices can detect much lighter DM, of meV to eV mass, via dark matter absorption on a conduction electron, followed by emission of an athermal phonon. Lastly, we demonstrate the power of this setup for relic kinetically mixed hidden photons, pseudoscalars, and scalars, showing that the reach can exceedmore » current astrophysical and terrestrial constraints with only a moderate exposure.« less

  5. Ferric iron in primitive asteroids - A 0.43-micron absorption feature

    NASA Technical Reports Server (NTRS)

    Vilas, Faith; Hatch, Erin C.; Larson, Stephen M.; Sawyer, Scott R.; Gaffey, Michael J.

    1993-01-01

    A search of reflectance spectra of C- P-, D- and S-class asteroids to hunt for the Soret band near 0.4 micron that is indicative of porphyrins yielded an identification of an 0.43 micron absorption feature in 11 primitive asteroids of the C, P, and G classes and in one S-class asteroid. It is proposed that the feature is an Fe(3+) spin-forbidden transition in aqueously altered material, possibly located near 0.43 micron due to an enhancement effect similar to the mechanism operating in jarosite. The significance of the feature for the aqueous alteration history of these asteroids is addressed.

  6. Salient object detection method based on multiple semantic features

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

  7. Interstellar proteins and the discovery of a new absorption feature at lambda = 2800 A

    NASA Astrophysics Data System (ADS)

    Karim, L. M.; Hoyle, F.; Wickramasinghe, N. C.

    1983-07-01

    In order to check the presence of biogenic materials in interstellar grains, the spectra of three early-type, heavily reddened stars recorded by the IUE were examined. These stars showed comparatively weak absorption at 2200 A, minimizing the effect of graphite grains. A broad absorption feature centered on 2800 A is discovered in HD 14250 and interpreted to be due to the amino acid tryptophan. Comparison of the spectrum with that of the calculated extinction behavior of graphite spheres of radii 0.02 microns suggests that the latter are not responsible for the observed spectrum.

  8. Phase-dependent absorption features in X-ray spectra of X-ray Dim Isolated Neutron Stars

    NASA Astrophysics Data System (ADS)

    Borghese, A.; Rea, N.; Coti Zelati, F.; Turolla, R.; Tiengo, A.; Zane, S.

    2017-12-01

    A detailed phase-resolved spectroscopy of archival XMM-Newton observations of X-ray Dim Isolated Neutron Stars (XDINSs) led to the discovery of narrow and strongly phase-dependent absorption features in two of these sources. The first was discovered in the X-ray spectrum of RX J0720.4-3125, followed by a new possible candidate in RX J1308.6+2127. Both spectral lines have similar properties: they are detected for only ˜ 20% of the rotational cycle and appear to be stable over the timespan covered by the observations. We performed Monte Carlo simulations to test the significance of these phase-variable features and in both cases the outcome has confirmed the detection with a confidence level > 4.6σ. Because of the narrow width and the strong dependence on the pulsar rotational phase, the most likely interpretation for these spectral features is in terms of resonant proton cyclotron absorption scattering in a confined high-B structure close to the stellar surface. Within the framework of this interpretation, our results provide evidence for deviations from a pure dipole magnetic field on small scales for highly magnetized neutron stars and support the proposed scenario of XDINSs being aged magnetars, with a strong non-dipolar crustal B-field component.

  9. Molecular detection with terahertz waves based on absorption-induced transparency metamaterials

    NASA Astrophysics Data System (ADS)

    G. Rodrigo, Sergio; Martín-Moreno, L.

    2016-10-01

    A system for the detection of spectral signatures of chemical compounds at the Terahertz regime is presented. The system consists on a holey metal film whereby the presence of a given substance provokes the appearance of spectral features in transmission and reflection induced by the molecular specimen. These induced effects can be regarded as an extraordinary optical transmission phenomenon called absorption-induced transparency (AIT). The phenomenon consist precisely in the appearance of peaks in transmission and dips in reflection after sputtering of a chemical compound onto an initially opaque holey metal film. The spectral signatures due to AIT occur unexpectedly close to the absorption energies of the molecules. The presence of a target, a chemical compound, would be thus revealed as a strong drop in reflectivity measurements. We theoretically predict the AIT based system would serve to detect amounts of hydrocyanic acid (HCN) at low rate concentrations.

  10. The origin of absorptive features in the two-dimensional electronic spectra of rhodopsin.

    PubMed

    Farag, Marwa H; Jansen, Thomas L C; Knoester, Jasper

    2018-05-09

    In rhodopsin, the absorption of a photon causes the isomerization of the 11-cis isomer of the retinal chromophore to its all-trans isomer. This isomerization is known to occur through a conical intersection (CI) and the internal conversion through the CI is known to be vibrationally coherent. Recently measured two-dimensional electronic spectra (2DES) showed dramatic absorptive spectral features at early waiting times associated with the transition through the CI. The common two-state two-mode model Hamiltonian was unable to elucidate the origin of these features. To rationalize the source of these features, we employ a three-state three-mode model Hamiltonian where the hydrogen out-of plane (HOOP) mode and a higher-lying electronic state are included. The 2DES of the retinal chromophore in rhodopsin are calculated and compared with the experiment. Our analysis shows that the source of the observed features in the measured 2DES is the excited state absorption to a higher-lying electronic state and not the HOOP mode.

  11. Searching for dark absorption with direct detection experiments

    SciTech Connect

    Bloch, Itay M.; Essig, Rouven; Tobioka, Kohsaku

    We consider the absorption by bound electrons of dark matter in the form of dark photons and axion-like particles, as well as of dark photons from the Sun, in current and next-generation direct detection experiments. Experiments sensitive to electron recoils can detect such particles with masses between a few eV to more than 10 keV. For dark photon dark matter, we update a previous bound based on XENON10 data and derive new bounds based on data from XENON100 and CDMSlite. We find these experiments to disfavor previously allowed parameter space. Moreover, we derive sensitivity projections for SuperCDMS at SNOLAB formore » silicon and germanium targets, as well as for various possible experiments with scintillating targets (cesium iodide, sodium iodide, and gallium arsenide). The projected sensitivity can probe large new regions of parameter space. For axion-like particles, the same current direction detection data improves on previously known direct-detection constraints but does not bound new parameter space beyond known stellar cooling bounds. However, projected sensitivities of the upcoming SuperCDMS SNOLAB using germanium can go beyond these and even probe parameter space consistent with possible hints from the white dwarf luminosity function. We find similar results for dark photons from the sun. For all cases, direct-detection experiments can have unprecedented sensitivity to dark-sector particles.« less

  12. Searching for dark absorption with direct detection experiments

    DOE PAGES

    Bloch, Itay M.; Essig, Rouven; Tobioka, Kohsaku; ...

    2017-06-16

    We consider the absorption by bound electrons of dark matter in the form of dark photons and axion-like particles, as well as of dark photons from the Sun, in current and next-generation direct detection experiments. Experiments sensitive to electron recoils can detect such particles with masses between a few eV to more than 10 keV. For dark photon dark matter, we update a previous bound based on XENON10 data and derive new bounds based on data from XENON100 and CDMSlite. We find these experiments to disfavor previously allowed parameter space. Moreover, we derive sensitivity projections for SuperCDMS at SNOLAB formore » silicon and germanium targets, as well as for various possible experiments with scintillating targets (cesium iodide, sodium iodide, and gallium arsenide). The projected sensitivity can probe large new regions of parameter space. For axion-like particles, the same current direction detection data improves on previously known direct-detection constraints but does not bound new parameter space beyond known stellar cooling bounds. However, projected sensitivities of the upcoming SuperCDMS SNOLAB using germanium can go beyond these and even probe parameter space consistent with possible hints from the white dwarf luminosity function. We find similar results for dark photons from the sun. For all cases, direct-detection experiments can have unprecedented sensitivity to dark-sector particles.« less

  13. Understanding the features in the ultrafast transient absorption spectra of CdSe quantum dots

    NASA Astrophysics Data System (ADS)

    Zhang, Cheng; Do, Thanh Nhut; Ong, Xuanwei; Chan, Yinthai; Tan, Howe-Siang

    2016-12-01

    We describe a model to explain the features of the ultrafast transient absorption (TA) spectra of CdSe core type quantum dots (QDs). The measured TA spectrum consists of contributions by the ground state bleach (GSB), stimulated emission (SE) and excited state absorption (ESA) processes associated with the three lowest energy transition of the QDs. We model the shapes of the GSB, SE and ESA spectral components after fits to the linear absorption. The spectral positions of the ESA components take into account the biexcitonic binding energy. In order to obtain the correct weightage of the GSB, SE and ESA components to the TA spectrum, we enumerate the set of coherence transfer pathways associated with these processes. From our fits of the experimental TA spectra of 65 Å diameter QDs, biexcitonic binding energies for the three lowest energy transitions are obtained.

  14. Feature Selection and Pedestrian Detection Based on Sparse Representation.

    PubMed

    Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei

    2015-01-01

    Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony.

  15. Breast Cancer Detection with Reduced Feature Set.

    PubMed

    Mert, Ahmet; Kılıç, Niyazi; Bilgili, Erdem; Akan, Aydin

    2015-01-01

    This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%-40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.

  16. Automatic mine detection based on multiple features

    NASA Astrophysics Data System (ADS)

    Yu, Ssu-Hsin; Gandhe, Avinash; Witten, Thomas R.; Mehra, Raman K.

    2000-08-01

    Recent research sponsored by the Army, Navy and DARPA has significantly advanced the sensor technologies for mine detection. Several innovative sensor systems have been developed and prototypes were built to investigate their performance in practice. Most of the research has been focused on hardware design. However, in order for the systems to be in wide use instead of in limited use by a small group of well-trained experts, an automatic process for mine detection is needed to make the final decision process on mine vs. no mine easier and more straightforward. In this paper, we describe an automatic mine detection process consisting of three stage, (1) signal enhancement, (2) pixel-level mine detection, and (3) object-level mine detection. The final output of the system is a confidence measure that quantifies the presence of a mine. The resulting system was applied to real data collected using radar and acoustic technologies.

  17. Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 μm

    USGS Publications Warehouse

    Kokaly, Raymond F.; Skidmore, Andrew K

    2015-01-01

    Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic C-H bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r2 = 0.79, RMSE = 2.1%, n = 56), and canopy (r2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the

  18. Clustering approaches to feature change detection

    NASA Astrophysics Data System (ADS)

    G-Michael, Tesfaye; Gunzburger, Max; Peterson, Janet

    2018-05-01

    The automated detection of changes occurring between multi-temporal images is of significant importance in a wide range of medical, environmental, safety, as well as many other settings. The usage of k-means clustering is explored as a means for detecting objects added to a scene. The silhouette score for the clustering is used to define the optimal number of clusters that should be used. For simple images having a limited number of colors, new objects can be detected by examining the change between the optimal number of clusters for the original and modified images. For more complex images, new objects may need to be identified by examining the relative areas covered by corresponding clusters in the original and modified images. Which method is preferable depends on the composition and range of colors present in the images. In addition to describing the clustering and change detection methodology of our proposed approach, we provide some simple illustrations of its application.

  19. Morphological feature detection for cervical cancer screening

    NASA Astrophysics Data System (ADS)

    Narayanswamy, Ramkumar; Sharpe, John P.; Duke, Heather J.; Stewart, Rosemary J.; Johnson, Kristina M.

    1995-03-01

    An optoelectronic system has been designed to pre-screen pap-smear slides and detect the suspicious cells using the hit/miss transform. Computer simulation of the algorithm tested on 184 pap-smear images detected 95% of the suspicious region as suspect while tagging just 5% of the normal regions as suspect. An optoelectronic implementation of the hit/miss transform using a 4f Vander-Lugt correlator architecture is proposed and demonstrated with experimental results.

  20. Mask Matching for Linear Feature Detection.

    DTIC Science & Technology

    1987-01-01

    decide which matched masks are part of a linear feature by sim- ple thresholding of the confidence measures. However, it is shown in a compan - ion report...Laboratory, Center for Automation Research, University of Maryland, January 1987. 3. E.M. Allen, R.H. Trigg, and R.J. Wood, The Maryland Artificial ... Intelligence Group Franz Lisp Environment, Variation 3.5, TR-1226, Department of Computer Science, University of Maryland, December 1984. 4. D.E. Knuth, The

  1. Ice detection systems : experimental feature : final report.

    DOT National Transportation Integrated Search

    1986-01-01

    In the fall of 1980, an experimental ice detection system was installed on the Fremont Bridge in Portland, Oregon. this bridge, which caries I-405 over the Willamette River, has a history of icing problem when the deck is wet and the temperature hove...

  2. Mg I absorption features in the solar spectrum near 9 and 12 microns

    NASA Technical Reports Server (NTRS)

    Glenar, David A.; Reuter, Dennis C.; Deming, Drake; Chang, Edward S.

    1988-01-01

    High-resolution FTS observations from the Kitt Peak National Solar Observatory and the Spacelab 3 ATMOS experiment have revealed additional infrared transitions due to Mg I in the spectra of both quiet sun and sunspot penumbra. In contrast to previous observations, these transitions are seen in absorption, not emission. Absorption intensities range from 1 to 7 percent of the continuum in the quiet sun. In the penumbra, the same features appear to show Zeeman splitting. Modeling of the line profiles in the photospheric spectrum shows evidence for a factor of three overabundance in the n = 5 or more levels of Mg I in the upper photosphere, but with no deviations from a Planck source function. It is concluded that whatever the process that produces the emission (including the Lemke and Holweger mechanism), it must occur well above the tau(5000) = 0.01 level.

  3. Local processes in preattentive feature detection.

    PubMed

    Bacon, W F; Egeth, H E

    1991-02-01

    Sagi and Julesz (1987) claimed that for a target to be detected preattentively, it must be within some small critical distance of a nontarget. The independent effects of separation and display size, which were confounded in the Sagi and Julesz experiments, were examined. Experiments 1 and 2 revealed that in tasks requiring search for a color-defined target, target-nontarget separation had no effect on reaction time (RT). Display size, however, was inversely related to RT. Experiment 3 ruled out the possibility that the decreasing function of RT with display size was due to arousal caused by higher display luminance. When nontarget grouping was inhibited, (Exp. 4) it was found that RT no longer decreased with increasing display size. This suggests that nontarget grouping may have been the cause of the improved performance at larger display sizes. Experiments 5 and 6 extended the results to line segments, the stimuli used by Sagi and Julesz.

  4. Space moving target detection using time domain feature

    NASA Astrophysics Data System (ADS)

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

    2018-01-01

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

  5. Ocean Striations Detecting and Its Features

    NASA Astrophysics Data System (ADS)

    Guan, Y. P.; Zhang, Y.; Chen, Z.; Liu, H.; Yu, Y.; Huang, R. X.

    2016-02-01

    Over the past 10 years or so, ocean striations has been one of the research frontiers as reported in many investigators. With suitable filtering subroutines, striations can be revealed from many different types of ocean datasets. It is clear that striations are some types of meso-scale phenomena in the large-scale circulation system, which in the form of alternating band-like structure. We present a comprehensive study on the effectiveness of the different detection approaches to unveiling the striations. Three one-dimensional filtering methods: Gaussian smoothing, Hanning and Chebyshev high-pass filtering. Our results show that all three methods can reveal ocean banded structures, but the Chebyshev filtering is the best choice. The Gaussian smoothing is not a high pass filter, and it can merely bring regional striations, such as those in the Eastern Pacific, to light. The Hanning high pass filter can introduce a northward shifting of stripes, so it is not as good as the Chebyshev filter. On the other hand, striations in the open ocean are mostly zonally oriented; however, there are always exceptions. In particular, in coastal ocean, due to topography constraint and along shore currents, striations can titled in the meridional direction. We examined the band-like structure of striation for some selected regions of the open ocean and the semi-closed sub-basins, such as the South China sea, the Gulf of Mexico, the Mediterranean Sea and the Japan Sea. A reasonable interpretation is given here.

  6. Feature Detection Systems Enhance Satellite Imagery

    NASA Technical Reports Server (NTRS)

    2009-01-01

    -resolution satellites, which provide the benefit of images detailed enough to reveal large features like highways while still broad enough for global coverage, continue to scan the entirety of the Earth s surface. In 2012, NASA plans to launch the Landsat Data Continuity Mission (LDCM), or Landsat 8, to extend the Landsat program s contributions to cartography, water management, natural disaster relief planning, and more.

  7. Ultrafast transient absorption revisited: Phase-flips, spectral fingers, and other dynamical features

    SciTech Connect

    Cina, Jeffrey A., E-mail: cina@uoregon.edu; Kovac, Philip A.; Jumper, Chanelle C.

    We rebuild the theory of ultrafast transient-absorption/transmission spectroscopy starting from the optical response of an individual molecule to incident femtosecond pump and probe pulses. The resulting description makes use of pulse propagators and free molecular evolution operators to arrive at compact expressions for the several contributions to a transient-absorption signal. In this alternative description, which is physically equivalent to the conventional response-function formalism, these signal contributions are conveniently expressed as quantum mechanical overlaps between nuclear wave packets that have undergone different sequences of pulse-driven optical transitions and time-evolution on different electronic potential-energy surfaces. Using this setup in application to amore » simple, multimode model of the light-harvesting chromophores of PC577, we develop wave-packet pictures of certain generic features of ultrafast transient-absorption signals related to the probed-frequency dependence of vibrational quantum beats. These include a Stokes-shifting node at the time-evolving peak emission frequency, antiphasing between vibrational oscillations on opposite sides (i.e., to the red or blue) of this node, and spectral fingering due to vibrational overtones and combinations. Our calculations make a vibrationally abrupt approximation for the incident pump and probe pulses, but properly account for temporal pulse overlap and signal turn-on, rather than neglecting pulse overlap or assuming delta-function excitations, as are sometimes done.« less

  8. Optical Path Switching Based Differential Absorption Radiometry for Substance Detection

    NASA Technical Reports Server (NTRS)

    Sachse, Glen W. (Inventor)

    2000-01-01

    A system and method are provided for detecting one or more substances. An optical path switch divides sample path radiation into a time series of alternating first polarized components and second polarized components. The first polarized components are transmitted along a first optical path and the second polarized components along a second optical path. A first gasless optical filter train filters the first polarized components to isolate at least a first wavelength band thereby generating first filtered radiation. A second gasless optical filter train filters the second polarized components to isolate at least a second wavelength band thereby generating second filtered radiation. The first wavelength band and second wavelength band are unique. Further, spectral absorption of a substance of interest is different at the first wavelength band as compared to the second wavelength band. A beam combiner combines the first and second filtered radiation to form a combined beam of radiation. A detector is disposed to monitor magnitude of at least a portion of the combined beam alternately at the first wavelength band and the second wavelength band as an indication of the concentration of the substance in the sample path.

  9. PCA-HOG symmetrical feature based diseased cell detection

    NASA Astrophysics Data System (ADS)

    Wan, Min-jie

    2016-04-01

    A histogram of oriented gradient (HOG) feature is applied to the field of diseased cell detection, which can detect diseased cells in high resolution tissue images rapidly, accurately and efficiently. Firstly, motivated by symmetrical cellular forms, a new HOG symmetrical feature based on the traditional HOG feature is proposed to meet the condition of cell detection. Secondly, considering the high feature dimension of traditional HOG feature leads to plenty of memory resources and long runtime in practical applications, a classical dimension reduction method called principal component analysis (PCA) is used to reduce the dimension of high-dimensional HOG descriptor. Because of that, computational speed is increased greatly, and the accuracy of detection can be controlled in a proper range at the same time. Thirdly, support vector machine (SVM) classifier is trained with PCA-HOG symmetrical features proposed above. At last, practical tissue images is detected and analyzed by SVM classifier. In order to verify the effectiveness of this new algorithm, it is practically applied to conduct diseased cell detection which takes 200 pieces of H&E (hematoxylin & eosin) high resolution staining histopathological images collected from 20 breast cancer patients as a sample. The experiment shows that the average processing rate can be 25 frames per second and the detection accuracy can be 92.1%.

  10. Detecting Image Splicing Using Merged Features in Chroma Space

    PubMed Central

    Liu, Guangjie; Dai, Yuewei

    2014-01-01

    Image splicing is an image editing method to copy a part of an image and paste it onto another image, and it is commonly followed by postprocessing such as local/global blurring, compression, and resizing. To detect this kind of forgery, the image rich models, a feature set successfully used in the steganalysis is evaluated on the splicing image dataset at first, and the dominant submodel is selected as the first kind of feature. The selected feature and the DCT Markov features are used together to detect splicing forgery in the chroma channel, which is convinced effective in splicing detection. The experimental results indicate that the proposed method can detect splicing forgeries with lower error rate compared to the previous literature. PMID:24574877

  11. Detecting image splicing using merged features in chroma space.

    PubMed

    Xu, Bo; Liu, Guangjie; Dai, Yuewei

    2014-01-01

    Image splicing is an image editing method to copy a part of an image and paste it onto another image, and it is commonly followed by postprocessing such as local/global blurring, compression, and resizing. To detect this kind of forgery, the image rich models, a feature set successfully used in the steganalysis is evaluated on the splicing image dataset at first, and the dominant submodel is selected as the first kind of feature. The selected feature and the DCT Markov features are used together to detect splicing forgery in the chroma channel, which is convinced effective in splicing detection. The experimental results indicate that the proposed method can detect splicing forgeries with lower error rate compared to the previous literature.

  12. Ultraviolet spectra of quenched carbonaceous composite derivatives: Comparison to the '217 nanometer' interstellar absorption feature

    NASA Technical Reports Server (NTRS)

    Sakata, Akira; Wada, Setsuko; Tokunaga, Alan T.; Narisawa, Takatoshi; Nakagawa, Hidehiro; Ono, Hiroshi

    1994-01-01

    QCCs (quenched carbonaceous composite) are amorphus carbonaceous material formed from a hydrocarbon plasma. We present the UV-visible spectra of 'filmy QCC; (obtained outside of the beam ejected from the hydrocarbon plasma) and 'dark QCC' (obtained very near to the beam) for comparison to the stellar extinction curve. When filmy QCC is heated to 500-700 C (thermally altered), the wavelength of the absorption maximum increases form 204 nm to 220-222 nm. The dark QCC has an absorption maximum at 217-222 nm. In addition, the thermally altered filmy QCC has a slope change at about 500 nm which resmbles that in the interstellar extinction curve. The resemblance of the extinction curve of the QCCs to that of the interstellar medium suggests that QCC derivatives may be representative of the type of interstellar material that produces the 217 nm interstellar medium feature. The peak extinction of the dark QCC is higher than the average interstellar extinction curve while that of the thermally altered filmy QCC is lower, so that a mixture of dark and thermally altered filmy QCC can match the peak extinction observed in the interstellar medium. It is shown from electron micrographs that most of the thermally altered flimy QCC is in the form of small grainy structure less than 4 nm in diameter. This shows that the structure unit causing the 217-222 nm feature in QCC is very small.

  13. Adapting Local Features for Face Detection in Thermal Image.

    PubMed

    Ma, Chao; Trung, Ngo Thanh; Uchiyama, Hideaki; Nagahara, Hajime; Shimada, Atsushi; Taniguchi, Rin-Ichiro

    2017-11-27

    A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses). We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results.

  14. Convolutional neural network features based change detection in satellite images

    NASA Astrophysics Data System (ADS)

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

    2016-07-01

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

  15. Feature Integration Theory Revisited: Dissociating Feature Detection and Attentional Guidance in Visual Search

    ERIC Educational Resources Information Center

    Chan, Louis K. H.; Hayward, William G.

    2009-01-01

    In feature integration theory (FIT; A. Treisman & S. Sato, 1990), feature detection is driven by independent dimensional modules, and other searches are driven by a master map of locations that integrates dimensional information into salience signals. Although recent theoretical models have largely abandoned this distinction, some observed…

  16. Targeted Feature Detection for Data-Dependent Shotgun Proteomics

    PubMed Central

    2017-01-01

    Label-free quantification of shotgun LC–MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification (“FFId”), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between “internal” and “external” (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the “uncertain” feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards

  17. Targeted Feature Detection for Data-Dependent Shotgun Proteomics.

    PubMed

    Weisser, Hendrik; Choudhary, Jyoti S

    2017-08-04

    Label-free quantification of shotgun LC-MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification ("FFId"), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between "internal" and "external" (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the "uncertain" feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards that provide a known

  18. Picture Detection in Rapid Serial Visual Presentation: Features or Identity?

    ERIC Educational Resources Information Center

    Potter, Mary C.; Wyble, Brad; Pandav, Rijuta; Olejarczyk, Jennifer

    2010-01-01

    A pictured object can be readily detected in a rapid serial visual presentation sequence when the target is specified by a superordinate category name such as "animal" or "vehicle". Are category features the initial basis for detection, with identification of the specific object occurring in a second stage (Evans &…

  19. Hybrid feature selection for supporting lightweight intrusion detection systems

    NASA Astrophysics Data System (ADS)

    Song, Jianglong; Zhao, Wentao; Liu, Qiang; Wang, Xin

    2017-08-01

    Redundant and irrelevant features not only cause high resource consumption but also degrade the performance of Intrusion Detection Systems (IDS), especially when coping with big data. These features slow down the process of training and testing in network traffic classification. Therefore, a hybrid feature selection approach in combination with wrapper and filter selection is designed in this paper to build a lightweight intrusion detection system. Two main phases are involved in this method. The first phase conducts a preliminary search for an optimal subset of features, in which the chi-square feature selection is utilized. The selected set of features from the previous phase is further refined in the second phase in a wrapper manner, in which the Random Forest(RF) is used to guide the selection process and retain an optimized set of features. After that, we build an RF-based detection model and make a fair comparison with other approaches. The experimental results on NSL-KDD datasets show that our approach results are in higher detection accuracy as well as faster training and testing processes.

  20. Boosting instance prototypes to detect local dermoscopic features.

    PubMed

    Situ, Ning; Yuan, Xiaojing; Zouridakis, George

    2010-01-01

    Local dermoscopic features are useful in many dermoscopic criteria for skin cancer detection. We address the problem of detecting local dermoscopic features from epiluminescence (ELM) microscopy skin lesion images. We formulate the recognition of local dermoscopic features as a multi-instance learning (MIL) problem. We employ the method of diverse density (DD) and evidence confidence (EC) function to convert MIL to a single-instance learning (SIL) problem. We apply Adaboost to improve the classification performance with support vector machines (SVMs) as the base classifier. We also propose to boost the selection of instance prototypes through changing the data weights in the DD function. We validate the methods on detecting ten local dermoscopic features from a dataset with 360 images. We compare the performance of the MIL approach, its boosting version, and a baseline method without using MIL. Our results show that boosting can provide performance improvement compared to the other two methods.

  1. UV absorption investigation of ferromagnetically filled ultra-thick carbon onions, carriers of the 217.5 nm Interstellar Absorption Feature

    NASA Astrophysics Data System (ADS)

    Boi, Filippo S.; Zhang, Xiaotian; Ivaturi, Sameera; Liu, Qianyang; Wen, Jiqiu; Wang, Shanling

    2017-12-01

    Carbon nano-onions (CNOs) are fullerene-like structures which consist of quasi-spherical closed carbon shells. These structures have become a subject of great interest thanks to their characteristic absorption feature of interstellar origin (at 217.5 nm, 4.6 μm-1). An additional extinction peak at 3.8 μm-1 has also been reported and attributed to absorption by graphitic residues between the as-grown CNOs. Here, we report the ultraviolet absorption properties of ultra-thick CNOs filled with FePt3 crystals, which also exhibit two main absorption peaks—features located at 4.58 μm-1 and 3.44 μm-1. The presence of this additional feature is surprising and is attributed to nonmagnetic graphite flakes produced as a by-product in the pyrolysis experiment (as confirmed by magnetic separation methods). Instead, the feature at 4.58 μm-1 is associated with the π-plasmonic resonance of the CNOs structures. The FePt3 filled CNOs were fabricated in situ by an advanced one-step fast process consisting in the direct sublimation and pyrolysis of two molecular precursors, namely, ferrocene and dichloro-cyclooctadiene-platinum in a chemical vapour deposition system. The morphological, structural, and magnetic properties of the as-grown filled CNOs were characterized by a means of scanning and transmission electron microscopy, X-ray diffraction, and magnetometry.

  2. Hazardous Gas Detection Sensor Using Broadband Light-Emitting Diode-Based Absorption Spectroscopy for Space Applications

    SciTech Connect

    Terracciano, Anthony; Thurmond, Kyle; Villar, Michael

    As space travel matures and extended duration voyages become increasingly common, it will be necessary to include arrays of early fire detection systems aboard spacefaring vessels, space habitats, and in spacesuits. As gasses that are relevant to combustion and pyrolysis have absorption features in the midinfrared range, it is possible to utilize absorption spectroscopy as a means of detecting and quantifying the concentration of these hazardous compounds. Within this work, a sensor for detecting carbon dioxide has been designed and tested autonomously on a high-altitude balloon flight. The sensor utilizes a 4.2-mm lightemitting diode source, amplitude modulation to characterize speciesmore » concentrations, and frequency modulation to characterize ambient temperature. Future work will include expanding the sensor design to detect other gases, and demonstrating suborbital flight capability.« less

  3. Hazardous Gas Detection Sensor Using Broadband Light-Emitting Diode-Based Absorption Spectroscopy for Space Applications

    DOE PAGES

    Terracciano, Anthony; Thurmond, Kyle; Villar, Michael; ...

    2018-03-12

    As space travel matures and extended duration voyages become increasingly common, it will be necessary to include arrays of early fire detection systems aboard spacefaring vessels, space habitats, and in spacesuits. As gasses that are relevant to combustion and pyrolysis have absorption features in the midinfrared range, it is possible to utilize absorption spectroscopy as a means of detecting and quantifying the concentration of these hazardous compounds. Within this work, a sensor for detecting carbon dioxide has been designed and tested autonomously on a high-altitude balloon flight. The sensor utilizes a 4.2-mm lightemitting diode source, amplitude modulation to characterize speciesmore » concentrations, and frequency modulation to characterize ambient temperature. Future work will include expanding the sensor design to detect other gases, and demonstrating suborbital flight capability.« less

  4. Effect of cell-size on the energy absorption features of closed-cell aluminium foams

    NASA Astrophysics Data System (ADS)

    Nammi, S. K.; Edwards, G.; Shirvani, H.

    2016-11-01

    The effect of cell-size on the compressive response and energy absorption features of closed-cell aluminium (Al) foam were investigated by finite element method. Micromechanical models were constructed with a repeating unit-cell (RUC) which was sectioned from tetrakaidecahedra structure. Using this RUC, three Al foam models with different cell-sizes (large, medium and small) and all of same density, were built. These three different cell-size pieces of foam occupy the same volume and their domains contained 8, 27 and 64 RUCs respectively. However, the smaller cell-size foam has larger surface area to volume ratio compared to other two. Mechanical behaviour was modelled under uniaxial loading. All three aggregates (3D arrays of RUCs) of different cell-sizes showed an elastic region at the initial stage, then followed by a plateau, and finally, a densification region. The smaller cell size foam exhibited a higher peak-stress and a greater densification strain comparing other two cell-sizes investigated. It was demonstrated that energy absorption capabilities of smaller cell-size foams was higher compared to the larger cell-sizes examined.

  5. Old stellar populations. 5: Absorption feature indices for the complete LICK/IDS sample of stars

    NASA Technical Reports Server (NTRS)

    Worthey, Guy; Faber, S. M.; Gonzalez, J. Jesus; Burstein, D.

    1994-01-01

    Twenty-one optical absorption features, 11 of which have been previously defined, are automatically measured in a sample of 460 stars. Following Gorgas et al., the indices are summarized in fitting functions that give index strengths as functions of stellar temperature, gravity, and (Fe/H). This project was carried out with the purpose of predicting index strengths in the integrated light of stellar populations of different ages and metallicities, but the data should be valuable for stellar studies in the Galaxy as well. Several of the new indices appear to be promising indicators of metallicity for old stellar populations. A complete list of index data and atmospheric parameters is available in computer-readable form.

  6. Development of a Near-Ir Cavity Enhanced Absorption Spectrometer for the Detection of Atmospheric Oxidation Products and Organoamines

    NASA Astrophysics Data System (ADS)

    Eddingsaas, Nathan C.; Jewell, Breanna; Thurnherr, Emily

    2014-06-01

    An estimated 10,000 to 100,000 different compounds have been measured in the atmosphere, each one undergoes many oxidation reactions that may or may not degrade air quality. To date, the fate of even some of the most abundant hydrocarbons in the atmosphere is poorly understood. One difficulty is the detection of atmospheric oxidation products that are very labile and decompose during analysis. To study labile species under atmospheric conditions, a highly sensitive, non-destructive technique is needed. Here we describe a near-IR incoherent broadband cavity enhanced absorption spectroscopy (IBBCEAS) setup that we are developing to meet this end. We have chosen to utilize the near-IR, where vibrational overtone absorptions are observed, due to the clean spectral windows and better spectral separation of absorption features. In one spectral window we can simultaneously and continuously monitor the composition of alcohols, hydroperoxides, and carboxylic acids in an air mass. In addition, we have used our CEAS setup to detect organoamines. The long effective path length of CEAS allows for low detection limits, even of the overtone absorption features, at ppb and ppt levels.

  7. GBT Detection of Polarization-Dependent HI Absorption and HI Outflows in Local ULIRGs and Quasars

    NASA Technical Reports Server (NTRS)

    Teng, Stacy H.; Veilleux, Sylvain; Baker, Andrew J.

    2013-01-01

    We present the results of a 21-cm HI survey of 27 local massive gas-rich late-stage mergers and merger remnants with the Green Bank Telescope (GBT). These remnants were selected from the Quasar/ULIRG Evolution Study (QUEST) sample of ultraluminous infrared galaxies (ULIRGs; L(sub 8 - 1000 micron) > 10(exp 12) solar L) and quasars; our targets are all bolometrically dominated by active galactic nuclei (AGN) and sample the later phases of the proposed ULIRG-to-quasar evolutionary sequence. We find the prevalence of HI absorption (emission) to be 100% (29%) in ULIRGs with HI detections, 100% (88%) in FIR-strong quasars, and 63% (100%) in FIR-weak quasars. The absorption features are associated with powerful neutral outflows that change from being mainly driven by star formation in ULIRGs to being driven by the AGN in the quasars. These outflows have velocities that exceed 1500 km/s in some cases. Unexpectedly, we find polarization-dependent HI absorption in 57% of our spectra (88% and 63% of the FIR-strong and FIR-weak quasars, respectively). We attribute this result to absorption of polarized continuum emission from these sources by foreground HI clouds. About 60% of the quasars displaying polarized spectra are radio-loud, far higher than the approx 10% observed in the general AGN population. This discrepancy suggests that radio jets play an important role in shaping the environments in these galaxies. These systems may represent a transition phase in the evolution of gas-rich mergers into "mature" radio galaxies.

  8. Abnormal Circulation Changes in the Winter Stratosphere, Detected Through Variations of D Region Ionospheric Absorption

    NASA Technical Reports Server (NTRS)

    Delamorena, B. A.

    1984-01-01

    A method to detect stratospheric warmings using ionospheric absorption records obtained by an Absorption Meter (method A3) is introduced. The activity of the stratospheric circulation and the D region ionospheric absorption as well as other atmospheric parameters during the winter anomaly experience an abnormal variation. A simultaneity was found in the beginning of abnormal variation in the mentioned parameters, using the absorption records for detecting the initiation of the stratospheric warming. Results of this scientific experience of forecasting in the El Arenosillo Range, are presented.

  9. Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

    PubMed

    Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

    2014-08-01

    Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature

  10. Prostate cancer detection: Fusion of cytological and textural features.

    PubMed

    Nguyen, Kien; Jain, Anil K; Sabata, Bikash

    2011-01-01

    A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i) to locate cancer regions in a large digitized tissue biopsy, and (ii) to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue regions. In this paper, we address the first stage by presenting a cancer detection approach for the whole slide tissue image. We propose a novel method to extract a cytological feature, namely the presence of cancer nuclei (nuclei with prominent nucleoli) in the tissue, and apply this feature to detect the cancer regions. Additionally, conventional image texture features which have been widely used in the literature are also considered. The performance comparison among the proposed cytological textural feature combination method, the texture-based method and the cytological feature-based method demonstrates the robustness of the extracted cytological feature. At a false positive rate of 6%, the proposed method is able to achieve a sensitivity of 78% on a dataset including six training images (each of which has approximately 4,000×7,000 pixels) and 1 1 whole-slide test images (each of which has approximately 5,000×23,000 pixels). All images are at 20X magnification.

  11. Prostate cancer detection: Fusion of cytological and textural features

    PubMed Central

    Nguyen, Kien; Jain, Anil K.; Sabata, Bikash

    2011-01-01

    A computer-assisted system for histological prostate cancer diagnosis can assist pathologists in two stages: (i) to locate cancer regions in a large digitized tissue biopsy, and (ii) to assign Gleason grades to the regions detected in stage 1. Most previous studies on this topic have primarily addressed the second stage by classifying the preselected tissue regions. In this paper, we address the first stage by presenting a cancer detection approach for the whole slide tissue image. We propose a novel method to extract a cytological feature, namely the presence of cancer nuclei (nuclei with prominent nucleoli) in the tissue, and apply this feature to detect the cancer regions. Additionally, conventional image texture features which have been widely used in the literature are also considered. The performance comparison among the proposed cytological textural feature combination method, the texture-based method and the cytological feature-based method demonstrates the robustness of the extracted cytological feature. At a false positive rate of 6%, the proposed method is able to achieve a sensitivity of 78% on a dataset including six training images (each of which has approximately 4,000×7,000 pixels) and 1 1 whole-slide test images (each of which has approximately 5,000×23,000 pixels). All images are at 20X magnification. PMID:22811959

  12. Cascade Classification with Adaptive Feature Extraction for Arrhythmia Detection.

    PubMed

    Park, Juyoung; Kang, Mingon; Gao, Jean; Kim, Younghoon; Kang, Kyungtae

    2017-01-01

    Detecting arrhythmia from ECG data is now feasible on mobile devices, but in this environment it is necessary to trade computational efficiency against accuracy. We propose an adaptive strategy for feature extraction that only considers normalized beat morphology features when running in a resource-constrained environment; but in a high-performance environment it takes account of a wider range of ECG features. This process is augmented by a cascaded random forest classifier. Experiments on data from the MIT-BIH Arrhythmia Database showed classification accuracies from 96.59% to 98.51%, which are comparable to state-of-the art methods.

  13. Hemorrhage detection in MRI brain images using images features

    NASA Astrophysics Data System (ADS)

    Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

    2013-11-01

    The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

  14. Accurate feature detection and estimation using nonlinear and multiresolution analysis

    NASA Astrophysics Data System (ADS)

    Rudin, Leonid; Osher, Stanley

    1994-11-01

    A program for feature detection and estimation using nonlinear and multiscale analysis was completed. The state-of-the-art edge detection was combined with multiscale restoration (as suggested by the first author) and robust results in the presence of noise were obtained. Successful applications to numerous images of interest to DOD were made. Also, a new market in the criminal justice field was developed, based in part, on this work.

  15. 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.

  16. Environmental Temperature Effect on the Far-Infrared Absorption Features of Aromatic-Based Titan's Aerosol Analogs

    NASA Technical Reports Server (NTRS)

    Gautier, Thomas; Trainer, Melissa G.; Loeffler, Mark J.; Sebree, Joshua A.; Anderson, Carrie M.

    2016-01-01

    Benzene detection has been reported in Titans atmosphere both in the stratosphere at ppb levels by remote sensing and in the thermosphere at ppm levels by the Cassini's Ion and Neutral Mass Spectrometer. This detection supports the idea that aromatic and heteroaromatic reaction pathways may play an important role in Titans atmospheric chemistry, especially in the formation of aerosols. Indeed, aromatic molecules are easily dissociated by ultraviolet radiation and can therefore contribute significantly to aerosol formation. It has been shown recently that aerosol analogs produced from a gas mixture containing a low concentration of aromatic and/or heteroaromatic molecules (benzene, naphthalene, pyridine, quinoline and isoquinoline) have spectral signatures below 500/cm, a first step towards reproducing the aerosol spectral features observed by Cassini's Composite InfraRed Spectrometer (CIRS) in the far infrared. In this work we investigate the influence of environmental temperature on the absorption spectra of such aerosol samples, simulating the temperature range to which aerosols, once formed, are exposed during their transport through Titans stratosphere. Our results show that environmental temperature does not have any major effect on the spectral shape of these aerosol analogs in the far-infrared, which is consistent with the CIRS observations.

  17. MULTI-WAVELENGTH STUDIES OF SPECTACULAR RAM PRESSURE STRIPPING OF A GALAXY: DISCOVERY OF AN X-RAY ABSORPTION FEATURE

    SciTech Connect

    Gu, Liyi; Makishima, Kazuo; Yagi, Masafumi

    We report the detection of an X-ray absorption feature near the galaxy M86 in the Virgo cluster. The absorber has a column density of 2-3 × 10{sup 20} cm{sup –2}, and its position coincides with the peak of an intracluster H I cloud which was removed from the galaxy NGC 4388 presumably by ram pressure. These results indicate that the H I cloud is located in front of M86 along the line-of-sight, and suggest that the stripping was primarily created by an interaction between NGC 4388 and the hot plasmas of the Virgo cluster, not the M86 halo. By calculatingmore » an X-ray temperature map, we further detected an X-ray counterpart of the H I cloud up to ≈3' south of M86. It has a temperature of 0.89 keV and a mass of ∼4.5 × 10{sup 8} M {sub ☉}, exceeding the estimated H I gas mass. The high hot-to-cold gas ratio in the cloud indicates a significant evaporation of the H I gas, probably by thermal conduction from the hotter cluster plasma with a sub-Spitzer rate.« less

  18. Environmental temperature effect on the far-infrared absorption features of aromatic-based Titan's aerosol analogs

    NASA Astrophysics Data System (ADS)

    Gautier, Thomas; Trainer, Melissa G.; Loeffler, Mark J.; Sebree, Joshua A.; Anderson, Carrie M.

    2017-01-01

    Benzene detection has been reported in Titan's atmosphere both in the stratosphere at ppb levels by remote sensing (Coustenis et al., 2007; Vinatier et al., 2007) and in the thermosphere at ppm levels by the Cassini's Ion and Neutral Mass Spectrometer (Waite et al., 2007). This detection supports the idea that aromatic and heteroaromatic reaction pathways may play an important role in Titan's atmospheric chemistry, especially in the formation of aerosols. Indeed, aromatic molecules are easily dissociated by ultraviolet radiation and can therefore contribute significantly to aerosol formation. It has been shown recently that aerosol analogs produced from a gas mixture containing a low concentration of aromatic and/or heteroaromatic molecules (benzene, naphthalene, pyridine, quinoline and isoquinoline) have spectral signatures below 500 cm-1, a first step towards reproducing the aerosol spectral features observed by Cassini's Composite InfraRed Spectrometer (CIRS) in the far infrared (Anderson and Samuelson 2011, and references therein). In this work we investigate the influence of environmental temperature on the absorption spectra of such aerosol samples, simulating the temperature range to which aerosols, once formed, are exposed during their transport through Titan's stratosphere. Our results show that environmental temperature does not have any major effect on the spectral shape of these aerosol analogs in the far-infrared, which is consistent with the CIRS observations.

  19. The origin of blueshifted absorption features in the X-ray spectrum of PG 1211+143: outflow or disc

    NASA Astrophysics Data System (ADS)

    Gallo, L. C.; Fabian, A. C.

    2013-07-01

    In some radio-quiet active galactic nuclei (AGN), high-energy absorption features in the X-ray spectra have been interpreted as ultrafast outflows (UFOs) - highly ionized material (e.g. Fe XXV and Fe XXVI) ejected at mildly relativistic velocities. In some cases, these outflows can carry energy in excess of the binding energy of the host galaxy. Needless to say, these features demand our attention as they are strong signatures of AGN feedback and will influence galaxy evolution. For the same reason, alternative models need to be discussed and refuted or confirmed. Gallo and Fabian proposed that some of these features could arise from resonance absorption of the reflected spectrum in a layer of ionized material located above and corotating with the accretion disc. Therefore, the absorbing medium would be subjected to similar blurring effects as seen in the disc. A priori, the existence of such plasma above the disc is as plausible as a fast wind. In this work, we highlight the ambiguity by demonstrating that the absorption model can describe the ˜7.6 keV absorption feature (and possibly other features) in the quasar PG 1211+143, an AGN that is often described as a classic example of a UFO. In this model, the 2-10 keV spectrum would be largely reflection dominated (as opposed to power law dominated in the wind models) and the resonance absorption would be originating in a layer between about 6 and 60 gravitational radii. The studies of such features constitute a cornerstone for future X-ray observatories like Astro-H and Athena+. Should our model prove correct, or at least important in some cases, then absorption will provide another diagnostic tool with which to probe the inner accretion flow with future missions.

  20. Detection of fungal damaged popcorn using image property covariance features

    USDA-ARS?s Scientific Manuscript database

    Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that cause a symptom called “blue-eye.” This infection of popcorn kernels causes economic losses because of their poor appearance and the frequently disagreeable flavor of the popped kernels. Images of ker...

  1. PCA feature extraction for change detection in multidimensional unlabeled data.

    PubMed

    Kuncheva, Ludmila I; Faithfull, William J

    2014-01-01

    When classifiers are deployed in real-world applications, it is assumed that the distribution of the incoming data matches the distribution of the data used to train the classifier. This assumption is often incorrect, which necessitates some form of change detection or adaptive classification. While there has been a lot of work on change detection based on the classification error monitored over the course of the operation of the classifier, finding changes in multidimensional unlabeled data is still a challenge. Here, we propose to apply principal component analysis (PCA) for feature extraction prior to the change detection. Supported by a theoretical example, we argue that the components with the lowest variance should be retained as the extracted features because they are more likely to be affected by a change. We chose a recently proposed semiparametric log-likelihood change detection criterion that is sensitive to changes in both mean and variance of the multidimensional distribution. An experiment with 35 datasets and an illustration with a simple video segmentation demonstrate the advantage of using extracted features compared to raw data. Further analysis shows that feature extraction through PCA is beneficial, specifically for data with multiple balanced classes.

  2. Radial measurements of IMF-sensitive absorption features in two massive ETGs

    NASA Astrophysics Data System (ADS)

    Vaughan, Sam P.; Davies, Roger L.; Zieleniewski, Simon; Houghton, Ryan C. W.

    2018-03-01

    We make radial measurements of stellar initial mass function (IMF) sensitive absorption features in the two massive early-type galaxies NGC 1277 and IC 843. Using the Oxford Short Wavelength Integral Field specTrogaph (SWIFT), we obtain resolved measurements of the Na I 0.82 and FeH 0.99 indices, amongst others, finding both galaxies show strong gradients in Na I absorption combined with flat FeH profiles at ˜0.4 Å. We find these measurements may be explained by radial gradients in the IMF, appropriate abundance gradients in [Na/Fe] and [Fe/H], or a combination of the two, and our data are unable to break this degeneracy. We also use full spectral fitting to infer global properties from an integrated spectrum of each object, deriving a unimodal IMF slope consistent with Salpeter in IC 843 (x = 2.27 ± 0.17) but steeper than Salpeter in NGC 1277 (x = 2.69 ± 0.11), despite their similar FeH equivalent widths. Independently, we fit the strength of the FeH feature and compare to the E-MILES and CvD12 stellar population libraries, finding agreement between the models. The IMF values derived in this way are in close agreement with those from spectral fitting in NGC 1277 (x_{CvD}=2.59^{+0.25}_{-0.48}, x_{E-MILES}=2.77± 0.31), but are less consistent in IC 843, with the IMF derived from FeH alone leading to steeper slopes than when fitting the full spectrum (x_{CvD}=2.57^{+0.30}_{-0.41}, x_{E-MILES}=2.72± 0.25). This work highlights the importance of a large wavelength coverage for breaking the degeneracy between abundance and IMF variations, and may bring into doubt the use of the Wing-Ford band as an IMF index if used without other spectral information.

  3. Crowding with detection and coarse discrimination of simple visual features.

    PubMed

    Põder, Endel

    2008-04-24

    Some recent studies have suggested that there are actually no crowding effects with detection and coarse discrimination of simple visual features. The present study tests the generality of this idea. A target Gabor patch, surrounded by either 2 or 6 flanker Gabors, was presented briefly at 4 deg eccentricity of the visual field. Each Gabor patch was oriented either vertically or horizontally (selected randomly). Observers' task was either to detect the presence of the target (presented with probability 0.5) or to identify the orientation of the target. The target-flanker distance was varied. Results were similar for the two tasks but different for 2 and 6 flankers. The idea that feature detection and coarse discrimination are immune to crowding may be valid for the two-flanker condition only. With six flankers, a normal crowding effect was observed. It is suggested that the complexity of the full pattern (target plus flankers) could explain the difference.

  4. Anomaly Detection Using an Ensemble of Feature Models

    PubMed Central

    Noto, Keith; Brodley, Carla; Slonim, Donna

    2011-01-01

    We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model that will be able to distinguish examples in the future that do not belong to the same class. Traditional approaches typically compare the position of a new data point to the set of “normal” training data points in a chosen representation of the feature space. For some data sets, the normal data may not have discernible positions in feature space, but do have consistent relationships among some features that fail to appear in the anomalous examples. Our approach learns to predict the values of training set features from the values of other features. After we have formed an ensemble of predictors, we apply this ensemble to new data points. To combine the contribution of each predictor in our ensemble, we have developed a novel, information-theoretic anomaly measure that our experimental results show selects against noisy and irrelevant features. Our results on 47 data sets show that for most data sets, this approach significantly improves performance over current state-of-the-art feature space distance and density-based approaches. PMID:22020249

  5. Visual Saliency Detection Based on Multiscale Deep CNN Features.

    PubMed

    Guanbin Li; Yizhou Yu

    2016-11-01

    Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. The penultimate layer of our neural network has been confirmed to be a discriminative high-level feature vector for saliency detection, which we call deep contrast feature. To generate a more robust feature, we integrate handcrafted low-level features with our deep contrast feature. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving the state-of-the-art performance on all public benchmarks, improving the F-measure by 6.12% and 10%, respectively, on the DUT-OMRON data set and our new data set (HKU-IS), and lowering the mean absolute error by 9% and 35.3%, respectively, on these two data sets.

  6. UV Absorption Spectroscopy in Water-Filled Antiresonant Hollow Core Fibers for Pharmaceutical Detection.

    PubMed

    Nissen, Mona; Doherty, Brenda; Hamperl, Jonas; Kobelke, Jens; Weber, Karina; Henkel, Thomas; Schmidt, Markus A

    2018-02-06

    Due to a worldwide increased use of pharmaceuticals and, in particular, antibiotics, a growing number of these substance residues now contaminate natural water resources and drinking supplies. This triggers a considerable demand for low-cost, high-sensitivity methods for monitoring water quality. Since many biological substances exhibit strong and characteristic absorption features at wavelengths shorter than 300 nm, UV spectroscopy presents a suitable approach for the quantitative identification of such water-contaminating species. However, current UV spectroscopic devices often show limited light-matter interaction lengths, demand sophisticated and bulky experimental infrastructure which is not compatible with microfluidics, and leave large fractions of the sample analyte unused. Here, we introduce the concept of UV spectroscopy in liquid-filled anti-resonant hollow core fibers, with large core diameters and lengths of approximately 1 m, as a means to overcome such limitations. This extended light-matter interaction length principally improves the concentration detection limit by two orders of magnitude while using almost the entire sample volume-that is three orders of magnitude smaller compared to cuvette based approaches. By integrating the fibers into an optofluidic chip environment and operating within the lowest experimentally feasible transmission band, concentrations of the application-relevant pharmaceutical substances, sulfamethoxazole (SMX) and sodium salicylate (SS), were detectable down to 0.1 µM (26 ppb) and 0.4 µM (64 ppb), respectively, with the potential to reach significantly lower detection limits for further device integration.

  7. UV Absorption Spectroscopy in Water-Filled Antiresonant Hollow Core Fibers for Pharmaceutical Detection

    PubMed Central

    Nissen, Mona; Doherty, Brenda; Hamperl, Jonas; Kobelke, Jens; Weber, Karina; Henkel, Thomas; Schmidt, Markus A.

    2018-01-01

    Due to a worldwide increased use of pharmaceuticals and, in particular, antibiotics, a growing number of these substance residues now contaminate natural water resources and drinking supplies. This triggers a considerable demand for low-cost, high-sensitivity methods for monitoring water quality. Since many biological substances exhibit strong and characteristic absorption features at wavelengths shorter than 300 nm, UV spectroscopy presents a suitable approach for the quantitative identification of such water-contaminating species. However, current UV spectroscopic devices often show limited light-matter interaction lengths, demand sophisticated and bulky experimental infrastructure which is not compatible with microfluidics, and leave large fractions of the sample analyte unused. Here, we introduce the concept of UV spectroscopy in liquid-filled anti-resonant hollow core fibers, with large core diameters and lengths of approximately 1 m, as a means to overcome such limitations. This extended light-matter interaction length principally improves the concentration detection limit by two orders of magnitude while using almost the entire sample volume—that is three orders of magnitude smaller compared to cuvette based approaches. By integrating the fibers into an optofluidic chip environment and operating within the lowest experimentally feasible transmission band, concentrations of the application-relevant pharmaceutical substances, sulfamethoxazole (SMX) and sodium salicylate (SS), were detectable down to 0.1 µM (26 ppb) and 0.4 µM (64 ppb), respectively, with the potential to reach significantly lower detection limits for further device integration. PMID:29415468

  8. Electrochemical and Infrared Absorption Spectroscopy Detection of SF₆ Decomposition Products.

    PubMed

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

    2017-11-15

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

  9. Breast cancer detection in rotational thermography images using texture features

    NASA Astrophysics Data System (ADS)

    Francis, Sheeja V.; Sasikala, M.; Bhavani Bharathi, G.; Jaipurkar, Sandeep D.

    2014-11-01

    Breast cancer is a major cause of mortality in young women in the developing countries. Early diagnosis is the key to improve survival rate in cancer patients. Breast thermography is a diagnostic procedure that non-invasively images the infrared emissions from breast surface to aid in the early detection of breast cancer. Due to limitations in imaging protocol, abnormality detection by conventional breast thermography, is often a challenging task. Rotational thermography is a novel technique developed in order to overcome the limitations of conventional breast thermography. This paper evaluates this technique's potential for automatic detection of breast abnormality, from the perspective of cold challenge. Texture features are extracted in the spatial domain, from rotational thermogram series, prior to and post the application of cold challenge. These features are fed to a support vector machine for automatic classification of normal and malignant breasts, resulting in a classification accuracy of 83.3%. Feature reduction has been performed by principal component analysis. As a novel attempt, the ability of this technique to locate the abnormality has been studied. The results of the study indicate that rotational thermography holds great potential as a screening tool for breast cancer detection.

  10. Detection of metal stress in boreal forest species using the 0.67-micron chlorophyll absorption band

    NASA Technical Reports Server (NTRS)

    Singhroy, Vernon H.; Kruse, Fred A.

    1991-01-01

    Several recent studies have shown that a shift of the red-edge inflection near 0.70 micron in vegetation reflectance spectra is an indicator of metal stress, partially attributable to changes in chlorophyll concentration. This 'red-edge shift', however, is difficult to detect and has been reported both toward longer (red) and shorter (blue) wavelengths. Our work demonstrates that direct measurement of the depth and width of the chlorophyll absorption band at 0.67 micron using digital feature extraction and absorption band characterization procedures developed for the analysis of mineral spectra is a more consistent indicator of metal stress. Additionally, the magnitude of these parameters is generally greater than that of the red edge shift and thus should be more amenable to detection and mapping using field and aircraft spectrometers.

  11. First Time Rapid and Accurate Detection of Massive Number of Metal Absorption Lines in the Early Universe Using Deep Neural Network

    NASA Astrophysics Data System (ADS)

    Zhao, Yinan; Ge, Jian; Yuan, Xiaoyong; Li, Xiaolin; Zhao, Tiffany; Wang, Cindy

    2018-01-01

    Metal absorption line systems in the distant quasar spectra have been used as one of the most powerful tools to probe gas content in the early Universe. The MgII λλ 2796, 2803 doublet is one of the most popular metal absorption lines and has been used to trace gas and global star formation at redshifts between ~0.5 to 2.5. In the past, machine learning algorithms have been used to detect absorption lines systems in the large sky survey, such as Principle Component Analysis, Gaussian Process and decision tree, but the overall detection process is not only complicated, but also time consuming. It usually takes a few months to go through the entire quasar spectral dataset from each of the Sloan Digital Sky Survey (SDSS) data release. In this work, we applied the deep neural network, or “ deep learning” algorithms, in the most recently SDSS DR14 quasar spectra and were able to randomly search 20000 quasar spectra and detect 2887 strong Mg II absorption features in just 9 seconds. Our detection algorithms were verified with previously released DR12 and DR7 data and published Mg II catalog and the detection accuracy is 90%. This is the first time that deep neural network has demonstrated its promising power in both speed and accuracy in replacing tedious, repetitive human work in searching for narrow absorption patterns in a big dataset. We will present our detection algorithms and also statistical results of the newly detected Mg II absorption lines.

  12. Cloud Detection by Fusing Multi-Scale Convolutional Features

    NASA Astrophysics Data System (ADS)

    Li, Zhiwei; Shen, Huanfeng; Wei, Yancong; Cheng, Qing; Yuan, Qiangqiang

    2018-04-01

    Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.

  13. Face liveness detection using shearlet-based feature descriptors

    NASA Astrophysics Data System (ADS)

    Feng, Litong; Po, Lai-Man; Li, Yuming; Yuan, Fang

    2016-07-01

    Face recognition is a widely used biometric technology due to its convenience but it is vulnerable to spoofing attacks made by nonreal faces such as photographs or videos of valid users. The antispoof problem must be well resolved before widely applying face recognition in our daily life. Face liveness detection is a core technology to make sure that the input face is a live person. However, this is still very challenging using conventional liveness detection approaches of texture analysis and motion detection. The aim of this paper is to propose a feature descriptor and an efficient framework that can be used to effectively deal with the face liveness detection problem. In this framework, new feature descriptors are defined using a multiscale directional transform (shearlet transform). Then, stacked autoencoders and a softmax classifier are concatenated to detect face liveness. We evaluated this approach using the CASIA Face antispoofing database and replay-attack database. The experimental results show that our approach performs better than the state-of-the-art techniques following the provided protocols of these databases, and it is possible to significantly enhance the security of the face recognition biometric system. In addition, the experimental results also demonstrate that this framework can be easily extended to classify different spoofing attacks.

  14. A Robust Shape Reconstruction Method for Facial Feature Point Detection.

    PubMed

    Tan, Shuqiu; Chen, Dongyi; Guo, Chenggang; Huang, Zhiqi

    2017-01-01

    Facial feature point detection has been receiving great research advances in recent years. Numerous methods have been developed and applied in practical face analysis systems. However, it is still a quite challenging task because of the large variability in expression and gestures and the existence of occlusions in real-world photo shoot. In this paper, we present a robust sparse reconstruction method for the face alignment problems. Instead of a direct regression between the feature space and the shape space, the concept of shape increment reconstruction is introduced. Moreover, a set of coupled overcomplete dictionaries termed the shape increment dictionary and the local appearance dictionary are learned in a regressive manner to select robust features and fit shape increments. Additionally, to make the learned model more generalized, we select the best matched parameter set through extensive validation tests. Experimental results on three public datasets demonstrate that the proposed method achieves a better robustness over the state-of-the-art methods.

  15. Feature detection in satellite images using neural network technology

    NASA Technical Reports Server (NTRS)

    Augusteijn, Marijke F.; Dimalanta, Arturo S.

    1992-01-01

    A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.

  16. FE K EMISSION AND ABSORPTION FEATURES IN THE XMM-EPIC SPECTRUM OF THE SEYFERT GALAXY IC 4329A

    NASA Technical Reports Server (NTRS)

    Markowitz, A.; Reeves, J. N.; Braito, V.

    2001-01-01

    We present a re-analysis of the XMM-Newton long-look of the X-ray bright Seyfert galaxy IC 4329a. The Fe K bandpass is dominated by two peaks, consistent with emission from neutral or near-neutral Fe Ka and KP. A relativistic diskline model whereby both peaks are the result of one doubly-peaked diskline profile is found to be a poor description of the data. Models using two relativistic disklines are found to describe the emission profile well. A low-inclination, moderately-relativistic dual-diskline model is possible if the contribution from narrow components, due to distant material, is small or absent. A high-inclination, moderately relativistic profile for each peak is possible if there are roughly equal contributions from both the broad and narrow components. Upper limits on Fe XXV and Fe XXVI emission and absorption at the systemic velocity of IC 4329a are obtained. We also present the results of RXTE monitoring of this source obtained so far; the combined XMM-Newton and RXTE data sets allow us to explore the time-resolved spectral behavior of this source on time scales ranging from hours to 2 years. We find no strong evidence for variability of the Fe Ka emission line on any time scale probed, likely due to the minimal level of continuum variability. We detect a narrow absorption line, at a energy of 7.68 keV in the rest frame of the source; its significance has been confirmed using Monte Carlo simulations. This feature is most likely due to absorption from Fe XXVI blueshifted to approximately 0.1c relative to the systemic velocity, making IC 4329a the lowest-redshift AGN known with a high-velocity, highly-ionized outflow component. As is often the case with similar outflows seen in high-luminosity quasars, the estimated mass outflow rate is larger than the inflow accretion rate, signaling that the outflow represents a substantial portion of the total energy budget of the AGN. The outflow could arise from a radiatively-driven disk wind, or it may be in the

  17. Optical detection of random features for high security applications

    NASA Astrophysics Data System (ADS)

    Haist, T.; Tiziani, H. J.

    1998-02-01

    Optical detection of random features in combination with digital signatures based on public key codes in order to recognize counterfeit objects will be discussed. Without applying expensive production techniques objects are protected against counterfeiting. Verification is done off-line by optical means without a central authority. The method is applied for protecting banknotes. Experimental results for this application are presented. The method is also applicable for identity verification of a credit- or chip-card holder.

  18. Near-infrared incoherent broadband cavity enhanced absorption spectroscopy (NIR-IBBCEAS) for detection and quantification of natural gas components.

    PubMed

    Prakash, Neeraj; Ramachandran, Arun; Varma, Ravi; Chen, Jun; Mazzoleni, Claudio; Du, Ke

    2018-06-28

    The principle of near-infrared incoherent broadband cavity enhanced absorption spectroscopy was employed to develop a novel instrument for detecting natural gas leaks as well as for testing the quality of natural gas mixtures. The instrument utilizes the absorption features of methane, butane, ethane, and propane in the wavelength region of 1100 nm to 1250 nm. The absorption cross-section spectrum in this region for methane was adopted from the HITRAN database, and those for the other three gases were measured in the laboratory. A singular-value decomposition (SVD) based analysis scheme was employed for quantifying methane, butane, ethane, and propane by performing a linear least-square fit. The developed instrument achieved a detection limit of 460 ppm, 141 ppm, 175 ppm and 173 ppm for methane, butane, ethane, and propane, respectively, with a measurement time of 1 second and a cavity length of 0.59 m. These detection limits are less than 1% of the Lower Explosive Limit (LEL) for each gas. The sensitivity can be further enhanced by changing the experimental parameters (such as cavity length, lamp power etc.) and using longer averaging intervals. The detection system is a low-cost and portable instrument suitable for performing field monitorings. The results obtained on the gas mixture emphasize the instrument's potential for deployment at industrial facilities dealing with natural gas, where potential leaks pose a threat to public safety.

  19. Feature integration theory revisited: dissociating feature detection and attentional guidance in visual search.

    PubMed

    Chan, Louis K H; Hayward, William G

    2009-02-01

    In feature integration theory (FIT; A. Treisman & S. Sato, 1990), feature detection is driven by independent dimensional modules, and other searches are driven by a master map of locations that integrates dimensional information into salience signals. Although recent theoretical models have largely abandoned this distinction, some observed results are difficult to explain in its absence. The present study measured dimension-specific performance during detection and localization, tasks that require operation of dimensional modules and the master map, respectively. Results showed a dissociation between tasks in terms of both dimension-switching costs and cross-dimension attentional capture, reflecting a dimension-specific nature for detection tasks and a dimension-general nature for localization tasks. In a feature-discrimination task, results precluded an explanation based on response mode. These results are interpreted to support FIT's postulation that different mechanisms are involved in parallel and focal attention searches. This indicates that the FIT architecture should be adopted to explain the current results and that a variety of visual attention findings can be addressed within this framework. Copyright 2009 APA, all rights reserved.

  20. The Associated Absorption Features in Quasar Spectra of the Sloan Digital Sky Survey. I. Mg II Absorption Doublets

    NASA Astrophysics Data System (ADS)

    Chen, Zhi-Fu; Huang, Wei-Rong; Pang, Ting-Ting; Huang, Hong-Yan; Pan, Da-Sheng; Yao, Min; Nong, Wei-Jing; Lu, Mei-Mei

    2018-03-01

    Using the SDSS spectra of quasars included in the DR7Q or DR12Q catalogs, we search for Mg II λλ2796, 2803 narrow absorption doublets in the spectra data around Mg II λ2798 emission lines. We obtain 17,316 Mg II doublets, within the redshift range of 0.3299 ≤ z abs ≤ 2.5663. We find that a velocity offset of υ r < 6000 km s‑1 is a safe boundary to constrain the vast majority of associated Mg II systems, although we find some doublets at υ r > 6000 km s‑1. If associated Mg II absorbers are defined by υ r < 6000 km s‑1, ∼33.3% of the absorbers are supposed to be contaminants of intervening systems. Removing the 33.3% contaminants, ∼4.5% of the quasars present at least one associated Mg II system with {W}{{r}}λ 2796≥slant 0.2 \\mathringA . The fraction of associated Mg II systems with high-velocity outflows correlates with the average luminosities of their central quasars, indicating a relationship between outflows and the quasar feedback power. The υ r distribution of the outflow Mg II absorbers is peaked at 1023 km s‑1, which is smaller than the corresponding value of the outflow C IV absorbers. The redshift number density evolution of absorbers (dn/dz) limited by υ r > ‑3000 km s‑1 differs from that of absorbers constrained by υ r > 2000 km s‑1. Absorbers limited by υ r > 2000 km s‑1 and higher values exhibit profiles similar to dn/dz. In addition, the dn/dz is smaller when absorbers are constrained with larger υ r . The distributions of equivalent widths, and the ratio of {W}rλ 2796/{W}rλ 2803, are the same for associated and intervening systems, and independent of quasar luminosity.

  1. Deep PDF parsing to extract features for detecting embedded malware.

    SciTech Connect

    Munson, Miles Arthur; Cross, Jesse S.

    2011-09-01

    The number of PDF files with embedded malicious code has risen significantly in the past few years. This is due to the portability of the file format, the ways Adobe Reader recovers from corrupt PDF files, the addition of many multimedia and scripting extensions to the file format, and many format properties the malware author may use to disguise the presence of malware. Current research focuses on executable, MS Office, and HTML formats. In this paper, several features and properties of PDF Files are identified. Features are extracted using an instrumented open source PDF viewer. The feature descriptions of benignmore » and malicious PDFs can be used to construct a machine learning model for detecting possible malware in future PDF files. The detection rate of PDF malware by current antivirus software is very low. A PDF file is easy to edit and manipulate because it is a text format, providing a low barrier to malware authors. Analyzing PDF files for malware is nonetheless difficult because of (a) the complexity of the formatting language, (b) the parsing idiosyncrasies in Adobe Reader, and (c) undocumented correction techniques employed in Adobe Reader. In May 2011, Esparza demonstrated that PDF malware could be hidden from 42 of 43 antivirus packages by combining multiple obfuscation techniques [4]. One reason current antivirus software fails is the ease of varying byte sequences in PDF malware, thereby rendering conventional signature-based virus detection useless. The compression and encryption functions produce sequences of bytes that are each functions of multiple input bytes. As a result, padding the malware payload with some whitespace before compression/encryption can change many of the bytes in the final payload. In this study we analyzed a corpus of 2591 benign and 87 malicious PDF files. While this corpus is admittedly small, it allowed us to test a system for collecting indicators of embedded PDF malware. We will call these indicators features

  2. Feature selection from hyperspectral imaging for guava fruit defects detection

    NASA Astrophysics Data System (ADS)

    Mat Jafri, Mohd. Zubir; Tan, Sou Ching

    2017-06-01

    Development of technology makes hyperspectral imaging commonly used for defect detection. In this research, a hyperspectral imaging system was setup in lab to target for guava fruits defect detection. Guava fruit was selected as the object as to our knowledge, there is fewer attempts were made for guava defect detection based on hyperspectral imaging. The common fluorescent light source was used to represent the uncontrolled lighting condition in lab and analysis was carried out in a specific wavelength range due to inefficiency of this particular light source. Based on the data, the reflectance intensity of this specific setup could be categorized in two groups. Sequential feature selection with linear discriminant (LD) and quadratic discriminant (QD) function were used to select features that could potentially be used in defects detection. Besides the ordinary training method, training dataset in discriminant was separated in two to cater for the uncontrolled lighting condition. These two parts were separated based on the brighter and dimmer area. Four evaluation matrixes were evaluated which are LD with common training method, QD with common training method, LD with two part training method and QD with two part training method. These evaluation matrixes were evaluated using F1-score with total 48 defected areas. Experiment shown that F1-score of linear discriminant with the compensated method hitting 0.8 score, which is the highest score among all.

  3. Airborne Measurements of CO2 Column Absorption and Range Using a Pulsed Direct-Detection Integrated Path Differential Absorption Lidar

    NASA Technical Reports Server (NTRS)

    Abshire, James B.; Riris, Haris; Weaver, Clark J.; Mao, Jianping; Allan, Graham R.; Hasselbrack, William E.; Browell, Edward V.

    2013-01-01

    We report on airborne CO2 column absorption measurements made in 2009 with a pulsed direct-detection lidar operating at 1572.33 nm and utilizing the integrated path differential absorption technique. We demonstrated these at different altitudes from an aircraft in July and August in flights over four locations in the central and eastern United States. The results show clear CO2 line shape and absorption signals, which follow the expected changes with aircraft altitude from 3 to 13 km. The lidar measurement statistics were also calculated for each flight as a function of altitude. The optical depth varied nearly linearly with altitude, consistent with calculations based on atmospheric models. The scatter in the optical depth measurements varied with aircraft altitude as expected, and the median measurement precisions for the column varied from 0.9 to 1.2 ppm. The altitude range with the lowest scatter was 810 km, and the majority of measurements for the column within it had precisions between 0.2 and 0.9 ppm.

  4. Non-contact feature detection using ultrasonic Lamb waves

    DOEpatents

    Sinha, Dipen N [Los Alamos, NM

    2011-06-28

    Apparatus and method for non-contact ultrasonic detection of features on or within the walls of hollow pipes are described. An air-coupled, high-power ultrasonic transducer for generating guided waves in the pipe wall, and a high-sensitivity, air-coupled transducer for detecting these waves, are disposed at a distance apart and at chosen angle with respect to the surface of the pipe, either inside of or outside of the pipe. Measurements may be made in reflection or transmission modes depending on the relative position of the transducers and the pipe. Data are taken by sweeping the frequency of the incident ultrasonic waves, using a tracking narrow-band filter to reduce detected noise, and transforming the frequency domain data into the time domain using fast Fourier transformation, if required.

  5. [Study on intestinal absorption features of oligosaccharides in Morinda officinalis How. with sigle-pass perfusion].

    PubMed

    Deng, Shao-Dong; Zhang, Peng; Lin, Li; Xiao, Feng-Xia; Lin, Jing-Ran

    2015-01-01

    To study the in situ intestinal absorption of five oligosaccharides contained in Morinda officinalis How. (sucrose, kestose, nystose, 1F-Fructofuranosyinystose and Bajijiasu). The absorption of the five oligosaccharides in small intestine (duodenum, jejunum and ileum) and colon of rats and their contents were investigated by using in situ single-pass perfusion model and HPLC-ELSD. The effects of drug concentration, pH in perfusate and P-glycoprotein inhibitor on the intestinal absorption were investigated to define the intestinal absorption mechanism of the five oligosaccharides in rats. According to the results, all of the five oligosaccharides were absorbed in the whole intestine, and their absorption rates were affected by the pH of the perfusion solution, drug concentration and intestinal segments. Verapamil Hydrochloride could significantly increase the absorptive amount of sucrose and Bajijiasu, suggesting sucrose and Bajijiasu are P-gp's substrate. The five oligosaccharides are absorbed mainly through passive diffusion in the intestinal segments, without saturated absorption. They are absorbed well in all intestines and mainly in duodenum and jejunum.

  6. Special Features of Light Absorption by the Dimer of Bilayer Microparticles

    NASA Astrophysics Data System (ADS)

    Geints, Yu. É.; Panina, E. K.; Zemlyanov, A. A.

    2018-05-01

    Results of numerical simulation of light absorption by the dimer of bilayer spherical particles consisting of a water core and a polymer shell absorbing radiation are presented. The spatial distribution and the amplitude characteristics of the volume density of the absorbed power are investigated. It is shown that for a certain spatial dimer configuration, the maximal achievable density of the absorbed power is realized. It is also established that for closely spaced microcapsules with high shell absorption indices, the total power absorbed in the dimer volume can increase in comparison with the radiation absorption by two insulated microparticles.

  7. Gas trace detection with cavity enhanced absorption spectroscopy: a review of its process in the field

    NASA Astrophysics Data System (ADS)

    Liu, Siqi; Luo, Zhifu; Tan, Zhongqi; Long, Xingwu

    2016-11-01

    Cavity-enhanced absorption spectroscopy (CEAS) is a technology in which the intracavity absorption is deduced from the intensity of light transmitted by the high finesse optical cavity. Then the samples' parameters, such as their species, concentration and absorption cross section, would be detection. It was first proposed and demonstrated by Engeln R. [1] and O'Keefe[2] in 1998. This technology has extraordinary detection sensitivity, high resolution and good practicability, so it is used in many fields , such as clinical medicine, gas detection and basic physics research. In this paper, we focus on the use of gas trace detection, including the advance of CEAS over the past twenty years, the newest research progresses, and the prediction of this technology's development direction in the future.

  8. Feature detection on 3D images of dental imprints

    NASA Astrophysics Data System (ADS)

    Mokhtari, Marielle; Laurendeau, Denis

    1994-09-01

    A computer vision approach for the extraction of feature points on 3D images of dental imprints is presented. The position of feature points are needed for the measurement of a set of parameters for automatic diagnosis of malocclusion problems in orthodontics. The system for the acquisition of the 3D profile of the imprint, the procedure for the detection of the interstices between teeth, and the approach for the identification of the type of tooth are described, as well as the algorithm for the reconstruction of the surface of each type of tooth. A new approach for the detection of feature points, called the watershed algorithm, is described in detail. The algorithm is a two-stage procedure which tracks the position of local minima at four different scales and produces a final map of the position of the minima. Experimental results of the application of the watershed algorithm on actual 3D images of dental imprints are presented for molars, premolars and canines. The segmentation approach for the analysis of the shape of incisors is also described in detail.

  9. Hdr Imaging for Feature Detection on Detailed Architectural Scenes

    NASA Astrophysics Data System (ADS)

    Kontogianni, G.; Stathopoulou, E. K.; Georgopoulos, A.; Doulamis, A.

    2015-02-01

    3D reconstruction relies on accurate detection, extraction, description and matching of image features. This is even truer for complex architectural scenes that pose needs for 3D models of high quality, without any loss of detail in geometry or color. Illumination conditions influence the radiometric quality of images, as standard sensors cannot depict properly a wide range of intensities in the same scene. Indeed, overexposed or underexposed pixels cause irreplaceable information loss and degrade digital representation. Images taken under extreme lighting environments may be thus prohibitive for feature detection/extraction and consequently for matching and 3D reconstruction. High Dynamic Range (HDR) images could be helpful for these operators because they broaden the limits of illumination range that Standard or Low Dynamic Range (SDR/LDR) images can capture and increase in this way the amount of details contained in the image. Experimental results of this study prove this assumption as they examine state of the art feature detectors applied both on standard dynamic range and HDR images.

  10. Detection of ocean glint and ozone absorption using LCROSS Earth observations

    SciTech Connect

    Robinson, Tyler D.; Ennico, Kimberly; Meadows, Victoria S.

    The Lunar CRater Observation and Sensing Satellite (LCROSS) observed the distant Earth on three occasions in 2009. These data span a range of phase angles, including a rare crescent phase view. For each epoch, the satellite acquired near-infrared and mid-infrared full-disk images, and partial-disk spectra at 0.26-0.65 μm (λ/Δλ ∼ 500) and 1.17-2.48 μm (λ/Δλ ∼ 50). Spectra show strong absorption features due to water vapor and ozone, which is a biosignature gas. We perform a significant recalibration of the UV-visible spectra and provide the first comparison of high-resolution visible Earth spectra to the NASA Astrobiology Institute's Virtual Planetary Laboratorymore » three-dimensional spectral Earth model. We find good agreement with the observations, reproducing the absolute brightness and dynamic range at all wavelengths for all observation epochs, thus validating the model to within the ∼10% data calibration uncertainty. Data-model comparisons reveal a strong ocean glint signature in the crescent phase data set, which is well matched by our model predictions throughout the observed wavelength range. This provides the first observational test of a technique that could be used to determine exoplanet habitability from disk-integrated observations at visible and near-infrared wavelengths, where the glint signal is strongest. We examine the detection of the ozone 255 nm Hartley and 400-700 nm Chappuis bands. While the Hartley band is the strongest ozone feature in Earth's spectrum, false positives for its detection could exist. Finally, we discuss the implications of these findings for future exoplanet characterization missions.« less

  11. Interstellar molecules. [detection from Copernicus satellite UV absorption data

    NASA Technical Reports Server (NTRS)

    Drake, J. F.

    1974-01-01

    The Princeton equipment on the Copernicus satellite provides the means to study interstellar molecules between the satellite and stars from 20 to 1000 pc distant. The study is limited to stars relatively unobscured by dust which strongly attenuates the ultraviolet continuum flux used as a source to probe the interstellar medium. Of the 14 molecules searched for only three have been detected including molecular hydrogen, molecular HD, and carbon monoxide.

  12. Infrared absorption of CH3OSO detected with time-resolved Fourier-transform spectroscopy.

    PubMed

    Chen, Jin-Dah; Lee, Yuan-Pern

    2011-03-07

    A step-scan Fourier-transform spectrometer coupled with a multipass absorption cell was employed to detect temporally resolved infrared absorption spectra of CH(3)OSO produced upon irradiation of a flowing gaseous mixture of CH(3)OS(O)Cl in N(2) or CO(2) at 248 nm. Two intense transient features with origins near 1152 and 994 cm(-1) are assigned to syn-CH(3)OSO; the former is attributed to overlapping bands at 1154 ± 3 and 1151 ± 3 cm(-1), assigned to the S=O stretching mixed with CH(3) rocking (ν(8)) and the S=O stretching mixed with CH(3) wagging (ν(9)) modes, respectively, and the latter to the C-O stretching (ν(10)) mode at 994 ± 6 cm(-1). Two weak bands at 2991 ± 6 and 2956 ± 3 cm(-1) are assigned as the CH(3) antisymmetric stretching (ν(2)) and symmetric stretching (ν(3)) modes, respectively. Observed vibrational transition wavenumbers agree satisfactorily with those predicted with quantum-chemical calculations at level B3P86∕aug-cc-pVTZ. Based on rotational parameters predicted at that level, the simulated rotational contours of these bands agree satisfactorily with experimental results. The simulation indicates that the S=O stretching mode of anti-CH(3)OSO near 1164 cm(-1) likely makes a small contribution to the observed band near 1152 cm(-1). A simple kinetic model of self-reaction is employed to account for the decay of CH(3)OSO and yields a second-order rate coefficient k=(4 ± 2)×10(-10) cm(3)molecule(-1)s(-1). © 2011 American Institute of Physics.

  13. Cepstrum based feature extraction method for fungus detection

    NASA Astrophysics Data System (ADS)

    Yorulmaz, Onur; Pearson, Tom C.; Çetin, A. Enis

    2011-06-01

    In this paper, a method for detection of popcorn kernels infected by a fungus is developed using image processing. The method is based on two dimensional (2D) mel and Mellin-cepstrum computation from popcorn kernel images. Cepstral features that were extracted from popcorn images are classified using Support Vector Machines (SVM). Experimental results show that high recognition rates of up to 93.93% can be achieved for both damaged and healthy popcorn kernels using 2D mel-cepstrum. The success rate for healthy popcorn kernels was found to be 97.41% and the recognition rate for damaged kernels was found to be 89.43%.

  14. Multi-epoch Detections of Water Ice Absorption in Edge-on Disks around Herbig Ae Stars: PDS 144N and PDS 453

    NASA Astrophysics Data System (ADS)

    Terada, Hiroshi; Tokunaga, Alan T.

    2017-01-01

    We report the multi-epoch detections of water ice in 2.8-4.2 μ {{m}} spectra of two Herbig Ae stars, PDS 144N (A2 IVe) and PDS 453 (F2 Ve), which have an edge-on circumstellar disk. The detected water ice absorption is found to originate from their protoplanetary disks. The spectra show a relatively shallow absorption of water ice of around 3.1 μ {{m}} for both objects. The optical depths of the water ice absorption are ˜0.1 and ˜0.2 for PDS 144N and PDS 453, respectively. Compared to the water ice previously detected in low-mass young stellar objects with an edge-on disk with a similar inclination angle, these optical depths are significantly lower. It suggests that stronger UV radiation from the central stars effectively decreases the water ice abundance around the Herbig Ae stars through photodesorption. The water ice absorption in PDS 453 shows a possible variation of the feature among the six observing epochs. This variation could be due to a change of absorption materials passing through our line of sight to the central star. The overall profile of the water ice absorption in PDS 453 is quite similar to the absorption previously reported in the edge-on disk object d216-0939, and this unique profile may be seen only at a high inclination angle in the range of 76°-80°.

  15. Camouflaged target detection based on polarized spectral features

    NASA Astrophysics Data System (ADS)

    Tan, Jian; Zhang, Junping; Zou, Bin

    2016-05-01

    The polarized hyperspectral images (PHSI) include polarization, spectral, spatial and radiant features, which provide more information about objects and scenes than traditional intensity or spectrum ones. And polarization can suppress the background and highlight the object, leading to the high potential to improve camouflaged target detection. So polarized hyperspectral imaging technique has aroused extensive concern in the last few years. Nowadays, the detection methods are still not very mature, most of which are rooted in the detection of hyperspectral image. And before using these algorithms, Stokes vector is used to process the original four-dimensional polarized hyperspectral data firstly. However, when the data is large and complex, the amount of calculation and error will increase. In this paper, tensor is applied to reconstruct the original four-dimensional data into new three-dimensional data, then, the constraint energy minimization (CEM) is used to process the new data, which adds the polarization information to construct the polarized spectral filter operator and takes full advantages of spectral and polarized information. This way deals with the original data without extracting the Stokes vector, so as to reduce the computation and error greatly. The experimental results also show that the proposed method in this paper is more suitable for the target detection of the PHSI.

  16. Colitis detection on abdominal CT scans by rich feature hierarchies

    NASA Astrophysics Data System (ADS)

    Liu, Jiamin; Lay, Nathan; Wei, Zhuoshi; Lu, Le; Kim, Lauren; Turkbey, Evrim; Summers, Ronald M.

    2016-03-01

    Colitis is inflammation of the colon due to neutropenia, inflammatory bowel disease (such as Crohn disease), infection and immune compromise. Colitis is often associated with thickening of the colon wall. The wall of a colon afflicted with colitis is much thicker than normal. For example, the mean wall thickness in Crohn disease is 11-13 mm compared to the wall of the normal colon that should measure less than 3 mm. Colitis can be debilitating or life threatening, and early detection is essential to initiate proper treatment. In this work, we apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals to detect potential colitis on CT scans. Our method first generates around 3000 category-independent region proposals for each slice of the input CT scan using selective search. Then, a fixed-length feature vector is extracted from each region proposal using a CNN. Finally, each region proposal is classified and assigned a confidence score with linear SVMs. We applied the detection method to 260 images from 26 CT scans of patients with colitis for evaluation. The detection system can achieve 0.85 sensitivity at 1 false positive per image.

  17. Feature-fused SSD: fast detection for small objects

    NASA Astrophysics Data System (ADS)

    Cao, Guimei; Xie, Xuemei; Yang, Wenzhe; Liao, Quan; Shi, Guangming; Wu, Jinjian

    2018-04-01

    Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off as base architecture. We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects. In detailed fusion operation, we design two feature fusion modules, concatenation module and element-sum module, different in the way of adding contextual information. Experimental results show that these two fusion modules obtain higher mAP on PASCAL VOC2007 than baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points improvement on some small objects categories. The testing speed of them is 43 and 40 FPS respectively, superior to the state of the art Deconvolutional single shot detector (DSSD) by 29.4 and 26.4 FPS.

  18. Detection and analysis of diamond fingerprinting feature and its application

    NASA Astrophysics Data System (ADS)

    Li, Xin; Huang, Guoliang; Li, Qiang; Chen, Shengyi

    2011-01-01

    Before becoming a jewelry diamonds need to be carved artistically with some special geometric features as the structure of the polyhedron. There are subtle differences in the structure of this polyhedron in each diamond. With the spatial frequency spectrum analysis of diamond surface structure, we can obtain the diamond fingerprint information which represents the "Diamond ID" and has good specificity. Based on the optical Fourier Transform spatial spectrum analysis, the fingerprinting identification of surface structure of diamond in spatial frequency domain was studied in this paper. We constructed both the completely coherent diamond fingerprinting detection system illuminated by laser and the partially coherent diamond fingerprinting detection system illuminated by led, and analyzed the effect of the coherence of light source to the diamond fingerprinting feature. We studied rotation invariance and translation invariance of the diamond fingerprinting and verified the feasibility of real-time and accurate identification of diamond fingerprint. With the profit of this work, we can provide customs, jewelers and consumers with a real-time and reliable diamonds identification instrument, which will curb diamond smuggling, theft and other crimes, and ensure the healthy development of the diamond industry.

  19. Attosecond transient absorption probing of electronic superpositions of bound states in neon. Detection of quantum beats

    DOE PAGES

    Beck, Annelise R; Bernhardt, Birgitta; Warrick, Erika R.; ...

    2014-11-07

    Electronic wavepackets composed of multiple bound excited states of atomic neon lying between 19.6 and 21.5 eV are launched using an isolated attosecond pulse. Individual quantum beats of the wavepacket are detected by perturbing the induced polarization of the medium with a time-delayed few-femtosecond near-infrared (NIR) pulse via coupling the individual states to multiple neighboring levels. All of the initially excited states are monitored simultaneously in the attosecond transient absorption spectrum, revealing Lorentzian to Fano lineshape spectral changes as well as quantum beats. The most prominent beating of the several that were observed was in the spin–orbit split 3d absorptionmore » features, which has a 40 femtosecond period that corresponds to the spin–orbit splitting of 0.1 eV. The few-level models and multilevel calculations confirm that the observed magnitude of oscillation depends strongly on the spectral bandwidth and tuning of the NIR pulse and on the location of possible coupling states.« less

  20. 4.6 micron absorption features due to solid phase CO and cyano group molecules toward compact infrared sources

    NASA Technical Reports Server (NTRS)

    Lacy, J. H.; Baas, F.; Allamandola, L. J.; Van De Bult, C. E. P.; Persson, S. E.; Mcgregor, P. J.; Lonsdale, C. J.; Geballe, T. R.

    1984-01-01

    Spectra obtained at a resolving power of 840, for seven protostellar sources in the region of the 4.67-micron fundamental vibrational band of CO, indicate that the deep absorption feature in W33A near 4.61 microns consists of three features which are seen in other sources, but with varying relative strength. UV-irradiation laboratory experiments with 'dirty ice' temperature cycling allow the identification of two of the features cited with solid CO and CO complexed to other molecules. Cyano group-containing molecules have a lower vapor pressure than CO, and can therefore survive in much warmer environments. The formation and location of the CO- and CN-bearing grain mantles and sources of UV irradiation in cold molecular clouds are discussed. Plausible UV light sources can produce the observed cyano group features, but only under conditions in which local heat sources do not cause evaporation of the CO molecules prior to their photoprocessing.

  1. Force-detected nanoscale absorption spectroscopy in water at room temperature using an optical trap

    NASA Astrophysics Data System (ADS)

    Parobek, Alexander; Black, Jacob W.; Kamenetska, Maria; Ganim, Ziad

    2018-04-01

    Measuring absorption spectra of single molecules presents a fundamental challenge for standard transmission-based instruments because of the inherently low signal relative to the large background of the excitation source. Here we demonstrate a new approach for performing absorption spectroscopy in solution using a force measurement to read out optical excitation at the nanoscale. The photoinduced force between model chromophores and an optically trapped gold nanoshell has been measured in water at room temperature. This photoinduced force is characterized as a function of wavelength to yield the force spectrum, which is shown to be correlated to the absorption spectrum for four model systems. The instrument constructed for these measurements combines an optical tweezer with frequency domain absorption spectroscopy over the 400-800 nm range. These measurements provide proof-of-principle experiments for force-detected nanoscale spectroscopies that operate under ambient chemical conditions.

  2. Sonographic features of incidentally detected, small, nonpalpable ovarian dermoids.

    PubMed

    Serafini, G; Quadri, P G; Gandolfo, N G; Gandolfo, N; Martinoli, C; Derchi, L E

    1999-09-01

    We describe the transvaginal sonographic features of incidentally detected, small, nonpalpable ovarian dermoid cysts. A total of 38 small (less than 3 cm in diameter), nonpalpable, incidentally discovered ovarian dermoids in 35 women were retrospectively reviewed; 3 patients had small bilateral lesions, and 7 had a small ovarian dermoid detected during preoperative evaluation of a symptomatic, large, contralateral lesion. Transvaginal sonography permitted identification of all 38 dermoids, whereas abdominal sonography detected only 22 of the lesions. Three main structural patterns were observed with transvaginal sonography: (1) 20 of 38 lesions had a solid, hyperechoic appearance, either homogeneous (11) or heterogeneous (9); (2) a fluid-filled area with a hyperechoic focus in its wall was seen in 10 cases; and (3) a mixed pattern, with solid and liquid areas, was seen in 8 cases. Calcifications were appreciated in 7 lesions. Acoustic shadowing was noted in 30 cases, either as a shadow posterior to the hyperechoic portion of the mass or as an edge shadow lateral to the lesion. Doppler studies were obtained for 20 lesions but proved inconclusive: 4 mixed-pattern dermoids had a few internal signals with a low resistance pattern; in the remaining cases, there were signals at the periphery of the cysts, but it could not be determined whether these were from vessels within the lesions or from vessels in the surrounding ovarian parenchyma. Surgery confirmed benign cystic dermoids in all 38 cases. Sonographically, small ovarian dermoids have a variety of textural patterns quite similar to those encountered in large, symptomatic lesions. The increased resolution capabilities provided by transvaginal sonography allow incidental detection of previously unsuspected dermoids and permit identification of their nature. Copyright 1999 John Wiley & Sons, Inc.

  3. Learning to detect and combine the features of an object

    PubMed Central

    Suchow, Jordan W.; Pelli, Denis G.

    2013-01-01

    To recognize an object, it is widely supposed that we first detect and then combine its features. Familiar objects are recognized effortlessly, but unfamiliar objects—like new faces or foreign-language letters—are hard to distinguish and must be learned through practice. Here, we describe a method that separates detection and combination and reveals how each improves as the observer learns. We dissociate the steps by two independent manipulations: For each step, we do or do not provide a bionic crutch that performs it optimally. Thus, the two steps may be performed solely by the human, solely by the crutches, or cooperatively, when the human takes one step and a crutch takes the other. The crutches reveal a double dissociation between detecting and combining. Relative to the two-step ideal, the human observer’s overall efficiency for unconstrained identification equals the product of the efficiencies with which the human performs the steps separately. The two-step strategy is inefficient: Constraining the ideal to take two steps roughly halves its identification efficiency. In contrast, we find that humans constrained to take two steps perform just as well as when unconstrained, which suggests that they normally take two steps. Measuring threshold contrast (the faintness of a barely identifiable letter) as it improves with practice, we find that detection is inefficient and learned slowly. Combining is learned at a rate that is 4× higher and, after 1,000 trials, 7× more efficient. This difference explains much of the diversity of rates reported in perceptual learning studies, including effects of complexity and familiarity. PMID:23267067

  4. Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680 nm absorption feature with continuum removal

    USGS Publications Warehouse

    Sanches, Ieda Del´Arco; Souza Filho, Carlos Roberto de; Kokaly, Raymond F.

    2014-01-01

    This paper explores the use of spectral feature analysis to detect plant stress in visible/near infrared wavelengths. A time series of close range leaf and canopy reflectance data of two plant species grown in hydrocarbon-contaminated soil was acquired with a portable spectrometer. The ProSpecTIR-VS airborne imaging spectrometer was used to obtain far range hyperspectral remote sensing data over the field experiment. Parameters describing the chlorophyll 680 nm absorption feature (depth, width, and area) were derived using continuum removal applied to the spectra. A new index, the Plant Stress Detection Index (PSDI), was calculated using continuum-removed values near the chlorophyll feature centre (680 nm) and on the green-edge (560 and 575 nm). Chlorophyll feature’s depth, width and area, the PSDI and a narrow-band normalised difference vegetation index were evaluated for their ability to detect stressed plants. The objective was to analyse how the parameters/indices were affected by increasing degrees of plant stress and to examine their utility as plant stress indicators at the remote sensing level (e.g. airborne sensor). For leaf data, PSDI and the chlorophyll feature area revealed the highest percentage (67–70%) of stressed plants. The PSDI also proved to be the best constraint for detecting the stress in hydrocarbon-impacted plants with field canopy spectra and airborne imaging spectroscopy data. This was particularly true using thresholds based on the ASD canopy data and considering the combination of higher percentage of stressed plants detected (across the thresholds) and fewer false-positives.

  5. Detection of tuberculosis using hybrid features from chest radiographs

    NASA Astrophysics Data System (ADS)

    Fatima, Ayesha; Akram, M. Usman; Akhtar, Mahmood; Shafique, Irrum

    2017-02-01

    Tuberculosis is an infectious disease and becomes a major threat all over the world but still diagnosis of tuberculosis is a challenging task. In literature, chest radiographs are considered as most commonly used medical images in under developed countries for the diagnosis of TB. Different methods have been proposed but they are not helpful for radiologists due to cost and accuracy issues. Our paper presents a methodology in which different combinations of features are extracted based on intensities, shape and texture of chest radiograph and given to classifier for the detection of TB. The performance of our methodology is evaluated using publically available standard dataset Montgomery Country (MC) which contains 138 CXRs among which 80 CXRs are normal and 58 CXRs are abnormal including effusion and miliary patterns etc. The accuracy of 81.16% was achieved and the results show that proposed method have outperformed existing state of the art methods on MC dataset.

  6. Sodium Absorption from the Exoplanetary Atmosphere of HD 189733b Detected in the Optical Transmission Spectrum

    NASA Astrophysics Data System (ADS)

    Redfield, Seth; Endl, Michael; Cochran, William D.; Koesterke, Lars

    2008-01-01

    We present the first ground-based detection of sodium absorption in the transmission spectrum of an extrasolar planet. Absorption due to the atmosphere of the extrasolar planet HD 189733b is detected in both lines of the Na I doublet. High spectral resolution observations were taken of 11 transits with the High Resolution Spectrograph (HRS) on the 9.2 m Hobby-Eberly Telescope (HET). The Na I absorption in the transmission spectrum due to HD 189733b is (- 67.2 +/- 20.7) × 10-5 deeper in the "narrow" spectral band that encompasses both lines relative to adjacent bands. The 1 σ error includes both random and systematic errors, and the detection is >3 σ. This amount of relative absorption in Na I for HD 189733b is ~3 times larger than that detected for HD 209458b by Charbonneau et al. (2002) and indicates that these two hot Jupiters may have significantly different atmospheric properties. Based on observations obtained with the Hobby-Eberly Telescope, which is a joint project of the University of Texas at Austin, the Pennsylvania State University, Stanford University, Ludwig-Maximilians-Universität München, and Georg-August-Universität Göttingen.

  7. Cryptography based on the absorption/emission features of multicolor semiconductor nanocrystal quantum dots.

    PubMed

    Zhou, Ming; Chang, Shoude; Grover, Chander

    2004-06-28

    Further to the optical coding based on fluorescent semiconductor quantum dots (QDs), a concept of using mixtures of multiple single-color QDs for creating highly secret cryptograms based on their absorption/emission properties was demonstrated. The key to readout of the optical codes is a group of excitation lights with the predetermined wavelengths programmed in a secret manner. The cryptograms can be printed on the surfaces of different objects such as valuable documents for security purposes.

  8. Detection of the Galactic Warm Neutral Medium in HI 21cm absorption

    NASA Astrophysics Data System (ADS)

    Patra, Narendra Nath; Kanekar, Nissim; Chengalur, Jayaram N.; Roy, Nirupam

    2018-05-01

    We report a deep Giant Metrewave Radio Telescope (GMRT) search for Galactic HI 21cm absorption towards the quasar B0438-436, yielding the detection of wide, weak HI 21cm absorption, with a velocity-integrated HI 21cm optical depth of 0.0188 ± 0.0036 km s-1. Comparing this with the HI column density measured in the Parkes Galactic All-Sky Survey gives a column density-weighted harmonic mean spin temperature of 3760 ± 365 K, one of the highest measured in the Galaxy. This is consistent with most of the HI along the sightline arising in the stable warm neutral medium (WNM). The low peak HI 21cm optical depth towards B0438-436 implies negligible self-absorption, allowing a multi-Gaussian joint decomposition of the HI 21cm absorption and emission spectra. This yields a gas kinetic temperature of T_k ≤ (4910 ± 1900) K, and a spin temperature of T_s = (1000 ± 345) K for the gas that gives rise to the HI 21cm absorption. Our data are consistent with the HI 21cm absorption arising from either the stable WNM, with T_s ≪ T_k, T_k ≈ 5000 K, and little penetration of the background Lyman-α radiation field into the neutral hydrogen, or from the unstable neutral medium, with T_s ≈ T_k ≈ 1000K.

  9. Portable 4.6 Micrometers Laser Absorption Spectrometer for Carbon Monoxide Monitoring and Fire Detection

    NASA Technical Reports Server (NTRS)

    Briggs, Ryan M.; Frez, Clifford; Forouhar, Siamak; May, Randy D.; Ruff, Gary A.

    2013-01-01

    The air quality aboard manned spacecraft must be continuously monitored to ensure crew safety and identify equipment malfunctions. In particular, accurate real-time monitoring of carbon monoxide (CO) levels helps to prevent chronic exposure and can also provide early detection of combustion-related hazards. For long-duration missions, environmental monitoring grows in importance, but the mass and volume of monitoring instruments must be minimized. Furthermore, environmental analysis beyond low-Earth orbit must be performed in-situ, as sample return becomes impractical. Due to their small size, low power draw, and performance reliability, semiconductor-laser-based absorption spectrometers are viable candidates for this purpose. To reduce instrument form factor and complexity, the emission wavelength of the laser source should coincide with strong fundamental absorption lines of the target gases, which occur in the 3 to 5 micrometers wavelength range for most combustion products of interest, thereby reducing the absorption path length required for low-level concentration measurements. To address the needs of current and future NASA missions, we have developed a prototype absorption spectrometer using a semiconductor quantum cascade laser source operating near 4.6 micrometers that can be used to detect low concentrations of CO with a compact single-pass absorption cell. In this study, we present the design of the prototype instrument and report on measurements of CO emissions from the combustion of a variety of aerospace plastics.

  10. Phenotype detection in morphological mutant mice using deformation features.

    PubMed

    Roy, Sharmili; Liang, Xi; Kitamoto, Asanobu; Tamura, Masaru; Shiroishi, Toshihiko; Brown, Michael S

    2013-01-01

    Large-scale global efforts are underway to knockout each of the approximately 25,000 mouse genes and interpret their roles in shaping the mammalian embryo. Given the tremendous amount of data generated by imaging mutated prenatal mice, high-throughput image analysis systems are inevitable to characterize mammalian development and diseases. Current state-of-the-art computational systems offer only differential volumetric analysis of pre-defined anatomical structures between various gene-knockout mice strains. For subtle anatomical phenotypes, embryo phenotyping still relies on the laborious histological techniques that are clearly unsuitable in such big data environment. This paper presents a system that automatically detects known phenotypes and assists in discovering novel phenotypes in muCT images of mutant mice. Deformation features obtained from non-linear registration of mutant embryo to a normal consensus average image are extracted and analyzed to compute phenotypic and candidate phenotypic areas. The presented system is evaluated using C57BL/10 embryo images. All cases of ventricular septum defect and polydactyly, well-known to be present in this strain, are successfully detected. The system predicts potential phenotypic areas in the liver that are under active histological evaluation for possible phenotype of this mouse line.

  11. Differential absorption lidar observation on small-time-scale features of water vapor in the atmospheric boundary layer

    NASA Astrophysics Data System (ADS)

    Kong, Wei; Li, Jiatang; Liu, Hao; Chen, Tao; Hong, Guanglie; Shu, Rong

    2017-11-01

    Observation on small-time-scale features of water vapor density is essential for turbulence, convection and many other fast atmospheric processes study. For the high signal-to-noise signal of elastic signal acquired by differential absorption lidar, it has great potential for all-day water vapor turbulence observation. This paper presents a set of differential absorption lidar at 935nm developed by Shanghai Institute of Technical Physics of the Chinese Academy of Science for water vapor turbulence observation. A case at the midday is presented to demonstrate the daytime observation ability of this system. "Autocovariance method" is used to separate the contribution of water vapor fluctuation from random error. The results show that the relative error is less than 10% at temporal and spatial resolution of 10 seconds and 60 meters in the ABL. This indicate that the system has excellent performance for daytime water vapor turbulence observation.

  12. Mass detection with digitized screening mammograms by using Gabor features

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Agyepong, Kwabena

    2007-03-01

    Breast cancer is the leading cancer among American women. The current lifetime risk of developing breast cancer is 13.4% (one in seven). Mammography is the most effective technology presently available for breast cancer screening. With digital mammograms computer-aided detection (CAD) has proven to be a useful tool for radiologists. In this paper, we focus on mass detection that is a common category of breast cancers relative to calcification and architecture distortion. We propose a new mass detection algorithm utilizing Gabor filters, termed as "Gabor Mass Detection" (GMD). There are three steps in the GMD algorithm, (1) preprocessing, (2) generating alarms and (3) classification (reducing false alarms). Down-sampling, quantization, denoising and enhancement are done in the preprocessing step. Then a total of 30 Gabor filtered images (along 6 bands by 5 orientations) are produced. Alarm segments are generated by thresholding four Gabor images of full orientations (Stage-I classification) with image-dependent thresholds computed via histogram analysis. Next a set of edge histogram descriptors (EHD) are extracted from 24 Gabor images (6 by 4) that will be used for Stage-II classification. After clustering EHD features with fuzzy C-means clustering method, a k-nearest neighbor classifier is used to reduce the number of false alarms. We initially analyzed 431 digitized mammograms (159 normal images vs. 272 cancerous images, from the DDSM project, University of South Florida) with the proposed GMD algorithm. And a ten-fold cross validation was used for testing the GMD algorithm upon the available data. The GMD performance is as follows: sensitivity (true positive rate) = 0.88 at false positives per image (FPI) = 1.25, and the area under the ROC curve = 0.83. The overall performance of the GMD algorithm is satisfactory and the accuracy of locating masses (highlighting the boundaries of suspicious areas) is relatively high. Furthermore, the GMD algorithm can

  13. Detection of Ne VIII in an Intervening Multiphase Absorption System Toward 3C 263

    NASA Astrophysics Data System (ADS)

    Narayanan, Anand; Wakker, Bart P.; Savage, Blair D.

    2009-09-01

    We report the detection of Ne VIII in an intervening multiphase absorption line system at z = 0.32566 in the Far Ultraviolet Spectroscopic Explorer spectrum of the quasar 3C 263 (zem = 0.646). The Ne VIII λ770 Å detection has a 3.9σ significance. At the same velocity, we also find absorption lines from C IV, O III, O IV, and N IV. The line parameter measurements yield log [N(Ne VIII) cm-2] = 13.98+0.10 -0.13 and b = 49.8 ± 5.5 km s-1. We find that the ionization mechanism in the gas phase giving rise to the Ne VIII absorption is inconsistent with photoionization. The absorber has a multiphase structure, with the intermediate ions produced in cool photoionized gas and the Ne VIII most likely in a warm collisionally ionized medium in the temperature range (0.5-1.0) × 106 K. This is the second ever detection of an intervening Ne VIII absorption system. Its properties resemble the previous Ne VIII absorber reported by Savage and colleagues. Direct observations of H I and O VI are needed to better constrain the physical conditions in the collisionally ionized gas phase of this absorber. Based on observations with the NASA-CNES-CSA Far Ultraviolet Spectroscopic Explorer operated by Johns Hopkins University, supported by NASA contract NAS5-32985.

  14. Spectral feature characterization methods for blood stain detection in crime scene backgrounds

    NASA Astrophysics Data System (ADS)

    Yang, Jie; Mathew, Jobin J.; Dube, Roger R.; Messinger, David W.

    2016-05-01

    Blood stains are one of 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. Blood spectral signatures containing unique reflectance or absorption features are important both for forensic on-site investigation and laboratory testing. They can be used for target detection and identification applied to crime scene hyperspectral imagery, and also be utilized to analyze the spectral variation of blood on various backgrounds. Non-blood stains often mislead the detection and can generate false alarms at a real crime scene, especially for dark and red backgrounds. This paper measured the reflectance of liquid blood and 9 kinds of non-blood samples in the range of 350 nm - 2500 nm in various crime scene backgrounds, such as pure samples contained in petri dish with various thicknesses, mixed samples with different colors and materials of fabrics, and mixed samples with wood, all of which are examined to provide sub-visual evidence for detecting and recognizing blood from non-blood samples in a realistic crime scene. The spectral difference between blood and non-blood samples are examined and spectral features such as "peaks" and "depths" of reflectance are selected. Two blood stain detection methods are proposed in this paper. The first method uses index to denote the ratio of "depth" minus "peak" over"depth" add"peak" within a wavelength range of the reflectance spectrum. The second method uses relative band depth of the selected wavelength ranges of the reflectance spectrum. Results show that the index method is able to discriminate blood from non-blood samples in most tested crime scene backgrounds, but is not able to detect it from black felt. Whereas the relative band depth method is able to discriminate blood from non-blood samples on all of the tested background material types and colors.

  15. A signal-detection-based diagnostic-feature-detection model of eyewitness identification.

    PubMed

    Wixted, John T; Mickes, Laura

    2014-04-01

    The theoretical understanding of eyewitness identifications made from a police lineup has long been guided by the distinction between absolute and relative decision strategies. In addition, the accuracy of identifications associated with different eyewitness memory procedures has long been evaluated using measures like the diagnosticity ratio (the correct identification rate divided by the false identification rate). Framed in terms of signal-detection theory, both the absolute/relative distinction and the diagnosticity ratio are mainly relevant to response bias while remaining silent about the key issue of diagnostic accuracy, or discriminability (i.e., the ability to tell the difference between innocent and guilty suspects in a lineup). Here, we propose a signal-detection-based model of eyewitness identification, one that encourages the use of (and helps to conceptualize) receiver operating characteristic (ROC) analysis to measure discriminability. Recent ROC analyses indicate that the simultaneous presentation of faces in a lineup yields higher discriminability than the presentation of faces in isolation, and we propose a diagnostic feature-detection hypothesis to account for that result. According to this hypothesis, the simultaneous presentation of faces allows the eyewitness to appreciate that certain facial features (viz., those that are shared by everyone in the lineup) are non-diagnostic of guilt. To the extent that those non-diagnostic features are discounted in favor of potentially more diagnostic features, the ability to discriminate innocent from guilty suspects will be enhanced.

  16. Adaptive skin segmentation via feature-based face detection

    NASA Astrophysics Data System (ADS)

    Taylor, Michael J.; Morris, Tim

    2014-05-01

    Variations in illumination can have significant effects on the apparent colour of skin, which can be damaging to the efficacy of any colour-based segmentation approach. We attempt to overcome this issue by presenting a new adaptive approach, capable of generating skin colour models at run-time. Our approach adopts a Viola-Jones feature-based face detector, in a moderate-recall, high-precision configuration, to sample faces within an image, with an emphasis on avoiding potentially detrimental false positives. From these samples, we extract a set of pixels that are likely to be from skin regions, filter them according to their relative luma values in an attempt to eliminate typical non-skin facial features (eyes, mouths, nostrils, etc.), and hence establish a set of pixels that we can be confident represent skin. Using this representative set, we train a unimodal Gaussian function to model the skin colour in the given image in the normalised rg colour space - a combination of modelling approach and colour space that benefits us in a number of ways. A generated function can subsequently be applied to every pixel in the given image, and, hence, the probability that any given pixel represents skin can be determined. Segmentation of the skin, therefore, can be as simple as applying a binary threshold to the calculated probabilities. In this paper, we touch upon a number of existing approaches, describe the methods behind our new system, present the results of its application to arbitrary images of people with detectable faces, which we have found to be extremely encouraging, and investigate its potential to be used as part of real-time systems.

  17. Alternating absorption features during attosecond-pulse propagation in a laser-controlled gaseous medium

    NASA Astrophysics Data System (ADS)

    Pfeiffer, Adrian N.; Bell, M. Justine; Beck, Annelise R.; Mashiko, Hiroki; Neumark, Daniel M.; Leone, Stephen R.

    2013-11-01

    Recording the transmitted spectrum of a weak attosecond pulse through a medium, while a strong femtosecond pulse copropagates at variable delay, probes the strong-field dynamics of atoms, molecules, and solids. Usually, the interpretation of these measurements is based on the assumption of a thin medium. Here, the propagation through a macroscopic medium of helium atoms in the region of fully allowed resonances is investigated both theoretically and experimentally. The propagation has dramatic effects on the transient spectrum even at relatively low pressures (50 mbar) and short propagation lengths (1 mm). The absorption does not evolve monotonically with the product of propagation distance and pressure, but regions with characteristics of Lorentz line shapes and characteristics of Fano line shapes alternate. Criteria are deduced to estimate whether macroscopic effects can be neglected or not in a transient absorption experiment. Furthermore, the theory in the limit of single-atom response yields a general equation for Lorentz- and Fano-type line shapes at variable pulse delay.

  18. MRI for the detection of calcific features of vertebral haemangioma.

    PubMed

    Bender, Y Y; Böker, S M; Diederichs, G; Walter, T; Wagner, M; Fallenberg, E; Liebig, T; Rickert, M; Hamm, B; Makowski, M R

    2017-08-01

    To evaluate the diagnostic performance of susceptibility-weighted-magnetic-resonance imaging (SW-MRI) for the detection of vertebral haemangiomas (VHs) compared to T1/T2-weighted MRI sequences, radiographs, and computed tomography (CT). The study was approved by the local ethics review board. An SW-MRI sequence was added to the clinical spine imaging protocol. The image-based diagnosis of 56 VHs in 46 patients was established using T1/T2 MRI in combination with radiography/CT as the reference standard. VHs were assessed based on T1/T2-weighted MRI images alone and in combination with SW-MRI, while radiographs/CT images were excluded from the analysis. Fifty-one of 56 VHs could be identified on T1/T2 MRI images alone, if radiographs/CT images were excluded from analysis. In five cases (9.1%), additional radiographs/CT images were required for the imaging-based diagnosis. If T1/T2 and SW-MRI images were used in combination, all VHs could be diagnosed, without the need for radiography/CT. Size measurements revealed a close correlation between CT and SW-MRI (R 2 =0.94; p<0.05). This study demonstrates that SW-MRI enables reliable detection of the typical calcified features of VHs. This is of importance for routine MRI of the spine, as the use of additional CT/radiography can be minimized. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

  19. Drowsiness detection during different times of day using multiple features.

    PubMed

    Sahayadhas, Arun; Sundaraj, Kenneth; Murugappan, Murugappan

    2013-06-01

    Driver drowsiness has been one of the major causes of road accidents that lead to severe trauma, such as physical injury, death, and economic loss, which highlights the need to develop a system that can alert drivers of their drowsy state prior to accidents. Researchers have therefore attempted to develop systems that can determine driver drowsiness using the following four measures: (1) subjective ratings from drivers, (2) vehicle-based measures, (3) behavioral measures and (4) physiological measures. In this study, we analyzed the various factors that contribute towards drowsiness. A total of 15 male subjects were asked to drive for 2 h at three different times of the day (00:00-02:00, 03:00-05:00 and 15:00-17:00 h) when the circadian rhythm is low. The less intrusive physiological signal measurements, ECG and EMG, are analyzed during this driving task. Statistically significant differences in the features of ECG and sEMG signals were observed between the alert and drowsy states of the drivers during different times of day. In the future, these physiological measures can be fused with vision-based measures for the development of an efficient drowsiness detection system.

  20. Structural, thermal and optical absorption features of heavy metal oxides doped tellurite rich glasses

    NASA Astrophysics Data System (ADS)

    Kaky, Kawa M.; Lakshminarayana, G.; Baki, S. O.; Kityk, I. V.; Taufiq-Yap, Y. H.; Mahdi, M. A.

    In order to improve tellurite glass stability to be applicable for optical fiber amplifier applications, glasses with the composition of (70 - x)TeO2. (10)ZnO. (10)WO3. (5)Na2O. (5)TiO2. (x)Bi2O3 (x = 1, 2, 3, 4, and 5 mol%) have been produced and characterized using the related methods. Structural properties were investigated using X-ray diffraction (XRD) which confirms the non-crystalline structure and scanning electron microscopy (SEM) micrographs also confirm the XRD results. The energy dispersive X-ray (EDX) analysis profiles show that all the mentioned elements are present in the prepared glasses. Following the IR spectra, all the tellurium bonds such as stretching vibrations of TeO4 tbp and TeO3/TeO3+1 unit are revealed. Raman spectra confirm the presence of different functional groups, actually, it shows bands mainly in four spectral regions: R1 (65-150) cm-1, R2 (280-550) cm-1, R3 (880-950) cm-1 and R4 (916-926) cm-1 and the identified bands are assigned to respective molecular groups. The thermal study was carried out using Differential scanning calorimetry (DSC) which indicates good thermal stability of the synthesized glasses with increasing Bi concentration. From the optical absorption spectra, we evaluated cut-off edge wavelengths and found increasing cutoff wavelength with an increase in Bi2O3 concentration. In the UV-Visible region, optical band gap energy and allowed transitions were investigated using three methods; direct, indirect, and absorption spectrum fitting (ASF), and band gaps from indirect and ASF were matched.

  1. GREEN BANK TELESCOPE DETECTION OF POLARIZATION-DEPENDENT H I ABSORPTION AND H I OUTFLOWS IN LOCAL ULIRGs AND QUASARS

    SciTech Connect

    Teng, Stacy H.; Veilleux, Sylvain; Baker, Andrew J., E-mail: stacy.h.teng@nasa.gov

    2013-03-10

    We present the results of a 21 cm H I survey of 27 local massive gas-rich late-stage mergers and merger remnants with the Robert C. Byrd Green Bank Telescope. These remnants were selected from the Quasar/ULIRG Evolution Study sample of ultraluminous infrared galaxies (ULIRGs; L{sub 8{sub -{sub 1000{sub {mu}m}}}} > 10{sup 12} L{sub Sun }) and quasars; our targets are all bolometrically dominated by active galactic nuclei (AGNs) and sample the later phases of the proposed ULIRG-to-quasar evolutionary sequence. We find the prevalence of H I absorption (emission) to be 100% (29%) in ULIRGs with H I detections, 100% (88%)more » in FIR-strong quasars, and 63% (100%) in FIR-weak quasars. The absorption features are associated with powerful neutral outflows that change from being mainly driven by star formation in ULIRGs to being driven by the AGN in the quasars. These outflows have velocities that exceed 1500 km s{sup -1} in some cases. Unexpectedly, we find polarization-dependent H I absorption in 57% of our spectra (88% and 63% of the FIR-strong and FIR-weak quasars, respectively). We attribute this result to absorption of polarized continuum emission from these sources by foreground H I clouds. About 60% of the quasars displaying polarized spectra are radio-loud, far higher than the {approx}10% observed in the general AGN population. This discrepancy suggests that radio jets play an important role in shaping the environments in these galaxies. These systems may represent a transition phase in the evolution of gas-rich mergers into ''mature'' radio galaxies.« less

  2. Fast linear feature detection using multiple directional non-maximum suppression.

    PubMed

    Sun, C; Vallotton, P

    2009-05-01

    The capacity to detect linear features is central to image analysis, computer vision and pattern recognition and has practical applications in areas such as neurite outgrowth detection, retinal vessel extraction, skin hair removal, plant root analysis and road detection. Linear feature detection often represents the starting point for image segmentation and image interpretation. In this paper, we present a new algorithm for linear feature detection using multiple directional non-maximum suppression with symmetry checking and gap linking. Given its low computational complexity, the algorithm is very fast. We show in several examples that it performs very well in terms of both sensitivity and continuity of detected linear features.

  3. Mid and Near-IR Absorption Spectra of PAH Neutrals and Ions in H20 Ice to Facilitate their Astronomical Detection

    NASA Technical Reports Server (NTRS)

    Bernstein, Max P.; Sandford, Scott A.; Allamandola, Louis J.

    2004-01-01

    Polycyclic aromatic hydrocarbons (PAHs) are believed to be the most abundant and widespread class of organic compounds in the universe, having been observed in emission towards energetic regions and absorption towards colder ones.We will present IR spectra of PAHs and their cations in H20 ice measured in the laboratory in the hopes that this will facilitate the detection of these features in the interstellar medium.

  4. Core-based intrinsic fiber-optic absorption sensor for the detection of volatile organic compounds

    NASA Astrophysics Data System (ADS)

    Klunder, Gregory L.; Russo, Richard E.

    1995-03-01

    A core-based intrinsic fiber-optic absorption sensor has been developed and tested for the detection of volatile organic compounds. The distal ends of transmitting and receiving fibers are connected by a small cylindrical section of an optically clear silicone rubber. The silicone rubber acts both as a light pipe and as a selective membrane into which the analyte molecules can diffuse. The sensor has been used to detect volatile organics (trichloroethylene, 1,1-dichloroethylene, and benzene) in both aqueous solutions and in the vapor phase or headspace. Absorption spectra obtained in the near-infrared (near-IR) provide qualitative and quantitative information about the analyte. Water, which has strong broad-band absorption in the near-IR, is excluded from the spectra because of the hydrophobic properties of the silicone rubber. The rate-limiting step is shown to be the diffusion through the Nernstian boundary layer surrounding the sensor and not the diffusion through the silicone polymer. The rate of analyte diffusion into the sensor, as measured by the t(sub 90) values (the time required for the sensor to reach 90% of the equilibrium value), is 30 min for measurements in aqueous solutions and approximately 3 min for measurements made in the headspace. The limit of detection obtained with this sensor is approximately 1.1 ppm for trichloroethylene in an aqueous solution.

  5. Application of backscatter absorption gas imaging to the detection of chemicals related to drug production

    NASA Astrophysics Data System (ADS)

    Kulp, Thomas J.; Garvis, Darrel G.; Kennedy, Randall B.; McRae, Thomas G.

    1991-08-01

    The application of backscatter absorption gas imaging (BAGI) to the detection of gaseous chemical species associated with the production of illegal drugs is considered. BAGI is a gas visualization technique that allows the imaging of over 70 organic vapors at minimum concentrations of a few to several hundred ppm-m. Present BAGI capabilities at Lawrence Livermore National Laboratory and Laser Imaging Systems are discussed. Eighteen different species of interest in drug-law enforcement are identified as being detectable by BAGI. The chemical remote sensing needs of law enforcement officials are described, and the use of BAGI in meeting some of these needs is outlined.

  6. [Gas pipeline leak detection based on tunable diode laser absorption spectroscopy].

    PubMed

    Zhang, Qi-Xing; Wang, Jin-Jun; Liu, Bing-Hai; Cai, Ting-Li; Qiao, Li-Feng; Zhang, Yong-Ming

    2009-08-01

    The principle of tunable diode laser absorption spectroscopy and harmonic detection technique was introduced. An experimental device was developed by point sampling through small multi-reflection gas cell. A specific line near 1 653. 7 nm was targeted for methane measurement using a distributed feedback diode laser as tunable light source. The linearity between the intensity of second harmonic signal and the concentration of methane was determined. The background content of methane in air was measured. The results show that gas sensors using tunable diode lasers provide a high sensitivity and high selectivity method for city gas pipeline leak detection.

  7. Wavelength modulation spectroscopy--digital detection of gas absorption harmonics based on Fourier analysis.

    PubMed

    Mei, Liang; Svanberg, Sune

    2015-03-20

    This work presents a detailed study of the theoretical aspects of the Fourier analysis method, which has been utilized for gas absorption harmonic detection in wavelength modulation spectroscopy (WMS). The lock-in detection of the harmonic signal is accomplished by studying the phase term of the inverse Fourier transform of the Fourier spectrum that corresponds to the harmonic signal. The mathematics and the corresponding simulation results are given for each procedure when applying the Fourier analysis method. The present work provides a detailed view of the WMS technique when applying the Fourier analysis method.

  8. Ultraviolet Broad Absorption Features and the Spectral Energy Distribution of the QSO PG 1351+64. 3.1

    NASA Technical Reports Server (NTRS)

    Zheng, W.; Kriss, G. A.; Wang, J. X.; Brotherton, M.; Oegerle, W. R.; Blair, W. P.; Davidsen, A. F.; Green, R. F.; Hutchings, J. B.; Kaiser, M. E.; hide

    2001-01-01

    We present a moderate-resolution (approximately 20 km s(exp -1) spectrum of the mini broad absorption line QSO PG 1351+64 between 915-1180 A, obtained with the Far Ultraviolet Spectroscopic Explorer (FUSE). Additional low-resolution spectra at longer wavelengths were also obtained with the Hubble Space Telescope (HST) and ground-based telescopes. Broad absorption is present on the blue wings of C III (lambda)977, Ly(beta), O VI (lambda)(lambda)1032,1038, Ly(alpha), N V (lambda)(lambda)1238,1242, Si IV (lambda)(lambda)1393,1402, and C IV (lambda)(lambda)1548,1450. The absorption profile can be fitted with five components at velocities of approximately -780, -1049, -1629, -1833, and -3054 km s(exp -1) with respect to the emission-line redshift of z = 0.088. All the absorption components cover a large fraction of the continuum source as well as the broad-line region. The O VI emission feature is very weak, and the O VI/Ly(alpha) flux ratio is 0.08, one of the lowest among low-redshift active galaxies and QSOs. The UV (ultraviolet) continuum shows a significant change in slope near 1050 A in the restframe. The steeper continuum shortward of the Lyman limit extrapolates well to the observed weak X-ray flux level. The absorbers' properties are similar to those of high-redshift broad absorption-line QSOs. The derived total column density of the UV absorbers is on the order of 10(exp 21) cm(exp -2), unlikely to produce significant opacity above 1 keV in the X-ray. Unless there is a separate, high-ionization X-ray absorber, the QSO's weak X-ray flux may be intrinsic. The ionization level of the absorbing components is comparable to that anticipated in the broad-line region, therefore the absorbers may be related to broad-line clouds along the line of sight.

  9. Ultraviolet Broad Absorption Features and the Spectral Energy Distribution of the QSO PG 1351+641. 2.5

    NASA Technical Reports Server (NTRS)

    Zheng, W.; Kriss, G. A.; Wang, J. X.; Brotherton, M.; Oegerle, W. R.; Blair, W. P.; Davidsen, A. F.; Green, R. F.; Hutchings, J. B.; Kaiser, M. E.; hide

    2001-01-01

    We present a moderate-resolution (approximately 20 km/s) spectrum of the broad-absorption line QSO PG 1351+64 between 915-1180 angstroms, obtained with the Far Ultraviolet Spectroscopic Explorer (FUSE). Additional low-resolution spectra at longer wavelengths were also obtained with the Hubble Space Telescope (HST) and ground-based telescopes. Broad absorption is present on the blue wings of C III lambda977, Ly-beta, O VI lambda-lambda-1032,1038, Ly-alpha, N V lambda-lambda-1238,1242, Si IV lambda-lambda-1393,1402, and C IV lambda-lambda-1548,1450. The absorption profile can be fitted with five components at velocities of approximately -780, -1049, -1629, -1833, and -3054 km/s with respect to the emission-line redshift of z = 0.088. All the absorption components cover a large fraction of the continuum source as well as the broad-line region. The O VI emission feature is very weak, and the O VI/Ly-alpha flux ratio is 0.08, one of the lowest among low-redshift active galaxies and QSOs. The ultraviolet continuum shows a significant change in slope near 1050 angstroms in the restframe. The steeper continuum shortward of the Lyman limit extrapolates well to the observed weak X-ray flux level. The absorbers' properties are similar to those of high-redshift broad absorption-line QSOs. The derived total column density of the UV absorbers is on the order of 10(exp 21)/s, unlikely to produce significant opacity above 1 keV in the X-ray. Unless there is a separate, high-ionization X-ray absorber, the QSO's weak X-ray flux may be intrinsic. The ionization level of the absorbing components is comparable to that anticipated in the broad-line region, therefore the absorbers may be related to broad-line clouds along the line of sight.

  10. Tunable Diode Laser Atomic Absorption Spectroscopy for Detection of Potassium under Optically Thick Conditions.

    PubMed

    Qu, Zhechao; Steinvall, Erik; Ghorbani, Ramin; Schmidt, Florian M

    2016-04-05

    Potassium (K) is an important element related to ash and fine-particle formation in biomass combustion processes. In situ measurements of gaseous atomic potassium, K(g), using robust optical absorption techniques can provide valuable insight into the K chemistry. However, for typical parts per billion K(g) concentrations in biomass flames and reactor gases, the product of atomic line strength and absorption path length can give rise to such high absorbance that the sample becomes opaque around the transition line center. We present a tunable diode laser atomic absorption spectroscopy (TDLAAS) methodology that enables accurate, calibration-free species quantification even under optically thick conditions, given that Beer-Lambert's law is valid. Analyte concentration and collisional line shape broadening are simultaneously determined by a least-squares fit of simulated to measured absorption profiles. Method validation measurements of K(g) concentrations in saturated potassium hydroxide vapor in the temperature range 950-1200 K showed excellent agreement with equilibrium calculations, and a dynamic range from 40 pptv cm to 40 ppmv cm. The applicability of the compact TDLAAS sensor is demonstrated by real-time detection of K(g) concentrations close to biomass pellets during atmospheric combustion in a laboratory reactor.

  11. Laser Radar Study Using Resonance Absorption for Remote Detection Of Air Pollutants

    NASA Technical Reports Server (NTRS)

    Igarashi, Takashi

    1973-01-01

    A laser radar using resonance absorption has an advantage of increased detection range and sensitivity compared with that achieved by Raman or resonance back scattering. In this paper, new laser radar system using resonance absorption is proposed and results obtained from this laser radar system are discussed. NO2, SO2 gas has an absorption spectrum at 4500 A and 3000 A respectively as shown in Fig. 1. A laser light including at least a set of an absorption peak (lambda)1 and a valley (lambda)2 is emitted into a pollutant atmosphere. The light reflected with a topographical reflector or an atmospheric Mie scattering as distributed reflectors is received and divided into two wavelength components (lambda)1 and (lambda)2. The laser radar system used in the investigation is shown in Fig', 2 and consists of a dye laser transmitter, an optical receiver with a special monochrometer and a digital processer. Table 1 shows the molecular constants of NO2, and SO2 and the dye laser used in this experiment. In this system, the absolute concentration of the pollutant gas can be measured in comparison with a standard gas cell. The concentration of NO2, SO2 as low as 0.1 ppm have been measured at 100 m depth resolution. For a 1 mJ laser output, the observable range of this system achieved up to 300 m using the distributed Mie reflector. The capability and technical limitation of the system will be discussed in detail.

  12. High sensitivity liquid phase measurements using broadband cavity enhanced absorption spectroscopy (BBCEAS) featuring a low cost webcam based prism spectrometer.

    PubMed

    Qu, Zhechao; Engstrom, Julia; Wong, Donald; Islam, Meez; Kaminski, Clemens F

    2013-11-07

    Cavity enhanced techniques enable high sensitivity absorption measurements in the liquid phase but are typically more complex, and much more expensive, to perform than conventional absorption methods. The latter attributes have so far prevented a wide spread use of these methods in the analytical sciences. In this study we demonstrate a novel BBCEAS instrument that is sensitive, yet simple and economical to set up and operate. We use a prism spectrometer with a low cost webcam as the detector in conjunction with an optical cavity consisting of two R = 0.99 dielectric mirrors and a white light LED source for illumination. High sensitivity liquid phase measurements were made on samples contained in 1 cm quartz cuvettes placed at normal incidence to the light beam in the optical cavity. The cavity enhancement factor (CEF) with water as the solvent was determined directly by phase shift cavity ring down spectroscopy (PS-CRDS) and also by calibration with Rhodamine 6G solutions. Both methods yielded closely matching CEF values of ~60. The minimum detectable change in absorption (αmin) was determined to be 6.5 × 10(-5) cm(-1) at 527 nm and was limited only by the 8 bit resolution of the particular webcam detector used, thus offering scope for further improvement. The instrument was used to make representative measurements on dye solutions and in the determination of nitrite concentrations in a variation of the widely used Griess Assay. Limits of detection (LOD) were ~850 pM for Rhodamine 6G and 3.7 nM for nitrite, respectively. The sensitivity of the instrument compares favourably with previous cavity based liquid phase studies whilst being achieved at a small fraction of the cost hitherto reported, thus opening the door to widespread use in the community. Further means of improving sensitivity are discussed in the paper.

  13. Searches for 3.5 keV Absorption Features in Cluster AGN Spectra

    NASA Astrophysics Data System (ADS)

    Conlon, Joseph P.

    2018-06-01

    We investigate possible evidence for a spectral dip around 3.5 keV in central cluster AGNs, motivated by previous results for archival Chandra observations of the Perseus cluster and the general interest in novel spectral features around 3.5 keV that may arise from dark matter physics. We use two deep Chandra observations of the Perseus and Virgo clusters that have recently been made public. In both cases, mild improvements in the fit (Δχ2 = 4.2 and Δχ2 = 2.5) are found by including such a dip at 3.5 keV into the spectrum. A comparable result (Δχ2 = 6.5) is found re-analysing archival on-axis Chandra ACIS-S observations of the centre of the Perseus cluster.

  14. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression

    USGS Publications Warehouse

    Kokaly, R.F.; Clark, R.N.

    1999-01-01

    We develop a new method for estimating the biochemistry of plant material using spectroscopy. Normalized band depths calculated from the continuum-removed reflectance spectra of dried and ground leaves were used to estimate their concentrations of nitrogen, lignin, and cellulose. Stepwise multiple linear regression was used to select wavelengths in the broad absorption features centered at 1.73 ??m, 2.10 ??m, and 2.30 ??m that were highly correlated with the chemistry of samples from eastern U.S. forests. Band depths of absorption features at these wavelengths were found to also be highly correlated with the chemistry of four other sites. A subset of data from the eastern U.S. forest sites was used to derive linear equations that were applied to the remaining data to successfully estimate their nitrogen, lignin, and cellulose concentrations. Correlations were highest for nitrogen (R2 from 0.75 to 0.94). The consistent results indicate the possibility of establishing a single equation capable of estimating the chemical concentrations in a wide variety of species from the reflectance spectra of dried leaves. The extension of this method to remote sensing was investigated. The effects of leaf water content, sensor signal-to-noise and bandpass, atmospheric effects, and background soil exposure were examined. Leaf water was found to be the greatest challenge to extending this empirical method to the analysis of fresh whole leaves and complete vegetation canopies. The influence of leaf water on reflectance spectra must be removed to within 10%. Other effects were reduced by continuum removal and normalization of band depths. If the effects of leaf water can be compensated for, it might be possible to extend this method to remote sensing data acquired by imaging spectrometers to give estimates of nitrogen, lignin, and cellulose concentrations over large areas for use in ecosystem studies.We develop a new method for estimating the biochemistry of plant material using

  15. Narrow phase-dependent features in X-ray dim isolated neutron stars: a new detection and upper limits

    NASA Astrophysics Data System (ADS)

    Borghese, A.; Rea, N.; Coti Zelati, F.; Tiengo, A.; Turolla, R.; Zane, S.

    2017-07-01

    We report on the results of a detailed phase-resolved spectroscopy of archival XMM-Newton observations of X-ray dim isolated neutron stars (XDINSs). Our analysis revealed a narrow and phase-variable absorption feature in the X-ray spectrum of RX J1308.6+2127. The feature has an energy of ˜740 eV and an equivalent width of ˜15 eV. It is detected only in ˜1/5 of the phase cycle, and appears to be present for the entire timespan covered by the observations (2001 December to 2007 June). The strong dependence on the pulsar rotation and the narrow width suggest that the feature is likely due to resonant cyclotron absorption/scattering in a confined high-B structure close to the stellar surface. Assuming a proton cyclotron line, the magnetic field strength in the loop is Bloop ˜ 1.7 × 1014 G, about a factor of ˜5 higher than the surface dipolar magnetic field (Bsurf ˜ 3.4 × 1013 G). This feature is similar to that recently detected in another XDINS, RX J0720.4-3125, showing (as expected by theoretical simulations) that small-scale magnetic loops close to the surface might be common to many highly magnetic neutron stars (although difficult to detect with current X-ray instruments). Furthermore, we investigated the available XMM-Newton data of all XDINSs in search for similar narrow phase-dependent features, but could derive only upper limits for all the other sources.

  16. Investigation of kinematic features for dismount detection and tracking

    NASA Astrophysics Data System (ADS)

    Narayanaswami, Ranga; Tyurina, Anastasia; Diel, David; Mehra, Raman K.; Chinn, Janice M.

    2012-05-01

    With recent changes in threats and methods of warfighting and the use of unmanned aircrafts, ISR (Intelligence, Surveillance and Reconnaissance) activities have become critical to the military's efforts to maintain situational awareness and neutralize the enemy's activities. The identification and tracking of dismounts from surveillance video is an important step in this direction. Our approach combines advanced ultra fast registration techniques to identify moving objects with a classification algorithm based on both static and kinematic features of the objects. Our objective was to push the acceptable resolution beyond the capability of industry standard feature extraction methods such as SIFT (Scale Invariant Feature Transform) based features and inspired by it, SURF (Speeded-Up Robust Feature). Both of these methods utilize single frame images. We exploited the temporal component of the video signal to develop kinematic features. Of particular interest were the easily distinguishable frequencies characteristic of bipedal human versus quadrupedal animal motion. We examine limits of performance, frame rates and resolution required for human, animal and vehicles discrimination. A few seconds of video signal with the acceptable frame rate allow us to lower resolution requirements for individual frames as much as by a factor of five, which translates into the corresponding increase of the acceptable standoff distance between the sensor and the object of interest.

  17. Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection.

    PubMed

    Bogaarts, J G; Hilkman, D M W; Gommer, E D; van Kranen-Mastenbroek, V H J M; Reulen, J P H

    2016-12-01

    Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential. Recently, the median decaying memory (MDM) approach was determined to be the best method of normalization. MDM uses a sliding baseline buffer of EEG epochs to calculate feature normalization constants. However, while this method does include non-seizure EEG epochs, it also includes EEG activity that can have a detrimental effect on the normalization and subsequent seizure detection performance. In this study, EEG data that is to be incorporated into the baseline buffer are automatically selected based on a novelty detection algorithm (Novelty-MDM). Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity-specificity ROC curve. This evaluation compares three different EEG normalization methods, namely a fixed baseline buffer (FB), the median decaying memory (MDM) approach, and our novelty median decaying memory (Novelty-MDM) method. It is demonstrated that MDM did not improve overall performance compared to FB (p < 0.27), partly because seizure like episodes were included in the baseline. More importantly, Novelty-MDM significantly outperforms both FB (p = 0.015) and MDM (p = 0.0065).

  18. Automated feature detection and identification in digital point-ordered signals

    DOEpatents

    Oppenlander, Jane E.; Loomis, Kent C.; Brudnoy, David M.; Levy, Arthur J.

    1998-01-01

    A computer-based automated method to detect and identify features in digital point-ordered signals. The method is used for processing of non-destructive test signals, such as eddy current signals obtained from calibration standards. The signals are first automatically processed to remove noise and to determine a baseline. Next, features are detected in the signals using mathematical morphology filters. Finally, verification of the features is made using an expert system of pattern recognition methods and geometric criteria. The method has the advantage that standard features can be, located without prior knowledge of the number or sequence of the features. Further advantages are that standard features can be differentiated from irrelevant signal features such as noise, and detected features are automatically verified by parameters extracted from the signals. The method proceeds fully automatically without initial operator set-up and without subjective operator feature judgement.

  19. A light-emitting diode- (LED-) based absorption sensor for simultaneous detection of carbon monoxide and carbon dioxide

    DOE PAGES

    Thurmond, Kyle; Loparo, Zachary; Partridge, Jr., William P.; ...

    2016-04-18

    Here, a sensor was developed for simultaneous measurements of carbon monoxide (CO) and carbon dioxide (CO 2) fluctuations in internal combustion engine exhaust gases. This sensor utilizes low-cost and compact light-emitting diodes (LEDs) that emit in the 3–5 µm wavelength range. An affordable, fast response sensor that can measure these gases has a broad application that can lead to more efficient, fuel-flexible engines and regulation of harmful emissions. Light emission from LEDs is spectrally broader and more spatially divergent when compared to that of lasers, which presented many design challenges. Optical design studies addressed some of the non-ideal characteristics ofmore » the LED emissions. Measurements of CO and CO 2 were conducted using their fundamental absorption bands centered at 4.7 µm and 4.3 µm, respectively, while a 3.6 µm reference LED was used to account for scattering losses (due to soot, window deposits, etc.) common to the three measurement LEDs. Instrument validation and calibration was performed using a laboratory flow cell and bottled-gas mixtures. The sensor was able to detect CO 2 and CO concentration changes as small as 30 ppm and 400 ppm, respectively. Because of the many control and monitor species with infra-red absorption features, which can be measured using the strategy described, this work demonstrates proof of concept for a wider range of fast (250 Hz) and low-cost sensors for gas measurement and process monitoring.« less

  20. A General Purpose Feature Extractor for Light Detection and Ranging Data

    DTIC Science & Technology

    2010-11-17

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

  1. Speech recognition features for EEG signal description in detection of neonatal seizures.

    PubMed

    Temko, A; Boylan, G; Marnane, W; Lightbody, G

    2010-01-01

    In this work, features which are usually employed in automatic speech recognition (ASR) are used for the detection of neonatal seizures in newborn EEG. Three conventional ASR feature sets are compared to the feature set which has been previously developed for this task. The results indicate that the thoroughly-studied spectral envelope based ASR features perform reasonably well on their own. Additionally, the SVM Recursive Feature Elimination routine is applied to all extracted features pooled together. It is shown that ASR features consistently appear among the top-rank features.

  2. Quantum cascade laser-based multipass absorption system for hydrogen peroxide detection

    NASA Astrophysics Data System (ADS)

    Cao, Yingchun; Sanchez, Nancy P.; Jiang, Wenzhe; Ren, Wei; Lewicki, Rafal; Jiang, Dongfang; Griffin, Robert J.; Tittel, Frank K.

    2015-01-01

    Hydrogen peroxide (H2O2) is a relevant molecular trace gas species, that is related to the oxidative capacity of the atmosphere, the production of radical species such as OH, the generation of sulfate aerosol via oxidation of S(IV) to S(VI), and the formation of acid rain. The detection of atmospheric H2O2 involves specific challenges due to its high reactivity and low concentration (ppbv to sub-ppbv level). Traditional methods for measuring atmospheric H2O2 concentration are often based on wet-chemistry methods that require a transfer from the gas- to liquid-phase for a subsequent determination by techniques such as fluorescence spectroscopy, which can lead to problems such as sampling artifacts and interference by other atmospheric constituents. A quartz-enhanced photoacoustic spectroscopy-based system for the measurement of atmospheric H2O2 with a detection limit of 75 ppb for 1-s integration time was previously reported. In this paper, an updated H2O2 detection system based on long-optical-path-length absorption spectroscopy by using a distributed feedback quantum cascade laser (DFB-QCL) will be described. A 7.73-μm CW-DFB-QCL and a thermoelectrically cooled infrared detector, optimized for a wavelength of 8 μm, are employed for theH2O2 sensor system. A commercial astigmatic Herriott multi-pass cell with an effective optical path-length of 76 m is utilized for the reported QCL multipass absorption system. Wavelength modulation spectroscopy (WMS) with second harmonic detection is used for enhancing the signal-to-noise-ratio. A minimum detection limit of 13.4 ppb is achieved with a 2 s sampling time. Based on an Allan-Werle deviation analysis the minimum detection limit can be improved to 1.5 ppb when using an averaging time of 300 s.

  3. Temporal intracavity detection of parasitic infrared absorption in Ti:Sapphire lasers

    NASA Astrophysics Data System (ADS)

    Deleva, A. D.; Peshev, Z. Y.; Aneva, Z. I.

    1993-12-01

    An intracavity technique with temporal sensitivity to optical losses is used to detect parasitic infrared absorption (PIRA) in Ti:sapphire crystals with high active-center concentrations. By means of comparative analysis, re-emission is established of part of the parasitically absorbed energy back into the laser action channel. A method is proposed for approximate quantitative determination of the relative part of re-emitting PIRA-centers with respect to their total number; for the highly-doped crystal described, it is estimated at about 11%.

  4. Forensic applications of chemical imaging: latent fingerprint detection using visible absorption and luminescence.

    PubMed

    Exline, David L; Wallace, Christie; Roux, Claude; Lennard, Chris; Nelson, Matthew P; Treado, Patrick J

    2003-09-01

    Chemical imaging technology is a rapid examination technique that combines molecular spectroscopy and digital imaging, providing information on morphology, composition, structure, and concentration of a material. Among many other applications, chemical imaging offers an array of novel analytical testing methods, which limits sample preparation and provides high-quality imaging data essential in the detection of latent fingerprints. Luminescence chemical imaging and visible absorbance chemical imaging have been successfully applied to ninhydrin, DFO, cyanoacrylate, and luminescent dye-treated latent fingerprints, demonstrating the potential of this technology to aid forensic investigations. In addition, visible absorption chemical imaging has been applied successfully to visualize untreated latent fingerprints.

  5. Automatic Detection of Sand Ripple Features in Sidescan Sonar Imagery

    DTIC Science & Technology

    2014-07-09

    Among the features used in forensic scientific fingerprint analysis are terminations or bifurcations of print ridges. Sidescan sonar imagery of ripple...always be pathological cases. The size of the blocks of pixels used in determining the ripple wavelength is evident in the output images on the right in

  6. Detection of hypertensive retinopathy using vessel measurements and textural features.

    PubMed

    Agurto, Carla; Joshi, Vinayak; Nemeth, Sheila; Soliz, Peter; Barriga, Simon

    2014-01-01

    Features that indicate hypertensive retinopathy have been well described in the medical literature. This paper presents a new system to automatically classify subjects with hypertensive retinopathy (HR) using digital color fundus images. Our method consists of the following steps: 1) normalization and enhancement of the image; 2) determination of regions of interest based on automatic location of the optic disc; 3) segmentation of the retinal vasculature and measurement of vessel width and tortuosity; 4) extraction of color features; 5) classification of vessel segments as arteries or veins; 6) calculation of artery-vein ratios using the six widest (major) vessels for each category; 7) calculation of mean red intensity and saturation values for all arteries; 8) calculation of amplitude-modulation frequency-modulation (AM-FM) features for entire image; and 9) classification of features into HR and non-HR using linear regression. This approach was tested on 74 digital color fundus photographs taken with TOPCON and CANON retinal cameras using leave-one out cross validation. An area under the ROC curve (AUC) of 0.84 was achieved with sensitivity and specificity of 90% and 67%, respectively.

  7. Detection of Abnormal Events via Optical Flow Feature Analysis

    PubMed Central

    Wang, Tian; Snoussi, Hichem

    2015-01-01

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

  8. Empirical Evaluation of Different Feature Representations for Social Circles Detection

    DTIC Science & Technology

    2015-06-16

    study and compare the performance on the available labelled Facebook data from the Kaggle competition on learning social circles in networks . We...Kaggle competition on learning social circles in networks [5]. The data consist of hand- labelled friendship egonets from Facebook and a set of 57...16. SECURITY CLASSIFICATION OF: Social circles detection is a special case of community detection in social network that is currently attracting a

  9. Detection of interstellar sodium hydroxide in self-absorption toward the galactic center

    NASA Technical Reports Server (NTRS)

    Hollis, J. M.; Rhodes, P. J.

    1982-01-01

    A weak self-absorbed emission line, which is identified as the J = 4-3 transition of sodium hydroxide, has been detected in the direction of Sgr B2(OH). The correspondingly weak Sgr B2(QH) emission line U75406, previously reported as an unidentified spectral feature by other investigators, is consistent with the J = 3-2 transition of sodium hydroxide. This detection may represent the first evidence of a grain reaction formation mechanism for simple metal hydroxides. The detection of H62 Delta toward Orion A is also reported.

  10. [Study of the Detecting System of CH4 and SO2 Based on Spectral Absorption Method and UV Fluorescence Method].

    PubMed

    Wang, Shu-tao; Wang, Zhi-fang; Liu, Ming-hua; Wei, Meng; Chen, Dong-ying; Wang, Xing-long

    2016-01-01

    According to the spectral absorption characteristics of polluting gases and fluorescence characteristics, a time-division multiplexing detection system is designed. Through this system we can detect Methane (CH4) and sulfur dioxide (SO2) by using spectral absorption method and the SO2 can be detected by using UV fluorescence method. The system consists of four parts: a combination of a light source which could be switched, the common optical path, the air chamber and the signal processing section. The spectral absorption characteristics and fluorescence characteristics are measured first. Then the experiment of detecting CH4 and SO2 through spectral absorption method and the experiment of detecting SO2 through UV fluorescence method are conducted, respectively. Through measuring characteristics of spectral absorption and fluorescence, we get excitation wavelengths of SO2 and CH4 measured by spectral absorption method at the absorption peak are 280 nm and 1.64 μm, respectively, and the optimal excitation wavelength of SO2 measured by UV fluorescence method is 220 nm. we acquire the linear relation between the concentration of CH4 and relative intensity and the linear relation between the concentration of SO2 and output voltage after conducting the experiment of spectral absorption method, and the linearity are 98.7%, 99.2% respectively. Through the experiment of UV fluorescence method we acquire that the relation between the concentration of SO2 and the voltage is linear, and the linearity is 99.5%. Research shows that the system is able to be applied to detect the polluted gas by absorption spectrum method and UV fluorescence method. Combing these two measurement methods decreases the costing and the volume, and this system can also be used to measure the other gases. Such system has a certain value of application.

  11. The ship edge feature detection based on high and low threshold for remote sensing image

    NASA Astrophysics Data System (ADS)

    Li, Xuan; Li, Shengyang

    2018-05-01

    In this paper, a method based on high and low threshold is proposed to detect the ship edge feature due to the low accuracy rate caused by the noise. Analyze the relationship between human vision system and the target features, and to determine the ship target by detecting the edge feature. Firstly, using the second-order differential method to enhance the quality of image; Secondly, to improvement the edge operator, we introduction of high and low threshold contrast to enhancement image edge and non-edge points, and the edge as the foreground image, non-edge as a background image using image segmentation to achieve edge detection, and remove the false edges; Finally, the edge features are described based on the result of edge features detection, and determine the ship target. The experimental results show that the proposed method can effectively reduce the number of false edges in edge detection, and has the high accuracy of remote sensing ship edge detection.

  12. The Effect of Resolution on Detecting Visually Salient Preattentive Features

    DTIC Science & Technology

    2015-06-01

    resolutions in descending order (a–e). The plot compiles the areas of interest displayed in the images and each symbol represents 1 of the images. Data...to particular regions in a scene by highly salient 2 features, for example, the color of the flower discussed in the previous example. These...descending order (a–e). The plot compiles the areas of interest displayed in the images and each symbol represents 1 of the images. Data clusters

  13. Image Recognition and Feature Detection in Solar Physics

    NASA Astrophysics Data System (ADS)

    Martens, Petrus C.

    2012-05-01

    The Solar Dynamics Observatory (SDO) data repository will dwarf the archives of all previous solar physics missions put together. NASA recognized early on that the traditional methods of analyzing the data -- solar scientists and grad students in particular analyzing the images by hand -- would simply not work and tasked our Feature Finding Team (FFT) with developing automated feature recognition modules for solar events and phenomena likely to be observed by SDO. Having these metadata available on-line will enable solar scientist to conduct statistical studies involving large sets of events that would be impossible now with traditional means. We have followed a two-track approach in our project: we have been developing some existing task-specific solar feature finding modules to be "pipe-line" ready for the stream of SDO data, plus we are designing a few new modules. Secondly, we took it upon us to develop an entirely new "trainable" module that would be capable of identifying different types of solar phenomena starting from a limited number of user-provided examples. Both approaches are now reaching fruition, and I will show examples and movies with results from several of our feature finding modules. In the second part of my presentation I will focus on our “trainable” module, which is the most innovative in character. First, there is the strong similarity between solar and medical X-ray images with regard to their texture, which has allowed us to apply some advances made in medical image recognition. Second, we have found that there is a strong similarity between the way our trainable module works and the way our brain recognizes images. The brain can quickly recognize similar images from key characteristics, just as our code does. We conclude from that that our approach represents the beginning of a more human-like procedure for computer image recognition.

  14. Chromatic information and feature detection in fast visual analysis

    DOE PAGES

    Del Viva, Maria M.; Punzi, Giovanni; Shevell, Steven K.; ...

    2016-08-01

    The visual system is able to recognize a scene based on a sketch made of very simple features. This ability is likely crucial for survival, when fast image recognition is necessary, and it is believed that a primal sketch is extracted very early in the visual processing. Such highly simplified representations can be sufficient for accurate object discrimination, but an open question is the role played by color in this process. Rich color information is available in natural scenes, yet artist's sketches are usually monochromatic; and, black-andwhite movies provide compelling representations of real world scenes. Also, the contrast sensitivity ofmore » color is low at fine spatial scales. We approach the question from the perspective of optimal information processing by a system endowed with limited computational resources. We show that when such limitations are taken into account, the intrinsic statistical properties of natural scenes imply that the most effective strategy is to ignore fine-scale color features and devote most of the bandwidth to gray-scale information. We find confirmation of these information-based predictions from psychophysics measurements of fast-viewing discrimination of natural scenes. As a result, we conclude that the lack of colored features in our visual representation, and our overall low sensitivity to high-frequency color components, are a consequence of an adaptation process, optimizing the size and power consumption of our brain for the visual world we live in.« less

  15. Chromatic information and feature detection in fast visual analysis

    SciTech Connect

    Del Viva, Maria M.; Punzi, Giovanni; Shevell, Steven K.

    The visual system is able to recognize a scene based on a sketch made of very simple features. This ability is likely crucial for survival, when fast image recognition is necessary, and it is believed that a primal sketch is extracted very early in the visual processing. Such highly simplified representations can be sufficient for accurate object discrimination, but an open question is the role played by color in this process. Rich color information is available in natural scenes, yet artist's sketches are usually monochromatic; and, black-andwhite movies provide compelling representations of real world scenes. Also, the contrast sensitivity ofmore » color is low at fine spatial scales. We approach the question from the perspective of optimal information processing by a system endowed with limited computational resources. We show that when such limitations are taken into account, the intrinsic statistical properties of natural scenes imply that the most effective strategy is to ignore fine-scale color features and devote most of the bandwidth to gray-scale information. We find confirmation of these information-based predictions from psychophysics measurements of fast-viewing discrimination of natural scenes. As a result, we conclude that the lack of colored features in our visual representation, and our overall low sensitivity to high-frequency color components, are a consequence of an adaptation process, optimizing the size and power consumption of our brain for the visual world we live in.« less

  16. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

    NASA Astrophysics Data System (ADS)

    Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

    2017-07-01

    Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

  17. Multi-species detection using multi-mode absorption spectroscopy (MUMAS)

    NASA Astrophysics Data System (ADS)

    Northern, J. H.; Thompson, A. W. J.; Hamilton, M. L.; Ewart, P.

    2013-06-01

    The detection of multiple species using a single laser and single detector employing multi-mode absorption spectroscopy (MUMAS) is reported. An in-house constructed, diode-pumped, Er:Yb:glass micro-laser operating at 1,565 nm with 10 modes separated by 18 GHz was used to record MUMAS signals in a gas mixture containing C2H2, N2O and CO. The components of the mixture were detected simultaneously by identifying multiple transitions in each of the species. By using temperature- and pressure-dependent modelled spectral fits to the data, partial pressures of each species in the mixture were determined with an uncertainty of ±2 %.

  18. Feature Sampling in Detection: Implications for the Measurement of Perceptual Independence

    ERIC Educational Resources Information Center

    Macho, Siegfried

    2007-01-01

    The article presents the feature sampling signal detection (FS-SDT) model, an extension of the multivariate signal detection (SDT) model. The FS-SDT model assumes that, because of attentional shifts, different subsets of features are sampled for different presentations of the same multidimensional stimulus. Contrary to the SDT model, the FS-SDT…

  19. A biomimetic algorithm for the improved detection of microarray features

    NASA Astrophysics Data System (ADS)

    Nicolau, Dan V., Jr.; Nicolau, Dan V.; Maini, Philip K.

    2007-02-01

    One the major difficulties of microarray technology relate to the processing of large and - importantly - error-loaded images of the dots on the chip surface. Whatever the source of these errors, those obtained in the first stage of data acquisition - segmentation - are passed down to the subsequent processes, with deleterious results. As it has been demonstrated recently that biological systems have evolved algorithms that are mathematically efficient, this contribution attempts to test an algorithm that mimics a bacterial-"patented" algorithm for the search of available space and nutrients to find, "zero-in" and eventually delimitate the features existent on the microarray surface.

  20. Pixel color feature enhancement for road signs detection

    NASA Astrophysics Data System (ADS)

    Zhang, Qieshi; Kamata, Sei-ichiro

    2010-02-01

    Road signs play an important role in our daily life which used to guide drivers to notice variety of road conditions and cautions. They provide important visual information that can help drivers operating their vehicles in a manner for enhancing traffic safety. The occurrence of some accidents can be reduced by using automatic road signs recognition system which can alert the drivers. This research attempts to develop a warning system to alert the drivers to notice the important road signs early enough to refrain road accidents from happening. For solving this, a non-linear weighted color enhancement method by pixels is presented. Due to the advantage of proposed method, different road signs can be detected from videos effectively. With suitably coefficients and operations, the experimental results have proved that the proposed method is robust, accurate and powerful in road signs detection.

  1. Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

    NASA Astrophysics Data System (ADS)

    Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

    2018-04-01

    In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

  2. Features versus context: An approach for precise and detailed detection and delineation of faces and facial features.

    PubMed

    Ding, Liya; Martinez, Aleix M

    2010-11-01

    The appearance-based approach to face detection has seen great advances in the last several years. In this approach, we learn the image statistics describing the texture pattern (appearance) of the object class we want to detect, e.g., the face. However, this approach has had limited success in providing an accurate and detailed description of the internal facial features, i.e., eyes, brows, nose, and mouth. In general, this is due to the limited information carried by the learned statistical model. While the face template is relatively rich in texture, facial features (e.g., eyes, nose, and mouth) do not carry enough discriminative information to tell them apart from all possible background images. We resolve this problem by adding the context information of each facial feature in the design of the statistical model. In the proposed approach, the context information defines the image statistics most correlated with the surroundings of each facial component. This means that when we search for a face or facial feature, we look for those locations which most resemble the feature yet are most dissimilar to its context. This dissimilarity with the context features forces the detector to gravitate toward an accurate estimate of the position of the facial feature. Learning to discriminate between feature and context templates is difficult, however, because the context and the texture of the facial features vary widely under changing expression, pose, and illumination, and may even resemble one another. We address this problem with the use of subclass divisions. We derive two algorithms to automatically divide the training samples of each facial feature into a set of subclasses, each representing a distinct construction of the same facial component (e.g., closed versus open eyes) or its context (e.g., different hairstyles). The first algorithm is based on a discriminant analysis formulation. The second algorithm is an extension of the AdaBoost approach. We provide

  3. Fluorescence detection of white-beam X-ray absorption anisotropy: towards element-sensitive projections of local atomic structure

    PubMed Central

    Korecki, P.; Tolkiehn, M.; Dąbrowski, K. M.; Novikov, D. V.

    2011-01-01

    Projections of the atomic structure around Nb atoms in a LiNbO3 single crystal were obtained from a white-beam X-ray absorption anisotropy (XAA) pattern detected using Nb K fluorescence. This kind of anisotropy results from the interference of X-rays inside a sample and, owing to the short coherence length of a white beam, is visible only at small angles around interatomic directions. Consequently, the main features of the recorded XAA corresponded to distorted real-space projections of dense-packed atomic planes and atomic rows. A quantitative analysis of XAA was carried out using a wavelet transform and allowed well resolved projections of Nb atoms to be obtained up to distances of 10 Å. The signal of nearest O atoms was detected indirectly by a comparison with model calculations. The measurement of white-beam XAA using characteristic radiation indicates the possibility of obtaining element-sensitive projections of the local atomic structure in more complex samples. PMID:21997909

  4. Optical resonance-enhanced absorption-based near-field immunochip biosensor for allergen detection.

    PubMed

    Maier, Irene; Morgan, Michael R A; Lindner, Wolfgang; Pittner, Fritz

    2008-04-15

    An optical immunochip biosensor has been developed as a rapid method for allergen detection in complex food matrixes, and its application evaluated for the detection of the egg white allergens, ovalbumin and ovomucoid. The optical near-field phenomenon underlying the basic principle of the sensor design is called resonance-enhanced absorption (REA), which utilizes gold nanoparticles (Au NPs) as signal transducers in a highly sensitive interferometric setup. Using this approach, a novel, simple, and rapid colorimetric solid-phase immunoassay on a planar chip substrate was realized in direct and sandwich assay formats, with a detection system that does not require any instrumentation for readout. Semiquantitative immunochemical responses are directly visible to the naked eye of the analyst. The biosensor shows concentration-dependent color development by capturing antibody-functionalized Au NPs on allergen-coated chips and has a detection limit of 1 ng/mL. To establish a rapid method, we took advantage of the physicochemical microenvironment of the Au NP-antibody bioconjugate to be bound directly over an interacting poly(styrene-methyl methacrylate) interlayer by an immobilized antigen. In the direct assay format, a coating time with allergen of only 5 min under "soft" nondenaturing conditions was sufficient for accurate reproducibility and sensitivity. In conclusion, the REA-based immunochip sensor is easy to fabricate, is reproducible and selective in its performance, has minimal technical requirements, and will enable high-throughput screening of affinity binding interactions in technological and medical applications.

  5. CHANDRA Detects Relativistic Broad Absorption Lines from APM 08279+5255

    NASA Astrophysics Data System (ADS)

    Chartas, G.; Brandt, W. N.; Gallagher, S. C.; Garmire, G. P.

    2002-11-01

    We report the discovery of X-ray broad absorption lines (BALs) from the BAL quasar APM 08279+5255 originating from material moving at relativistic velocities with respect to the central source. The large flux magnification by a factor of ~100 provided by the gravitational lens effect combined with the large redshift (z=3.91) of the quasar have facilitated the acquisition of the first high signal-to-noise X-ray spectrum of a quasar containing X-ray BALs. Our analysis of the X-ray spectrum of APM 08279+5255 places the rest-frame energies of the two observed absorption lines at 8.1 and 9.8 keV. The detection of each of these lines is significant at a greater than 99.9% confidence level based on the F-test. Assuming that the absorption lines are from Fe XXV Kα, the implied bulk velocities of the X-ray BALs are ~0.2c and ~0.4c, respectively. The observed high bulk velocities of the X-ray BALs combined with the relatively short recombination timescales of the X-ray-absorbing gas imply that the absorbers responsible for the X-ray BALs are located at radii of <~2×1017 cm, within the expected location of the UV absorber. With this implied geometry, the X-ray gas could provide the necessary shielding to prevent the UV absorber from being completely ionized by the central X-ray source, consistent with hydrodynamical simulations of line-driven disk winds. Estimated mass-outflow rates for the gas creating the X-ray BALs are typically less than a solar mass per year. Our spectral analysis also indicates that the continuum X-ray emission of APM 08279+5255 is consistent with that of a typical radio-quiet quasar with a spectral slope of Γ=1.72+0.06-0.05.

  6. Possible Detection of an Emission Cyclotron Resonance Scattering Feature from the Accretion-Powered Pulsar 4U 1626-67

    NASA Technical Reports Server (NTRS)

    Iwakiri, W. B.; Terada, Y.; Tashiro, M. S.; Mihara, T.; Angelini, L.; Yamada, S.; Enoto, T.; Makishima, K.; Nakajima, M.; Yoshida, A.

    2012-01-01

    We present analysis of 4U 1626-67, a 7.7 s pulsar in a low-mass X-ray binary system, observed with the hard X-ray detector of the Japanese X-ray satellite Suzaku in 2006 March for a net exposure of 88 ks. The source was detected at an average 10-60 keY flux of approx 4 x 10-10 erg / sq cm/ s. The phase-averaged spectrum is reproduced well by combining a negative and positive power-law times exponential cutoff (NPEX) model modified at approx 37 keY by a cyclotron resonance scattering feature (CRSF). The phase-resolved analysis shows that the spectra at the bright phases are well fit by the NPEX with CRSF model. On the other hand. the spectrum in the dim phase lacks the NPEX high-energy cutoff component, and the CRSF can be reproduced by either an emission or an absorption profile. When fitting the dim phase spectrum with the NPEX plus Gaussian model. we find that the feature is better described in terms of an emission rather than an absorption profile. The statistical significance of this result, evaluated by means of an F test, is between 2.91 x 10(exp -3) and 1.53 x 10(exp -5), taking into account the systematic errors in the background evaluation of HXD-PIN. We find that the emission profile is more feasible than the absorption one for comparing the physical parameters in other phases. Therefore, we have possibly detected an emission line at the cyclotron resonance energy in the dim phase.

  7. Invisible ink mark detection in the visible spectrum using absorption difference.

    PubMed

    Lee, Joong; Kong, Seong G; Kang, Tae-Yi; Kim, Byounghyun; Jeon, Oc-Yeub

    2014-03-01

    One of popular techniques in gambling fraud involves the use of invisible ink marks printed on the back surface of playing cards. Such covert patterns are transparent in the visible spectrum and therefore invisible to unaided human eyes. Invisible patterns can be made visible with ultraviolet (UV) illumination or a CCD camera installed with an infrared (IR) filter depending on the type of ink materials used. Cheating gamers often wear contact lenses or eyeglasses made of IR or UV filters to recognize the secret marks on the playing cards. This paper presents an image processing technique to reveal invisible ink patterns in the visible spectrum without the aid of special equipment such as UV lighting or IR filters. A printed invisible ink pattern leaves a thin coating on the surface with different refractive index for different wavelengths of light, which results in color dispersion or absorption difference. The proposed method finds the differences of color components caused by absorption difference to detect invisible ink patterns on the surface. Experiment results show that the proposed scheme is effective for both UV-active and IR-active invisible ink materials. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  8. Broadband magnetometry by infrared-absorption detection of nitrogen-vacancy ensembles in diamond

    SciTech Connect

    Acosta, V. M.; Bauch, E.; Jarmola, A.

    We demonstrate magnetometry by detection of the spin state of high-density nitrogen-vacancy ensembles in diamond using optical absorption at 1042 nm. With this technique, measurement contrast, and collection efficiency can approach unity, leading to an increase in magnetic sensitivity compared to the more common method of collecting red fluorescence. Working at 75 K with a sensor with effective volume 50x50x300 {mu}m{sup 3}, we project photon shot-noise limited sensitivity of 5 pT in one second of acquisition and bandwidth from dc to a few megahertz. Operation in a gradiometer configuration yields a noise floor of 7 nT{sub rms} at {approx}110 Hzmore » in one second of acquisition.« less

  9. Method of analyzing multiple sample simultaneously by detecting absorption and systems for use in such a method

    DOEpatents

    Yeung, Edward S.; Gong, Xiaoyi

    2004-09-07

    The present invention provides a method of analyzing multiple samples simultaneously by absorption detection. The method comprises: (i) providing a planar array of multiple containers, each of which contains a sample comprising at least one absorbing species, (ii) irradiating the planar array of multiple containers with a light source and (iii) detecting absorption of light with a detetion means that is in line with the light source at a distance of at leaat about 10 times a cross-sectional distance of a container in the planar array of multiple containers. The absorption of light by a sample indicates the presence of an absorbing species in it. The method can further comprise: (iv) measuring the amount of absorption of light detected in (iii) indicating the amount of the absorbing species in the sample. Also provided by the present invention is a system for use in the abov metho.The system comprises; (i) a light source comrnpising or consisting essentially of at leaat one wavelength of light, the absorption of which is to be detected, (ii) a planar array of multiple containers, and (iii) a detection means that is in line with the light source and is positioned in line with and parallel to the planar array of multiple contiainers at a distance of at least about 10 times a cross-sectional distance of a container.

  10. Diffuse-light absorption spectroscopy by fiber optics for detecting and quantifying the adulteration of extra virgin olive oil

    NASA Astrophysics Data System (ADS)

    Mignani, A. G.; Ciaccheri, L.; Ottevaere, H.; Thienpont, H.; Conte, L.; Marega, M.; Cichelli, A.; Attilio, C.; Cimato, A.

    2010-09-01

    A fiber optic setup for diffuse-light absorption spectroscopy in the wide 400-1700 nm spectral range is experimented for detecting and quantifying the adulteration of extra virgin olive oil caused by lower-grade olive oils. Absorption measurements provide spectral fingerprints of authentic and adulterated oils. A multivariate processing of spectroscopic data is applied for discriminating the type of adulterant and for predicting its fraction.

  11. Automatic detection of solar features in HSOS full-disk solar images using guided filter

    NASA Astrophysics Data System (ADS)

    Yuan, Fei; Lin, Jiaben; Guo, Jingjing; Wang, Gang; Tong, Liyue; Zhang, Xinwei; Wang, Bingxiang

    2018-02-01

    A procedure is introduced for the automatic detection of solar features using full-disk solar images from Huairou Solar Observing Station (HSOS), National Astronomical Observatories of China. In image preprocessing, median filter is applied to remove the noises. Guided filter is adopted to enhance the edges of solar features and restrain the solar limb darkening, which is first introduced into the astronomical target detection. Then specific features are detected by Otsu algorithm and further threshold processing technique. Compared with other automatic detection procedures, our procedure has some advantages such as real time and reliability as well as no need of local threshold. Also, it reduces the amount of computation largely, which is benefited from the efficient guided filter algorithm. The procedure has been tested on one month sequences (December 2013) of HSOS full-disk solar images and the result shows that the number of features detected by our procedure is well consistent with the manual one.

  12. Detecting submerged features in water: modeling, sensors, and measurements

    NASA Astrophysics Data System (ADS)

    Bostater, Charles R., Jr.; Bassetti, Luce

    2004-11-01

    It is becoming more important to understand the remote sensing systems and associated autonomous or semi-autonomous methodologies (robotic & mechatronics) that may be utilized in freshwater and marine aquatic environments. This need comes from several issues related not only to advances in our scientific understanding and technological capabilities, but also from the desire to insure that the risk associated with UXO (unexploded ordnance), related submerged mines, as well as submerged targets (such as submerged aquatic vegetation) and debris left from previous human activities are remotely sensed and identified followed by reduced risks through detection and removal. This paper will describe (a) remote sensing systems, (b) platforms (fixed and mobile, as well as to demonstrate (c) the value of thinking in terms of scalability as well as modularity in the design and application of new systems now being constructed within our laboratory and other laboratories, as well as future systems. New remote sensing systems - moving or fixed sensing systems, as well as autonomous or semi-autonomous robotic and mechatronic systems will be essential to secure domestic preparedness for humanitarian reasons. These remote sensing systems hold tremendous value, if thoughtfully designed for other applications which include environmental monitoring in ambient environments.

  13. Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter

    NASA Astrophysics Data System (ADS)

    Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin

    2018-02-01

    This paper presents a novel signal processing scheme, feature selection based multi-scale morphological filter (MMF), for train axle bearing fault detection. In this scheme, more than 30 feature indicators of vibration signals are calculated for axle bearings with different conditions and the features which can reflect fault characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on diagnosis of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting fault features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing faults.

  14. A Light-Emitting Diode- (LED-) Based Absorption Sensor for Simultaneous Detection of Carbon Monoxide and Carbon Dioxide.

    PubMed

    Thurmond, Kyle; Loparo, Zachary; Partridge, William; Vasu, Subith S

    2016-06-01

    A sensor was developed for simultaneous measurements of carbon monoxide (CO) and carbon dioxide (CO2) fluctuations in internal combustion engine exhaust gases. This sensor utilizes low-cost and compact light-emitting diodes (LEDs) that emit in the 3-5 µm wavelength range. An affordable, fast response sensor that can measure these gases has a broad application that can lead to more efficient, fuel-flexible engines and regulation of harmful emissions. Light emission from LEDs is spectrally broader and more spatially divergent when compared to that of lasers, which presented many design challenges. Optical design studies addressed some of the non-ideal characteristics of the LED emissions. Measurements of CO and CO2 were conducted using their fundamental absorption bands centered at 4.7 µm and 4.3 µm, respectively, while a 3.6 µm reference LED was used to account for scattering losses (due to soot, window deposits, etc.) common to the three measurement LEDs. Instrument validation and calibration was performed using a laboratory flow cell and bottled-gas mixtures. The sensor was able to detect CO2 and CO concentration changes as small as 30 ppm and 400 ppm, respectively. Because of the many control and monitor species with infra-red absorption features, which can be measured using the strategy described, this work demonstrates proof of concept for a wider range of fast (250 Hz) and low-cost sensors for gas measurement and process monitoring. © The Author(s) 2016.

  15. Evidence for ultra-fast outflows in radio-quiet AGNs. I. Detection and statistical incidence of Fe K-shell absorption lines

    NASA Astrophysics Data System (ADS)

    Tombesi, F.; Cappi, M.; Reeves, J. N.; Palumbo, G. G. C.; Yaqoob, T.; Braito, V.; Dadina, M.

    2010-10-01

    Context. Blue-shifted Fe K absorption lines have been detected in recent years between 7 and 10 keV in the X-ray spectra of several radio-quiet AGNs. The derived blue-shifted velocities of the lines can often reach mildly relativistic values, up to 0.2-0.4c. These findings are important because they suggest the presence of a previously unknown massive and highly ionized absorbing material outflowing from their nuclei, possibly connected with accretion disk winds/outflows. Aims: The scope of the present work is to statistically quantify the parameters and incidence of the blue-shifted Fe K absorption lines through a uniform analysis on a large sample of radio-quiet AGNs. This allows us to assess their global detection significance and to overcome any possible publication bias. Methods: We performed a blind search for narrow absorption features at energies greater than 6.4 keV in a sample of 42 radio-quiet AGNs observed with XMM-Newton. A simple uniform model composed by an absorbed power-law plus Gaussian emission and absorption lines provided a good fit for all the data sets. We derived the absorption lines parameters and calculated their detailed detection significance making use of the classical F-test and extensive Monte Carlo simulations. Results: We detect 36 narrow absorption lines on a total of 101 XMM-Newton EPIC pn observations. The number of absorption lines at rest-frame energies higher than 7 keV is 22. Their global probability to be generated by random fluctuations is very low, less than 3 × 10-8, and their detection have been independently confirmed by a spectral analysis of the MOS data, with associated random probability <10-7. We identify the lines as Fe XXV and Fe XXVI K-shell resonant absorption. They are systematically blue-shifted, with a velocity distribution ranging from zero up to ~0.3c, with a peak and mean value at ~0.1c. We detect variability of the lines on both EWs and blue-shifted velocities among different XMM-Newton observations

  16. Intensity-Stabilized Fast-Scanned Direct Absorption Spectroscopy Instrumentation Based on a Distributed Feedback Laser with Detection Sensitivity down to 4 × 10−6

    PubMed Central

    Zhao, Gang; Tan, Wei; Jia, Mengyuan; Hou, Jiajuan; Ma, Weiguang; Dong, Lei; Zhang, Lei; Feng, Xiaoxia; Wu, Xuechun; Yin, Wangbao; Xiao, Liantuan; Axner, Ove; Jia, Suotang

    2016-01-01

    A novel, intensity-stabilized, fast-scanned, direct absorption spectroscopy (IS-FS-DAS) instrumentation, based on a distributed feedback (DFB) diode laser, is developed. A fiber-coupled polarization rotator and a fiber-coupled polarizer are used to stabilize the intensity of the laser, which significantly reduces its relative intensity noise (RIN). The influence of white noise is reduced by fast scanning over the spectral feature (at 1 kHz), followed by averaging. By combining these two noise-reducing techniques, it is demonstrated that direct absorption spectroscopy (DAS) can be swiftly performed down to a limit of detection (LOD) (1σ) of 4 × 10−6, which opens up a number of new applications. PMID:27657082

  17. A systematic exploration of the micro-blog feature space for teens stress detection.

    PubMed

    Zhao, Liang; Li, Qi; Xue, Yuanyuan; Jia, Jia; Feng, Ling

    2016-01-01

    In the modern stressful society, growing teenagers experience severe stress from different aspects from school to friends, from self-cognition to inter-personal relationship, which negatively influences their smooth and healthy development. Being timely and accurately aware of teenagers psychological stress and providing effective measures to help immature teenagers to cope with stress are highly valuable to both teenagers and human society. Previous work demonstrates the feasibility to sense teenagers' stress from their tweeting contents and context on the open social media platform-micro-blog. However, a tweet is still too short for teens to express their stressful status in a comprehensive way. Considering the topic continuity from the tweeting content to the follow-up comments and responses between the teenager and his/her friends, we combine the content of comments and responses under the tweet to supplement the tweet content. Also, such friends' caring comments like "what happened?", "Don't worry!", "Cheer up!", etc. provide hints to teenager's stressful status. Hence, in this paper, we propose to systematically explore the micro-blog feature space, comprised of four kinds of features [tweeting content features (FW), posting features (FP), interaction features (FI), and comment-response features (FC) between teenagers and friends] for teenager' stress category and stress level detection. We extract and analyze these feature values and their impacts on teens stress detection. We evaluate the framework through a real user study of 36 high school students aged 17. Different classifiers are employed to detect potential stress categories and corresponding stress levels. Experimental results show that all the features in the feature space positively affect stress detection, and linguistic negative emotion, proportion of negative sentences, friends' caring comments and teen's reply rate play more significant roles than the rest features. Micro-blog platform provides

  18. Crack Damage Detection Method via Multiple Visual Features and Efficient Multi-Task Learning Model.

    PubMed

    Wang, Baoxian; Zhao, Weigang; Gao, Po; Zhang, Yufeng; Wang, Zhe

    2018-06-02

    This paper proposes an effective and efficient model for concrete crack detection. The presented work consists of two modules: multi-view image feature extraction and multi-task crack region detection. Specifically, multiple visual features (such as texture, edge, etc.) of image regions are calculated, which can suppress various background noises (such as illumination, pockmark, stripe, blurring, etc.). With the computed multiple visual features, a novel crack region detector is advocated using a multi-task learning framework, which involves restraining the variability for different crack region features and emphasizing the separability between crack region features and complex background ones. Furthermore, the extreme learning machine is utilized to construct this multi-task learning model, thereby leading to high computing efficiency and good generalization. Experimental results of the practical concrete images demonstrate that the developed algorithm can achieve favorable crack detection performance compared with traditional crack detectors.

  19. Optimized feature-detection for on-board vision-based surveillance

    NASA Astrophysics Data System (ADS)

    Gond, Laetitia; Monnin, David; Schneider, Armin

    2012-06-01

    The detection and matching of robust features in images is an important step in many computer vision applications. In this paper, the importance of the keypoint detection algorithms and their inherent parameters in the particular context of an image-based change detection system for IED detection is studied. Through extensive application-oriented experiments, we draw an evaluation and comparison of the most popular feature detectors proposed by the computer vision community. We analyze how to automatically adjust these algorithms to changing imaging conditions and suggest improvements in order to achieve more exibility and robustness in their practical implementation.

  20. Synthetic aperture radar target detection, feature extraction, and image formation techniques

    NASA Technical Reports Server (NTRS)

    Li, Jian

    1994-01-01

    This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images.

  1. Sequential feature selection for detecting buried objects using forward looking ground penetrating radar

    NASA Astrophysics Data System (ADS)

    Shaw, Darren; Stone, Kevin; Ho, K. C.; Keller, James M.; Luke, Robert H.; Burns, Brian P.

    2016-05-01

    Forward looking ground penetrating radar (FLGPR) has the benefit of detecting objects at a significant standoff distance. The FLGPR signal is radiated over a large surface area and the radar signal return is often weak. Improving detection, especially for buried in road targets, while maintaining an acceptable false alarm rate remains to be a challenging task. Various kinds of features have been developed over the years to increase the FLGPR detection performance. This paper focuses on investigating the use of as many features as possible for detecting buried targets and uses the sequential feature selection technique to automatically choose the features that contribute most for improving performance. Experimental results using data collected at a government test site are presented.

  2. Wavelength modulation absorption spectroscopy with 2 f detection using multiplexed diode lasers for rapid temperature measurements in gaseous flows

    NASA Astrophysics Data System (ADS)

    Liu, J. T. C.; Jeffries, J. B.; Hanson, R. K.

    Multiplexed fiber-coupled diode lasers are used to probe second-harmonic line shapes of two near-infrared water absorption features, at 1343 nm and 1392 nm, in order to infer temperatures in gases containing water vapor, such as combustion flows. Wavelength modulation is performed at 170 kHz, and is superimposed on 1-kHz wavelength scans in order to recover full second-harmonic line shapes. Digital waveform generation and lock-in detection are performed using a data-acquisition card installed in a PC. An optimal selection of the modulation indices is shown to greatly simplify data interpretation over extended temperature ranges and to minimize the need for calibration when performing 2 f ratio thermometry. A theoretical discussion of this optimized strategy for 2 f ratio thermometry, as well as results from experimental validations in a heated cell, at pressures up to atmospheric, are presented in order to illustrate the utility of this technique for rapid temperature measurements in gaseous flow fields.

  3. Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and CNN features.

    PubMed

    Xiao Jia; Meng, Max Q-H

    2017-07-01

    Gastrointestinal (GI) bleeding detection plays an essential role in wireless capsule endoscopy (WCE) examination. In this paper, we present a new approach for WCE bleeding detection that combines handcrafted (HC) features and convolutional neural network (CNN) features. Compared with our previous work, a smaller-scale CNN architecture is constructed to lower the computational cost. In experiments, we show that the proposed strategy is highly capable when training data is limited, and yields comparable or better results than the latest methods.

  4. Mitosis detection using generic features and an ensemble of cascade adaboosts.

    PubMed

    Tek, F Boray

    2013-01-01

    Mitosis count is one of the factors that pathologists use to assess the risk of metastasis and survival of the patients, which are affected by the breast cancer. We investigate an application of a set of generic features and an ensemble of cascade adaboosts to the automated mitosis detection. Calculation of the features rely minimally on object-level descriptions and thus require minimal segmentation. The proposed work was developed and tested on International Conference on Pattern Recognition (ICPR) 2012 mitosis detection contest data. We plotted receiver operating characteristics curves of true positive versus false positive rates; calculated recall, precision, F-measure, and region overlap ratio measures. WE TESTED OUR FEATURES WITH TWO DIFFERENT CLASSIFIER CONFIGURATIONS: 1) An ensemble of single adaboosts, 2) an ensemble of cascade adaboosts. On the ICPR 2012 mitosis detection contest evaluation, the cascade ensemble scored 54, 62.7, and 58, whereas the non-cascade version scored 68, 28.1, and 39.7 for the recall, precision, and F-measure measures, respectively. Mostly used features in the adaboost classifier rules were a shape-based feature, which counted granularity and a color-based feature, which relied on Red, Green, and Blue channel statistics. The features, which express the granular structure and color variations, are found useful for mitosis detection. The ensemble of adaboosts performs better than the individual adaboost classifiers. Moreover, the ensemble of cascaded adaboosts was better than the ensemble of single adaboosts for mitosis detection.

  5. Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture

    DOEpatents

    West, Phillip B [Idaho Falls, ID; Novascone, Stephen R [Idaho Falls, ID; Wright, Jerry P [Idaho Falls, ID

    2012-05-29

    Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture are described. According to one embodiment, an earth analysis method includes engaging a device with the earth, analyzing the earth in a single substantially lineal direction using the device during the engaging, and providing information regarding a subsurface feature of the earth using the analysis.

  6. Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture

    DOEpatents

    West, Phillip B [Idaho Falls, ID; Novascone, Stephen R [Idaho Falls, ID; Wright, Jerry P [Idaho Falls, ID

    2011-09-27

    Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture are described. According to one embodiment, an earth analysis method includes engaging a device with the earth, analyzing the earth in a single substantially lineal direction using the device during the engaging, and providing information regarding a subsurface feature of the earth using the analysis.

  7. An extensive analysis of various texture feature extractors to detect Diabetes Mellitus using facial specific regions.

    PubMed

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

    2017-04-01

    Researchers have recently discovered that Diabetes Mellitus can be detected through non-invasive computerized method. However, the focus has been on facial block color features. In this paper, we extensively study the effects of texture features extracted from facial specific regions at detecting Diabetes Mellitus using eight texture extractors. The eight methods are from four texture feature families: (1) statistical texture feature family: Image Gray-scale Histogram, Gray-level Co-occurance Matrix, and Local Binary Pattern, (2) structural texture feature family: Voronoi Tessellation, (3) signal processing based texture feature family: Gaussian, Steerable, and Gabor filters, and (4) model based texture feature family: Markov Random Field. In order to determine the most appropriate extractor with optimal parameter(s), various parameter(s) of each extractor are experimented. For each extractor, the same dataset (284 Diabetes Mellitus and 231 Healthy samples), classifiers (k-Nearest Neighbors and Support Vector Machines), and validation method (10-fold cross validation) are used. According to the experiments, the first and third families achieved a better outcome at detecting Diabetes Mellitus than the other two. The best texture feature extractor for Diabetes Mellitus detection is the Image Gray-scale Histogram with bin number=256, obtaining an accuracy of 99.02%, a sensitivity of 99.64%, and a specificity of 98.26% by using SVM. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies.

    PubMed

    Hussain, Lal; Ahmed, Adeel; Saeed, Sharjil; Rathore, Saima; Awan, Imtiaz Ahmed; Shah, Saeed Arif; Majid, Abdul; Idris, Adnan; Awan, Anees Ahmed

    2018-02-06

    Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.

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

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

  10. Evaluation of wavelet spectral features in pathological detection and discrimination of yellow rust and powdery mildew in winter wheat with hyperspectral reflectance data

    NASA Astrophysics Data System (ADS)

    Shi, Yue; Huang, Wenjiang; Zhou, Xianfeng

    2017-04-01

    Hyperspectral absorption features are important indicators of characterizing plant biophysical variables for the automatic diagnosis of crop diseases. Continuous wavelet analysis has proven to be an advanced hyperspectral analysis technique for extracting absorption features; however, specific wavelet features (WFs) and their relationship with pathological characteristics induced by different infestations have rarely been summarized. The aim of this research is to determine the most sensitive WFs for identifying specific pathological lesions from yellow rust and powdery mildew in winter wheat, based on 314 hyperspectral samples measured in field experiments in China in 2002, 2003, 2005, and 2012. The resultant WFs could be used as proxies to capture the major spectral absorption features caused by infestation of yellow rust or powdery mildew. Multivariate regression analysis based on these WFs outperformed conventional spectral features in disease detection; meanwhile, a Fisher discrimination model exhibited considerable potential for generating separable clusters for each infestation. Optimal classification returned an overall accuracy of 91.9% with a Kappa of 0.89. This paper also emphasizes the WFs and their relationship with pathological characteristics in order to provide a foundation for the further application of this approach in monitoring winter wheat diseases at the regional scale.

  11. Serendipitous Detection of {\\rm{H}}\\,{\\rm{I}} Absorption Sets the True Redshift of 4C +15.05 to z = 0.833

    NASA Astrophysics Data System (ADS)

    Jones, K. M.; Ghosh, T.; Salter, C. J.

    2018-06-01

    4C+15.05 (also known as NRAO 91, PKS 0202+14, or J0204+15) is a quintessential blazar. It has a luminous, variable radio spectrum, a super-luminal jet, and gamma-ray detections. Arecibo observations with the 700–800 MHz receiver on the 305 m diameter William E. Gordon Telescope detected, serendipitously, H I in absorption against 4C+15.05 while using it as a bandpass calibrator for another object in an H I absorption project. Although the redshift we derive is different from that commonly in use in the literature (nominally z = 0.405), it agrees very well with the value of z = 0.833 determined by Stickel et al. This absorption feature is best fitted by a sum of three Gaussians, which yield an average redshift of z = 0.8336 ± 0.0004, although without corresponding high-resolution imaging it is not possible to say whether the components are parts of outflows or inflows. A total column density of N(H I) = 2.39 ± 0.13 × 1021 cm‑2 is derived, relatively high compared to many radio-loud sources. These results are compared to various relationships in the literature.

  12. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

    PubMed Central

    Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

    2016-01-01

    Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated

  13. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection.

    PubMed

    Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

    2016-07-19

    Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated

  14. Attention in the processing of complex visual displays: detecting features and their combinations.

    PubMed

    Farell, B

    1984-02-01

    The distinction between operations in visual processing that are parallel and preattentive and those that are serial and attentional receives both theoretical and empirical support. According to Treisman's feature-integration theory, independent features are available preattentively, but attention is required to veridically combine features into objects. Certain evidence supporting this theory is consistent with a different interpretation, which was tested in four experiments. The first experiment compared the detection of features and feature combinations while eliminating a factor that confounded earlier comparisons. The resulting priority of access to combinatorial information suggests that features and nonlocal combinations of features are not connected solely by a bottom-up hierarchical convergence. Causes of the disparity between the results of Experiment 1 and the results of previous research were investigated in three subsequent experiments. The results showed that of the two confounded factors, it was the difference in the mapping of alternatives onto responses, not the differing attentional demands of features and objects, that underlaid the results of the previous research. The present results are thus counterexamples to the feature-integration theory. Aspects of this theory are shown to be subsumed by more general principles, which are discussed in terms of attentional processes in the detection of features, objects, and stimulus alternatives.

  15. LC-IMS-MS Feature Finder. Detecting Multidimensional Liquid Chromatography, Ion Mobility, and Mass Spectrometry Features in Complex Datasets

    SciTech Connect

    Crowell, Kevin L.; Slysz, Gordon W.; Baker, Erin Shammel

    2013-09-05

    We introduce a command line software application LC-IMS-MS Feature Finder that searches for molecular ion signatures in multidimensional liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data by clustering deisotoped peaks with similar monoisotopic mass, charge state, LC elution time, and ion mobility drift time values. The software application includes an algorithm for detecting and quantifying co-eluting chemical species, including species that exist in multiple conformations that may have been separated in the IMS dimension.

  16. Feature Detection of Curve Traffic Sign Image on The Bandung - Jakarta Highway

    NASA Astrophysics Data System (ADS)

    Naseer, M.; Supriadi, I.; Supangkat, S. H.

    2018-03-01

    Unsealed roadside and problems with the road surface are common causes of road crashes, particularly when those are combined with curves. Curve traffic sign is an important component for giving early warning to driver on traffic, especially on high-speed traffic like on the highway. Traffic sign detection has became a very interesting research now, and in this paper will be discussed about the detection of curve traffic sign. There are two types of curve signs are discussed, namely the curve turn to the left and the curve turn to the right and the all data sample used are the curves taken / recorded from some signs on the Bandung - Jakarta Highway. Feature detection of the curve signs use Speed Up Robust Feature (SURF) method, where the detected scene image is 800x450. From 45 curve turn to the right images, the system can detect the feature well to 35 images, where the success rate is 77,78%, while from the 45 curve turn to the left images, the system can detect the feature well to 34 images and the success rate is 75,56%, so the average accuracy in the detection process is 76,67%. While the average time for the detection process is 0.411 seconds.

  17. Detection of Emotional Faces: Salient Physical Features Guide Effective Visual Search

    ERIC Educational Resources Information Center

    Calvo, Manuel G.; Nummenmaa, Lauri

    2008-01-01

    In this study, the authors investigated how salient visual features capture attention and facilitate detection of emotional facial expressions. In a visual search task, a target emotional face (happy, disgusted, fearful, angry, sad, or surprised) was presented in an array of neutral faces. Faster detection of happy and, to a lesser extent,…

  18. Detection of emotional faces: salient physical features guide effective visual search.

    PubMed

    Calvo, Manuel G; Nummenmaa, Lauri

    2008-08-01

    In this study, the authors investigated how salient visual features capture attention and facilitate detection of emotional facial expressions. In a visual search task, a target emotional face (happy, disgusted, fearful, angry, sad, or surprised) was presented in an array of neutral faces. Faster detection of happy and, to a lesser extent, surprised and disgusted faces was found both under upright and inverted display conditions. Inversion slowed down the detection of these faces less than that of others (fearful, angry, and sad). Accordingly, the detection advantage involves processing of featural rather than configural information. The facial features responsible for the detection advantage are located in the mouth rather than the eye region. Computationally modeled visual saliency predicted both attentional orienting and detection. Saliency was greatest for the faces (happy) and regions (mouth) that were fixated earlier and detected faster, and there was close correspondence between the onset of the modeled saliency peak and the time at which observers initially fixated the faces. The authors conclude that visual saliency of specific facial features--especially the smiling mouth--is responsible for facilitated initial orienting, which thus shortens detection. (PsycINFO Database Record (c) 2008 APA, all rights reserved).

  19. Detection of corn and weed species by the combination of spectral, shape and textural features

    USDA-ARS?s Scientific Manuscript database

    Accurate detection of weeds in farmland can help reduce pesticide use and protect the agricultural environment. To develop intelligent equipment for weed detection, this study used an imaging spectrometer system, which supports micro-scale plant feature analysis by acquiring high-resolution hyper sp...

  20. Personalized features for attention detection in children with Attention Deficit Hyperactivity Disorder.

    PubMed

    Fahimi, Fatemeh; Guan, Cuntai; Wooi Boon Goh; Kai Keng Ang; Choon Guan Lim; Tih Shih Lee

    2017-07-01

    Measuring attention from electroencephalogram (EEG) has found applications in the treatment of Attention Deficit Hyperactivity Disorder (ADHD). It is of great interest to understand what features in EEG are most representative of attention. Intensive research has been done in the past and it has been proven that frequency band powers and their ratios are effective features in detecting attention. However, there are still unanswered questions, like, what features in EEG are most discriminative between attentive and non-attentive states? Are these features common among all subjects or are they subject-specific and must be optimized for each subject? Using Mutual Information (MI) to perform subject-specific feature selection on a large data set including 120 ADHD children, we found that besides theta beta ratio (TBR) which is commonly used in attention detection and neurofeedback, the relative beta power and theta/(alpha+beta) (TBAR) are also equally significant and informative for attention detection. Interestingly, we found that the relative theta power (which is also commonly used) may not have sufficient discriminative information itself (it is informative only for 3.26% of ADHD children). We have also demonstrated that although these features (relative beta power, TBR and TBAR) are the most important measures to detect attention on average, different subjects have different set of most discriminative features.

  1. Image feature detection and extraction techniques performance evaluation for development of panorama under different light conditions

    NASA Astrophysics Data System (ADS)

    Patil, Venkat P.; Gohatre, Umakant B.

    2018-04-01

    The technique of obtaining a wider field-of-view of an image to get high resolution integrated image is normally required for development of panorama of a photographic images or scene from a sequence of part of multiple views. There are various image stitching methods developed recently. For image stitching five basic steps are adopted stitching which are Feature detection and extraction, Image registration, computing homography, image warping and Blending. This paper provides review of some of the existing available image feature detection and extraction techniques and image stitching algorithms by categorizing them into several methods. For each category, the basic concepts are first described and later on the necessary modifications made to the fundamental concepts by different researchers are elaborated. This paper also highlights about the some of the fundamental techniques for the process of photographic image feature detection and extraction methods under various illumination conditions. The Importance of Image stitching is applicable in the various fields such as medical imaging, astrophotography and computer vision. For comparing performance evaluation of the techniques used for image features detection three methods are considered i.e. ORB, SURF, HESSIAN and time required for input images feature detection is measured. Results obtained finally concludes that for daylight condition, ORB algorithm found better due to the fact that less tome is required for more features extracted where as for images under night light condition it shows that SURF detector performs better than ORB/HESSIAN detectors.

  2. Features

    Science.gov Websites

    Los Alamos National Laboratory Search Site submit About Mission Business Newsroom Publications Los Innovation in New Mexico Los Alamos Collaboration for Explosives Detection (LACED) SensorNexus Exascale Computing Project (ECP) User Facilities Center for Integrated Nanotechnologies (CINT) Los Alamos Neutron

  3. Enhancement of the Feature Extraction Capability in Global Damage Detection Using Wavelet Theory

    NASA Technical Reports Server (NTRS)

    Saleeb, Atef F.; Ponnaluru, Gopi Krishna

    2006-01-01

    The main objective of this study is to assess the specific capabilities of the defect energy parameter technique for global damage detection developed by Saleeb and coworkers. The feature extraction is the most important capability in any damage-detection technique. Features are any parameters extracted from the processed measurement data in order to enhance damage detection. The damage feature extraction capability was studied extensively by analyzing various simulation results. The practical significance in structural health monitoring is that the detection at early stages of small-size defects is always desirable. The amount of changes in the structure's response due to these small defects was determined to show the needed level of accuracy in the experimental methods. The arrangement of fine/extensive sensor network to measure required data for the detection is an "unlimited" ability, but there is a difficulty to place extensive number of sensors on a structure. Therefore, an investigation was conducted using the measurements of coarse sensor network. The white and the pink noises, which cover most of the frequency ranges that are typically encountered in the many measuring devices used (e.g., accelerometers, strain gauges, etc.) are added to the displacements to investigate the effect of noisy measurements in the detection technique. The noisy displacements and the noisy damage parameter values are used to study the signal feature reconstruction using wavelets. The enhancement of the feature extraction capability was successfully achieved by the wavelet theory.

  4. The relationship study between image features and detection probability based on psychology experiments

    NASA Astrophysics Data System (ADS)

    Lin, Wei; Chen, Yu-hua; Wang, Ji-yuan; Gao, Hong-sheng; Wang, Ji-jun; Su, Rong-hua; Mao, Wei

    2011-04-01

    Detection probability is an important index to represent and estimate target viability, which provides basis for target recognition and decision-making. But it will expend a mass of time and manpower to obtain detection probability in reality. At the same time, due to the different interpretation of personnel practice knowledge and experience, a great difference will often exist in the datum obtained. By means of studying the relationship between image features and perception quantity based on psychology experiments, the probability model has been established, in which the process is as following.Firstly, four image features have been extracted and quantified, which affect directly detection. Four feature similarity degrees between target and background were defined. Secondly, the relationship between single image feature similarity degree and perception quantity was set up based on psychological principle, and psychological experiments of target interpretation were designed which includes about five hundred people for interpretation and two hundred images. In order to reduce image features correlativity, a lot of artificial synthesis images have been made which include images with single brightness feature difference, images with single chromaticity feature difference, images with single texture feature difference and images with single shape feature difference. By analyzing and fitting a mass of experiments datum, the model quantitys have been determined. Finally, by applying statistical decision theory and experimental results, the relationship between perception quantity with target detection probability has been found. With the verification of a great deal of target interpretation in practice, the target detection probability can be obtained by the model quickly and objectively.

  5. Load-Differential Features for Automated Detection of Fatigue Cracks Using Guided Waves (Preprint)

    DTIC Science & Technology

    2011-11-01

    AFRL-RX-WP-TP-2011-4363 LOAD-DIFFERENTIAL FEATURES FOR AUTOMATED DETECTION OF FATIGUE CRACKS USING GUIDED WAVES (PREPRINT) Jennifer E...AUTOMATED DETECTION OF FATIGUE CRACKS USING GUIDED WAVES (PREPRINT) 5a. CONTRACT NUMBER FA8650-09-C-5206 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER...tensile loads open fatigue cracks and thus enhance their detectability using ultrasonic methods. Here we introduce a class of load-differential methods

  6. Exploration of available feature detection and identification systems and their performance on radiographs

    NASA Astrophysics Data System (ADS)

    Wantuch, Andrew C.; Vita, Joshua A.; Jimenez, Edward S.; Bray, Iliana E.

    2016-10-01

    Despite object detection, recognition, and identification being very active areas of computer vision research, many of the available tools to aid in these processes are designed with only photographs in mind. Although some algorithms used specifically for feature detection and identification may not take explicit advantage of the colors available in the image, they still under-perform on radiographs, which are grayscale images. We are especially interested in the robustness of these algorithms, specifically their performance on a preexisting database of X-ray radiographs in compressed JPEG form, with multiple ways of describing pixel information. We will review various aspects of the performance of available feature detection and identification systems, including MATLABs Computer Vision toolbox, VLFeat, and OpenCV on our non-ideal database. In the process, we will explore possible reasons for the algorithms' lessened ability to detect and identify features from the X-ray radiographs.

  7. Characterizing Protein Complexes with UV absorption, Light Scattering, and Refractive Index Detection.

    NASA Astrophysics Data System (ADS)

    Trainoff, Steven

    2009-03-01

    Many modern pharmaceuticals and naturally occurring biomolecules consist of complexes of proteins and polyethylene glycol or carbohydrates. In the case of vaccine development, these complexes are often used to induce or amplify immune responses. For protein therapeutics they are used to modify solubility and function, or to control the rate of degradation and elimination of a drug from the body. Characterizing the stoichiometry of these complexes is an important industrial problem that presents a formidable challenge to analytical instrument designers. Traditional analytical methods, such as using florescent tagging, chemical assays, and mass spectrometry perturb the system so dramatically that the complexes are often destroyed or uncontrollably modified by the measurement. A solution to this problem consists of fractionating the samples and then measuring the fractions using sequential non-invasive detectors that are sensitive to different components of the complex. We present results using UV absorption, which is primarily sensitive to the protein fraction, Light Scattering, which measures the total weight average molar mass, and Refractive Index detection, which measures the net concentration. We also present a solution of the problem inter-detector band-broadening problem that has heretofore made this approach impractical. Presented will be instrumentation and an analysis method that overcome these obstacles and make this technique a reliable and robust way of non-invasively characterizing these industrially important compounds.

  8. Confocal Light Absorption and Scattering Spectroscopic (CLASS) imaging: From cancer detection to sub-cellular function

    NASA Astrophysics Data System (ADS)

    Qiu, Le

    Light scattering spectroscopy (LSS), an optical technique that relates the spectroscopic properties of light elastically scattered by small particles to their size, refractive index and shape, has been recently successfully employed for sensing morphological and biochemical properties of epithelial tissues and cells in vivo. LSS does not require exogenous markers, is non-invasive, and, due to its multispectral nature, can sense biological structures well beyond the diffraction limit. All that makes LSS be a very good candidate to be used both in clinical medicine for in vivo detection of disease and in cell biology to monitor cell function on the organelle scale. Recently we developed two LSS-based imaging modalities: clinical Polarized LSS (PLSS) Endoscopic Technique for locating early pre-cancerous changes in GI tract and Confocal Light Absorption and Scattering Spectroscopic (CLASS) Microscopy for studying cells in vivo without exogenous markers. One important application of the clinical PLSS endoscopic instrument, a noncontact scanning imaging device compatible with the standard clinical endoscopes and capable of detecting dysplastic changes, is to serve as a guide for biopsy in Barrett's esophagus (BE). The instrument detects parallel and perpendicular components of the polarized light, backscattered from epithelial tissues, and determines characteristics of epithelial nuclei from the residual spectra. It also can find tissue oxygenation, hemoglobin content and other properties from the diffuse light component. By rapidly scanning esophagus the PLSS endoscopic instrument makes sure the entire BE portion is scanned and examined for the presence of dysplasia. CLASS microscopy, on the other hand, combines principles of light scattering spectroscopy (LSS) with confocal microscopy. Its main purpose is to image cells on organelle scale in vivo without the use of exogenous labels which may affect the cell function. The confocal geometry selects specific region and

  9. Application of tunable diode laser absorption spectroscopy in the detection of oxygen

    NASA Astrophysics Data System (ADS)

    Zhou, Xin; Jin, Xing

    2015-10-01

    Most aircrafts is driven by chemic energy which is released in the combustion process. For improving the capability of engine and controlling the running on-time, the processes of fuel physics and chemistry need to be analysis by kinds of high quality sensor. In the research of designing and improving the processes of fuel physics and chemistry, the concentration, temperature and velocity of kinds of gas in the combustor need to be detected and measured. In addition, these engines and research equipments are always in the harsh environment of high temperature, high pressure and high speed. The harsh environment needs the sensor to be high reliability, well repetition, no cross- sensitivity between gases, and the traditional measurement system can't satisfy the metrical requirement well. Tunable diode laser absorption spectroscopy (TDLAS) analytic measurement technology can well satisfy the measurement in the harsh environment, which can support the whole measurement plan and high quality measurement system. Because the TDLAS sensor has the excellence of small bulk, light weight, high reliability and well specifically measurement, the TDLAS measurement technology has wide prospects. Different from most measurements, only a beam of laser can be pass through the measured environment by TDLAS, and the measurement equipment needn't be set in the harsh environment. So, the TDLAS equipment can't be interrupted by the measured equipment. The ability of subsistence in the harsh environment is very valuable, especially in the measurement on the subject of aerospace with environment of high speed, combustion and plasma. This paper focuses on the collecting the articles on the subject of oxygen detection of TDLAS. By analyzing the research and results of the articles, we conclude the central issues, difficulties and results. And we can get some instructive conclusions.

  10. Infrared video based gas leak detection method using modified FAST features

    NASA Astrophysics Data System (ADS)

    Wang, Min; Hong, Hanyu; Huang, Likun

    2018-03-01

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

  11. LC-IMS-MS Feature Finder: detecting multidimensional liquid chromatography, ion mobility and mass spectrometry features in complex datasets.

    PubMed

    Crowell, Kevin L; Slysz, Gordon W; Baker, Erin S; LaMarche, Brian L; Monroe, Matthew E; Ibrahim, Yehia M; Payne, Samuel H; Anderson, Gordon A; Smith, Richard D

    2013-11-01

    The addition of ion mobility spectrometry to liquid chromatography-mass spectrometry experiments requires new, or updated, software tools to facilitate data processing. We introduce a command line software application LC-IMS-MS Feature Finder that searches for molecular ion signatures in multidimensional liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data by clustering deisotoped peaks with similar monoisotopic mass, charge state, LC elution time and ion mobility drift time values. The software application includes an algorithm for detecting and quantifying co-eluting chemical species, including species that exist in multiple conformations that may have been separated in the IMS dimension. LC-IMS-MS Feature Finder is available as a command-line tool for download at http://omics.pnl.gov/software/LC-IMS-MS_Feature_Finder.php. The Microsoft.NET Framework 4.0 is required to run the software. All other dependencies are included with the software package. Usage of this software is limited to non-profit research to use (see README). rds@pnnl.gov. Supplementary data are available at Bioinformatics online.

  12. Combining heterogeneous features for colonic polyp detection in CTC based on semi-definite programming

    NASA Astrophysics Data System (ADS)

    Wang, Shijun; Yao, Jianhua; Petrick, Nicholas A.; Summers, Ronald M.

    2009-02-01

    Colon cancer is the second leading cause of cancer-related deaths in the United States. Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible combination for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features, called histogram of curvature features, are rotation, translation and scale invariant and can be treated as complementing our existing feature set. Then in order to make full use of the traditional features (defined as group A) and the new features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to identify an optimized classification kernel based on the combined set of features. We did leave-one-patient-out test on a CTC dataset which contained scans from 50 patients (with 90 6-9mm polyp detections). Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per patient rate of 7, the sensitivity on 6-9mm polyps using the combined features improved from 0.78 (Group A) and 0.73 (Group B) to 0.82 (p<=0.01).

  13. A ROC-based feature selection method for computer-aided detection and diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Songyuan; Zhang, Guopeng; Liao, Qimei; Zhang, Junying; Jiao, Chun; Lu, Hongbing

    2014-03-01

    Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier's suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.

  14. Fabric defect detection based on visual saliency using deep feature and low-rank recovery

    NASA Astrophysics Data System (ADS)

    Liu, Zhoufeng; Wang, Baorui; Li, Chunlei; Li, Bicao; Dong, Yan

    2018-04-01

    Fabric defect detection plays an important role in improving the quality of fabric product. In this paper, a novel fabric defect detection method based on visual saliency using deep feature and low-rank recovery was proposed. First, unsupervised training is carried out by the initial network parameters based on MNIST large datasets. The supervised fine-tuning of fabric image library based on Convolutional Neural Networks (CNNs) is implemented, and then more accurate deep neural network model is generated. Second, the fabric images are uniformly divided into the image block with the same size, then we extract their multi-layer deep features using the trained deep network. Thereafter, all the extracted features are concentrated into a feature matrix. Third, low-rank matrix recovery is adopted to divide the feature matrix into the low-rank matrix which indicates the background and the sparse matrix which indicates the salient defect. In the end, the iterative optimal threshold segmentation algorithm is utilized to segment the saliency maps generated by the sparse matrix to locate the fabric defect area. Experimental results demonstrate that the feature extracted by CNN is more suitable for characterizing the fabric texture than the traditional LBP, HOG and other hand-crafted features extraction method, and the proposed method can accurately detect the defect regions of various fabric defects, even for the image with complex texture.

  15. Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

    PubMed Central

    Adam, Asrul; Mohd Tumari, Mohd Zaidi; Mohamad, Mohd Saberi

    2014-01-01

    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

  16. Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.

    PubMed

    Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan

    2014-01-01

    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

  17. [Acoustic detection of absorption of millimeter-band electromagnetic waves in biological objects].

    PubMed

    Polnikov, I G; Putvinskiĭ, A V

    1988-01-01

    Principles of photoacoustic spectroscopy were applied to elaborate a new method for controlling millimeter electromagnetic waves absorption in biological objects. The method was used in investigations of frequency dependence of millimeter wave power absorption in vitro and in vivo in the commonly used experimental irradiation systems.

  18. The physics of heterodyne detection in the far-infrared: Transition from electric-field to photon-absorption detection in a simple system

    NASA Technical Reports Server (NTRS)

    Teich, M. C.

    1980-01-01

    The history of heterodyne detection is reviewed from the radiowave to the optical regions of the electromagnetic spectrum with emphasion the submillimeter/far infrared. The transition from electric field to photon absorption detection in a simple system is investigated. The response of an isolated two level detector to a coherent source of incident radiation is calculated for both heterodyne and video detection. When the processes of photon absorption and photon emission cannot be distinguished, the relative detected power at double- and sum-frequencies is found to be multiplied by a coefficient, which is less than or equal to unity, and which depends on the incident photon energy and on the effective temperature of the system.

  19. The effect of feature selection methods on computer-aided detection of masses in mammograms

    NASA Astrophysics Data System (ADS)

    Hupse, Rianne; Karssemeijer, Nico

    2010-05-01

    In computer-aided diagnosis (CAD) research, feature selection methods are often used to improve generalization performance of classifiers and shorten computation times. In an application that detects malignant masses in mammograms, we investigated the effect of using a selection criterion that is similar to the final performance measure we are optimizing, namely the mean sensitivity of the system in a predefined range of the free-response receiver operating characteristics (FROC). To obtain the generalization performance of the selected feature subsets, a cross validation procedure was performed on a dataset containing 351 abnormal and 7879 normal regions, each region providing a set of 71 mass features. The same number of noise features, not containing any information, were added to investigate the ability of the feature selection algorithms to distinguish between useful and non-useful features. It was found that significantly higher performances were obtained using feature sets selected by the general test statistic Wilks' lambda than using feature sets selected by the more specific FROC measure. Feature selection leads to better performance when compared to a system in which all features were used.

  20. Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

    NASA Astrophysics Data System (ADS)

    Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

    2014-07-01

    Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection technique is introduced in the task of epileptic seizure detection. The raw data are electroencephalography (EEG) signals. Using discrete wavelet transform, the biomedical signals were decomposed into several sets of wavelet coefficients. To reduce the dimension of these wavelet coefficients, a feature selection method that combines the strength of both filter and wrapper methods is proposed. Principal component analysis (PCA) is used as part of the filter method. As for wrapper method, the evolutionary harmony search (HS) algorithm is employed. This metaheuristic method aims at finding the best discriminating set of features from the original data. The obtained features were then used as input for an automated classifier, namely wavelet neural networks (WNNs). The WNNs model was trained to perform a binary classification task, that is, to determine whether a given EEG signal was normal or epileptic. For comparison purposes, different sets of features were also used as input. Simulation results showed that the WNNs that used the features chosen by the hybrid algorithm achieved the highest overall classification accuracy.

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

    PubMed

    Lakshmi, Priya G G; Domnic, S

    2014-12-01

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

  2. Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

    NASA Astrophysics Data System (ADS)

    Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

    2014-03-01

    Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

  3. GridMass: a fast two-dimensional feature detection method for LC/MS.

    PubMed

    Treviño, Victor; Yañez-Garza, Irma-Luz; Rodriguez-López, Carlos E; Urrea-López, Rafael; Garza-Rodriguez, Maria-Lourdes; Barrera-Saldaña, Hugo-Alberto; Tamez-Peña, José G; Winkler, Robert; Díaz de-la-Garza, Rocío-Isabel

    2015-01-01

    One of the initial and critical procedures for the analysis of metabolomics data using liquid chromatography and mass spectrometry is feature detection. Feature detection is the process to detect boundaries of the mass surface from raw data. It consists of detected abundances arranged in a two-dimensional (2D) matrix of mass/charge and elution time. MZmine 2 is one of the leading software environments that provide a full analysis pipeline for these data. However, the feature detection algorithms provided in MZmine 2 are based mainly on the analysis of one-dimension at a time. We propose GridMass, an efficient algorithm for 2D feature detection. The algorithm is based on landing probes across the chromatographic space that are moved to find local maxima providing accurate boundary estimations. We tested GridMass on a controlled marker experiment, on plasma samples, on plant fruits, and in a proteome sample. Compared with other algorithms, GridMass is faster and may achieve comparable or better sensitivity and specificity. As a proof of concept, GridMass has been implemented in Java under the MZmine 2 environment and is available at http://www.bioinformatica.mty.itesm.mx/GridMass and MASSyPup. It has also been submitted to the MZmine 2 developing community. Copyright © 2015 John Wiley & Sons, Ltd.

  4. Absorption features of chromophoric dissolved organic matter (CDOM) and tracing implication for dissolved organic carbon (DOC) in Changjiang Estuary, China

    NASA Astrophysics Data System (ADS)

    Zhang, X. Y.; Chen, X.; Deng, H.; Du, Y.; Jin, H. Y.

    2013-07-01

    Chromophoric dissolved organic matter (CDOM) represents the light absorbing fraction of dissolved organic carbon (DOC). Studies have shown that the optical properties of CDOM can be used to infer the distribution and diffusion characteristics of DOC in the estuary and coastal zone. The inversion of DOC concentrations from remote sensing has been implemented in certain regions. In this study we investigate the potential of tracing DOC from CDOM by the measurement of DOC, absorption spectrum of CDOM, Chla concentration, suspended sediment (SS), and salinity from cruises in different seasons around the Changjiang estuary. Our results show that around the Changjiang estuary the absorption coefficients of CDOM in general have the similar spatial and temporal characteristics as that of DOC, but the strength of the correlation between CDOM and DOC varies locally and seasonally. The input of pollutants from outside the estuary, the bloom of phytoplankton in spring, re-suspension of deposited sediment, and light bleaching all contribute to the local and seasonal variation of the correlation between DOC and CDOM. An inversion model for the determination of DOC from CDOM is established, but the stability of model parameters and its application in different environments need further study. We find that relative to the absorption coefficient of CDOM, the fitted parameters of the absorption spectrum of DOM are better indictors for the composition of DOC. In addition, it is found that the terrestrial input of DOC to Changjiang estuary is a typical two-stage dilution process instead of a linear diffusion process.

  5. Detection of obstacles on runway using Ego-Motion compensation and tracking of significant features

    NASA Technical Reports Server (NTRS)

    Kasturi, Rangachar (Principal Investigator); Camps, Octavia (Principal Investigator); Gandhi, Tarak; Devadiga, Sadashiva

    1996-01-01

    This report describes a method for obstacle detection on a runway for autonomous navigation and landing of an aircraft. Detection is done in the presence of extraneous features such as tiremarks. Suitable features are extracted from the image and warping using approximately known camera and plane parameters is performed in order to compensate ego-motion as far as possible. Residual disparity after warping is estimated using an optical flow algorithm. Features are tracked from frame to frame so as to obtain more reliable estimates of their motion. Corrections are made to motion parameters with the residual disparities using a robust method, and features having large residual disparities are signaled as obstacles. Sensitivity analysis of the procedure is also studied. Nelson's optical flow constraint is proposed to separate moving obstacles from stationary ones. A Bayesian framework is used at every stage so that the confidence in the estimates can be determined.

  6. A general purpose feature extractor for light detection and ranging data.

    PubMed

    Li, Yangming; Olson, Edwin B

    2010-01-01

    Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset.

  7. A General Purpose Feature Extractor for Light Detection and Ranging Data

    PubMed Central

    Li, Yangming; Olson, Edwin B.

    2010-01-01

    Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset. PMID:22163474

  8. Max-AUC Feature Selection in Computer-Aided Detection of Polyps in CT Colonography

    PubMed Central

    Xu, Jian-Wu; Suzuki, Kenji

    2014-01-01

    We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level. PMID:24608058

  9. Max-AUC feature selection in computer-aided detection of polyps in CT colonography.

    PubMed

    Xu, Jian-Wu; Suzuki, Kenji

    2014-03-01

    We propose a feature selection method based on a sequential forward floating selection (SFFS) procedure to improve the performance of a classifier in computerized detection of polyps in CT colonography (CTC). The feature selection method is coupled with a nonlinear support vector machine (SVM) classifier. Unlike the conventional linear method based on Wilks' lambda, the proposed method selected the most relevant features that would maximize the area under the receiver operating characteristic curve (AUC), which directly maximizes classification performance, evaluated based on AUC value, in the computer-aided detection (CADe) scheme. We presented two variants of the proposed method with different stopping criteria used in the SFFS procedure. The first variant searched all feature combinations allowed in the SFFS procedure and selected the subsets that maximize the AUC values. The second variant performed a statistical test at each step during the SFFS procedure, and it was terminated if the increase in the AUC value was not statistically significant. The advantage of the second variant is its lower computational cost. To test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda for a colonic-polyp database (25 polyps and 2624 nonpolyps). We extracted 75 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions. The two variants of the proposed feature selection method chose 29 and 7 features, respectively. Two SVM classifiers trained with these selected features yielded a 96% by-polyp sensitivity at false-positive (FP) rates of 4.1 and 6.5 per patient, respectively. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda that yielded 18.0 FPs per patient at the same sensitivity level.

  10. Combining Statistical and Geometric Features for Colonic Polyp Detection in CTC Based on Multiple Kernel Learning

    PubMed Central

    Wang, Shijun; Yao, Jianhua; Petrick, Nicholas; Summers, Ronald M.

    2010-01-01

    Colon cancer is the second leading cause of cancer-related deaths in the United States. Computed tomographic colonography (CTC) combined with a computer aided detection system provides a feasible approach for improving colonic polyps detection and increasing the use of CTC for colon cancer screening. To distinguish true polyps from false positives, various features extracted from polyp candidates have been proposed. Most of these traditional features try to capture the shape information of polyp candidates or neighborhood knowledge about the surrounding structures (fold, colon wall, etc.). In this paper, we propose a new set of shape descriptors for polyp candidates based on statistical curvature information. These features called histograms of curvature features are rotation, translation and scale invariant and can be treated as complementing existing feature set. Then in order to make full use of the traditional geometric features (defined as group A) and the new statistical features (group B) which are highly heterogeneous, we employed a multiple kernel learning method based on semi-definite programming to learn an optimized classification kernel from the two groups of features. We conducted leave-one-patient-out test on a CTC dataset which contained scans from 66 patients. Experimental results show that a support vector machine (SVM) based on the combined feature set and the semi-definite optimization kernel achieved higher FROC performance compared to SVMs using the two groups of features separately. At a false positive per scan rate of 5, the sensitivity of the SVM using the combined features improved from 0.77 (Group A) and 0.73 (Group B) to 0.83 (p ≤ 0.01). PMID:20953299

  11. Solar absorption Fourier Transform Infrared spectroscopy applied to detect SO2 plumes above Mexico City

    NASA Astrophysics Data System (ADS)

    Aldana-Vazquez, A.; Stremme, W.; Grutter, M.

    2010-12-01

    There are sources of emissions of sulfur dioxide (SO2) that disperse to the Metropolitan Area of Mexico City (MCMA). The sources can be divided into three categories: a) The active Popocatepetl volcano located 70 km SE from the center of Mexico City, b) the industrial area located approximately 70 km to the and c) other local sources located in the surroundings from the measurement.. Solar absorption infrared spectra are being recorded since 2007 above the campus of the Universidad Nacional Autónoma de México (UNAM, 19.33 N, 99.18 W, 2260 m.a.s.l.). The column of SO2 was retrieved from all the spectra recorded in 2008 with the retrieval code SFIT2. Enhancement of the SO2 column could be identified in different time periods. The origin of the detected SO2 is determined by correlating the SO2 column with a) its surface concentration measured in the surroundings by the monitoring stations from the city’s monitoring network of (RAMA), b) the height of the mixing layer measured at UNAM, and c) meteorological wind data (REDMET, NCEP-NARR, and SMN). The result shows that the extraordinary events are correlated with the mentioned sources, and the analysis confirms prior studies that the plume travels at different altitudes. The plume of the Popocatepetl volcano is transported according to the wind at 5000 m.a.s.l. while emissions from the industrial area northwest of the MCMA are dispersed at lower altitudes within the mixing layer.

  12. Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

    NASA Astrophysics Data System (ADS)

    Chan, Yi-Tung; Wang, Shuenn-Jyi; Tsai, Chung-Hsien

    2017-09-01

    Public safety is a matter of national security and people's livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method's performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.

  13. Significance of MPEG-7 textural features for improved mass detection in mammography.

    PubMed

    Eltonsy, Nevine H; Tourassi, Georgia D; Fadeev, Aleksey; Elmaghraby, Adel S

    2006-01-01

    The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to evaluate the performance of each set of features independently and merged into a back-propagation artificial neural network (BPANN) using the leave-one-out sampling scheme (LOOSS). The study was based on a database of 668 mammographic ROIs (340 depicting cancer regions and 328 depicting normal parenchyma). Overall, the ROC area index of the BPANN using the directional morphological features was Az=0.85+/-0.01. The MPEG-7 edge histogram descriptor-based BPNN showed an ROC area index of Az=0.71+/-0.01 while homogeneous textural descriptors using 30 and 120 channels helped the BPNN achieve similar ROC area indexes of Az=0.882+/-0.02 and Az=0.877+/-0.01 respectively. After merging the MPEG-7 homogeneous textural features with the directional neighborhood features the performance of the BPANN increased providing an ROC area index of Az=0.91+/-0.01. MPEG-7 homogeneous textural descriptor significantly improved the morphology-based detection scheme.

  14. Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall

    NASA Astrophysics Data System (ADS)

    Bouma, Henri; Baan, Jan; Burghouts, Gertjan J.; Eendebak, Pieter T.; van Huis, Jasper R.; Dijk, Judith; van Rest, Jeroen H. C.

    2014-10-01

    Proactive detection of incidents is required to decrease the cost of security incidents. This paper focusses on the automatic early detection of suspicious behavior of pickpockets with track-based features in a crowded shopping mall. Our method consists of several steps: pedestrian tracking, feature computation and pickpocket recognition. This is challenging because the environment is crowded, people move freely through areas which cannot be covered by a single camera, because the actual snatch is a subtle action, and because collaboration is complex social behavior. We carried out an experiment with more than 20 validated pickpocket incidents. We used a top-down approach to translate expert knowledge in features and rules, and a bottom-up approach to learn discriminating patterns with a classifier. The classifier was used to separate the pickpockets from normal passers-by who are shopping in the mall. We performed a cross validation to train and evaluate our system. In this paper, we describe our method, identify the most valuable features, and analyze the results that were obtained in the experiment. We estimate the quality of these features and the performance of automatic detection of (collaborating) pickpockets. The results show that many of the pickpockets can be detected at a low false alarm rate.

  15. Automated retrieval of cloud and aerosol properties from the ARM Raman lidar, part 1: feature detection

    SciTech Connect

    Thorsen, Tyler J.; Fu, Qiang; Newsom, Rob K.

    A Feature detection and EXtinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement (ARM) program’s Raman lidar (RL) has been developed. Presented here is part 1 of the FEX algorithm: the detection of features including both clouds and aerosols. The approach of FEX is to use multiple quantities— scattering ratios derived using elastic and nitro-gen channel signals from two fields of view, the scattering ratio derived using only the elastic channel, and the total volume depolarization ratio— to identify features using range-dependent detection thresholds. FEX is designed to be context-sensitive with thresholds determined for each profile by calculating the expectedmore » clear-sky signal and noise. The use of multiple quantities pro-vides complementary depictions of cloud and aerosol locations and allows for consistency checks to improve the accuracy of the feature mask. The depolarization ratio is shown to be particularly effective at detecting optically-thin features containing non-spherical particles such as cirrus clouds. Improve-ments over the existing ARM RL cloud mask are shown. The performance of FEX is validated against a collocated micropulse lidar and observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the ARM Darwin, Australia site. While we focus on a specific lidar system, the FEX framework presented here is suitable for other Raman or high spectral resolution lidars.« less

  16. Mouse epileptic seizure detection with multiple EEG features and simple thresholding technique

    NASA Astrophysics Data System (ADS)

    Tieng, Quang M.; Anbazhagan, Ashwin; Chen, Min; Reutens, David C.

    2017-12-01

    Objective. Epilepsy is a common neurological disorder characterized by recurrent, unprovoked seizures. The search for new treatments for seizures and epilepsy relies upon studies in animal models of epilepsy. To capture data on seizures, many applications require prolonged electroencephalography (EEG) with recordings that generate voluminous data. The desire for efficient evaluation of these recordings motivates the development of automated seizure detection algorithms. Approach. A new seizure detection method is proposed, based on multiple features and a simple thresholding technique. The features are derived from chaos theory, information theory and the power spectrum of EEG recordings and optimally exploit both linear and nonlinear characteristics of EEG data. Main result. The proposed method was tested with real EEG data from an experimental mouse model of epilepsy and distinguished seizures from other patterns with high sensitivity and specificity. Significance. The proposed approach introduces two new features: negative logarithm of adaptive correlation integral and power spectral coherence ratio. The combination of these new features with two previously described features, entropy and phase coherence, improved seizure detection accuracy significantly. Negative logarithm of adaptive correlation integral can also be used to compute the duration of automatically detected seizures.

  17. Semi supervised Learning of Feature Hierarchies for Object Detection in a Video (Open Access)

    DTIC Science & Technology

    2013-10-03

    dataset: PETS2009 Dataset, Oxford Town Center dataset [3], PNNL Parking Lot datasets [25] and CAVIAR cols1 dataset [1] for human detection. Be- sides, we...level features from TownCen- ter, ParkingLot, PETS09 and CAVIAR . As we can see that, the four set of features are visually very different from each other...information is more distinguished for detecting a person in the TownCen- ter than CAVIAR . Comparing figure 5(a) with 6(a), interest- ingly, the color

  18. Encoding properties of haltere neurons enable motion feature detection in a biological gyroscope

    PubMed Central

    Fox, Jessica L.; Fairhall, Adrienne L.; Daniel, Thomas L.

    2010-01-01

    The halteres of dipteran insects are essential sensory organs for flight control. They are believed to detect Coriolis and other inertial forces associated with body rotation during flight. Flies use this information for rapid flight control. We show that the primary afferent neurons of the haltere’s mechanoreceptors respond selectively with high temporal precision to multiple stimulus features. Although we are able to identify many stimulus features contributing to the response using principal component analysis, predictive models using only two features, common across the cell population, capture most of the cells’ encoding activity. However, different sensitivity to these two features permits each cell to respond to sinusoidal stimuli with a different preferred phase. This feature similarity, combined with diverse phase encoding, allows the haltere to transmit information at a high rate about numerous inertial forces, including Coriolis forces. PMID:20133721

  19. A new feature constituting approach to detection of vocal fold pathology

    NASA Astrophysics Data System (ADS)

    Hariharan, M.; Polat, Kemal; Yaacob, Sazali

    2014-08-01

    In the last two decades, non-invasive methods through acoustic analysis of voice signal have been proved to be excellent and reliable tool to diagnose vocal fold pathologies. This paper proposes a new feature vector based on the wavelet packet transform and singular value decomposition for the detection of vocal fold pathology. k-means clustering based feature weighting is proposed to increase the distinguishing performance of the proposed features. In this work, two databases Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and MAPACI speech pathology database are used. Four different supervised classifiers such as k-nearest neighbour (k-NN), least-square support vector machine, probabilistic neural network and general regression neural network are employed for testing the proposed features. The experimental results uncover that the proposed features give very promising classification accuracy of 100% for both MEEI database and MAPACI speech pathology database.

  20. Deep Spatial-Temporal Joint Feature Representation for Video Object Detection.

    PubMed

    Zhao, Baojun; Zhao, Boya; Tang, Linbo; Han, Yuqi; Wang, Wenzheng

    2018-03-04

    With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP).

  1. Detection of Coronal Mass Ejections Using Multiple Features and Space-Time Continuity

    NASA Astrophysics Data System (ADS)

    Zhang, Ling; Yin, Jian-qin; Lin, Jia-ben; Feng, Zhi-quan; Zhou, Jin

    2017-07-01

    Coronal Mass Ejections (CMEs) release tremendous amounts of energy in the solar system, which has an impact on satellites, power facilities and wireless transmission. To effectively detect a CME in Large Angle Spectrometric Coronagraph (LASCO) C2 images, we propose a novel algorithm to locate the suspected CME regions, using the Extreme Learning Machine (ELM) method and taking into account the features of the grayscale and the texture. Furthermore, space-time continuity is used in the detection algorithm to exclude the false CME regions. The algorithm includes three steps: i) define the feature vector which contains textural and grayscale features of a running difference image; ii) design the detection algorithm based on the ELM method according to the feature vector; iii) improve the detection accuracy rate by using the decision rule of the space-time continuum. Experimental results show the efficiency and the superiority of the proposed algorithm in the detection of CMEs compared with other traditional methods. In addition, our algorithm is insensitive to most noise.

  2. Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT.

    PubMed

    Lopez-Martin, Manuel; Carro, Belen; Sanchez-Esguevillas, Antonio; Lloret, Jaime

    2017-08-26

    The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.

  3. Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

    PubMed Central

    Carro, Belen; Sanchez-Esguevillas, Antonio

    2017-01-01

    The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery. PMID:28846608

  4. Change Detection in Uav Video Mosaics Combining a Feature Based Approach and Extended Image Differencing

    NASA Astrophysics Data System (ADS)

    Saur, Günter; Krüger, Wolfgang

    2016-06-01

    Change detection is an important task when using unmanned aerial vehicles (UAV) for video surveillance. We address changes of short time scale using observations in time distances of a few hours. Each observation (previous and current) is a short video sequence acquired by UAV in near-Nadir view. Relevant changes are, e.g., recently parked or moved vehicles. Examples for non-relevant changes are parallaxes caused by 3D structures of the scene, shadow and illumination changes, and compression or transmission artifacts. In this paper we present (1) a new feature based approach to change detection, (2) a combination with extended image differencing (Saur et al., 2014), and (3) the application to video sequences using temporal filtering. In the feature based approach, information about local image features, e.g., corners, is extracted in both images. The label "new object" is generated at image points, where features occur in the current image and no or weaker features are present in the previous image. The label "vanished object" corresponds to missing or weaker features in the current image and present features in the previous image. This leads to two "directed" change masks and differs from image differencing where only one "undirected" change mask is extracted which combines both label types to the single label "changed object". The combination of both algorithms is performed by merging the change masks of both approaches. A color mask showing the different contributions is used for visual inspection by a human image interpreter.

  5. Vehicle parts detection based on Faster - RCNN with location constraints of vehicle parts feature point

    NASA Astrophysics Data System (ADS)

    Yang, Liqin; Sang, Nong; Gao, Changxin

    2018-03-01

    Vehicle parts detection plays an important role in public transportation safety and mobility. The detection of vehicle parts is to detect the position of each vehicle part. We propose a new approach by combining Faster RCNN and three level cascaded convolutional neural network (DCNN). The output of Faster RCNN is a series of bounding boxes with coordinate information, from which we can locate vehicle parts. DCNN can precisely predict feature point position, which is the center of vehicle part. We design an output strategy by combining these two results. There are two advantages for this. The quality of the bounding boxes are greatly improved, which means vehicle parts feature point position can be located more precise. Meanwhile we preserve the position relationship between vehicle parts and effectively improve the validity and reliability of the result. By using our algorithm, the performance of the vehicle parts detection improve obviously compared with Faster RCNN.

  6. Behavioral features recognition and oestrus detection based on fast approximate clustering algorithm in dairy cows

    NASA Astrophysics Data System (ADS)

    Tian, Fuyang; Cao, Dong; Dong, Xiaoning; Zhao, Xinqiang; Li, Fade; Wang, Zhonghua

    2017-06-01

    Behavioral features recognition was an important effect to detect oestrus and sickness in dairy herds and there is a need for heat detection aid. The detection method was based on the measure of the individual behavioural activity, standing time, and temperature of dairy using vibrational sensor and temperature sensor in this paper. The data of behavioural activity index, standing time, lying time and walking time were sent to computer by lower power consumption wireless communication system. The fast approximate K-means algorithm (FAKM) was proposed to deal the data of the sensor for behavioral features recognition. As a result of technical progress in monitoring cows using computers, automatic oestrus detection has become possible.

  7. Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images.

    PubMed

    Rakotomamonjy, Alain; Petitjean, Caroline; Salaün, Mathieu; Thiberville, Luc

    2014-06-01

    To assess the feasibility of lung cancer diagnosis using fibered confocal fluorescence microscopy (FCFM) imaging technique and scattering features for pattern recognition. FCFM imaging technique is a new medical imaging technique for which interest has yet to be established for diagnosis. This paper addresses the problem of lung cancer detection using FCFM images and, as a first contribution, assesses the feasibility of computer-aided diagnosis through these images. Towards this aim, we have built a pattern recognition scheme which involves a feature extraction stage and a classification stage. The second contribution relies on the features used for discrimination. Indeed, we have employed the so-called scattering transform for extracting discriminative features, which are robust to small deformations in the images. We have also compared and combined these features with classical yet powerful features like local binary patterns (LBP) and their variants denoted as local quinary patterns (LQP). We show that scattering features yielded to better recognition performances than classical features like LBP and their LQP variants for the FCFM image classification problems. Another finding is that LBP-based and scattering-based features provide complementary discriminative information and, in some situations, we empirically establish that performance can be improved when jointly using LBP, LQP and scattering features. In this work we analyze the joint capability of FCFM images and scattering features for lung cancer diagnosis. The proposed method achieves a good recognition rate for such a diagnosis problem. It also performs well when used in conjunction with other features for other classical medical imaging classification problems. Copyright © 2014 Elsevier B.V. All rights reserved.

  8. Vision-based in-line fabric defect detection using yarn-specific shape features

    NASA Astrophysics Data System (ADS)

    Schneider, Dorian; Aach, Til

    2012-01-01

    We develop a methodology for automatic in-line flaw detection in industrial woven fabrics. Where state of the art detection algorithms apply texture analysis methods to operate on low-resolved ({200 ppi) image data, we describe here a process flow to segment single yarns in high-resolved ({1000 ppi) textile images. Four yarn shape features are extracted, allowing a precise detection and measurement of defects. The degree of precision reached allows a classification of detected defects according to their nature, providing an innovation in the field of automatic fabric flaw detection. The design has been carried out to meet real time requirements and face adverse conditions caused by loom vibrations and dirt. The entire process flow is discussed followed by an evaluation using a database with real-life industrial fabric images. This work pertains to the construction of an on-loom defect detection system to be used in manufacturing practice.

  9. An on-board pedestrian detection and warning system with features of side pedestrian

    NASA Astrophysics Data System (ADS)

    Cheng, Ruzhong; Zhao, Yong; Wong, ChupChung; Chan, KwokPo; Xu, Jiayao; Wang, Xin'an

    2012-01-01

    Automotive Active Safety(AAS) is the main branch of intelligence automobile study and pedestrian detection is the key problem of AAS, because it is related with the casualties of most vehicle accidents. For on-board pedestrian detection algorithms, the main problem is to balance efficiency and accuracy to make the on-board system available in real scenes, so an on-board pedestrian detection and warning system with the algorithm considered the features of side pedestrian is proposed. The system includes two modules, pedestrian detecting and warning module. Haar feature and a cascade of stage classifiers trained by Adaboost are first applied, and then HOG feature and SVM classifier are used to refine false positives. To make these time-consuming algorithms available in real-time use, a divide-window method together with operator context scanning(OCS) method are applied to increase efficiency. To merge the velocity information of the automotive, the distance of the detected pedestrian is also obtained, so the system could judge if there is a potential danger for the pedestrian in the front. With a new dataset captured in urban environment with side pedestrians on zebra, the embedded system and its algorithm perform an on-board available result on side pedestrian detection.

  10. Feature Transformation Detection Method with Best Spectral Band Selection Process for Hyper-spectral Imaging

    NASA Astrophysics Data System (ADS)

    Chen, Hai-Wen; McGurr, Mike; Brickhouse, Mark

    2015-11-01

    We present a newly developed feature transformation (FT) detection method for hyper-spectral imagery (HSI) sensors. In essence, the FT method, by transforming the original features (spectral bands) to a different feature domain, may considerably increase the statistical separation between the target and background probability density functions, and thus may significantly improve the target detection and identification performance, as evidenced by the test results in this paper. We show that by differentiating the original spectral, one can completely separate targets from the background using a single spectral band, leading to perfect detection results. In addition, we have proposed an automated best spectral band selection process with a double-threshold scheme that can rank the available spectral bands from the best to the worst for target detection. Finally, we have also proposed an automated cross-spectrum fusion process to further improve the detection performance in lower spectral range (<1000 nm) by selecting the best spectral band pair with multivariate analysis. Promising detection performance has been achieved using a small background material signature library for concept-proving, and has then been further evaluated and verified using a real background HSI scene collected by a HYDICE sensor.

  11. FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection.

    PubMed

    Noto, Keith; Brodley, Carla; Slonim, Donna

    2012-01-01

    Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called "normal" instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised anomaly detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised anomaly detection task, compared to well-known state-of-the-art anomaly detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach.

  12. FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection

    PubMed Central

    Brodley, Carla; Slonim, Donna

    2011-01-01

    Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called “normal” instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised anomaly detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised anomaly detection task, compared to well-known state-of-the-art anomaly detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach. PMID:22639542

  13. Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

    NASA Astrophysics Data System (ADS)

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-03-01

    Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by

  14. 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.

  15. Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis.

    PubMed

    Zhang, Hanyuan; Tian, Xuemin; Deng, Xiaogang; Cao, Yuping

    2018-05-16

    As an attractive nonlinear dynamic data analysis tool, global preserving kernel slow feature analysis (GKSFA) has achieved great success in extracting the high nonlinearity and inherently time-varying dynamics of batch process. However, GKSFA is an unsupervised feature extraction method and lacks the ability to utilize batch process class label information, which may not offer the most effective means for dealing with batch process monitoring. To overcome this problem, we propose a novel batch process monitoring method based on the modified GKSFA, referred to as discriminant global preserving kernel slow feature analysis (DGKSFA), by closely integrating discriminant analysis and GKSFA. The proposed DGKSFA method can extract discriminant feature of batch process as well as preserve global and local geometrical structure information of observed data. For the purpose of fault detection, a monitoring statistic is constructed based on the distance between the optimal kernel feature vectors of test data and normal data. To tackle the challenging issue of nonlinear fault variable identification, a new nonlinear contribution plot method is also developed to help identifying the fault variable after a fault is detected, which is derived from the idea of variable pseudo-sample trajectory projection in DGKSFA nonlinear biplot. Simulation results conducted on a numerical nonlinear dynamic system and the benchmark fed-batch penicillin fermentation process demonstrate that the proposed process monitoring and fault diagnosis approach can effectively detect fault and distinguish fault variables from normal variables. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  16. Cue combination in a combined feature contrast detection and figure identification task.

    PubMed

    Meinhardt, Günter; Persike, Malte; Mesenholl, Björn; Hagemann, Cordula

    2006-11-01

    Target figures defined by feature contrast in spatial frequency, orientation or both cues had to be detected in Gabor random fields and their shape had to be identified in a dual task paradigm. Performance improved with increasing feature contrast and was strongly correlated among both tasks. Subjects performed significantly better with combined cues than with single cues. The improvement due to cue summation was stronger than predicted by the assumption of independent feature specific mechanisms, and increased with the performance level achieved with single cues until it was limited by ceiling effects. Further, cue summation was also strongly correlated among tasks: when there was benefit due to the additional cue in feature contrast detection, there was also benefit in figure identification. For the same performance level achieved with single cues, cue summation was generally larger in figure identification than in feature contrast detection, indicating more benefit when processes of shape and surface formation are involved. Our results suggest that cue combination improves spatial form completion and figure-ground segregation in noisy environments, and therefore leads to more stable object vision.

  17. Using Activity-Related Behavioural Features towards More Effective Automatic Stress Detection

    PubMed Central

    Giakoumis, Dimitris; Drosou, Anastasios; Cipresso, Pietro; Tzovaras, Dimitrios; Hassapis, George; Gaggioli, Andrea; Riva, Giuseppe

    2012-01-01

    This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing. PMID:23028461

  18. Comparing experts and novices in Martian surface feature change detection and identification

    NASA Astrophysics Data System (ADS)

    Wardlaw, Jessica; Sprinks, James; Houghton, Robert; Muller, Jan-Peter; Sidiropoulos, Panagiotis; Bamford, Steven; Marsh, Stuart

    2018-02-01

    Change detection in satellite images is a key concern of the Earth Observation field for environmental and climate change monitoring. Satellite images also provide important clues to both the past and present surface conditions of other planets, which cannot be validated on the ground. With the volume of satellite imagery continuing to grow, the inadequacy of computerised solutions to manage and process imagery to the required professional standard is of critical concern. Whilst studies find the crowd sourcing approach suitable for the counting of impact craters in single images, images of higher resolution contain a much wider range of features, and the performance of novices in identifying more complex features and detecting change, remains unknown. This paper presents a first step towards understanding whether novices can identify and annotate changes in different geomorphological features. A website was developed to enable visitors to flick between two images of the same location on Mars taken at different times and classify 1) if a surface feature changed and if so, 2) what feature had changed from a pre-defined list of six. Planetary scientists provided ;expert; data against which classifications made by novices could be compared when the project subsequently went public. Whilst no significant difference was found in images identified with surface changes by expert and novices, results exhibited differences in consensus within and between experts and novices when asked to classify the type of change. Experts demonstrated higher levels of agreement in classification of changes as dust devil tracks, slope streaks and impact craters than other features, whilst the consensus of novices was consistent across feature types; furthermore, the level of consensus amongst regardless of feature type. These trends are secondary to the low levels of consensus found, regardless of feature type or classifier expertise. These findings demand the attention of researchers who

  19. A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection

    Treesearch

    D. Earl Kline; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman; Robert L. Brisbin

    1993-01-01

    A multiple sensor machine vision prototype is being developed to scan full size hardwood lumber at industrial speeds for automatically detecting features such as knots holes, wane, stain, splits, checks, and color. The prototype integrates a multiple sensor imaging system, a materials handling system, a computer system, and application software. The prototype provides...

  20. Fast detection of vascular plaque in optical coherence tomography images using a reduced feature set

    NASA Astrophysics Data System (ADS)

    Prakash, Ammu; Ocana Macias, Mariano; Hewko, Mark; Sowa, Michael; Sherif, Sherif

    2018-03-01

    Optical coherence tomography (OCT) images are capable of detecting vascular plaque by using the full set of 26 Haralick textural features and a standard K-means clustering algorithm. However, the use of the full set of 26 textural features is computationally expensive and may not be feasible for real time implementation. In this work, we identified a reduced set of 3 textural feature which characterizes vascular plaque and used a generalized Fuzzy C-means clustering algorithm. Our work involves three steps: 1) the reduction of a full set 26 textural feature to a reduced set of 3 textural features by using genetic algorithm (GA) optimization method 2) the implementation of an unsupervised generalized clustering algorithm (Fuzzy C-means) on the reduced feature space, and 3) the validation of our results using histology and actual photographic images of vascular plaque. Our results show an excellent match with histology and actual photographic images of vascular tissue. Therefore, our results could provide an efficient pre-clinical tool for the detection of vascular plaque in real time OCT imaging.

  1. Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

    NASA Astrophysics Data System (ADS)

    Klomp, Sander; van der Sommen, Fons; Swager, Anne-Fré; Zinger, Svitlana; Schoon, Erik J.; Curvers, Wouter L.; Bergman, Jacques J.; de With, Peter H. N.

    2017-03-01

    Volumetric Laser Endomicroscopy (VLE) is a promising technique for the detection of early neoplasia in Barrett's Esophagus (BE). VLE generates hundreds of high resolution, grayscale, cross-sectional images of the esophagus. However, at present, classifying these images is a time consuming and cumbersome effort performed by an expert using a clinical prediction model. This paper explores the feasibility of using computer vision techniques to accurately predict the presence of dysplastic tissue in VLE BE images. Our contribution is threefold. First, a benchmarking is performed for widely applied machine learning techniques and feature extraction methods. Second, three new features based on the clinical detection model are proposed, having superior classification accuracy and speed, compared to earlier work. Third, we evaluate automated parameter tuning by applying simple grid search and feature selection methods. The results are evaluated on a clinically validated dataset of 30 dysplastic and 30 non-dysplastic VLE images. Optimal classification accuracy is obtained by applying a support vector machine and using our modified Haralick features and optimal image cropping, obtaining an area under the receiver operating characteristic of 0.95 compared to the clinical prediction model at 0.81. Optimal execution time is achieved using a proposed mean and median feature, which is extracted at least factor 2.5 faster than alternative features with comparable performance.

  2. Detecting Service Chains and Feature Interactions in Sensor-Driven Home Network Services

    PubMed Central

    Inada, Takuya; Igaki, Hiroshi; Ikegami, Kosuke; Matsumoto, Shinsuke; Nakamura, Masahide; Kusumoto, Shinji

    2012-01-01

    Sensor-driven services often cause chain reactions, since one service may generate an environmental impact that automatically triggers another service. We first propose a framework that can formalize and detect such service chains based on ECA (event, condition, action) rules. Although the service chain can be a major source of feature interactions, not all service chains lead to harmful interactions. Therefore, we then propose a method that identifies feature interactions within the service chains. Specifically, we characterize the degree of deviation of every service chain by evaluating the gap between expected and actual service states. An experimental evaluation demonstrates that the proposed method successfully detects 11 service chains and 6 feature interactions within 7 practical sensor-driven services. PMID:23012499

  3. Hybrid image representation learning model with invariant features for basal cell carcinoma detection

    NASA Astrophysics Data System (ADS)

    Arevalo, John; Cruz-Roa, Angel; González, Fabio A.

    2013-11-01

    This paper presents a novel method for basal-cell carcinoma detection, which combines state-of-the-art methods for unsupervised feature learning (UFL) and bag of features (BOF) representation. BOF, which is a form of representation learning, has shown a good performance in automatic histopathology image classi cation. In BOF, patches are usually represented using descriptors such as SIFT and DCT. We propose to use UFL to learn the patch representation itself. This is accomplished by applying a topographic UFL method (T-RICA), which automatically learns visual invariance properties of color, scale and rotation from an image collection. These learned features also reveals these visual properties associated to cancerous and healthy tissues and improves carcinoma detection results by 7% with respect to traditional autoencoders, and 6% with respect to standard DCT representations obtaining in average 92% in terms of F-score and 93% of balanced accuracy.

  4. Specification of Butyltins and Methyltins in Seawater and Marine Sediments by Hydride Derivatization and Atomic Absorption Detection.

    DTIC Science & Technology

    1985-07-01

    Corporation . - P.F. Seligman . . Naval Ocean Systems Center fTW G. Vafa University of Hawaii Research Corporation P.M. Stang DEC 0 4 198 1: San Diego State...NAVFAC 032) under the Marine Environmental Quality Assessment Program. Released by Under authority of P. F. Seligman , Head S. Yamamoto, Head Marine...SEDIMENTS BY HYDRIDE DERIVATIZATION AND ATOMIC ABSORPTION DETECTION 12 PERSONAL AUJTHORWS) A. 0. Valkirs, P. F. Seligman , G. Vafa, P. M. Stang, V. Homner

  5. The impact of signal normalization on seizure detection using line length features.

    PubMed

    Logesparan, Lojini; Rodriguez-Villegas, Esther; Casson, Alexander J

    2015-10-01

    Accurate automated seizure detection remains a desirable but elusive target for many neural monitoring systems. While much attention has been given to the different feature extractions that can be used to highlight seizure activity in the EEG, very little formal attention has been given to the normalization that these features are routinely paired with. This normalization is essential in patient-independent algorithms to correct for broad-level differences in the EEG amplitude between people, and in patient-dependent algorithms to correct for amplitude variations over time. It is crucial, however, that the normalization used does not have a detrimental effect on the seizure detection process. This paper presents the first formal investigation into the impact of signal normalization techniques on seizure discrimination performance when using the line length feature to emphasize seizure activity. Comparing five normalization methods, based upon the mean, median, standard deviation, signal peak and signal range, we demonstrate differences in seizure detection accuracy (assessed as the area under a sensitivity-specificity ROC curve) of up to 52 %. This is despite the same analysis feature being used in all cases. Further, changes in performance of up to 22 % are present depending on whether the normalization is applied to the raw EEG itself or directly to the line length feature. Our results highlight the median decaying memory as the best current approach for providing normalization when using line length features, and they quantify the under-appreciated challenge of providing signal normalization that does not impair seizure detection algorithm performance.

  6. Automatic Feature Detection, Description and Matching from Mobile Laser Scanning Data and Aerial Imagery

    NASA Astrophysics Data System (ADS)

    Hussnain, Zille; Oude Elberink, Sander; Vosselman, George

    2016-06-01

    In mobile laser scanning systems, the platform's position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of nadir aerial and MLSPC ortho images. In the data pre-processing step the MLSPC is patch-wise cropped and converted to ortho images. Furthermore, each aerial image patch covering the area of the corresponding MLSPC patch is also cropped from the aerial image. For feature detection, we implemented an adaptive variant of Harris-operator to automatically detect corner feature points on the vertices of road markings. In feature description phase, we used the LATCH binary descriptor, which is robust to data from different sensors. For descriptor matching, we developed an outlier filtering technique, which exploits the arrangements of relative Euclidean-distances and angles between corresponding sets of feature points. We found that the positioning accuracy of the computed correspondence has achieved the pixel level accuracy, where the image resolution is 12cm. Furthermore, the developed approach is reliable when enough road markings are available in the data sets. We conclude that, in urban areas, the developed approach can reliably extract features necessary to improve the MLSPC accuracy to pixel level.

  7. Fast detection of covert visuospatial attention using hybrid N2pc and SSVEP features

    NASA Astrophysics Data System (ADS)

    Xu, Minpeng; Wang, Yijun; Nakanishi, Masaki; Wang, Yu-Te; Qi, Hongzhi; Jung, Tzyy-Ping; Ming, Dong

    2016-12-01

    Objective. Detecting the shift of covert visuospatial attention (CVSA) is vital for gaze-independent brain-computer interfaces (BCIs), which might be the only communication approach for severely disabled patients who cannot move their eyes. Although previous studies had demonstrated that it is feasible to use CVSA-related electroencephalography (EEG) features to control a BCI system, the communication speed remains very low. This study aims to improve the speed and accuracy of CVSA detection by fusing EEG features of N2pc and steady-state visual evoked potential (SSVEP). Approach. A new paradigm was designed to code the left and right CVSA with the N2pc and SSVEP features, which were then decoded by a classification strategy based on canonical correlation analysis. Eleven subjects were recruited to perform an offline experiment in this study. Temporal waves, amplitudes, and topographies for brain responses related to N2pc and SSVEP were analyzed. The classification accuracy derived from the hybrid EEG features (SSVEP and N2pc) was compared with those using the single EEG features (SSVEP or N2pc). Main results. The N2pc could be significantly enhanced under certain conditions of SSVEP modulations. The hybrid EEG features achieved significantly higher accuracy than the single features. It obtained an average accuracy of 72.9% by using a data length of 400 ms after the attention shift. Moreover, the average accuracy reached ˜80% (peak values above 90%) when using 2 s long data. Significance. The results indicate that the combination of N2pc and SSVEP is effective for fast detection of CVSA. The proposed method could be a promising approach for implementing a gaze-independent BCI.

  8. Detection of blueshifted emission and absorption and a relativistic iron line in the X-ray spectrum of ESO323-G077

    NASA Astrophysics Data System (ADS)

    Jiménez-Bailón, E.; Krongold, Y.; Bianchi, S.; Matt, G.; Santos-Lleó, M.; Piconcelli, E.; Schartel, N.

    2008-12-01

    We report on the X-ray observation of the Seyfert 1 galaxy ESO323-G077 performed with XMM-Newton. The EPIC spectra show a complex spectrum with conspicuous absorption and emission features. The continuum emission can be modelled with a power law with an index of 1.99 +/- 0.02 in the whole XMM-Newton energy band, marginally consistent with typical values of type I objects. An absorption component with an uncommonly high equivalent hydrogen column (nH = 5.82+0.12-0.11 × 1022cm-2) is affecting the soft part of the spectrum. Additionally, two warm absorption components are also present in the spectrum. The lower ionized one, mainly imprinting the soft band of the spectrum, has an ionization parameter of logU = 2.14+0.06-0.07 and an outflowing velocity of v = 3200+600-200kms-1. Two absorption lines located at ~6.7 and ~7.0keV can be modelled with the highly ionized absorber. The ionization parameter and outflowing velocity of the gas measured are logU = 3.26+0.19-0.15 and v = 1700+600-400kms-1, respectively. Four emission lines were also detected in the soft energy band. The most likely explanation for these emission lines is that they are associated with an outflowing gas with a velocity of ~2000kms-1. The data suggest that the same gas which is causing the absorption could also being responsible of these emission features. Finally, the XMM-Newton spectrum shows the presence of a relativistic iron emission line likely originated in the accretion disc of a Kerr black hole with an inclination of ~25°. We propose a model to explain the observed X-ray properties which invokes the presence of a two-phase outflow with cone-like structure and a velocity of the order of 2000- 4000kms-1. The inner layer of the cone would be less ionized, or even neutral, than the outer layer. The inclination angle of the source would be lower than the opening angle of the outflowing cone. Partially based on observations obtained with XMM-Newton, an ESA science mission with instruments and

  9. Detecting paralinguistic events in audio stream using context in features and probabilistic decisions☆

    PubMed Central

    Gupta, Rahul; Audhkhasi, Kartik; Lee, Sungbok; Narayanan, Shrikanth

    2017-01-01

    Non-verbal communication involves encoding, transmission and decoding of non-lexical cues and is realized using vocal (e.g. prosody) or visual (e.g. gaze, body language) channels during conversation. These cues perform the function of maintaining conversational flow, expressing emotions, and marking personality and interpersonal attitude. In particular, non-verbal cues in speech such as paralanguage and non-verbal vocal events (e.g. laughters, sighs, cries) are used to nuance meaning and convey emotions, mood and attitude. For instance, laughters are associated with affective expressions while fillers (e.g. um, ah, um) are used to hold floor during a conversation. In this paper we present an automatic non-verbal vocal events detection system focusing on the detect of laughter and fillers. We extend our system presented during Interspeech 2013 Social Signals Sub-challenge (that was the winning entry in the challenge) for frame-wise event detection and test several schemes for incorporating local context during detection. Specifically, we incorporate context at two separate levels in our system: (i) the raw frame-wise features and, (ii) the output decisions. Furthermore, our system processes the output probabilities based on a few heuristic rules in order to reduce erroneous frame-based predictions. Our overall system achieves an Area Under the Receiver Operating Characteristics curve of 95.3% for detecting laughters and 90.4% for fillers on the test set drawn from the data specifications of the Interspeech 2013 Social Signals Sub-challenge. We perform further analysis to understand the interrelation between the features and obtained results. Specifically, we conduct a feature sensitivity analysis and correlate it with each feature's stand alone performance. The observations suggest that the trained system is more sensitive to a feature carrying higher discriminability with implications towards a better system design. PMID:28713197

  10. Computerized detection of unruptured aneurysms in MRA images: reduction of false positives using anatomical location features

    NASA Astrophysics Data System (ADS)

    Uchiyama, Yoshikazu; Gao, Xin; Hara, Takeshi; Fujita, Hiroshi; Ando, Hiromichi; Yamakawa, Hiroyasu; Asano, Takahiko; Kato, Hiroki; Iwama, Toru; Kanematsu, Masayuki; Hoshi, Hiroaki

    2008-03-01

    The detection of unruptured aneurysms is a major subject in magnetic resonance angiography (MRA). However, their accurate detection is often difficult because of the overlapping between the aneurysm and the adjacent vessels on maximum intensity projection images. The purpose of this study is to develop a computerized method for the detection of unruptured aneurysms in order to assist radiologists in image interpretation. The vessel regions were first segmented using gray-level thresholding and a region growing technique. The gradient concentration (GC) filter was then employed for the enhancement of the aneurysms. The initial candidates were identified in the GC image using a gray-level threshold. For the elimination of false positives (FPs), we determined shape features and an anatomical location feature. Finally, rule-based schemes and quadratic discriminant analysis were employed along with these features for distinguishing between the aneurysms and the FPs. The sensitivity for the detection of unruptured aneurysms was 90.0% with 1.52 FPs per patient. Our computerized scheme can be useful in assisting the radiologists in the detection of unruptured aneurysms in MRA images.

  11. Feature selection and definition for contours classification of thermograms in breast cancer detection

    NASA Astrophysics Data System (ADS)

    Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał; Oleszkiewicz, Witold; Cichosz, Paweł

    2016-09-01

    This contribution introduces the method of cancer pathologies detection on breast skin temperature distribution images. The use of thermosensitive foils applied to the breasts skin allows to create thermograms, which displays the amount of infrared energy emitted by all breast cells. The significant foci of hyperthermia or inflammation are typical for cancer cells. That foci can be recognized on thermograms as a contours, which are the areas of higher temperature. Every contour can be converted to a feature set that describe it, using the raw, central, Hu, outline, Fourier and colour moments of image pixels processing. This paper defines also the new way of describing a set of contours through theirs neighbourhood relations. Contribution introduces moreover the way of ranking and selecting most relevant features. Authors used Neural Network with Gevrey`s concept and recursive feature elimination, to estimate feature importance.

  12. Using Temporal Covariance of Motion and Geometric Features via Boosting for Human Fall Detection.

    PubMed

    Ali, Syed Farooq; Khan, Reamsha; Mahmood, Arif; Hassan, Malik Tahir; Jeon, And Moongu

    2018-06-12

    Fall induced damages are serious incidences for aged as well as young persons. A real-time automatic and accurate fall detection system can play a vital role in timely medication care which will ultimately help to decrease the damages and complications. In this paper, we propose a fast and more accurate real-time system which can detect people falling in videos captured by surveillance cameras. Novel temporal and spatial variance-based features are proposed which comprise the discriminatory motion, geometric orientation and location of the person. These features are used along with ensemble learning strategy of boosting with J48 and Adaboost classifiers. Experiments have been conducted on publicly available standard datasets including Multiple Cameras Fall ( with 2 classes and 3 classes ) and UR Fall Detection achieving percentage accuracies of 99.2, 99.25 and 99.0, respectively. Comparisons with nine state-of-the-art methods demonstrate the effectiveness of the proposed approach on both datasets.

  13. Near-infrared diode laser based spectroscopic detection of ammonia: a comparative study of photoacoustic and direct optical absorption methods

    NASA Technical Reports Server (NTRS)

    Bozoki, Zoltan; Mohacsi, Arpad; Szabo, Gabor; Bor, Zsolt; Erdelyi, Miklos; Chen, Weidong; Tittel, Frank K.

    2002-01-01

    A photoacoustic spectroscopic (PAS) and a direct optical absorption spectroscopic (OAS) gas sensor, both using continuous-wave room-temperature diode lasers operating at 1531.8 nm, were compared on the basis of ammonia detection. Excellent linear correlation between the detector signals of the two systems was found. Although the physical properties and the mode of operation of both sensors were significantly different, their performances were found to be remarkably similar, with a sub-ppm level minimum detectable concentration of ammonia and a fast response time in the range of a few minutes.

  14. The Physical Nature of the Sharp Spectral Feature at 7 keV Detected in 1H0707-495

    NASA Technical Reports Server (NTRS)

    Brandt, Niel

    2005-01-01

    XMM-Newton acquired data on the accepted target, 1H0707-495, on 2002 October 13 during revolution 0521. The observation was successful, with only about 5% data loss due to background flaring. We compared the data from this observation with earlier data taken on this Narrow-Line Seyfert 1 about two years before, performing interpretation studies in the context of the partial-covering model. Our second longer observation once again displays a sharp (< 200 eV) spectral drop above 7 keV. However, in comparison to the first observation, the edge depth and energy have changed significantly. In addition to changes in the edge parameters, the high-energy spectrum appears steeper. The changes in the high-energy spectrum can be adequately explained in terms of a partial-covering absorber out-flowing from the central region. The low-energy spectrum also shows significant long-term spectral variability, including (1) a substantial increase in the disk temperature, (2) detection of an approx. 0.9 keV emission feature, and (3) the presence of ionized absorption that was detected during the ASCA mission. The large increase in disk temperature, and the more modest rise in luminosity, can be understood if we consider a slim-disk model for 1H0707-495. In addition, the higher disk luminosity could be the driving force behind the outflow and the re-appearance of an ionized medium during the second XMM-Newton observation.

  15. EEG-based mild depressive detection using feature selection methods and classifiers.

    PubMed

    Li, Xiaowei; Hu, Bin; Sun, Shuting; Cai, Hanshu

    2016-11-01

    Depression has become a major health burden worldwide, and effectively detection of such disorder is a great challenge which requires latest technological tool, such as Electroencephalography (EEG). This EEG-based research seeks to find prominent frequency band and brain regions that are most related to mild depression, as well as an optimal combination of classification algorithms and feature selection methods which can be used in future mild depression detection. An experiment based on facial expression viewing task (Emo_block and Neu_block) was conducted, and EEG data of 37 university students were collected using a 128 channel HydroCel Geodesic Sensor Net (HCGSN). For discriminating mild depressive patients and normal controls, BayesNet (BN), Support Vector Machine (SVM), Logistic Regression (LR), k-nearest neighbor (KNN) and RandomForest (RF) classifiers were used. And BestFirst (BF), GreedyStepwise (GSW), GeneticSearch (GS), LinearForwordSelection (LFS) and RankSearch (RS) based on Correlation Features Selection (CFS) were applied for linear and non-linear EEG features selection. Independent Samples T-test with Bonferroni correction was used to find the significantly discriminant electrodes and features. Data mining results indicate that optimal performance is achieved using a combination of feature selection method GSW based on CFS and classifier KNN for beta frequency band. Accuracies achieved 92.00% and 98.00%, and AUC achieved 0.957 and 0.997, for Emo_block and Neu_block beta band data respectively. T-test results validate the effectiveness of selected features by search method GSW. Simplified EEG system with only FP1, FP2, F3, O2, T3 electrodes was also explored with linear features, which yielded accuracies of 91.70% and 96.00%, AUC of 0.952 and 0.972, for Emo_block and Neu_block respectively. Classification results obtained by GSW + KNN are encouraging and better than previously published results. In the spatial distribution of features, we find

  16. An Ensemble Method with Integration of Feature Selection and Classifier Selection to Detect the Landslides

    NASA Astrophysics Data System (ADS)

    Zhongqin, G.; Chen, Y.

    2017-12-01

    Abstract Quickly identify the spatial distribution of landslides automatically is essential for the prevention, mitigation and assessment of the landslide hazard. It's still a challenging job owing to the complicated characteristics and vague boundary of the landslide areas on the image. The high resolution remote sensing image has multi-scales, complex spatial distribution and abundant features, the object-oriented image classification methods can make full use of the above information and thus effectively detect the landslides after the hazard happened. In this research we present a new semi-supervised workflow, taking advantages of recent object-oriented image analysis and machine learning algorithms to quick locate the different origins of landslides of some areas on the southwest part of China. Besides a sequence of image segmentation, feature selection, object classification and error test, this workflow ensemble the feature selection and classifier selection. The feature this study utilized were normalized difference vegetation index (NDVI) change, textural feature derived from the gray level co-occurrence matrices (GLCM), spectral feature and etc. The improvement of this study shows this algorithm significantly removes some redundant feature and the classifiers get fully used. All these improvements lead to a higher accuracy on the determination of the shape of landslides on the high resolution remote sensing image, in particular the flexibility aimed at different kinds of landslides.

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

    PubMed

    Eleftheriadis, Stefanos; Rudovic, Ognjen; Pantic, Maja

    2016-10-05

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

  18. EEG error potentials detection and classification using time-frequency features for robot reinforcement learning.

    PubMed

    Boubchir, Larbi; Touati, Youcef; Daachi, Boubaker; Chérif, Arab Ali

    2015-08-01

    In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier.

  19. Flexible feature-space-construction architecture and its VLSI implementation for multi-scale object detection

    NASA Astrophysics Data System (ADS)

    Luo, Aiwen; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Huang, Zunkai; Jürgen Mattausch, Hans

    2018-04-01

    Feature extraction techniques are a cornerstone of object detection in computer-vision-based applications. The detection performance of vison-based detection systems is often degraded by, e.g., changes in the illumination intensity of the light source, foreground-background contrast variations or automatic gain control from the camera. In order to avoid such degradation effects, we present a block-based L1-norm-circuit architecture which is configurable for different image-cell sizes, cell-based feature descriptors and image resolutions according to customization parameters from the circuit input. The incorporated flexibility in both the image resolution and the cell size for multi-scale image pyramids leads to lower computational complexity and power consumption. Additionally, an object-detection prototype for performance evaluation in 65 nm CMOS implements the proposed L1-norm circuit together with a histogram of oriented gradients (HOG) descriptor and a support vector machine (SVM) classifier. The proposed parallel architecture with high hardware efficiency enables real-time processing, high detection robustness, small chip-core area as well as low power consumption for multi-scale object detection.

  20. L-Asparagine crystals with wide gap semiconductor features: optical absorption measurements and density functional theory computations.

    PubMed

    Zanatta, G; Gottfried, C; Silva, A M; Caetano, E W S; Sales, F A M; Freire, V N

    2014-03-28

    Results of optical absorption measurements are presented together with calculated structural, electronic, and optical properties for the anhydrous monoclinic L-asparagine crystal. Density functional theory (DFT) within the generalized gradient approximation (GGA) including dispersion effects (TS, Grimme) was employed to perform the calculations. The optical absorption measurements revealed that the anhydrous monoclinic L-asparagine crystal is a wide band gap material with 4.95 eV main gap energy. DFT-GGA+TS simulations, on the other hand, produced structural parameters in very good agreement with X-ray data. The lattice parameter differences Δa, Δb, Δc between theory and experiment were as small as 0.020, 0.051, and 0.022 Å, respectively. The calculated band gap energy is smaller than the experimental data by about 15%, with a 4.23 eV indirect band gap corresponding to Z → Γ and Z → β transitions. Three other indirect band gaps of 4.30 eV, 4.32 eV, and 4.36 eV are assigned to α3 → Γ, α1 → Γ, and α2 → Γ transitions, respectively. Δ-sol computations, on the other hand, predict a main band gap of 5.00 eV, just 50 meV above the experimental value. Electronic wavefunctions mainly originating from O 2p-carboxyl, C 2p-side chain, and C 2p-carboxyl orbitals contribute most significantly to the highest valence and lowest conduction energy bands, respectively. By varying the lattice parameters from their converged equilibrium values, we show that the unit cell is less stiff along the b direction than for the a and c directions. Effective mass calculations suggest that hole transport behavior is more anisotropic than electron transport, but the mass values allow for some charge mobility except along a direction perpendicular to the molecular layers of L-asparagine which form the crystal, so anhydrous monoclinic L-asparagine crystals could behave as wide gap semiconductors. Finally, the calculations point to a high degree of optical

  1. Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features.

    PubMed

    Tripathy, R K; Dandapat, S

    2016-06-01

    The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG.

  2. Detection of significant differences between absorption spectra of neutral helium and low temperature photoionized helium plasmas

    SciTech Connect

    Bartnik, A.; Wachulak, P.; Fiedorowicz, H.

    2013-11-15

    In this work, spectral investigations of photoionized He plasmas were performed. The photoionized plasmas were created by irradiation of helium stream, with intense pulses from laser-plasma extreme ultraviolet (EUV) source. The EUV source was based on a double-stream Xe/Ne gas-puff target irradiated with 10 ns/10 J Nd:YAG laser pulses. The most intense emission from the source spanned a relatively narrow spectral region below 20 nm, however, spectrally integrated intensity at longer wavelengths was also significant. The EUV radiation was focused onto a gas stream, injected into a vacuum chamber synchronously with the EUV pulse. The long-wavelength part of the EUVmore » radiation was used for backlighting of the photoionized plasmas to obtain absorption spectra. Both emission and absorption spectra in the EUV range were investigated. Significant differences between absorption spectra acquired for neutral helium and low temperature photoionized plasmas were demonstrated for the first time. Strong increase of intensities and spectral widths of absorption lines, together with a red shift of the K-edge, was shown.« less

  3. a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

    NASA Astrophysics Data System (ADS)

    Li, S.; Zhang, S.; Yang, D.

    2017-09-01

    Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

  4. Karst features detection and mapping using airphotos, DSMs and GIS techniques

    NASA Astrophysics Data System (ADS)

    Kakavas, M. P.; Nikolakopoulos, K. G.; Zagana, E.

    2015-10-01

    The aim of this work is to detect and qualify natural karst depressions in the Aitoloakarnania Prefecture, Western Greece, using remote sensing data in conjunction with the Geographical Information Systems - GIS. The study area is a part of the Ionian geotectonic zone, and its geological background consists of the Triassic Evaporates. The Triassic carbonate breccias where formed as a result of the tectonic and orogenetic setting of the external Hellenides and the diaper phenomena of the Triassic Evaporates. The landscape characterized by exokarst features closed depressions in the Triassic carbonate breccias. At the threshold of this study, an in situ observation was performed in order to identify dolines and swallow holes. The creation of sinkholes, in general, is based on the collapse of the surface layer due to chemical dissolution of carbonate rocks. In the current study airphotos stereopairs, DSMs and GIS were combined in order to detect and map the karst features. Thirty seven airphotos were imported in Leica Photogrammetry Suite and a stereo model of the study area was created. Then in 3D view possible karst features were detected and digitized. Those sites were verified during the in situ survey. ASTER GDEM, SRTM DEM, high resolution airphoto DSM created from the Greek Cadastral and a DEM from digitized contours from the 1/50,000 topographic were also evaluated in GIS environment for the automatic detection of the karst depressions. The results are presented in this study.

  5. Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

    PubMed Central

    2012-01-01

    Background Myocardial ischemia can be developed into more serious diseases. Early Detection of the ischemic syndrome in electrocardiogram (ECG) more accurately and automatically can prevent it from developing into a catastrophic disease. To this end, we propose a new method, which employs wavelets and simple feature selection. Methods For training and testing, the European ST-T database is used, which is comprised of 367 ischemic ST episodes in 90 records. We first remove baseline wandering, and detect time positions of QRS complexes by a method based on the discrete wavelet transform. Next, for each heart beat, we extract three features which can be used for differentiating ST episodes from normal: 1) the area between QRS offset and T-peak points, 2) the normalized and signed sum from QRS offset to effective zero voltage point, and 3) the slope from QRS onset to offset point. We average the feature values for successive five beats to reduce effects of outliers. Finally we apply classifiers to those features. Results We evaluated the algorithm by kernel density estimation (KDE) and support vector machine (SVM) methods. Sensitivity and specificity for KDE were 0.939 and 0.912, respectively. The KDE classifier detects 349 ischemic ST episodes out of total 367 ST episodes. Sensitivity and specificity of SVM were 0.941 and 0.923, respectively. The SVM classifier detects 355 ischemic ST episodes. Conclusions We proposed a new method for detecting ischemia in ECG. It contains signal processing techniques of removing baseline wandering and detecting time positions of QRS complexes by discrete wavelet transform, and feature extraction from morphology of ECG waveforms explicitly. It was shown that the number of selected features were sufficient to discriminate ischemic ST episodes from the normal ones. We also showed how the proposed KDE classifier can automatically select kernel bandwidths, meaning that the algorithm does not require any numerical values of the parameters

  6. Detection of hydrogen peroxide based on long-path absorption spectroscopy using a CW EC-QCL

    NASA Astrophysics Data System (ADS)

    Sanchez, N. P.; Yu, Y.; Dong, L.; Griffin, R.; Tittel, F. K.

    2016-02-01

    A sensor system based on a CW EC-QCL (mode-hop-free range 1225-1285 cm-1) coupled with long-path absorption spectroscopy was developed for the monitoring of gas-phase hydrogen peroxide (H2O2) using an interference-free absorption line located at 1234.055 cm-1. Wavelength modulation spectroscopy (WMS) with second harmonic detection was implemented for data processing. Optimum levels of pressure and modulation amplitude of the sensor system led to a minimum detection limit (MDL) of 25 ppb using an integration time of 280 sec. The selected absorption line for H2O2, which exhibits no interference from H2O, makes this sensor system suitable for sensitive and selective monitoring of H2O2 levels in decontamination and sterilization processes based on Vapor Phase Hydrogen Peroxide (VPHP) units, in which a mixture of H2O and H2O2 is generated. Furthermore, continuous realtime monitoring of H2O2 concentrations in industrial facilities employing this species can be achieved with this sensing system in order to evaluate average permissible exposure levels (PELs) and potential exceedances of guidelines established by the US Occupational Safety and Health Administration for H2O2.

  7. Fault detection of Tennessee Eastman process based on topological features and SVM

    NASA Astrophysics Data System (ADS)

    Zhao, Huiyang; Hu, Yanzhu; Ai, Xinbo; Hu, Yu; Meng, Zhen

    2018-03-01

    Fault detection in industrial process is a popular research topic. Although the distributed control system(DCS) has been introduced to monitor the state of industrial process, it still cannot satisfy all the requirements for fault detection of all the industrial systems. In this paper, we proposed a novel method based on topological features and support vector machine(SVM), for fault detection of industrial process. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The proposed method can be divided into four steps, i.e. network construction, network analysis, model training and model testing respectively. Finally, we apply the model to Tennessee Eastman process(TEP). The results show that this method works well and can be a useful supplement for fault detection of industrial process.

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

    PubMed

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

    2016-06-01

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

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

    NASA Astrophysics Data System (ADS)

    Wang, Wanli; Zhang, Xiuhua; Hong, Hanyu

    2015-12-01

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

  10. Modeling and Detecting Feature Interactions among Integrated Services of Home Network Systems

    NASA Astrophysics Data System (ADS)

    Igaki, Hiroshi; Nakamura, Masahide

    This paper presents a framework for formalizing and detecting feature interactions (FIs) in the emerging smart home domain. We first establish a model of home network system (HNS), where every networked appliance (or the HNS environment) is characterized as an object consisting of properties and methods. Then, every HNS service is defined as a sequence of method invocations of the appliances. Within the model, we next formalize two kinds of FIs: (a) appliance interactions and (b) environment interactions. An appliance interaction occurs when two method invocations conflict on the same appliance, whereas an environment interaction arises when two method invocations conflict indirectly via the environment. Finally, we propose offline and online methods that detect FIs before service deployment and during execution, respectively. Through a case study with seven practical services, it is shown that the proposed framework is generic enough to capture feature interactions in HNS integrated services. We also discuss several FI resolution schemes within the proposed framework.

  11. Synaptic State Matching: A Dynamical Architecture for Predictive Internal Representation and Feature Detection

    PubMed Central

    Tavazoie, Saeed

    2013-01-01

    Here we explore the possibility that a core function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single unifying computational framework. PMID:23991161

  12. Improving the detection of wind fields from LIDAR aerosol backscatter using feature extraction

    NASA Astrophysics Data System (ADS)

    Bickel, Brady R.; Rotthoff, Eric R.; Walters, Gage S.; Kane, Timothy J.; Mayor, Shane D.

    2016-04-01

    The tracking of winds and atmospheric features has many applications, from predicting and analyzing weather patterns in the upper and lower atmosphere to monitoring air movement from pig and chicken farms. Doppler LIDAR systems exist to quantify the underlying wind speeds, but cost of these systems can sometimes be relatively high, and processing limitations exist. The alternative is using an incoherent LIDAR system to analyze aerosol backscatter. Improving the detection and analysis of wind information from aerosol backscatter LIDAR systems will allow for the adoption of these relatively low cost instruments in environments where the size, complexity, and cost of other options are prohibitive. Using data from a simple aerosol backscatter LIDAR system, we attempt to extend the processing capabilities by calculating wind vectors through image correlation techniques to improve the detection of wind features.

  13. A change detection method for remote sensing image based on LBP and SURF feature

    NASA Astrophysics Data System (ADS)

    Hu, Lei; Yang, Hao; Li, Jin; Zhang, Yun

    2018-04-01

    Finding the change in multi-temporal remote sensing image is important in many the image application. Because of the infection of climate and illumination, the texture of the ground object is more stable relative to the gray in high-resolution remote sensing image. And the texture features of Local Binary Patterns (LBP) and Speeded Up Robust Features (SURF) are outstanding in extracting speed and illumination invariance. A method of change detection for matched remote sensing image pair is present, which compares the similarity by LBP and SURF to detect the change and unchanged of the block after blocking the image. And region growing is adopted to process the block edge zone. The experiment results show that the method can endure some illumination change and slight texture change of the ground object.

  14. Automatic detection of diabetic retinopathy features in ultra-wide field retinal images

    NASA Astrophysics Data System (ADS)

    Levenkova, Anastasia; Sowmya, Arcot; Kalloniatis, Michael; Ly, Angelica; Ho, Arthur

    2017-03-01

    Diabetic retinopathy (DR) is a major cause of irreversible vision loss. DR screening relies on retinal clinical signs (features). Opportunities for computer-aided DR feature detection have emerged with the development of Ultra-WideField (UWF) digital scanning laser technology. UWF imaging covers 82% greater retinal area (200°), against 45° in conventional cameras3 , allowing more clinically relevant retinopathy to be detected4 . UWF images also provide a high resolution of 3078 x 2702 pixels. Currently DR screening uses 7 overlapping conventional fundus images, and the UWF images provide similar results1,4. However, in 40% of cases, more retinopathy was found outside the 7-field ETDRS) fields by UWF and in 10% of cases, retinopathy was reclassified as more severe4 . This is because UWF imaging allows examination of both the central retina and more peripheral regions, with the latter implicated in DR6 . We have developed an algorithm for automatic recognition of DR features, including bright (cotton wool spots and exudates) and dark lesions (microaneurysms and blot, dot and flame haemorrhages) in UWF images. The algorithm extracts features from grayscale (green "red-free" laser light) and colour-composite UWF images, including intensity, Histogram-of-Gradient and Local binary patterns. Pixel-based classification is performed with three different classifiers. The main contribution is the automatic detection of DR features in the peripheral retina. The method is evaluated by leave-one-out cross-validation on 25 UWF retinal images with 167 bright lesions, and 61 other images with 1089 dark lesions. The SVM classifier performs best with AUC of 94.4% / 95.31% for bright / dark lesions.

  15. Spatial-temporal features of thermal images for Carpal Tunnel Syndrome detection

    NASA Astrophysics Data System (ADS)

    Estupinan Roldan, Kevin; Ortega Piedrahita, Marco A.; Benitez, Hernan D.

    2014-02-01

    Disorders associated with repeated trauma account for about 60% of all occupational illnesses, Carpal Tunnel Syndrome (CTS) being the most consulted today. Infrared Thermography (IT) has come to play an important role in the field of medicine. IT is non-invasive and detects diseases based on measuring temperature variations. IT represents a possible alternative to prevalent methods for diagnosis of CTS (i.e. nerve conduction studies and electromiography). This work presents a set of spatial-temporal features extracted from thermal images taken in healthy and ill patients. Support Vector Machine (SVM) classifiers test this feature space with Leave One Out (LOO) validation error. The results of the proposed approach show linear separability and lower validation errors when compared to features used in previous works that do not account for temperature spatial variability.

  16. Automatic Feature Selection and Improved Classification in SICADA Counterfeit Electronics Detection

    DTIC Science & Technology

    2017-03-20

    The SICADA methodology was developed to detect such counterfeit microelectronics by collecting power side channel data and applying machine learning...to identify counterfeits. This methodology has been extended to include a two-step automated feature selection process and now uses a one-class SVM...classifier. We describe this methodology and show results for empirical data collected from several types of Microchip dsPIC33F microcontrollers

  17. Performance Comparison of Feature Extraction Algorithms for Target Detection and Classification

    DTIC Science & Technology

    2013-01-01

    Detection and Classification⋆ Soheil Bahrampour1 Asok Ray2 Soumalya Sarkar2 Thyagaraju Damarla3 Nasser M. Nasrabadi3 Keywords: Feature Extraction...USA email:soheil@psu.edu 2A. Ray and S. Sarkar are with the Department of Mechanical Engineering, Pennsylvania State University, University Park, PA...no. 1, pp. 22–29, 2001. [5] G. Mallapragada, A. Ray , and X. Jin, “Symbolic dynamic filtering and language measure for behavior identification of mobile

  18. Detection of braking intention in diverse situations during simulated driving based on EEG feature combination

    NASA Astrophysics Data System (ADS)

    Kim, Il-Hwa; Kim, Jeong-Woo; Haufe, Stefan; Lee, Seong-Whan

    2015-02-01

    Objective. We developed a simulated driving environment for studying neural correlates of emergency braking in diversified driving situations. We further investigated to what extent these neural correlates can be used to detect a participant's braking intention prior to the behavioral response. Approach. We measured electroencephalographic (EEG) and electromyographic signals during simulated driving. Fifteen participants drove a virtual vehicle and were exposed to several kinds of traffic situations in a simulator system, while EEG signals were measured. After that, we extracted characteristic features to categorize whether the driver intended to brake or not. Main results. Our system shows excellent detection performance in a broad range of possible emergency situations. In particular, we were able to distinguish three different kinds of emergency situations (sudden stop of a preceding vehicle, sudden cutting-in of a vehicle from the side and unexpected appearance of a pedestrian) from non-emergency (soft) braking situations, as well as from situations in which no braking was required, but the sensory stimulation was similar to stimulations inducing an emergency situation (e.g., the sudden stop of a vehicle on a neighboring lane). Significance. We proposed a novel feature combination comprising movement-related potentials such as the readiness potential, event-related desynchronization features besides the event-related potentials (ERP) features used in a previous study. The performance of predicting braking intention based on our proposed feature combination was superior compared to using only ERP features. Our study suggests that emergency situations are characterized by specific neural patterns of sensory perception and processing, as well as motor preparation and execution, which can be utilized by neurotechnology based braking assistance systems.

  19. Detection of braking intention in diverse situations during simulated driving based on EEG feature combination.

    PubMed

    Kim, Il-Hwa; Kim, Jeong-Woo; Haufe, Stefan; Lee, Seong-Whan

    2015-02-01

    We developed a simulated driving environment for studying neural correlates of emergency braking in diversified driving situations. We further investigated to what extent these neural correlates can be used to detect a participant's braking intention prior to the behavioral response. We measured electroencephalographic (EEG) and electromyographic signals during simulated driving. Fifteen participants drove a virtual vehicle and were exposed to several kinds of traffic situations in a simulator system, while EEG signals were measured. After that, we extracted characteristic features to categorize whether the driver intended to brake or not. Our system shows excellent detection performance in a broad range of possible emergency situations. In particular, we were able to distinguish three different kinds of emergency situations (sudden stop of a preceding vehicle, sudden cutting-in of a vehicle from the side and unexpected appearance of a pedestrian) from non-emergency (soft) braking situations, as well as from situations in which no braking was required, but the sensory stimulation was similar to stimulations inducing an emergency situation (e.g., the sudden stop of a vehicle on a neighboring lane). We proposed a novel feature combination comprising movement-related potentials such as the readiness potential, event-related desynchronization features besides the event-related potentials (ERP) features used in a previous study. The performance of predicting braking intention based on our proposed feature combination was superior compared to using only ERP features. Our study suggests that emergency situations are characterized by specific neural patterns of sensory perception and processing, as well as motor preparation and execution, which can be utilized by neurotechnology based braking assistance systems.

  20. Crowdsourcing Broad Absorption Line Properties and Other Features of Quasar Outflow Using Zooniverse Citizen Science Project Platform

    NASA Astrophysics Data System (ADS)

    Crowe, Cassie; Lundgren, Britt; Grier, Catherine

    2018-01-01

    The Sloan Digital Sky Survey (SDSS) regularly publishes vast catalogs of quasars and other astronomical objects. Previously, the SDSS collaboration has used visual inspection to check quasar redshift validity and flag instances of broad absorption lines (BALs). This information helps researchers to easily single out the quasars with BAL properties and study their outflows and other intervening gas clouds. Due to the ever-growing number of new SDSS quasar observations, visual inspections are no longer possible using previous methods. Currently, BAL information is being determined entirely computationally, and the accuracy of that information is not precisely known. This project uses the Zooniverse citizen science platform to visually inspect quasar spectra for BAL properties, to check the accuracy of the current autonomous methods, and to flag multi-phase outflows and find candidates for in-falling gas into the quasar central engine. The layout and format of a Zooniverse project provides an easier way to inspect and record data on each spectrum and share the workload via crowdsourcing. Work done by the SDSS collaboration members is serving as a beta test for a public project upon the official release of the DR14 quasar catalog by SDSS.

  1. Detection of sub-kilometer craters in high resolution planetary images using shape and texture features

    NASA Astrophysics Data System (ADS)

    Bandeira, Lourenço; Ding, Wei; Stepinski, Tomasz F.

    2012-01-01

    Counting craters is a paramount tool of planetary analysis because it provides relative dating of planetary surfaces. Dating surfaces with high spatial resolution requires counting a very large number of small, sub-kilometer size craters. Exhaustive manual surveys of such craters over extensive regions are impractical, sparking interest in designing crater detection algorithms (CDAs). As a part of our effort to design a CDA, which is robust and practical for planetary research analysis, we propose a crater detection approach that utilizes both shape and texture features to identify efficiently sub-kilometer craters in high resolution panchromatic images. First, a mathematical morphology-based shape analysis is used to identify regions in an image that may contain craters; only those regions - crater candidates - are the subject of further processing. Second, image texture features in combination with the boosting ensemble supervised learning algorithm are used to accurately classify previously identified candidates into craters and non-craters. The design of the proposed CDA is described and its performance is evaluated using a high resolution image of Mars for which sub-kilometer craters have been manually identified. The overall detection rate of the proposed CDA is 81%, the branching factor is 0.14, and the overall quality factor is 72%. This performance is a significant improvement over the previous CDA based exclusively on the shape features. The combination of performance level and computational efficiency offered by this CDA makes it attractive for practical application.

  2. A robust indicator based on singular value decomposition for flaw feature detection from noisy ultrasonic signals

    NASA Astrophysics Data System (ADS)

    Cui, Ximing; Wang, Zhe; Kang, Yihua; Pu, Haiming; Deng, Zhiyang

    2018-05-01

    Singular value decomposition (SVD) has been proven to be an effective de-noising tool for flaw echo signal feature detection in ultrasonic non-destructive evaluation (NDE). However, the uncertainty in the arbitrary manner of the selection of an effective singular value weakens the robustness of this technique. Improper selection of effective singular values will lead to bad performance of SVD de-noising. What is more, the computational complexity of SVD is too large for it to be applied in real-time applications. In this paper, to eliminate the uncertainty in SVD de-noising, a novel flaw indicator, named the maximum singular value indicator (MSI), based on short-time SVD (STSVD), is proposed for flaw feature detection from a measured signal in ultrasonic NDE. In this technique, the measured signal is first truncated into overlapping short-time data segments to put feature information of a transient flaw echo signal in local field, and then the MSI can be obtained from the SVD of each short-time data segment. Research shows that this indicator can clearly indicate the location of ultrasonic flaw signals, and the computational complexity of this STSVD-based indicator is significantly reduced with the algorithm proposed in this paper. Both simulation and experiments show that this technique is very efficient for real-time application in flaw detection from noisy data.

  3. Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening.

    PubMed

    Seoud, Lama; Hurtut, Thomas; Chelbi, Jihed; Cheriet, Farida; Langlois, J M Pierre

    2016-04-01

    The development of an automatic telemedicine system for computer-aided screening and grading of diabetic retinopathy depends on reliable detection of retinal lesions in fundus images. In this paper, a novel method for automatic detection of both microaneurysms and hemorrhages in color fundus images is described and validated. The main contribution is a new set of shape features, called Dynamic Shape Features, that do not require precise segmentation of the regions to be classified. These features represent the evolution of the shape during image flooding and allow to discriminate between lesions and vessel segments. The method is validated per-lesion and per-image using six databases, four of which are publicly available. It proves to be robust with respect to variability in image resolution, quality and acquisition system. On the Retinopathy Online Challenge's database, the method achieves a FROC score of 0.420 which ranks it fourth. On the Messidor database, when detecting images with diabetic retinopathy, the proposed method achieves an area under the ROC curve of 0.899, comparable to the score of human experts, and it outperforms state-of-the-art approaches.

  4. Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW.

    PubMed

    Stewart, C M; Newlands, S D; Perachio, A A

    2004-12-01

    Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records.

  5. Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier

    PubMed Central

    Min, Yang; Zhu, Dingju

    2014-01-01

    Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier. PMID:25003153

  6. Obscenity detection using haar-like features and Gentle Adaboost classifier.

    PubMed

    Mustafa, Rashed; Min, Yang; Zhu, Dingju

    2014-01-01

    Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier.

  7. Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode.

    PubMed

    Ye, Tao; Wang, Baocheng; Song, Ping; Li, Juan

    2018-06-12

    Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN) is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net). It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

  8. Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error.

    PubMed

    Beheshti, Iman; Demirel, Hasan; Farokhian, Farnaz; Yang, Chunlan; Matsuda, Hiroshi

    2016-12-01

    This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that

  9. Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel

    PubMed Central

    2017-01-01

    Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health. The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG) channel. Four types of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for the analysis of original EEG signal and compared by ten state-of-the-art classifiers. Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF). The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications. The best combination of channel + features + classifier is subject-specific. In this work, the accuracy of FE as the feature is far greater than the Acc of other features. The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst. The impact of channel selection on the Acc is larger. The performance of various channels is very different. PMID:28255330

  10. Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel.

    PubMed

    Hu, Jianfeng

    2017-01-01

    Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health. The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG) channel. Four types of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for the analysis of original EEG signal and compared by ten state-of-the-art classifiers. Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF). The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications. The best combination of channel + features + classifier is subject-specific. In this work, the accuracy of FE as the feature is far greater than the Acc of other features. The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst. The impact of channel selection on the Acc is larger. The performance of various channels is very different.

  11. Absorption features in the quasar HS 1603 + 3820 II. Distance to the absorber obtained from photoionisation modelling

    NASA Astrophysics Data System (ADS)

    Różańska, A.; Nikołajuk, M.; Czerny, B.; Dobrzycki, A.; Hryniewicz, K.; Bechtold, J.; Ebeling, H.

    2014-04-01

    We present the photoionisation modelling of the intrinsic absorber in the bright quasar HS 1603 + 3820. We constructed the broad-band spectral energy distribution using the optical/UV/X-ray observations from different instruments as inputs for the photoionisation calculations. The spectra from the Keck telescope show extremely high CIV to HI ratios, for the first absorber in system A, named A1. This value, together with high column density of CIV ion, place strong constraints on the photoionisation model. We used two photoionisation codes to derive the hydrogen number density at the cloud illuminated surface. By estimating bolometric luminosity of HS 1603 + 3820 using the typical formula for quasars, we calculated the distance to A1. We could find one photoionization solution, by assuming either a constant density cloud (which was modelled using CLOUDY), or a stratified cloud (which was modelled using TITAN), as well as the solar abundances. This model explained both the ionic column density of CIV and the high CIV to HI ratio. The location of A1 is 0.1 pc, and it is situated even closer to the nucleus than the possible location of the Broad Line Region in this object. The upper limit of the distance is sensitive to the adopted covering factor and the carbon abundance. Photoionisation modelling always prefers dense clouds with the number density n0 = 1010 - 1012 cm-3, which explains intrinsic absorption in HS 1603 + 3820. This number density is of the same order as that in the disk atmosphere at the implied distance of A1. Therefore, our results show that the disk wind that escapes from the outermost accretion disk atmosphere can build up dense absorber in quasars.

  12. Minimal Data Fidelity for Successful detection of Stellar Features or Companions

    NASA Astrophysics Data System (ADS)

    Agarwal, S.; Wettlaufer, J. S.

    2017-12-01

    Technological advances in instrumentation have led to an exponential increase in exoplanet detection and scrutiny of stellar features such as spots and faculae. While the spots and faculae enable us to understand the stellar dynamics, exoplanets provide us with a glimpse into stellar evolution. While a clean set of data is always desirable, noise is ubiquitous in the data such as telluric, instrumental, or photonic, but combining this with increased spectrographic resolution compounds technological challenges. To account for these noise sources and resolution issues, using a temporal multifractal framework, we study data from the SOAP 2.0 tool, which simulates a stellar spectrum in the presence of a spot, a facula or a planet. Given these clean simulations, we vary the resolution as well as the signal-to- noise (S/N) ratio to obtain a lower limit on the resolution and S/N required to robustly detect features. We show that a spot and facula with a 1% coverage of the stellar disk can be robustly detected for a S/N (per resolution element) of 20 and 35 respectively for any resolution above 20,000, while a planet with an RV of 10ms-1 can be detected for a S/N (per resolution element) of 350. Rather than viewing noise as an impediment, this approach uses noise as a source of information.

  13. Wave field features of shallow vertical discontinuity and their application in non-destructive detection

    USGS Publications Warehouse

    Liu, J.; Xia, J.; Luo, Y.; Chen, C.; Li, X.; Huang, Y.

    2007-01-01

    The geotechnical integrity of critical infrastructure can be seriously compromised by the presence of fractures or crevices. Non-destructive techniques to accurately detect fractures in critical infrastructure such as dams and highways could be of significant benefit to the geotechnical industry. This paper investigates the application of shallow seismic and georadar methods to the detection of a vertical discontinuity using numerical simulations. The objective is to address the kinematical analysis of a vertical discontinuity, determine the resulting wave field characteristics, and provide the basis for determining the existence of vertical discontinuities based on the recorded signals. Simulation results demonstrate that: (1) A reflection from a vertical discontinuity produces a hyperbolic feature on a seismic or georadar profile; (2) In order for a reflection from a vertical discontinuity to be produced, a reflecting horizon below the discontinuity must exist, the offset between source and receiver (x0) must be non-zero, on the same side of the vertical discontinuity; (3) The range of distances from the vertical discontinuity where a reflection event is observed is proportional to its length and to x0; (4) Should the vertical crevice (or fracture) pass through a reflecting horizon, dual hyperbolic features can be observed on the records, and this can be used as a determining factor that the vertical crevice passes through the interface; and (5) diffractions from the edges of the discontinuity can be recorded with relatively smaller amplitude than reflections and their ranges are not constrained by the length of discontinuity. If the length of discontinuity is short enough, diffractions are the dominant feature. Real-world examples show that the shallow seismic reflection method and the georadar method are capable of recording the hyperbolic feature, which can be interpreted as vertical discontinuity. Thus, these methods show some promise as effective non

  14. A two-view ultrasound CAD system for spina bifida detection using Zernike features

    NASA Astrophysics Data System (ADS)

    Konur, Umut; Gürgen, Fikret; Varol, Füsun

    2011-03-01

    In this work, we address a very specific CAD (Computer Aided Detection/Diagnosis) problem and try to detect one of the relatively common birth defects - spina bifida, in the prenatal period. To do this, fetal ultrasound images are used as the input imaging modality, which is the most convenient so far. Our approach is to decide using two particular types of views of the fetal neural tube. Transcerebellar head (i.e. brain) and transverse (axial) spine images are processed to extract features which are then used to classify healthy (normal), suspicious (probably defective) and non-decidable cases. Decisions raised by two independent classifiers may be individually treated, or if desired and data related to both modalities are available, those decisions can be combined to keep matters more secure. Even more security can be attained by using more than two modalities and base the final decision on all those potential classifiers. Our current system relies on feature extraction from images for cases (for particular patients). The first step is image preprocessing and segmentation to get rid of useless image pixels and represent the input in a more compact domain, which is hopefully more representative for good classification performance. Next, a particular type of feature extraction, which uses Zernike moments computed on either B/W or gray-scale image segments, is performed. The aim here is to obtain values for indicative markers that signal the presence of spina bifida. Markers differ depending on the image modality being used. Either shape or texture information captured by moments may propose useful features. Finally, SVM is used to train classifiers to be used as decision makers. Our experimental results show that a promising CAD system can be actualized for the specific purpose. On the other hand, the performance of such a system would highly depend on the qualities of image preprocessing, segmentation, feature extraction and comprehensiveness of image data.

  15. Electrochemical and Infrared Absorption Spectroscopy Detection of SF6 Decomposition Products

    PubMed Central

    Dong, Ming; Ren, Ming; Ye, Rixin

    2017-01-01

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

  16. Demonstration of a portable near-infrared CH4 detection sensor based on tunable diode laser absorption spectroscopy

    NASA Astrophysics Data System (ADS)

    Zheng, Chuan-Tao; Huang, Jian-Qiang; Ye, Wei-Lin; Lv, Mo; Dang, Jing-Min; Cao, Tian-Shu; Chen, Chen; Wang, Yi-Ding

    2013-11-01

    A portable near-infrared (NIR) CH4 detection sensor based on a distributed feedback (DFB) laser modulated at 1.654 μm is experimentally demonstrated. Intelligent temperature controller with an accuracy of -0.07 to +0.09 °C as well as a scan and modulation module generating saw-wave and cosine-wave signals are developed to drive the DFB laser, and a cost effective lock-in amplifier used to extract the second harmonic signal is integrated. Thorough experiments are carried out to obtain detection performances, including detection range, accuracy, stability and the minimum detection limit (MDL). Measurement results show that the absolute detection error relative to the standard value is less than 7% within the range of 0-100%, and the MDL is estimated to be about 11 ppm under an absorption length of 0.2 m and a noise level of 2 mVpp. Twenty-four hours monitoring on two gas samples (0.1% and 20%) indicates that the absolute errors are less than 7% and 2.5%, respectively, suggesting good long term stability. The sensor reveals competitive characteristics compared with other reported portable or handheld sensors. The developed sensor can also be used for the detection of other gases by adopting other DFB lasers with different center-wavelength using the same hardware and slightly modified software.

  17. Heterodyne detection of the 752.033-GHz H2O rotational absorption line

    NASA Technical Reports Server (NTRS)

    Dionne, G. F.; Fitzgerald, J. F.; Chang, T. S.; Litvak, M. M.; Fetterman, H. R.

    1980-01-01

    A tunable high resolution two stage heterodyne radiometer was developed for the purpose of investigating the intensity and lineshape of the 752.033 GHz rotational transition of water vapor. Single-sideband system noise temperatures of approximately 45,000 K were obtained using a sensitive GaAs Schottky diode as the first stage mixer. First local oscillator power was supplied by a CO2 laser pumped formic acid laser (761.61 GHz), generating an X-band IF signal with theoretical line center at 9.5744 GHz. Second local oscillator power was provided by means of a 3 GHz waveguide cavity filter with only 9 dB insertion loss. In absorption measurements of the H2O taken from a laboratory simulation of a high altitude rocket plume, the center frequency of the 752 GHz line was determined to within 1 MHz of the reported value. A rotational temperature 75 K, a linewidth 5 MHz and a Doppler shift 3 MHz were measured with the line-of-sight intersecting the simulated-plume axis at a distance downstream of 30 nozzle diameters. These absorption data were obtained against continuum background radiation sources at temperatures of 1175 and 300 K.

  18. Absolute high spectral resolution measurements of surface solar radiation for detection of water vapour continuum absorption.

    PubMed

    Gardiner, T D; Coleman, M; Browning, H; Tallis, L; Ptashnik, I V; Shine, K P

    2012-06-13

    Solar-pointing Fourier transform infrared (FTIR) spectroscopy offers the capability to measure both the fine scale and broadband spectral structure of atmospheric transmission simultaneously across wide spectral regions. It is therefore suited to the study of both water vapour monomer and continuum absorption behaviours. However, in order to properly address this issue, it is necessary to radiatively calibrate the FTIR instrument response. A solar-pointing high-resolution FTIR spectrometer was deployed as part of the 'Continuum Absorption by Visible and Infrared radiation and its Atmospheric Relevance' (CAVIAR) consortium project. This paper describes the radiative calibration process using an ultra-high-temperature blackbody and the consideration of the related influence factors. The result is a radiatively calibrated measurement of the solar irradiation at the ground across the IR region from 2000 to 10 000 cm(-1) with an uncertainty of between 3.3 and 5.9 per cent. This measurement is shown to be in good general agreement with a radiative-transfer model. The results from the CAVIAR field measurements are being used in ongoing studies of atmospheric absorbers, in particular the water vapour continuum.

  19. Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management.

    PubMed

    Firdaus, Ahmad; Anuar, Nor Badrul; Razak, Mohd Faizal Ab; Hashem, Ibrahim Abaker Targio; Bachok, Syafiq; Sangaiah, Arun Kumar

    2018-05-04

    The increasing demand for Android mobile devices and blockchain has motivated malware creators to develop mobile malware to compromise the blockchain. Although the blockchain is secure, attackers have managed to gain access into the blockchain as legal users, thereby comprising important and crucial information. Examples of mobile malware include root exploit, botnets, and Trojans and root exploit is one of the most dangerous malware. It compromises the operating system kernel in order to gain root privileges which are then used by attackers to bypass the security mechanisms, to gain complete control of the operating system, to install other possible types of malware to the devices, and finally, to steal victims' private keys linked to the blockchain. For the purpose of maximizing the security of the blockchain-based medical data management (BMDM), it is crucial to investigate the novel features and approaches contained in root exploit malware. This study proposes to use the bio-inspired method of practical swarm optimization (PSO) which automatically select the exclusive features that contain the novel android debug bridge (ADB). This study also adopts boosting (adaboost, realadaboost, logitboost, and multiboost) to enhance the machine learning prediction that detects unknown root exploit, and scrutinized three categories of features including (1) system command, (2) directory path and (3) code-based. The evaluation gathered from this study suggests a marked accuracy value of 93% with Logitboost in the simulation. Logitboost also helped to predicted all the root exploit samples in our developed system, the root exploit detection system (RODS).

  20. Face detection on distorted images using perceptual quality-aware features

    NASA Astrophysics Data System (ADS)

    Gunasekar, Suriya; Ghosh, Joydeep; Bovik, Alan C.

    2014-02-01

    We quantify the degradation in performance of a popular and effective face detector when human-perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve face detection performance. These results can be used to guide resource or bandwidth allocation in a communication/delivery system that is associated with face detection tasks. A new face detector based on QualHOG features is also proposed that augments face-indicative HOG features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features, yielding improved tolerance against image distortions. The new detector provides statistically significant improvements over a strong baseline on a large database of face images representing a wide range of distortions. To facilitate this study, we created a new Distorted Face Database, containing face and non-face patches from images impaired by a variety of common distortion types and levels. This new dataset is available for download and further experimentation at www.ideal.ece.utexas.edu/˜suriya/DFD/.

  1. Enhanced flyby science with onboard computer vision: Tracking and surface feature detection at small bodies

    NASA Astrophysics Data System (ADS)

    Fuchs, Thomas J.; Thompson, David R.; Bue, Brian D.; Castillo-Rogez, Julie; Chien, Steve A.; Gharibian, Dero; Wagstaff, Kiri L.

    2015-10-01

    Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies.

  2. Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection.

    PubMed

    Li, Zhucui; Lu, Yan; Guo, Yufeng; Cao, Haijie; Wang, Qinhong; Shui, Wenqing

    2018-10-31

    Data analysis represents a key challenge for untargeted metabolomics studies and it commonly requires extensive processing of more than thousands of metabolite peaks included in raw high-resolution MS data. Although a number of software packages have been developed to facilitate untargeted data processing, they have not been comprehensively scrutinized in the capability of feature detection, quantification and marker selection using a well-defined benchmark sample set. In this study, we acquired a benchmark dataset from standard mixtures consisting of 1100 compounds with specified concentration ratios including 130 compounds with significant variation of concentrations. Five software evaluated here (MS-Dial, MZmine 2, XCMS, MarkerView, and Compound Discoverer) showed similar performance in detection of true features derived from compounds in the mixtures. However, significant differences between untargeted metabolomics software were observed in relative quantification of true features in the benchmark dataset. MZmine 2 outperformed the other software in terms of quantification accuracy and it reported the most true discriminating markers together with the fewest false markers. Furthermore, we assessed selection of discriminating markers by different software using both the benchmark dataset and a real-case metabolomics dataset to propose combined usage of two software for increasing confidence of biomarker identification. Our findings from comprehensive evaluation of untargeted metabolomics software would help guide future improvements of these widely used bioinformatics tools and enable users to properly interpret their metabolomics results. Copyright © 2018 Elsevier B.V. All rights reserved.

  3. Breast cancer mitosis detection in histopathological images with spatial feature extraction

    NASA Astrophysics Data System (ADS)

    Albayrak, Abdülkadir; Bilgin, Gökhan

    2013-12-01

    In this work, cellular mitosis detection in histopathological images has been investigated. Mitosis detection is very expensive and time consuming process. Development of digital imaging in pathology has enabled reasonable and effective solution to this problem. Segmentation of digital images provides easier analysis of cell structures in histopathological data. To differentiate normal and mitotic cells in histopathological images, feature extraction step is very crucial step for the system accuracy. A mitotic cell has more distinctive textural dissimilarities than the other normal cells. Hence, it is important to incorporate spatial information in feature extraction or in post-processing steps. As a main part of this study, Haralick texture descriptor has been proposed with different spatial window sizes in RGB and La*b* color spaces. So, spatial dependencies of normal and mitotic cellular pixels can be evaluated within different pixel neighborhoods. Extracted features are compared with various sample sizes by Support Vector Machines using k-fold cross validation method. According to the represented results, it has been shown that separation accuracy on mitotic and non-mitotic cellular pixels gets better with the increasing size of spatial window.

  4. LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

    PubMed

    Zhang, Tao; Chen, Wanzhong

    2017-08-01

    Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

  5. Automated Feature and Event Detection with SDO AIA and HMI Data

    NASA Astrophysics Data System (ADS)

    Davey, Alisdair; Martens, P. C. H.; Attrill, G. D. R.; Engell, A.; Farid, S.; Grigis, P. C.; Kasper, J.; Korreck, K.; Saar, S. H.; Su, Y.; Testa, P.; Wills-Davey, M.; Savcheva, A.; Bernasconi, P. N.; Raouafi, N.-E.; Delouille, V. A.; Hochedez, J. F..; Cirtain, J. W.; Deforest, C. E.; Angryk, R. A.; de Moortel, I.; Wiegelmann, T.; Georgouli, M. K.; McAteer, R. T. J.; Hurlburt, N.; Timmons, R.

    The Solar Dynamics Observatory (SDO) represents a new frontier in quantity and quality of solar data. At about 1.5 TB/day, the data will not be easily digestible by solar physicists using the same methods that have been employed for images from previous missions. In order for solar scientists to use the SDO data effectively they need meta-data that will allow them to identify and retrieve data sets that address their particular science questions. We are building a comprehensive computer vision pipeline for SDO, abstracting complete metadata on many of the features and events detectable on the Sun without human intervention. Our project unites more than a dozen individual, existing codes into a systematic tool that can be used by the entire solar community. The feature finding codes will run as part of the SDO Event Detection System (EDS) at the Joint Science Operations Center (JSOC; joint between Stanford and LMSAL). The metadata produced will be stored in the Heliophysics Event Knowledgebase (HEK), which will be accessible on-line for the rest of the world directly or via the Virtual Solar Observatory (VSO) . Solar scientists will be able to use the HEK to select event and feature data to download for science studies.

  6. A new feature detection mechanism and its application in secured ECG transmission with noise masking.

    PubMed

    Sufi, Fahim; Khalil, Ibrahim

    2009-04-01

    With cardiovascular disease as the number one killer of modern era, Electrocardiogram (ECG) is collected, stored and transmitted in greater frequency than ever before. However, in reality, ECG is rarely transmitted and stored in a secured manner. Recent research shows that eavesdropper can reveal the identity and cardiovascular condition from an intercepted ECG. Therefore, ECG data must be anonymized before transmission over the network and also stored as such in medical repositories. To achieve this, first of all, this paper presents a new ECG feature detection mechanism, which was compared against existing cross correlation (CC) based template matching algorithms. Two types of CC methods were used for comparison. Compared to the CC based approaches, which had 40% and 53% misclassification rates, the proposed detection algorithm did not perform any single misclassification. Secondly, a new ECG obfuscation method was designed and implemented on 15 subjects using added noises corresponding to each of the ECG features. This obfuscated ECG can be freely distributed over the internet without the necessity of encryption, since the original features needed to identify personal information of the patient remain concealed. Only authorized personnel possessing a secret key will be able to reconstruct the original ECG from the obfuscated ECG. Distribution of the would appear as regular ECG without encryption. Therefore, traditional decryption techniques including powerful brute force attack are useless against this obfuscation.

  7. Identification of Fourier transform infrared photoacoustic spectral features for detection of Aspergillus flavus infection in corn.

    PubMed

    Gordon, S H; Schudy, R B; Wheeler, B C; Wicklow, D T; Greene, R V

    1997-04-01

    Aspergillus flavus and other pathogenic fungi display typical infrared spectra which differ significantly from spectra of substrate materials such as corn. On this basis, specific spectral features have been identified which permit detection of fungal infection on the surface of corn kernels by photoacoustic infrared spectroscopy. In a blind study, ten corn kernels showing bright greenish yellow fluorescence (BGYF) in the germ or endosperm and ten BGYF-negative kernels were correctly classified as infected or not infected by Fourier transform infrared photoacoustic spectroscopy. Earlier studies have shown that BGYF-positive kernels contain the bulk of the aflatoxin contaminating grain at harvest. Ten major spectral features, identified by visual inspection of the photoacoustic spectra of A. flavus mycelium grown in culture versus uninfected corn, were interpreted and assigned by theoretical comparisons of the relative chemical compositions of fungi and corn. The spectral features can be built into either empirical or knowledge-based computer models (expert systems) for automatic infrared detection and segregation of grains or kernels containing aflatoxin from the food and feed supply.

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

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

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

  9. Development of gas fire detection system using tunable diode laser absorption spectroscopy

    NASA Astrophysics Data System (ADS)

    Jiang, Y. L.; Li, G.; Yang, T.; Wang, J. J.

    2017-01-01

    The conventional fire detection methods mainly produce an alarm through detecting the changes in smoke concentration, flame radiation, heat and other physical parameters in the environment, but are unable to provide an early warning of a fire emergency. We have designed a gas fire detection system with a high detection sensitivity and high selectivity using the tunable semiconductor diode laser as a light source and combining wavelength modulation and harmonic detection technology. This system can invert the second harmonic signal obtained to obtain the concentration of carbon monoxide gas (a fire characteristic gas) so as to provide an early warning of fire. We reduce the system offset noise and the background noise generated due to the laser interference by deducting the system background spectrum lines from the second harmonic signal. This can also eliminate the interference of other gas spectral lines to a large extent. We detected the concentration of the carbon monoxide gas generated in smoldering sandalwood fire and open beech wood fire with the homemade fire simulator, and tested the lowest detectable limit of system. The test results show that the lowest detectable limit can reach 5×10-6 the system can maintain stable operation for a long period of time and can automatically trigger a water mist fire extinguishing system, which can fully meet the needs of early fire warning.

  10. Topographic attributes as a guide for automated detection or highlighting of geological features

    NASA Astrophysics Data System (ADS)

    Viseur, Sophie; Le Men, Thibaud; Guglielmi, Yves

    2015-04-01

    Photogrammetry or LIDAR technology combined with photography allow geoscientists to obtain 3D high-resolution numerical representations of outcrops, generally termed as Digital Outcrop Models (DOM). For over a decade, these 3D numerical outcrops serve as support for precise and accurate interpretations of geological features such as fracture traces or plans, strata, facies mapping, etc. These interpretations have the benefit to be directly georeferenced and embedded into the 3D space. They are then easily integrated into GIS or geomodeler softwares for modelling in 3D the subsurface geological structures. However, numerical outcrops generally represent huge data sets that are heavy to manipulate and hence to interpret. This may be particularly tedious as soon as several scales of geological features must be investigated or as geological features are very dense and imbricated. Automated tools for interpreting geological features from DOMs would be then a significant help to process these kinds of data. Such technologies are commonly used for interpreting seismic or medical data. However, it may be noticed that even if many efforts have been devoted to easily and accurately acquire 3D topographic point clouds and photos and to visualize accurate 3D textured DOMs, few attentions have been paid to the development of algorithms for automated detection of the geological structures from DOMs. The automatic detection of objects on numerical data generally assumes that signals or attributes computed from this data allows the recognition of the targeted object boundaries. The first step consists then in defining attributes that highlight the objects or their boundaries. For DOM interpretations, some authors proposed to use differential operators computed on the surface such as normal or curvatures. These methods generally extract polylines corresponding to fracture traces or bed limits. Other approaches rely on the PCA technology to segregate different topographic plans

  11. Pulmonary embolism detection using localized vessel-based features in dual energy CT

    NASA Astrophysics Data System (ADS)

    Dicente Cid, Yashin; Depeursinge, Adrien; Foncubierta Rodríguez, Antonio; Platon, Alexandra; Poletti, Pierre-Alexandre; Müller, Henning

    2015-03-01

    Pulmonary embolism (PE) affects up to 600,000 patients and contributes to at least 100,000 deaths every year in the United States alone. Diagnosis of PE can be difficult as most symptoms are unspecific and early diagnosis is essential for successful treatment. Computed Tomography (CT) images can show morphological anomalies that suggest the existence of PE. Various image-based procedures have been proposed for improving computer-aided diagnosis of PE. We propose a novel method for detecting PE based on localized vessel-based features computed in Dual Energy CT (DECT) images. DECT provides 4D data indexed by the three spatial coordinates and the energy level. The proposed features encode the variation of the Hounsfield Units across the different levels and the CT attenuation related to the amount of iodine contrast in each vessel. A local classification of the vessels is obtained through the classification of these features. Moreover, the localization of the vessel in the lung provides better comparison between patients. Results show that the simple features designed are able to classify pulmonary embolism patients with an AUC (area under the receiver operating curve) of 0.71 on a lobe basis. Prior segmentation of the lung lobes is not necessary because an automatic atlas-based segmentation obtains similar AUC levels (0.65) for the same dataset. The automatic atlas reaches 0.80 AUC in a larger dataset with more control cases.

  12. Defect detection of castings in radiography images using a robust statistical feature.

    PubMed

    Zhao, Xinyue; He, Zaixing; Zhang, Shuyou

    2014-01-01

    One of the most commonly used optical methods for defect detection is radiographic inspection. Compared with methods that extract defects directly from the radiography image, model-based methods deal with the case of an object with complex structure well. However, detection of small low-contrast defects in nonuniformly illuminated images is still a major challenge for them. In this paper, we present a new method based on the grayscale arranging pairs (GAP) feature to detect casting defects in radiography images automatically. First, a model is built using pixel pairs with a stable intensity relationship based on the GAP feature from previously acquired images. Second, defects can be extracted by comparing the difference of intensity-difference signs between the input image and the model statistically. The robustness of the proposed method to noise and illumination variations has been verified on casting radioscopic images with defects. The experimental results showed that the average computation time of the proposed method in the testing stage is 28 ms per image on a computer with a Pentium Core 2 Duo 3.00 GHz processor. For the comparison, we also evaluated the performance of the proposed method as well as that of the mixture-of-Gaussian-based and crossing line profile methods. The proposed method achieved 2.7% and 2.0% false negative rates in the noise and illumination variation experiments, respectively.

  13. Archaeological Feature Detection from Archive Aerial Photography with a Sfm-Mvs and Image Enhancement Pipeline

    NASA Astrophysics Data System (ADS)

    Peppa, M. V.; Mills, J. P.; Fieber, K. D.; Haynes, I.; Turner, S.; Turner, A.; Douglas, M.; Bryan, P. G.

    2018-05-01

    Understanding and protecting cultural heritage involves the detection and long-term documentation of archaeological remains alongside the spatio-temporal analysis of their landscape evolution. Archive aerial photography can illuminate traces of ancient features which typically appear with different brightness values from their surrounding environment, but are not always well defined. This research investigates the implementation of the Structure-from-Motion - Multi-View Stereo image matching approach with an image enhancement algorithm to derive three epochs of orthomosaics and digital surface models from visible and near infrared historic aerial photography. The enhancement algorithm uses decorrelation stretching to improve the contrast of the orthomosaics so as archaeological features are better detected. Results include 2D / 3D locations of detected archaeological traces stored into a geodatabase for further archaeological interpretation and correlation with benchmark observations. The study also discusses the merits and difficulties of the process involved. This research is based on a European-wide project, entitled "Cultural Heritage Through Time", and the case study research was carried out as a component of the project in the UK.

  14. Real-Time Lane Region Detection Using a Combination of Geometrical and Image Features

    PubMed Central

    Cáceres Hernández, Danilo; Kurnianggoro, Laksono; Filonenko, Alexander; Jo, Kang Hyun

    2016-01-01

    Over the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performance. PMID:27869657

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

    NASA Astrophysics Data System (ADS)

    Gong, Yihong; Zhang, HongJiang

    1994-03-01

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

  16. Assignment of Pre-edge Features in the Ru K-edge X-ray Absorption Spectra of Organometallic Ruthenium Complexes

    PubMed Central

    Getty, Kendra; Delgado-Jaime, Mario Ulises

    2010-01-01

    The nature of the lowest energy bound-state transition in the Ru K-edge X-ray Absorption Spectra for a series of Grubbs-type ruthenium complexes was investigated. The pre-edge feature was unambiguously assigned as resulting from formally electric dipole forbidden Ru 4d←1s transitions. The intensities of these transitions are extremely sensitive to the ligand environment and the symmetry of the metal centre. In centrosymmetric complexes the pre-edge is very weak since it is limited by the weak electric quadrupole intensity mechanism. By contrast, upon breaking centrosymmetry, Ru 5p-4d mixing allows for introduction of electric dipole allowed character resulting in a dramatic increase in the pre-edge intensity. The information content of this approach is explored as it relates to complexes of importance in olefin metathesis and its relevance as a tool for the study of reactive intermediates. PMID:20151030

  17. Bright Retinal Lesions Detection using Colour Fundus Images Containing Reflective Features

    SciTech Connect

    Giancardo, Luca; Karnowski, Thomas Paul; Chaum, Edward

    2009-01-01

    In the last years the research community has developed many techniques to detect and diagnose diabetic retinopathy with retinal fundus images. This is a necessary step for the implementation of a large scale screening effort in rural areas where ophthalmologists are not available. In the United States of America, the incidence of diabetes is worryingly increasing among the young population. Retina fundus images of patients younger than 20 years old present a high amount of reflection due to the Nerve Fibre Layer (NFL), the younger the patient the more these reflections are visible. To our knowledge we are not awaremore » of algorithms able to explicitly deal with this type of reflection artefact. This paper presents a technique to detect bright lesions also in patients with a high degree of reflective NFL. First, the candidate bright lesions are detected using image equalization and relatively simple histogram analysis. Then, a classifier is trained using texture descriptor (Multi-scale Local Binary Patterns) and other features in order to remove the false positives in the lesion detection. Finally, the area of the lesions is used to diagnose diabetic retinopathy. Our database consists of 33 images from a telemedicine network currently developed. When determining moderate to high diabetic retinopathy using the bright lesions detected the algorithm achieves a sensitivity of 100% at a specificity of 100% using hold-one-out testing.« less

  18. CO and CO2 dual-gas detection based on mid-infrared wideband absorption spectroscopy

    NASA Astrophysics Data System (ADS)

    Dong, Ming; Zhong, Guo-qiang; Miao, Shu-zhuo; Zheng, Chuan-tao; Wang, Yi-ding

    2018-03-01

    A dual-gas sensor system is developed for CO and CO2 detection using a single broadband light source, pyroelectric detectors and time-division multiplexing (TDM) technique. A stepper motor based rotating system and a single-reflection spherical optical mirror are designed and adopted for realizing and enhancing dual-gas detection. Detailed measurements under static detection mode (without rotation) and dynamic mode (with rotation) are performed to study the performance of the sensor system for the two gas samples. The detection period is 7.9 s in one round of detection by scanning the two detectors. Based on an Allan deviation analysis, the 1σ detection limits under static operation are 3.0 parts per million (ppm) in volume and 2.6 ppm for CO and CO2, respectively, and those under dynamic operation are 9.4 ppm and 10.8 ppm for CO and CO2, respectively. The reported sensor has potential applications in various fields requiring CO and CO2 detection such as in the coal mine.

  19. The detection of carbon dioxide leaks using quasi-tomographic laser absorption spectroscopy measurements in variable wind

    DOE PAGES

    Levine, Zachary H.; Pintar, Adam L.; Dobler, Jeremy T.; ...

    2016-04-13

    Laser absorption spectroscopy (LAS) has been used over the last several decades for the measurement of trace gasses in the atmosphere. For over a decade, LAS measurements from multiple sources and tens of retroreflectors have been combined with sparse-sample tomography methods to estimate the 2-D distribution of trace gas concentrations and underlying fluxes from point-like sources. In this work, we consider the ability of such a system to detect and estimate the position and rate of a single point leak which may arise as a failure mode for carbon dioxide storage. The leak is assumed to be at a constant ratemore » giving rise to a plume with a concentration and distribution that depend on the wind velocity. Lastly, we demonstrate the ability of our approach to detect a leak using numerical simulation and also present a preliminary measurement.« less

  20. Truncated feature representation for automatic target detection using transformed data-based decomposition

    NASA Astrophysics Data System (ADS)

    Riasati, Vahid R.

    2016-05-01

    In this work, the data covariance matrix is diagonalized to provide an orthogonal bases set using the eigen vectors of the data. The eigen-vector decomposition of the data is transformed and filtered in the transform domain to truncate the data for robust features related to a specified set of targets. These truncated eigen features are then combined and reconstructed to utilize in a composite filter and consequently utilized for the automatic target detection of the same class of targets. The results associated with the testing of the current technique are evaluated using the peak-correlation and peak-correlation energy metrics and are presented in this work. The inverse transformed eigen-bases of the current technique may be thought of as an injected sparsity to minimize data in representing the skeletal data structure information associated with the set of targets under consideration.

  1. Observation chart design features affect the detection of patient deterioration: a systematic experimental evaluation.

    PubMed

    Christofidis, Melany J; Hill, Andrew; Horswill, Mark S; Watson, Marcus O

    2016-01-01

    To systematically evaluate the impact of several design features on chart-users' detection of patient deterioration on observation charts with early-warning scoring-systems. Research has shown that observation chart design affects the speed and accuracy with which abnormal observations are detected. However, little is known about the contribution of individual design features to these effects. A 2 × 2 × 2 × 2 mixed factorial design, with data-recording format (drawn dots vs. written numbers), scoring-system integration (integrated colour-based system vs. non-integrated tabular system) and scoring-row placement (grouped vs. separate) varied within-participants and scores (present vs. absent) varied between-participants by random assignment. 205 novice chart-users, tested between March 2011-March 2014, completed 64 trials where they saw real patient data presented on an observation chart. Each participant saw eight cases (four containing abnormal observations) on each of eight designs (which represented a factorial combination of the within-participants variables). On each trial, they assessed whether any of the observations were physiologically abnormal, or whether all observations were normal. Response times and error rates were recorded for each design. Participants responded faster (scores present and absent) and made fewer errors (scores absent) using drawn-dot (vs. written-number) observations and an integrated colour-based (vs. non-integrated tabular) scoring-system. Participants responded faster using grouped (vs. separate) scoring-rows when scores were absent, but separate scoring-rows when scores were present. Our findings suggest that several individual design features can affect novice chart-users' ability to detect patient deterioration. More broadly, the study further demonstrates the need to evaluate chart designs empirically. © 2015 John Wiley & Sons Ltd.

  2. Detection of Focal Cortical Dysplasia Lesions in MRI Using Textural Features

    NASA Astrophysics Data System (ADS)

    Loyek, Christian; Woermann, Friedrich G.; Nattkemper, Tim W.

    Focal cortical dysplasia (FCD) is a frequent cause of medically refractory partial epilepsy. The visual identification of FCD lesions on magnetic resonance images (MRI) is a challenging task in standard radiological analysis. Quantitative image analysis which tries to assist in the diagnosis of FCD lesions is an active field of research. In this work we investigate the potential of different texture features, in order to explore to what extent they are suitable for detecting lesional tissue. As a result we can show first promising results based on segmentation and texture classification.

  3. Performance Analysis of Grey-World-based Feature Detection and Matching for Mobile Positioning Systems

    NASA Astrophysics Data System (ADS)

    Bejuri, Wan Mohd Yaakob Wan; Mohamad, Mohd Murtadha

    2014-11-01

    This paper introduces a new grey-world-based feature detection and matching algorithm, intended for use with mobile positioning systems. This approach uses a combination of a wireless local area network (WLAN) and a mobile phone camera to determine positioning in an illumination environment using a practical and pervasive approach. The signal combination is based on retrieved signal strength from the WLAN access point and the image processing information from the building hallways. The results show our method can handle information better than Harlan Hile's method relative to the illumination environment, producing lower illumination error in five (5) different environments.

  4. Optimizing spectral resolutions for the classification of C3 and C4 grass species, using wavelengths of known absorption features

    NASA Astrophysics Data System (ADS)

    Adjorlolo, Clement; Cho, Moses A.; Mutanga, Onisimo; Ismail, Riyad

    2012-01-01

    Hyperspectral remote-sensing approaches are suitable for detection of the differences in 3-carbon (C3) and four carbon (C4) grass species phenology and composition. However, the application of hyperspectral sensors to vegetation has been hampered by high-dimensionality, spectral redundancy, and multicollinearity problems. In this experiment, resampling of hyperspectral data to wider wavelength intervals, around a few band-centers, sensitive to the biophysical and biochemical properties of C3 or C4 grass species is proposed. The approach accounts for an inherent property of vegetation spectral response: the asymmetrical nature of the inter-band correlations between a waveband and its shorter- and longer-wavelength neighbors. It involves constructing a curve of weighting threshold of correlation (Pearson's r) between a chosen band-center and its neighbors, as a function of wavelength. In addition, data were resampled to some multispectral sensors-ASTER, GeoEye-1, IKONOS, QuickBird, RapidEye, SPOT 5, and WorldView-2 satellites-for comparative purposes, with the proposed method. The resulting datasets were analyzed, using the random forest algorithm. The proposed resampling method achieved improved classification accuracy (κ=0.82), compared to the resampled multispectral datasets (κ=0.78, 0.65, 0.62, 0.59, 0.65, 0.62, 0.76, respectively). Overall, results from this study demonstrated that spectral resolutions for C3 and C4 grasses can be optimized and controlled for high dimensionality and multicollinearity problems, yet yielding high classification accuracies. The findings also provide a sound basis for programming wavebands for future sensors.

  5. A Widely Applicable Silver Sol for TLC Detection with Rich and Stable SERS Features.

    PubMed

    Zhu, Qingxia; Li, Hao; Lu, Feng; Chai, Yifeng; Yuan, Yongfang

    2016-12-01

    Thin-layer chromatography (TLC) coupled with surface-enhanced Raman spectroscopy (SERS) has gained tremendous popularity in the study of various complex systems. However, the detection of hydrophobic analytes is difficult, and the specificity still needs to be improved. In this study, a SERS-active non-aqueous silver sol which could activate the analytes to produce rich and stable spectral features was rapidly synthesized. Then, the optimized silver nanoparticles (AgNPs)-DMF sol was employed for TLC-SERS detection of hydrophobic (and also hydrophilic) analytes. SERS performance of this sol was superior to that of traditional Lee-Meisel AgNPs due to its high specificity, acceptable stability, and wide applicability. The non-aqueous AgNPs would be suitable for the TLC-SERS method, which shows great promise for applications in food safety assurance, environmental monitoring, medical diagnoses, and many other fields.

  6. A Widely Applicable Silver Sol for TLC Detection with Rich and Stable SERS Features

    NASA Astrophysics Data System (ADS)

    Zhu, Qingxia; Li, Hao; Lu, Feng; Chai, Yifeng; Yuan, Yongfang

    2016-04-01

    Thin-layer chromatography (TLC) coupled with surface-enhanced Raman spectroscopy (SERS) has gained tremendous popularity in the study of various complex systems. However, the detection of hydrophobic analytes is difficult, and the specificity still needs to be improved. In this study, a SERS-active non-aqueous silver sol which could activate the analytes to produce rich and stable spectral features was rapidly synthesized. Then, the optimized silver nanoparticles (AgNPs)-DMF sol was employed for TLC-SERS detection of hydrophobic (and also hydrophilic) analytes. SERS performance of this sol was superior to that of traditional Lee-Meisel AgNPs due to its high specificity, acceptable stability, and wide applicability. The non-aqueous AgNPs would be suitable for the TLC-SERS method, which shows great promise for applications in food safety assurance, environmental monitoring, medical diagnoses, and many other fields.

  7. Differentiation of animal fats from different origins: use of polymorphic features detected by Raman spectroscopy.

    PubMed

    Motoyama, Michiyo; Ando, Masahiro; Sasaki, Keisuke; Hamaguchi, Hiro-O

    2010-11-01

    Food safety requires the development of reliable techniques that ensure the origin of animal fats. In the present work, we try to verify the efficacy of using the polymorphic features of fats for discriminating animal-fat origins. We use Raman spectroscopy to collect the structural information of fat crystals. It is shown that a single Raman band at 1417 cm(-1) successfully differentiates pork fats from beef fats. This band is known to be characteristic of the β'-polymorph of fats. Pork fats show this band because they contain the β'-polymorph after rapid cooling to 0 °C. In beef-pork-fat mixtures, this band is not detected even in the presence of 50% pork fat; an addition of beef fat to pork fat is likely to produce a mixed fat with a completely different polymorphic behavior. This method seems to have the potential to detect beef products contaminated with pork-adipose tissue.

  8. Acoustic Longitudinal Field NIF Optic Feature Detection Map Using Time-Reversal & MUSIC

    SciTech Connect

    Lehman, S K

    2006-02-09

    We developed an ultrasonic longitudinal field time-reversal and MUltiple SIgnal Classification (MUSIC) based detection algorithm for identifying and mapping flaws in fused silica NIF optics. The algorithm requires a fully multistatic data set, that is one with multiple, independently operated, spatially diverse transducers, each transmitter of which, in succession, launches a pulse into the optic and the scattered signal measured and recorded at every receiver. We have successfully localized engineered ''defects'' larger than 1 mm in an optic. We confirmed detection and localization of 3 mm and 5 mm features in experimental data, and a 0.5 mm in simulated datamore » with sufficiently high signal-to-noise ratio. We present the theory, experimental results, and simulated results.« less

  9. On the use of feature selection to improve the detection of sea oil spills in SAR images

    NASA Astrophysics Data System (ADS)

    Mera, David; Bolon-Canedo, Veronica; Cotos, J. M.; Alonso-Betanzos, Amparo

    2017-03-01

    Fast and effective oil spill detection systems are crucial to ensure a proper response to environmental emergencies caused by hydrocarbon pollution on the ocean's surface. Typically, these systems uncover not only oil spills, but also a high number of look-alikes. The feature extraction is a critical and computationally intensive phase where each detected dark spot is independently examined. Traditionally, detection systems use an arbitrary set of features to discriminate between oil spills and look-alikes phenomena. However, Feature Selection (FS) methods based on Machine Learning (ML) have proved to be very useful in real domains for enhancing the generalization capabilities of the classifiers, while discarding the existing irrelevant features. In this work, we present a generic and systematic approach, based on FS methods, for choosing a concise and relevant set of features to improve the oil spill detection systems. We have compared five FS methods: Correlation-based feature selection (CFS), Consistency-based filter, Information Gain, ReliefF and Recursive Feature Elimination for Support Vector Machine (SVM-RFE). They were applied on a 141-input vector composed of features from a collection of outstanding studies. Selected features were validated via a Support Vector Machine (SVM) classifier and the results were compared with previous works. Test experiments revealed that the classifier trained with the 6-input feature vector proposed by SVM-RFE achieved the best accuracy and Cohen's kappa coefficient (87.1% and 74.06% respectively). This is a smaller feature combination with similar or even better classification accuracy than previous works. The presented finding allows to speed up the feature extraction phase without reducing the classifier accuracy. Experiments also confirmed the significance of the geometrical features since 75.0% of the different features selected by the applied FS methods as well as 66.67% of the proposed 6-input feature vector belong to

  10. Application of Geologic Mapping Techniques and Autonomous Feature Detection to Future Exploration of Europa

    NASA Astrophysics Data System (ADS)

    Bunte, M. K.; Tanaka, K. L.; Doggett, T.; Figueredo, P. H.; Lin, Y.; Greeley, R.; Saripalli, S.; Bell, J. F.

    2013-12-01

    Europa's extremely young surface age, evidence for extensive resurfacing, and indications of a sub-surface ocean elevate its astrobiological potential for habitable environments and make it a compelling focus for study. Knowledge of the global distribution and timing of Europan geologic units is a key step in understanding the history of the satellite and for identifying areas relevant for exploration. I have produced a 1:15M scale global geologic map of Europa which represents a proportionate distribution of four unit types and associated features: plains, linea, chaos, and crater materials. Mapping techniques differ somewhat from other planetary maps but do provide a method to establish stratigraphic markers and to illustrate the surface history through four periods of formation as a function of framework lineament cross-cutting relationships. Correlations of observed features on Europa with Earth analogs enforce a multi-process theory for formation rather than the typical reliance on the principle of parsimony. Lenticulae and microchaos are genetically similar and most likely form by diapirism. Platy and blocky chaos units, endmembers of archetypical chaos, are best explained by brine mobilization. Ridges account for the majority of lineaments and may form by a number of methods indicative of local conditions; most form by either tidal pumping or shear heating. The variety of morphologies exhibited by bands indicates that multiple formation mechanisms apply once fracturing of the brittle surface over a ductile subsurface is initiated. Mapping results support the interpretation that Europa's shell has thickened over time resulting in changes in the style and intensity of deformation. Mapping serves as an index for change detection and classification, aids in pre-encounter targeting, and supports the selection of potential landing sites. Highest priority target areas are those which indicate geophysical activity by the presence of volcanic plumes, outgassing, or

  11. Rotation-invariant features for multi-oriented text detection in natural images.

    PubMed

    Yao, Cong; Zhang, Xin; Bai, Xiang; Liu, Wenyu; Ma, Yi; Tu, Zhuowen

    2013-01-01

    Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes.

  12. [Spectral features analysis of Pinus massoniana with pest of Dendrolimus punctatus Walker and levels detection].

    PubMed

    Xu, Zhang-Hua; Liu, Jian; Yu, Kun-Yong; Gong, Cong-Hong; Xie, Wan-Jun; Tang, Meng-Ya; Lai, Ri-Wen; Li, Zeng-Lu

    2013-02-01

    Taking 51 field measured hyperspectral data with different pest levels in Yanping, Fujian Province as objects, the spectral reflectance and first derivative features of 4 levels of healthy, mild, moderate and severe insect pest were analyzed. On the basis of 7 detecting parameters construction, the pest level detecting models were built. The results showed that (1) the spectral reflectance of Pinus massoniana with pests were significantly lower than that of healthy state, and the higher the pest level, the lower the reflectance; (2) with the increase in pest level, the spectral reflectance curves' "green peak" and "red valley" of Pinus massoniana gradually disappeared, and the red edge was leveleds (3) the pest led to spectral "green peak" red shift, red edge position blue shift, but the changes in "red valley" and near-infrared position were complicated; (4) CARI, RES, REA and REDVI were highly relevant to pest levels, and the correlations between REP, RERVI, RENDVI and pest level were weak; (5) the multiple linear regression model with the variables of the 7 detection parameters could effectively detect the pest levels of Dendrolimus punctatus Walker, with both the estimation rate and accuracy above 0.85.

  13. Detection of tripping gait patterns in the elderly using autoregressive features and support vector machines.

    PubMed

    Lai, Daniel T H; Begg, Rezaul K; Taylor, Simon; Palaniswami, Marimuthu

    2008-01-01

    Elderly tripping falls cost billions annually in medical funds and result in high mortality rates often perpetrated by pulmonary embolism (internal bleeding) and infected fractures that do not heal well. In this paper, we propose an intelligent gait detection system (AR-SVM) for screening elderly individuals at risk of suffering tripping falls. The motivation of this system is to provide early detection of elderly gait reminiscent of tripping characteristics so that preventive measures could be administered. Our system is composed of two stages, a predictor model estimated by an autoregressive (AR) process and a support vector machine (SVM) classifier. The system input is a digital signal constructed from consecutive measurements of minimum toe clearance (MTC) representative of steady-state walking. The AR-SVM system was tested on 23 individuals (13 healthy and 10 having suffered at least one tripping fall in the past year) who each completed a minimum of 10 min of walking on a treadmill at a self-selected pace. In the first stage, a fourth order AR model required at least 64 MTC values to correctly detect all fallers and non-fallers. Detection was further improved to less than 1 min of walking when the model coefficients were used as input features to the SVM classifier. The system achieved a detection accuracy of 95.65% with the leave one out method using only 16 MTC samples, but was reduced to 69.57% when eight MTC samples were used. These results demonstrate a fast and efficient system requiring a small number of strides and only MTC measurements for accurate detection of tripping gait characteristics.

  14. Constraints on the OH-to-H Abundance Ratio in Infrared-bright Galaxies Derived from the Strength of the OH 35 μm Absorption Feature

    NASA Astrophysics Data System (ADS)

    Stone, Myra; Veilleux, Sylvain; González-Alfonso, Eduardo; Spoon, Henrik; Sturm, Eckhard

    2018-02-01

    We analyze Spitzer/InfraRed Spectrograph (IRS) observations of the OH 35 μm feature in 15 nearby (z ≲ 0.06) (ultra-)luminous infrared galaxies (U/LIRGs). All objects exhibit OH 35 μm purely in absorption, as expected. The small optical depth of this transition makes the strength of this feature a good indicator of the true OH column density. The measured OH 35 μm equivalent widths imply an average OH column density and a 1-σ standard deviation to the mean of {N}{OH}=1.31+/- 0.22× {10}17 cm‑2. This number is then compared with the hydrogen column density for a typical optical depth at 35 μm of ∼0.5 and gas-to-dust ratio of 125 to derive an OH-to-H abundance ratio of {X}{OH}=1.01+/- 0.15× {10}-6. This abundance ratio is formally a lower limit. It is consistent with the values generally assumed in the literature. The OH 35 μm line profiles predicted from published radiative transfer models constrained by observations of OH 65, 79, 84, and 119 μm in 5 objects (Mrk 231, Mrk 273, IRAS F05189-2524, IRAS F08572+3915, and IRAS F20551-4250) are also found to be consistent with the IRS OH 35 μm spectra.

  15. Synthesis temperature effect on the structural features and optical absorption of Zn(1-x)Co(x)Al2O4 oxides.

    PubMed

    Gaudon, M; Apheceixborde, A; Ménétrier, M; Le Nestour, A; Demourgues, A

    2009-10-05

    Zinc/cobalt aluminates with spinel-type structure were prepared by a polymeric route, leading to a pure phase with controlled grain size. The prepared pigments were characterized by powder X-ray diffraction Rietveld analyses in order to determine structural features, scanning electron microscopy for morphological investigation, helium pycnometry and (27)Al MAS NMR in order to highlight the occurrence of defects inside the structure, and UV-visible-near-IR spectroscopy to identify electronic transitions responsible for the compounds' color. The green-blue coloration of these pigments is known to be dependent on the sample thermal history. Here, for the first time, the Zn(1-x)Co(x)Al(2)O(4) color is newly interpreted. The pigment is green once synthesized at low temperature (i.e., with diminution of the pigment grain size); this variation was attributed to the appearance of a new absorption band located at about 500 nm, linked to a complex network feature involving Co ions in octahedral sites as well as oxygen and cationic vacancies. Hence, this work shows the possibility of easily getting a nonstoichiometric network with an abnormal cationic distribution from "chimie douce" processes with moderate synthesis temperature, and so various colorations for the same composition.

  16. A research of selected textural features for detection of asbestos-cement roofing sheets using orthoimages

    NASA Astrophysics Data System (ADS)

    Książek, Judyta

    2015-10-01

    At present, there has been a great interest in the development of texture based image classification methods in many different areas. This study presents the results of research carried out to assess the usefulness of selected textural features for detection of asbestos-cement roofs in orthophotomap classification. Two different orthophotomaps of southern Poland (with ground resolution: 5 cm and 25 cm) were used. On both orthoimages representative samples for two classes: asbestos-cement roofing sheets and other roofing materials were selected. Estimation of texture analysis usefulness was conducted using machine learning methods based on decision trees (C5.0 algorithm). For this purpose, various sets of texture parameters were calculated in MaZda software. During the calculation of decision trees different numbers of texture parameters groups were considered. In order to obtain the best settings for decision trees models cross-validation was performed. Decision trees models with the lowest mean classification error were selected. The accuracy of the classification was held based on validation data sets, which were not used for the classification learning. For 5 cm ground resolution samples, the lowest mean classification error was 15.6%. The lowest mean classification error in the case of 25 cm ground resolution was 20.0%. The obtained results confirm potential usefulness of the texture parameter image processing for detection of asbestos-cement roofing sheets. In order to improve the accuracy another extended study should be considered in which additional textural features as well as spectral characteristics should be analyzed.

  17. Pre-trained convolutional neural networks as feature extractors for tuberculosis detection.

    PubMed

    Lopes, U K; Valiati, J F

    2017-10-01

    It is estimated that in 2015, approximately 1.8 million people infected by tuberculosis died, most of them in developing countries. Many of those deaths could have been prevented if the disease had been detected at an earlier stage, but the most advanced diagnosis methods are still cost prohibitive for mass adoption. One of the most popular tuberculosis diagnosis methods is the analysis of frontal thoracic radiographs; however, the impact of this method is diminished by the need for individual analysis of each radiography by properly trained radiologists. Significant research can be found on automating diagnosis by applying computational techniques to medical images, thereby eliminating the need for individual image analysis and greatly diminishing overall costs. In addition, recent improvements on deep learning accomplished excellent results classifying images on diverse domains, but its application for tuberculosis diagnosis remains limited. Thus, the focus of this work is to produce an investigation that will advance the research in the area, presenting three proposals to the application of pre-trained convolutional neural networks as feature extractors to detect the disease. The proposals presented in this work are implemented and compared to the current literature. The obtained results are competitive with published works demonstrating the potential of pre-trained convolutional networks as medical image feature extractors. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Detecting features in the dark energy equation of state: a wavelet approach

    SciTech Connect

    Hojjati, Alireza; Pogosian, Levon; Zhao, Gong-Bo, E-mail: alireza_hojjati@sfu.ca, E-mail: levon@sfu.ca, E-mail: gong-bo.zhao@port.ac.uk

    2010-04-01

    We study the utility of wavelets for detecting the redshift evolution of the dark energy equation of state w(z) from the combination of supernovae (SNe), CMB and BAO data. We show that local features in w, such as bumps, can be detected efficiently using wavelets. To demonstrate, we first generate a mock supernovae data sample for a SNAP-like survey with a bump feature in w(z) hidden in, then successfully discover it by performing a blind wavelet analysis. We also apply our method to analyze the recently released ''Constitution'' SNe data, combined with WMAP and BAO from SDSS, and find weakmore » hints of dark energy dynamics. Namely, we find that models with w(z) < −1 for 0.2 < z < 0.5, and w(z) > −1 for 0.5 < z < 1, are mildly favored at 95% confidence level. This is in good agreement with several recent studies using other methods, such as redshift binning with principal component analysis (PCA) (e.g. Zhao and Zhang, arXiv: 0908.1568)« less

  19. Joint Spatial-Spectral Feature Space Clustering for Speech Activity Detection from ECoG Signals

    PubMed Central

    Kanas, Vasileios G.; Mporas, Iosif; Benz, Heather L.; Sgarbas, Kyriakos N.; Bezerianos, Anastasios; Crone, Nathan E.

    2014-01-01

    Brain machine interfaces for speech restoration have been extensively studied for more than two decades. The success of such a system will depend in part on selecting the best brain recording sites and signal features corresponding to speech production. The purpose of this study was to detect speech activity automatically from electrocorticographic signals based on joint spatial-frequency clustering of the ECoG feature space. For this study, the ECoG signals were recorded while a subject performed two different syllable repetition tasks. We found that the optimal frequency resolution to detect speech activity from ECoG signals was 8 Hz, achieving 98.8% accuracy by employing support vector machines (SVM) as a classifier. We also defined the cortical areas that held the most information about the discrimination of speech and non-speech time intervals. Additionally, the results shed light on the distinct cortical areas associated with the two syllable repetition tasks and may contribute to the development of portable ECoG-based communication. PMID:24658248

  20. Spinal focal lesion detection in multiple myeloma using multimodal image features

    NASA Astrophysics Data System (ADS)

    Fränzle, Andrea; Hillengass, Jens; Bendl, Rolf

    2015-03-01

    Multiple myeloma is a tumor disease in the bone marrow that affects the skeleton systemically, i.e. multiple lesions can occur in different sites in the skeleton. To quantify overall tumor mass for determining degree of disease and for analysis of therapy response, volumetry of all lesions is needed. Since the large amount of lesions in one patient impedes manual segmentation of all lesions, quantification of overall tumor volume is not possible until now. Therefore development of automatic lesion detection and segmentation methods is necessary. Since focal tumors in multiple myeloma show different characteristics in different modalities (changes in bone structure in CT images, hypointensity in T1 weighted MR images and hyperintensity in T2 weighted MR images), multimodal image analysis is necessary for the detection of focal tumors. In this paper a pattern recognition approach is presented that identifies focal lesions in lumbar vertebrae based on features from T1 and T2 weighted MR images. Image voxels within bone are classified using random forests based on plain intensities and intensity value derived features (maximum, minimum, mean, median) in a 5 x 5 neighborhood around a voxel from both T1 and T2 weighted MR images. A test data sample of lesions in 8 lumbar vertebrae from 4 multiple myeloma patients can be classified at an accuracy of 95% (using a leave-one-patient-out test). The approach provides a reasonable delineation of the example lesions. This is an important step towards automatic tumor volume quantification in multiple myeloma.

  1. Feature extraction for ultrasonic sensor based defect detection in ceramic components

    NASA Astrophysics Data System (ADS)

    Kesharaju, Manasa; Nagarajah, Romesh

    2014-02-01

    High density silicon carbide materials are commonly used as the ceramic element of hard armour inserts used in traditional body armour systems to reduce their weight, while providing improved hardness, strength and elastic response to stress. Currently, armour ceramic tiles are inspected visually offline using an X-ray technique that is time consuming and very expensive. In addition, from X-rays multiple defects are also misinterpreted as single defects. Therefore, to address these problems the ultrasonic non-destructive approach is being investigated. Ultrasound based inspection would be far more cost effective and reliable as the methodology is applicable for on-line quality control including implementation of accept/reject criteria. This paper describes a recently developed methodology to detect, locate and classify various manufacturing defects in ceramic tiles using sub band coding of ultrasonic test signals. The wavelet transform is applied to the ultrasonic signal and wavelet coefficients in the different frequency bands are extracted and used as input features to an artificial neural network (ANN) for purposes of signal classification. Two different classifiers, using artificial neural networks (supervised) and clustering (un-supervised) are supplied with features selected using Principal Component Analysis(PCA) and their classification performance compared. This investigation establishes experimentally that Principal Component Analysis(PCA) can be effectively used as a feature selection method that provides superior results for classifying various defects in the context of ultrasonic inspection in comparison with the X-ray technique.

  2. An ultra low power feature extraction and classification system for wearable seizure detection.

    PubMed

    Page, Adam; Pramod Tim Oates, Siddharth; Mohsenin, Tinoosh

    2015-01-01

    In this paper we explore the use of a variety of machine learning algorithms for designing a reliable and low-power, multi-channel EEG feature extractor and classifier for predicting seizures from electroencephalographic data (scalp EEG). Different machine learning classifiers including k-nearest neighbor, support vector machines, naïve Bayes, logistic regression, and neural networks are explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. The input to each machine learning classifier is a 198 feature vector containing 9 features for each of the 22 EEG channels obtained over 1-second windows. All classifiers were able to obtain F1 scores over 80% and onset sensitivity of 100% when tested on 10 patients. Among five different classifiers that were explored, logistic regression (LR) proved to have minimum hardware complexity while providing average F-1 score of 91%. Both ASIC and FPGA implementations of logistic regression are presented and show the smallest area, power consumption, and the lowest latency when compared to the previous work.

  3. Robust feature detection and local classification for surfaces based on moment analysis.

    PubMed

    Clarenz, Ulrich; Rumpf, Martin; Telea, Alexandru

    2004-01-01

    The stable local classification of discrete surfaces with respect to features such as edges and corners or concave and convex regions, respectively, is as quite difficult as well as indispensable for many surface processing applications. Usually, the feature detection is done via a local curvature analysis. If concerned with large triangular and irregular grids, e.g., generated via a marching cube algorithm, the detectors are tedious to treat and a robust classification is hard to achieve. Here, a local classification method on surfaces is presented which avoids the evaluation of discretized curvature quantities. Moreover, it provides an indicator for smoothness of a given discrete surface and comes together with a built-in multiscale. The proposed classification tool is based on local zero and first moments on the discrete surface. The corresponding integral quantities are stable to compute and they give less noisy results compared to discrete curvature quantities. The stencil width for the integration of the moments turns out to be the scale parameter. Prospective surface processing applications are the segmentation on surfaces, surface comparison, and matching and surface modeling. Here, a method for feature preserving fairing of surfaces is discussed to underline the applicability of the presented approach.

  4. Limiting Short-term Noise versus Optical Density in a Direct Absorption Spectrometer for Trace Gas Detection

    NASA Astrophysics Data System (ADS)

    Jervis, D.

    2016-12-01

    Field-deployable trace gas monitors are important for understanding a multitude of atmospheric processes: from forest photosynthesis and respiration [1], to fugitive methane emissions [2] and satellite measurement validation [3]. Consequently, a detailed knowledge of the performance limitations of these instruments is essential in order to establish reliable datasets. We present the short-term ( >1 Hz) performance of a long-pass direct absorption spectrometer as a function of the optical density of the absorption transition being probed. In particular, we identify fluctuations in the laser intensity as limiting the optical density uncertainty to 4x10-6/√Hz for weak transitions, and noise in the laser drive current as limiting the fractional noise in the optical density to 4x10-5/√Hz for deep transitions. We provide numerical and analytical predictions for both effects, as well as using the understanding of this phenomena to estimate how noise on neighboring strong and weak transitions couple to each other. All measurements were performed using the Aerodyne Research TILDAS Monitor, but are general to any instrument that uses direct absorption spectroscopy as a detection method. Wehr, R., et al. "Seasonality of temperate forest photosynthesis and daytime respiration." Nature 534.7609 (2016): 680-683. Conley, S., et al. "Methane emissions from the 2015 Aliso Canyon blowout in Los Angeles, CA." Science 351.6279 (2016): 1317-1320. Emmons, L. K., et al. "Validation of Measurements of Pollution in the Troposphere (MOPITT) CO retrievals with aircraft in situ profiles." Journal of Geophysical Research: Atmospheres 109.D3 (2004).

  5. Ion chromatography with the indirect ultraviolet detection of alkali metal ions and ammonium using imidazolium ionic liquid as ultraviolet absorption reagent and eluent.

    PubMed

    Liu, Yong-Qiang; Yu, Hong

    2016-08-01

    Indirect ultraviolet detection was conducted in ultraviolet-absorption-agent-added mobile phase to complete the detection of the absence of ultraviolet absorption functional group in analytes. Compared with precolumn derivatization or postcolumn derivatization, this method can be widely used, has the advantages of simple operation and good linear relationship. Chromatographic separation of Li(+) , Na(+) , K(+) , and NH4 (+) was performed on a carboxylic acid base cation exchange column using imidazolium ionic liquid/acid/organic solvent as the mobile phase, in which imidazolium ionic liquids acted as ultraviolet absorption reagent and eluting agent. The retention behaviors of four kinds of cations are discussed, and the mechanism of separation and detection are described. The main factors influencing the separation and detection were the background ultraviolet absorption reagent and the concentration of hydrogen ion in the ion chromatography-indirect ultraviolet detection. The successful separation and detection of Li(+) , Na(+) , K(+) , and NH4 (+) within 13 min was achieved using the selected chromatographic conditions, and the detection limits (S/N = 3) were 0.02, 0.11, 0.30, and 0.06 mg/L, respectively. A new separation and analysis method of alkali metal ions and ammonium by ion chromatography with indirect ultraviolet detection method was developed, and the application range of ionic liquid was expanded. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples

    PubMed Central

    White, James Robert; Nagarajan, Niranjan; Pop, Mihai

    2009-01-01

    Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them. We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment of the differences between these populations. The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects. Our methods are robust across datasets of varied complexity and sampling level. While designed for metagenomic applications, our software can also be applied

  7. Design of Advanced Atmospheric Water Vapor Differential Absorption Lidar (DIAL) Detection System

    NASA Technical Reports Server (NTRS)

    Refaat, Tamer F.; Luck, William S., Jr.; DeYoung, Russell J.

    1999-01-01

    The measurement of atmospheric water vapor is very important for understanding the Earth's climate and water cycle. The lidar atmospheric sensing experiment (LASE) is an instrument designed and operated by the Langley Research Center for high precision water vapor measurements. The design details of a new water vapor lidar detection system that improves the measurement sensitivity of the LASE instrument by a factor of 10 are discussed. The new system consists of an advanced, very low noise, avalanche photodiode (APD) and a state-of-the-art signal processing circuit. The new low-power system is also compact and lightweight so that it would be suitable for space flight and unpiloted atmospheric vehicles (UAV) applications. The whole system is contained on one small printed circuit board (9 x 15 sq cm). The detection system is mounted at the focal plane of a lidar receiver telescope, and the digital output is read by a personal computer with a digital data acquisition card.

  8. Detection of gastrointestinal cancer by elastic scattering and absorption spectroscopies with the Los Alamos Optical Biopsy System

    SciTech Connect

    Mourant, J.R.; Boyer, J.; Johnson, T.M.

    1995-03-01

    The Los Alamos National Laboratory has continued the development of the Optical Biopsy System (OBS) for noninvasive, real-time in situ diagnosis of tissue pathologies. In proceedings of earlier SPIE conferences we reported on clinical measurements in the bladder, and we report here on recent results of clinical tests in the gastrointestinal tract. With the OBS, tissue pathologies are detected/diagnosed using spectral measurements of the elastic optical transport properties (scattering and absorption) of the tissue over a wide range of wavelengths. The use of elastic scattering as the key to optical tissue diagnostics in the OBS is based on the factmore » that many tissue pathologies, including a majority of cancer forms, exhibit significant architectural changes at the cellular and sub-cellular level. Since the cellular components that cause elastic scattering have dimensions typically on the order of visible to near-IR wavelengths, the elastic (Mie) scattering properties will be wavelength dependent. Thus, morphology and size changes can be expected to cause significant changes m an optical signature that is derived from the wavelength-dependence of elastic scattering. Additionally, the optical geometry of the OBS beneficially enhances its sensitivity for measuring absorption bands. The OBS employs a small fiber-optic probe that is amenable to use with any endoscope or catheter, or to direct surface examination, as well as interstitial needle insertion. Data acquistion/display time is <1 second.« less

  9. Multilayer Cloud Detection with the MODIS Near-Infrared Water Vapor Absorption Band

    NASA Technical Reports Server (NTRS)

    Wind, Galina; Platnick, Steven; King, Michael D.; Hubanks, Paul A,; Pavolonis, Michael J.; Heidinger, Andrew K.; Yang, Ping; Baum, Bryan A.

    2009-01-01

    Data Collection 5 processing for the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the NASA Earth Observing System EOS Terra and Aqua spacecraft includes an algorithm for detecting multilayered clouds in daytime. The main objective of this algorithm is to detect multilayered cloud scenes, specifically optically thin ice cloud overlying a lower-level water cloud, that presents difficulties for retrieving cloud effective radius using single layer plane-parallel cloud models. The algorithm uses the MODIS 0.94 micron water vapor band along with CO2 bands to obtain two above-cloud precipitable water retrievals, the difference of which, in conjunction with additional tests, provides a map of where multilayered clouds might potentially exist. The presence of a multilayered cloud results in a large difference in retrievals of above-cloud properties between the CO2 and the 0.94 micron methods. In this paper the MODIS multilayered cloud algorithm is described, results of using the algorithm over example scenes are shown, and global statistics for multilayered clouds as observed by MODIS are discussed. A theoretical study of the algorithm behavior for simulated multilayered clouds is also given. Results are compared to two other comparable passive imager methods. A set of standard cloudy atmospheric profiles developed during the course of this investigation is also presented. The results lead to the conclusion that the MODIS multilayer cloud detection algorithm has some skill in identifying multilayered clouds with different thermodynamic phases

  10. Classification method, spectral diversity, band combination and accuracy assessment evaluation for urban feature detection

    NASA Astrophysics Data System (ADS)

    Erener, A.

    2013-04-01

    Automatic extraction of urban features from high resolution satellite images is one of the main applications in remote sensing. It is useful for wide scale applications, namely: urban planning, urban mapping, disaster management, GIS (geographic information systems) updating, and military target detection. One common approach to detecting urban features from high resolution images is to use automatic classification methods. This paper has four main objectives with respect to detecting buildings. The first objective is to compare the performance of the most notable supervised classification algorithms, including the maximum likelihood classifier (MLC) and the support vector machine (SVM). In this experiment the primary consideration is the impact of kernel configuration on the performance of the SVM. The second objective of the study is to explore the suitability of integrating additional bands, namely first principal component (1st PC) and the intensity image, for original data for multi classification approaches. The performance evaluation of classification results is done using two different accuracy assessment methods: pixel based and object based approaches, which reflect the third aim of the study. The objective here is to demonstrate the differences in the evaluation of accuracies of classification methods. Considering consistency, the same set of ground truth data which is produced by labeling the building boundaries in the GIS environment is used for accuracy assessment. Lastly, the fourth aim is to experimentally evaluate variation in the accuracy of classifiers for six different real situations in order to identify the impact of spatial and spectral diversity on results. The method is applied to Quickbird images for various urban complexity levels, extending from simple to complex urban patterns. The simple surface type includes a regular urban area with low density and systematic buildings with brick rooftops. The complex surface type involves almost all

  11. 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.

  12. 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

  13. MixDroid: A multi-features and multi-classifiers bagging system for Android malware detection

    NASA Astrophysics Data System (ADS)

    Huang, Weiqing; Hou, Erhang; Zheng, Liang; Feng, Weimiao

    2018-05-01

    In the past decade, Android platform has rapidly taken over the mobile market for its superior convenience and open source characteristics. However, with the popularity of Android, malwares targeting on Android devices are increasing rapidly, while the conventional rule-based and expert-experienced approaches are no longer able to handle such explosive growth. In this paper, combining with the theory of natural language processing and machine learning, we not only implement the basic feature extraction of permission application features, but also propose two innovative schemes of feature extraction: Dalvik opcode features and malicious code image, and implement an automatic Android malware detection system MixDroid which is based on multi-features and multi-classifiers. According to our experiment results on 20,000 Android applications, detection accuracy of MixDroid is 98.1%, which proves our schemes' effectiveness in Android malware detection.

  14. A TV Camera System Which Extracts Feature Points For Non-Contact Eye Movement Detection

    NASA Astrophysics Data System (ADS)

    Tomono, Akira; Iida, Muneo; Kobayashi, Yukio

    1990-04-01

    This paper proposes a highly efficient camera system which extracts, irrespective of background, feature points such as the pupil, corneal reflection image and dot-marks pasted on a human face in order to detect human eye movement by image processing. Two eye movement detection methods are sugested: One utilizing face orientation as well as pupil position, The other utilizing pupil and corneal reflection images. A method of extracting these feature points using LEDs as illumination devices and a new TV camera system designed to record eye movement are proposed. Two kinds of infra-red LEDs are used. These LEDs are set up a short distance apart and emit polarized light of different wavelengths. One light source beams from near the optical axis of the lens and the other is some distance from the optical axis. The LEDs are operated in synchronization with the camera. The camera includes 3 CCD image pick-up sensors and a prism system with 2 boundary layers. Incident rays are separated into 2 wavelengths by the first boundary layer of the prism. One set of rays forms an image on CCD-3. The other set is split by the half-mirror layer of the prism and forms an image including the regularly reflected component by placing a polarizing filter in front of CCD-1 or another image not including the component by not placing a polarizing filter in front of CCD-2. Thus, three images with different reflection characteristics are obtained by three CCDs. Through the experiment, it is shown that two kinds of subtraction operations between the three images output from CCDs accentuate three kinds of feature points: the pupil and corneal reflection images and the dot-marks. Since the S/N ratio of the subtracted image is extremely high, the thresholding process is simple and allows reducting the intensity of the infra-red illumination. A high speed image processing apparatus using this camera system is decribed. Realtime processing of the subtraction, thresholding and gravity position

  15. Spectroscopic, orbital, and physical properties of the binary Feige 24 and detection of transient He II absorption in the system

    NASA Technical Reports Server (NTRS)

    Vennes, Stephane; Thorstensen, John R.

    1994-01-01

    intensity. Because it is correlated with the passage of the white dwarf at inferior conjunction, the absorption may occur in some foreground plasma emanated by the red dwarf and accumulating near a Lagrangian point or, alternatively, it may originate in an accretion spot on the white dwarf surface coaligned with the major orbital axis. Either way, the He II detection may imply substantial mass loss from the red dwarf with a corollary reclassification of Feige 24 as a mixed He/H DAO white dwarf resulting from accretion of secondary mass-loss material. Feige 24 is the prototype of a class of young, EUV-emitting, binary systems comprising a late main sequence secondary and a hot H-rich white dwarf; the class is characterized by optical and ultraviolet photospheric He II absorption, circumstellar C IV lambda (1550) absorption, and by the presence of EUV-induced, phase-dependent Balmer fluorescence. These young systems present the best opportunity to constrain theory of common-envelope evolution.

  16. Towards real-time detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma

    DOE PAGES

    Wu, Lingfei; Wu, Kesheng; Sim, Alex; ...

    2016-06-01

    A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes tomore » detect and track blob-filaments in real time in fusion plasma. Here, on a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.« less

  17. Detection of Ozone and Nitric Oxide in Decomposition Products of Air-Insulated Switchgear Using Ultraviolet Differential Optical Absorption Spectroscopy (UV-DOAS).

    PubMed

    Li, Yalong; Zhang, Xiaoxing; Li, Xin; Cui, Zhaolun; Xiao, Hai

    2018-01-01

    Air-insulated switchgear cabinets play a role in the protection and control of the modern power grid, and partial discharge (PD) switchgear is a long-term process in the non-normal operation of one of the situations; thus, condition monitoring of the switchgear is important. The air-insulated switchgear during PD enables the decomposition of air components, namely, O 3 and NO. A set of experimental platforms was designed on the basis of the principle of ultraviolet differential optical absorption spectroscopy (UV-DOAS) to detect O 3 and NO concentrations in air-insulated switchgear. Differential absorption algorithm and wavelet transform were used to extract effective absorption spectra; a linear relationship between O 3 and NO concentrations and absorption spectrum data were established. O 3 detection linearity was up to 0.9992 and the detection limit was at 3.76 ppm. NO detection linearity was up to 0.9990 and the detection limit was at 0.64 ppm. Results indicate that detection platform is suitable for detecting trace O 3 and NO gases produced by PD of the air-insulated switchgear.

  18. Feasibility of detecting near-surface feature with Rayleigh-wave diffraction

    USGS Publications Warehouse

    Xia, J.; Nyquist, Jonathan E.; Xu, Y.; Roth, M.J.S.; Miller, R.D.

    2007-01-01

    Detection of near-surfaces features such as voids and faults is challenging due to the complexity of near-surface materials and the limited resolution of geophysical methods. Although multichannel, high-frequency, surface-wave techniques can provide reliable shear (S)-wave velocities in different geological settings, they are not suitable for detecting voids directly based on anomalies of the S-wave velocity because of limitations on the resolution of S-wave velocity profiles inverted from surface-wave phase velocities. Therefore, we studied the feasibility of directly detecting near-surfaces features with surface-wave diffractions. Based on the properties of surface waves, we have derived a Rayleigh-wave diffraction traveltime equation. We also have solved the equation for the depth to the top of a void and an average velocity of Rayleigh waves. Using these equations, the depth to the top of a void/fault can be determined based on traveltime data from a diffraction curve. In practice, only two diffraction times are necessary to define the depth to the top of a void/fault and the average Rayleigh-wave velocity that generates the diffraction curve. We used four two-dimensional square voids to demonstrate the feasibility of detecting a void with Rayleigh-wave diffractions: a 2??m by 2??m with a depth to the top of the void of 2??m, 4??m by 4??m with a depth to the top of the void of 7??m, and 6??m by 6??m with depths to the top of the void 12??m and 17??m. We also modeled surface waves due to a vertical fault. Rayleigh-wave diffractions were recognizable for all these models after FK filtering was applied to the synthetic data. The Rayleigh-wave diffraction traveltime equation was verified by the modeled data. Modeling results suggested that FK filtering is critical to enhance diffracted surface waves. A real-world example is presented to show how to utilize the derived equation of surface-wave diffractions. ?? 2006 Elsevier B.V. All rights reserved.

  19. Hyperspectral Feature Detection Onboard the Earth Observing One Spacecraft using Superpixel Segmentation and Endmember Extraction

    NASA Technical Reports Server (NTRS)

    Thompson, David R.; Bornstein, Benjamin; Bue, Brian D.; Tran, Daniel Q.; Chien, Steve A.; Castano, Rebecca

    2012-01-01

    We present a demonstration of onboard hyperspectral image processing with the potential to reduce mission downlink requirements. The system detects spectral endmembers and then uses them to map units of surface material. This summarizes the content of the scene, reveals spectral anomalies warranting fast response, and reduces data volume by two orders of magnitude. We have integrated this system into the Autonomous Science craft Experiment for operational use onboard the Earth Observing One (EO-1) Spacecraft. The system does not require prior knowledge about spectra of interest. We report on a series of trial overflights in which identical spacecraft commands are effective for autonomous spectral discovery and mapping for varied target features, scenes and imaging conditions.

  20. Multi-feature classifiers for burst detection in single EEG channels from preterm infants

    NASA Astrophysics Data System (ADS)

    Navarro, X.; Porée, F.; Kuchenbuch, M.; Chavez, M.; Beuchée, Alain; Carrault, G.

    2017-08-01

    Objective. The study of electroencephalographic (EEG) bursts in preterm infants provides valuable information about maturation or prognostication after perinatal asphyxia. Over the last two decades, a number of works proposed algorithms to automatically detect EEG bursts in preterm infants, but they were designed for populations under 35 weeks of post menstrual age (PMA). However, as the brain activity evolves rapidly during postnatal life, these solutions might be under-performing with increasing PMA. In this work we focused on preterm infants reaching term ages (PMA  ⩾36 weeks) using multi-feature classification on a single EEG channel. Approach. Five EEG burst detectors relying on different machine learning approaches were compared: logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (kNN), support vector machines (SVM) and thresholding (Th). Classifiers were trained by visually labeled EEG recordings from 14 very preterm infants (born after 28 weeks of gestation) with 36-41 weeks PMA. Main results. The most performing classifiers reached about 95% accuracy (kNN, SVM and LR) whereas Th obtained 84%. Compared to human-automatic agreements, LR provided the highest scores (Cohen’s kappa  =  0.71) using only three EEG features. Applying this classifier in an unlabeled database of 21 infants  ⩾36 weeks PMA, we found that long EEG bursts and short inter-burst periods are characteristic of infants with the highest PMA and weights. Significance. In view of these results, LR-based burst detection could be a suitable tool to study maturation in monitoring or portable devices using a single EEG channel.

  1. Affective video retrieval: violence detection in Hollywood movies by large-scale segmental feature extraction.

    PubMed

    Eyben, Florian; Weninger, Felix; Lehment, Nicolas; Schuller, Björn; Rigoll, Gerhard

    2013-01-01

    Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology "out of the lab" to real-world, diverse data. In this contribution, we address the problem of finding "disturbing" scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP) on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis.

  2. Affective Video Retrieval: Violence Detection in Hollywood Movies by Large-Scale Segmental Feature Extraction

    PubMed Central

    Eyben, Florian; Weninger, Felix; Lehment, Nicolas; Schuller, Björn; Rigoll, Gerhard

    2013-01-01

    Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology “out of the lab” to real-world, diverse data. In this contribution, we address the problem of finding “disturbing” scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP) on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis. PMID:24391704

  3. DETECTION OF SHARP SYMMETRIC FEATURES IN THE CIRCUMBINARY DISK AROUND AK Sco

    SciTech Connect

    Janson, Markus; Asensio-Torres, Ruben; Thalmann, Christian

    The Search for Planets Orbiting Two Stars survey aims to study the formation and distribution of planets in binary systems by detecting and characterizing circumbinary planets and their formation environments through direct imaging. With the SPHERE Extreme Adaptive Optics instrument, a good contrast can be achieved even at small (<300 mas) separations from bright stars, which enables studies of planets and disks in a separation range that was previously inaccessible. Here, we report the discovery of resolved scattered light emission from the circumbinary disk around the well-studied young double star AK Sco, at projected separations in the ∼13–40 AU range. Themore » sharp morphology of the imaged feature is surprising, given the smooth appearance of the disk in its spectral energy distribution. We show that the observed morphology can be represented either as a highly eccentric ring around AK Sco, or as two separate spiral arms in the disk, wound in opposite directions. The relative merits of these interpretations are discussed, as well as whether these features may have been caused by one or several circumbinary planets interacting with the disk.« less

  4. A Study of Feature Combination for Vehicle Detection Based on Image Processing

    PubMed Central

    2014-01-01

    Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification. PMID:24672299

  5. Feature recognition and detection for ancient architecture based on machine vision

    NASA Astrophysics Data System (ADS)

    Zou, Zheng; Wang, Niannian; Zhao, Peng; Zhao, Xuefeng

    2018-03-01

    Ancient architecture has a very high historical and artistic value. The ancient buildings have a wide variety of textures and decorative paintings, which contain a lot of historical meaning. Therefore, the research and statistics work of these different compositional and decorative features play an important role in the subsequent research. However, until recently, the statistics of those components are mainly by artificial method, which consumes a lot of labor and time, inefficiently. At present, as the strong support of big data and GPU accelerated training, machine vision with deep learning as the core has been rapidly developed and widely used in many fields. This paper proposes an idea to recognize and detect the textures, decorations and other features of ancient building based on machine vision. First, classify a large number of surface textures images of ancient building components manually as a set of samples. Then, using the convolution neural network to train the samples in order to get a classification detector. Finally verify its precision.

  6. Noise-robust speech recognition through auditory feature detection and spike sequence decoding.

    PubMed

    Schafer, Phillip B; Jin, Dezhe Z

    2014-03-01

    Speech recognition in noisy conditions is a major challenge for computer systems, but the human brain performs it routinely and accurately. Automatic speech recognition (ASR) systems that are inspired by neuroscience can potentially bridge the performance gap between humans and machines. We present a system for noise-robust isolated word recognition that works by decoding sequences of spikes from a population of simulated auditory feature-detecting neurons. Each neuron is trained to respond selectively to a brief spectrotemporal pattern, or feature, drawn from the simulated auditory nerve response to speech. The neural population conveys the time-dependent structure of a sound by its sequence of spikes. We compare two methods for decoding the spike sequences--one using a hidden Markov model-based recognizer, the other using a novel template-based recognition scheme. In the latter case, words are recognized by comparing their spike sequences to template sequences obtained from clean training data, using a similarity measure based on the length of the longest common sub-sequence. Using isolated spoken digits from the AURORA-2 database, we show that our combined system outperforms a state-of-the-art robust speech recognizer at low signal-to-noise ratios. Both the spike-based encoding scheme and the template-based decoding offer gains in noise robustness over traditional speech recognition methods. Our system highlights potential advantages of spike-based acoustic coding and provides a biologically motivated framework for robust ASR development.

  7. A primitive study of voxel feature generation by multiple stacked denoising autoencoders for detecting cerebral aneurysms on MRA

    NASA Astrophysics Data System (ADS)

    Nemoto, Mitsutaka; Hayashi, Naoto; Hanaoka, Shouhei; Nomura, Yukihiro; Miki, Soichiro; Yoshikawa, Takeharu; Ohtomo, Kuni

    2016-03-01

    The purpose of this study is to evaluate the feasibility of a novel feature generation, which is based on multiple deep neural networks (DNNs) with boosting, for computer-assisted detection (CADe). It is hard and time-consuming to optimize the hyperparameters for DNNs such as stacked denoising autoencoder (SdA). The proposed method allows using SdA based features without the burden of the hyperparameter setting. The proposed method was evaluated by an application for detecting cerebral aneurysms on magnetic resonance angiogram (MRA). A baseline CADe process included four components; scaling, candidate area limitation, candidate detection, and candidate classification. Proposed feature generation method was applied to extract the optimal features for candidate classification. Proposed method only required setting range of the hyperparameters for SdA. The optimal feature set was selected from a large quantity of SdA based features by multiple SdAs, each of which was trained using different hyperparameter set. The feature selection was operated through ada-boost ensemble learning method. Training of the baseline CADe process and proposed feature generation were operated with 200 MRA cases, and the evaluation was performed with 100 MRA cases. Proposed method successfully provided SdA based features just setting the range of some hyperparameters for SdA. The CADe process by using both previous voxel features and SdA based features had the best performance with 0.838 of an area under ROC curve and 0.312 of ANODE score. The results showed that proposed method was effective in the application for detecting cerebral aneurysms on MRA.

  8. Evaluating the Nature of So-Called S*-State Feature in Transient Absorption of Carotenoids in Light-Harvesting Complex 2 (LH2) from Purple Photosynthetic Bacteria.

    PubMed

    Niedzwiedzki, Dariusz M; Hunter, C Neil; Blankenship, Robert E

    2016-11-03

    Carotenoids are a class of natural pigments present in all phototrophic organisms, mainly in their light-harvesting proteins in which they play roles of accessory light absorbers and photoprotectors. Extensive time-resolved spectroscopic studies of these pigments have revealed unexpectedly complex photophysical properties, particularly for carotenoids in light-harvesting LH2 complexes from purple bacteria. An ambiguous, optically forbidden electronic excited state designated as S* has been postulated to be involved in carotenoid excitation relaxation and in an alternative carotenoid-to-bacteriochlorophyll energy transfer pathway, as well as being a precursor of the carotenoid triplet state. However, no definitive and satisfactory origin of the carotenoid S* state in these complexes has been established, despite a wide-ranging series of studies. Here, we resolve the ambiguous origin of the carotenoid S* state in LH2 complex from Rba. sphaeroides by showing that the S* feature can be seen as a combination of ground state absorption bleaching of the carotenoid pool converted to cations and the Stark spectrum of neighbor neutral carotenoids, induced by temporal electric field brought by the carotenoid cation-bacteriochlorophyll anion pair. These findings remove the need to assign an S* state, and thereby significantly simplify the photochemistry of carotenoids in these photosynthetic antenna complexes.

  9. Evaluating the Nature of So-Called S*-State Feature in Transient Absorption of Carotenoids in Light-Harvesting Complex 2 (LH2) from Purple Photosynthetic Bacteria

    PubMed Central

    2016-01-01

    Carotenoids are a class of natural pigments present in all phototrophic organisms, mainly in their light-harvesting proteins in which they play roles of accessory light absorbers and photoprotectors. Extensive time-resolved spectroscopic studies of these pigments have revealed unexpectedly complex photophysical properties, particularly for carotenoids in light-harvesting LH2 complexes from purple bacteria. An ambiguous, optically forbidden electronic excited state designated as S* has been postulated to be involved in carotenoid excitation relaxation and in an alternative carotenoid-to-bacteriochlorophyll energy transfer pathway, as well as being a precursor of the carotenoid triplet state. However, no definitive and satisfactory origin of the carotenoid S* state in these complexes has been established, despite a wide-ranging series of studies. Here, we resolve the ambiguous origin of the carotenoid S* state in LH2 complex from Rba. sphaeroides by showing that the S* feature can be seen as a combination of ground state absorption bleaching of the carotenoid pool converted to cations and the Stark spectrum of neighbor neutral carotenoids, induced by temporal electric field brought by the carotenoid cation–bacteriochlorophyll anion pair. These findings remove the need to assign an S* state, and thereby significantly simplify the photochemistry of carotenoids in these photosynthetic antenna complexes. PMID:27726397

  10. Evaluating the nature of so-called S*-State feature in transient absorption of carotenoids in light-harvesting complex 2 (LH2) from purple photosynthetic bacteria

    DOE PAGES

    Niedzwiedzki, Dariusz M.; Hunter, C. Neil; Blankenship, Robert E.

    2016-10-11

    Carotenoids are a class of natural pigments present in all phototrophic organisms, mainly in their light-harvesting proteins in which they play roles of accessory light absorbers and photoprotectors. Extensive time-resolved spectroscopic studies of these pigments have revealed unexpectedly complex photophysical properties, particularly for carotenoids in light-harvesting LH2 complexes from purple bacteria. An ambiguous, optically forbidden electronic excited state designated as S* has been postulated to be involved in carotenoid excitation relaxation and in an alternative carotenoid-to-bacteriochlorophyll energy transfer pathway, as well as being a precursor of the carotenoid triplet state. However, no definitive and satisfactory origin of the carotenoid S*more » state in these complexes has been established, despite a wide-ranging series of studies. Here, we resolve the ambiguous origin of the carotenoid S* state in LH2 complex from Rba. sphaeroides by showing that the S* feature can be seen as a combination of ground state absorption bleaching of the carotenoid pool converted to cations and the Stark spectrum of neighbor neutral carotenoids, induced by temporal electric field brought by the carotenoid cation- bacteriochlorophyll anion pair. Lastly, these findings remove the need to assign an S* state, and thereby significantly simplify the photochemistry of carotenoids in these photosynthetic antenna complexes.« less

  11. Evaluating the nature of so-called S*-State feature in transient absorption of carotenoids in light-harvesting complex 2 (LH2) from purple photosynthetic bacteria

    SciTech Connect

    Niedzwiedzki, Dariusz M.; Hunter, C. Neil; Blankenship, Robert E.

    Carotenoids are a class of natural pigments present in all phototrophic organisms, mainly in their light-harvesting proteins in which they play roles of accessory light absorbers and photoprotectors. Extensive time-resolved spectroscopic studies of these pigments have revealed unexpectedly complex photophysical properties, particularly for carotenoids in light-harvesting LH2 complexes from purple bacteria. An ambiguous, optically forbidden electronic excited state designated as S* has been postulated to be involved in carotenoid excitation relaxation and in an alternative carotenoid-to-bacteriochlorophyll energy transfer pathway, as well as being a precursor of the carotenoid triplet state. However, no definitive and satisfactory origin of the carotenoid S*more » state in these complexes has been established, despite a wide-ranging series of studies. Here, we resolve the ambiguous origin of the carotenoid S* state in LH2 complex from Rba. sphaeroides by showing that the S* feature can be seen as a combination of ground state absorption bleaching of the carotenoid pool converted to cations and the Stark spectrum of neighbor neutral carotenoids, induced by temporal electric field brought by the carotenoid cation- bacteriochlorophyll anion pair. Lastly, these findings remove the need to assign an S* state, and thereby significantly simplify the photochemistry of carotenoids in these photosynthetic antenna complexes.« less

  12. GPS Signal Feature Analysis to Detect Volcanic Plume on Mount Etna

    NASA Astrophysics Data System (ADS)

    Cannavo', Flavio; Aranzulla, Massimo; Scollo, Simona; Puglisi, Giuseppe; Imme', Giuseppina

    2014-05-01

    Volcanic ash produced during explosive eruptions can cause disruptions to aviation operations and to population living around active volcanoes. Thus, detection of volcanic plume becomes a crucial issue to reduce troubles connected to its presence. Nowadays, the volcanic plume detection is carried out by using different approaches such as satellites, radars and lidars. Recently, the capability of GPS to retrieve volcanic plumes has been also investigated and some tests applied to explosive activity of Etna have demonstrated that also the GPS may give useful information. In this work, we use the permanent and continuous GPS network of the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (Italy) that consists of 35 stations located all around volcano flanks. Data are processed by the GAMIT package developed by Massachusetts Institute of Technology. Here we investigate the possibility to quantify the volcanic plume through the GPS signal features and to estimate its spatial distribution by means of a tomographic inversion algorithm. The method is tested on volcanic plumes produced during the lava fountain of 4-5 September 2007, already used to confirm if weak explosive activity may or may not affect the GPS signals.

  13. A new feature extraction method for signal classification applied to cord dorsum potentials detection

    PubMed Central

    Vidaurre, D.; Rodríguez, E. E.; Bielza, C.; Larrañaga, P.; Rudomin, P.

    2012-01-01

    In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods. PMID:22929924

  14. A new feature extraction method for signal classification applied to cord dorsum potential detection.

    PubMed

    Vidaurre, D; Rodríguez, E E; Bielza, C; Larrañaga, P; Rudomin, P

    2012-10-01

    In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

  15. Non-invasive health status detection system using Gabor filters based on facial block texture features.

    PubMed

    Shu, Ting; Zhang, Bob

    2015-04-01

    Blood tests allow doctors to check for certain diseases and conditions. However, using a syringe to extract the blood can be deemed invasive, slightly painful, and its analysis time consuming. In this paper, we propose a new non-invasive system to detect the health status (Healthy or Diseased) of an individual based on facial block texture features extracted using the Gabor filter. Our system first uses a non-invasive capture device to collect facial images. Next, four facial blocks are located on these images to represent them. Afterwards, each facial block is convolved with a Gabor filter bank to calculate its texture value. Classification is finally performed using K-Nearest Neighbor and Support Vector Machines via a Library for Support Vector Machines (with four kernel functions). The system was tested on a dataset consisting of 100 Healthy and 100 Diseased (with 13 forms of illnesses) samples. Experimental results show that the proposed system can detect the health status with an accuracy of 93 %, a sensitivity of 94 %, a specificity of 92 %, using a combination of the Gabor filters and facial blocks.

  16. Diagnostic performance of 3D standing CT imaging for detection of knee osteoarthritis features.

    PubMed

    Segal, Neil A; Nevitt, Michael C; Lynch, John A; Niu, Jingbo; Torner, James C; Guermazi, Ali

    2015-07-01

    To determine the diagnostic performance of standing computerized tomography (SCT) of the knee for osteophytes and subchondral cysts compared with fixed-flexion radiography, using MRI as the reference standard. Twenty participants were recruited from the Multicenter Osteoarthritis Study. Participants' knees were imaged with SCT while standing in a knee-positioning frame, and with postero-anterior fixed-flexion radiography and 1T MRI. Medial and lateral marginal osteophytes and subchondral cysts were scored on bilateral radiographs and coronal SCT images using the OARSI grading system and on coronal MRI using Whole Organ MRI Scoring. Imaging modalities were read separately with images in random order. Sensitivity, specificity and accuracy for the detection of lesions were calculated and differences between modalities were tested using McNemar's test. Participants' mean age was 66.8 years, body mass index was 29.6 kg/m(2) and 50% were women. Of the 160 surfaces (medial and lateral femur and tibia for 40 knees), MRI revealed 84 osteophytes and 10 subchondral cysts. In comparison with osteophytes and subchondral cysts detected by MRI, SCT was significantly more sensitive (93 and 100%; p < 0.004) and accurate (95 and 99%; p < 0.001 for osteophytes) than plain radiographs (sensitivity 60 and 10% and accuracy 79 and 94%, respectively). For osteophytes, differences in sensitivity and accuracy were greatest at the medial femur (p = 0.002). In comparison with MRI, SCT imaging was more sensitive and accurate for detection of osteophytes and subchondral cysts than conventional fixed-flexion radiography. Additional study is warranted to assess diagnostic performance of SCT measures of joint space width, progression of OA features and the patellofemoral joint.

  17. The multi-mode polarization modulation spectrometer: part 1: simultaneous detection of absorption, turbidity, and optical activity.

    PubMed

    Arvinte, Tudor; Bui, Tam T T; Dahab, Ali A; Demeule, Barthélemy; Drake, Alex F; Elhag, Dhia; King, Peter

    2004-09-01

    Circular dichroism (CD) is an important spectroscopic technique for monitoring chirality and biological macromolecule conformation. However, during a CD measurement, absorbance, light scattering/turbidity, and fluorescence can also be detected. The simultaneous measurement of these different spectral features for a single sample is the basis of a multi-mode optical spectrometer. This allows time-efficient gathering of complementary information and provides a scheme to ensure that CD measurements are reliable. Aspects of circular polarization differential light scattering, pH, and temperature variation of a protein (antibody) solution are described. A procedure to help ensure that CD measurements are reliable is described.

  18. Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

    PubMed Central

    Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

    2016-01-01

    In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

  19. Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

    PubMed

    Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

    2016-07-30

    In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

  20. Imaging features of breast cancers on digital breast tomosynthesis according to molecular subtype: association with breast cancer detection.

    PubMed

    Lee, Su Hyun; Chang, Jung Min; Shin, Sung Ui; Chu, A Jung; Yi, Ann; Cho, Nariya; Moon, Woo Kyung

    2017-12-01

    To evaluate imaging features of breast cancers on digital breast tomosynthesis (DBT) according to molecular subtype and to determine whether the molecular subtype affects breast cancer detection on DBT. This was an institutional review board--approved study with a waiver of informed consent. DBT findings of 288 invasive breast cancers were reviewed according to Breast Imaging Reporting and Data System lexicon. Detectability of breast cancer was quantified by the number of readers (0-3) who correctly detected the cancer in an independent blinded review. DBT features and the cancer detectability score according to molecular subtype were compared using Fisher's exact test and analysis of variance. Of 288 invasive cancers, 194 were hormone receptor (HR)-positive, 48 were human epidermal growth factor receptor 2 (HER2) positive and 46 were triple negative breast cancers. The most common DBT findings were irregular spiculated masses for HR-positive cancer, fine pleomorphic or linear branching calcifications for HER2 positive cancer and irregular masses with circumscribed margins for triple negative breast cancers (p < 0.001). Cancer detectability on DBT was not significantly different according to molecular subtype (p = 0.213) but rather affected by tumour size, breast density and presence of mass or calcifications. Breast cancers showed different imaging features according to molecular subtype; however, it did not affect the cancer detectability on DBT. Advances in knowledge: DBT showed characteristic imaging features of breast cancers according to molecular subtype. However, cancer detectability on DBT was not affected by molecular subtype of breast cancers.

  1. A quick eye to anger: An investigation of a differential effect of facial features in detecting angry and happy expressions.

    PubMed

    Lo, L Y; Cheng, M Y

    2017-06-01

    Detection of angry and happy faces is generally found to be easier and faster than that of faces expressing emotions other than anger or happiness. This can be explained by the threatening account and the feature account. Few empirical studies have explored the interaction between these two accounts which are seemingly, but not necessarily, mutually exclusive. The present studies hypothesised that prominent facial features are important in facilitating the detection process of both angry and happy expressions; yet the detection of happy faces was more facilitated by the prominent features than angry faces. Results confirmed the hypotheses and indicated that participants reacted faster to the emotional expressions with prominent features (in Study 1) and the detection of happy faces was more facilitated by the prominent feature than angry faces (in Study 2). The findings are compatible with evolutionary speculation which suggests that the angry expression is an alarming signal of potential threats to survival. Compared to the angry faces, the happy faces need more salient physical features to obtain a similar level of processing efficiency. © 2015 International Union of Psychological Science.

  2. A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

    PubMed

    Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

    2017-06-09

    Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Detecting Small-Scale Topographic Changes and Relict Geomorphic Features on Barrier Islands Using SAR

    NASA Technical Reports Server (NTRS)

    Gibeaut, James C.; Crawford, Melba M.; Gutierrez, Roberto; Slatton, K. Clint; Neuenschwander, Amy L.; Ricard, Michael R.

    1997-01-01

    The shapes and elevations of barrier islands may change dramatically over a short period of time during a storm. Coastal scientists and engineers, however, are currently unable to measure these changes occurring over an entire barrier island at once. This three-year project, which is funded by NASA and jointly conducted by the Bureau of Economic Geology and the Center for Space Research at The University of Texas at Austin, is designed to overcome this problem by developing the use of interferometry from airborne synthetic aperture radar (AIRSAR) to measure coastal topography and to detect storm-induced changes in topography. Surrogate measures of topography observed in multiband, fully polarimetric AIRSAR (This type of data are now referred to as POLSAR data.) are also being investigated. Digital elevation models (DEM) of Galveston Island and Bolivar Peninsula, Texas obtained with Topographic SAR (TOPSAR) are compared with measurements by Global Positioning System (GPS) ground surveys and electronic total station surveys. In addition to topographic mapping, this project is evaluating the use of POLSAR to detect old features such as storm scarps, storm channels, former tidal inlets, and beach ridges that have been obscured by vegetation, erosion, deposition, and artificial filling. We have also expanded the work from the original proposal to include the mapping of coastal wetland vegetation and depositional environments. Methods developed during this project will provide coastal geologists with an unprecedented tool for monitoring and understanding barrier island systems. This understanding will improve overall coastal management policies and will help reduce the effects of natural and man-induced coastal hazards. This report summarizes our accomplishments during the second year of the study. Also included is a discussion of our planned activities for year 3 and a revised budget.

  4. Feature-Based Change Detection Reveals Inconsistent Individual Differences in Visual Working Memory Capacity.

    PubMed

    Ambrose, Joseph P; Wijeakumar, Sobanawartiny; Buss, Aaron T; Spencer, John P

    2016-01-01

    Visual working memory (VWM) is a key cognitive system that enables people to hold visual information in mind after a stimulus has been removed and compare past and present to detect changes that have occurred. VWM is severely capacity limited to around 3-4 items, although there are robust individual differences in this limit. Importantly, these individual differences are evident in neural measures of VWM capacity. Here, we capitalized on recent work showing that capacity is lower for more complex stimulus dimension. In particular, we asked whether individual differences in capacity remain consistent if capacity is shifted by a more demanding task, and, further, whether the correspondence between behavioral and neural measures holds across a shift in VWM capacity. Participants completed a change detection (CD) task with simple colors and complex shapes in an fMRI experiment. As expected, capacity was significantly lower for the shape dimension. Moreover, there were robust individual differences in behavioral estimates of VWM capacity across dimensions. Similarly, participants with a stronger BOLD response for color also showed a strong neural response for shape within the lateral occipital cortex, intraparietal sulcus (IPS), and superior IPS. Although there were robust individual differences in the behavioral and neural measures, we found little evidence of systematic brain-behavior correlations across feature dimensions. This suggests that behavioral and neural measures of capacity provide different views onto the processes that underlie VWM and CD. Recent theoretical approaches that attempt to bridge between behavioral and neural measures are well positioned to address these findings in future work.

  5. Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

    PubMed Central

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

    2018-01-01

    Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases. PMID:29495417

  6. Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors.

    PubMed

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

    2018-02-26

    Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

  7. Visual acuity of the honey bee retina and the limits for feature detection.

    PubMed

    Rigosi, Elisa; Wiederman, Steven D; O'Carroll, David C

    2017-04-06

    Visual abilities of the honey bee have been studied for more than 100 years, recently revealing unexpectedly sophisticated cognitive skills rivalling those of vertebrates. However, the physiological limits of the honey bee eye have been largely unaddressed and only studied in an unnatural, dark state. Using a bright display and intracellular recordings, we here systematically investigated the angular sensitivity across the light adapted eye of honey bee foragers. Angular sensitivity is a measure of photoreceptor receptive field size and thus small values indicate higher visual acuity. Our recordings reveal a fronto-ventral acute zone in which angular sensitivity falls below 1.9°, some 30% smaller than previously reported. By measuring receptor noise and responses to moving dark objects, we also obtained direct measures of the smallest features detectable by the retina. In the frontal eye, single photoreceptors respond to objects as small as 0.6° × 0.6°, with >99% reliability. This indicates that honey bee foragers possess significantly better resolution than previously reported or estimated behaviourally, and commonly assumed in modelling of bee acuity.

  8. Combined optimization of image-gathering and image-processing systems for scene feature detection

    NASA Technical Reports Server (NTRS)

    Halyo, Nesim; Arduini, Robert F.; Samms, Richard W.

    1987-01-01

    The relationship between the image gathering and image processing systems for minimum mean squared error estimation of scene characteristics is investigated. A stochastic optimization problem is formulated where the objective is to determine a spatial characteristic of the scene rather than a feature of the already blurred, sampled and noisy image data. An analytical solution for the optimal characteristic image processor is developed. The Wiener filter for the sampled image case is obtained as a special case, where the desired characteristic is scene restoration. Optimal edge detection is investigated using the Laplacian operator x G as the desired characteristic, where G is a two dimensional Gaussian distribution function. It is shown that the optimal edge detector compensates for the blurring introduced by the image gathering optics, and notably, that it is not circularly symmetric. The lack of circular symmetry is largely due to the geometric effects of the sampling lattice used in image acquisition. The optimal image gathering optical transfer function is also investigated and the results of a sensitivity analysis are shown.

  9. Linear feature detection algorithm for astronomical surveys - II. Defocusing effects on meteor tracks

    NASA Astrophysics Data System (ADS)

    Bektešević, Dino; Vinković, Dejan; Rasmussen, Andrew; Ivezić, Željko

    2018-03-01

    Given the current limited knowledge of meteor plasma micro-physics and its interaction with the surrounding atmosphere and ionosphere, meteors are a highly interesting observational target for high-resolution wide-field astronomical surveys. Such surveys are capable of resolving the physical size of meteor plasma heads, but they produce large volumes of images that need to be automatically inspected for possible existence of long linear features produced by meteors. Here, we show how big aperture sky survey telescopes detect meteors as defocused tracks with a central brightness depression. We derive an analytic expression for a defocused point source meteor track and use it to calculate brightness profiles of meteors modelled as uniform brightness discs. We apply our modelling to meteor images as seen by the Sloan Digital Sky Survey and Large Synoptic Survey Telescope telescopes. The expression is validated by Monte Carlo ray-tracing simulations of photons travelling through the atmosphere and the Large Synoptic Survey Telescope telescope optics. We show that estimates of the meteor distance and size can be extracted from the measured full width at half-maximum and the strength of the central dip in the observed brightness profile. However, this extraction becomes difficult when the defocused meteor track is distorted by the atmospheric seeing or contaminated by a long-lasting glowing meteor trail. The full width at half-maximum of satellite tracks is distinctly narrower than meteor values, which enables removal of a possible confusion between satellites and meteors.

  10. CATS Version 2 Aerosol Feature Detection and Applications for Data Assimilation

    NASA Technical Reports Server (NTRS)

    Nowottnick, E. P.; Yorks, J. E.; Selmer, P. A.; Palm, S. P.; Hlavka, D. L.; Pauly, R. M.; Ozog, S.; McGill, M. J.; Da Silva, A.

    2017-01-01

    The Cloud Aerosol Transport System (CATS) lidar has been operating onboard the International Space Station (ISS) since February 2015 and provides vertical observations of clouds and aerosols using total attenuated backscatter and depolarization measurements. From February March 2015, CATS operated in Mode 1, providing backscatter and depolarization measurements at 532 and 1064 nm. CATS began operation in Mode 2 in March 2015, providing backscatter and depolarization measurements at 1064 nm and has continued to operate to the present in this mode. CATS level 2 products are derived from these measurements, including feature detection, cloud aerosol discrimination, cloud and aerosol typing, and optical properties of cloud and aerosol layers. Here, we present changes to our level 2 algorithms, which were aimed at reducing several biases in our version 1 level 2 data products. These changes will be incorporated into our upcoming version 2 level 2 data release in summer 2017. Additionally, owing to the near real time (NRT) data downlinking capabilities of the ISS, CATS provides expedited NRT data products within 6 hours of observation time. This capability provides a unique opportunity for supporting field campaigns and for developing data assimilation techniques to improve simulated cloud and aerosol vertical distributions in models. We additionally present preliminary work toward assimilating CATS observations into the NASA Goddard Earth Observing System version 5 (GEOS-5) global atmospheric model and data assimilation system.

  11. The 2D analytic signal for envelope detection and feature extraction on ultrasound images.

    PubMed

    Wachinger, Christian; Klein, Tassilo; Navab, Nassir

    2012-08-01

    The fundamental property of the analytic signal is the split of identity, meaning the separation of qualitative and quantitative information in form of the local phase and the local amplitude, respectively. Especially the structural representation, independent of brightness and contrast, of the local phase is interesting for numerous image processing tasks. Recently, the extension of the analytic signal from 1D to 2D, covering also intrinsic 2D structures, was proposed. We show the advantages of this improved concept on ultrasound RF and B-mode images. Precisely, we use the 2D analytic signal for the envelope detection of RF data. This leads to advantages for the extraction of the information-bearing signal from the modulated carrier wave. We illustrate this, first, by visual assessment of the images, and second, by performing goodness-of-fit tests to a Nakagami distribution, indicating a clear improvement of statistical properties. The evaluation is performed for multiple window sizes and parameter estimation techniques. Finally, we show that the 2D analytic signal allows for an improved estimation of local features on B-mode images. Copyright © 2012 Elsevier B.V. All rights reserved.

  12. Real-Time Sensor Validation, Signal Reconstruction, and Feature Detection for an RLV Propulsion Testbed

    NASA Technical Reports Server (NTRS)

    Jankovsky, Amy L.; Fulton, Christopher E.; Binder, Michael P.; Maul, William A., III; Meyer, Claudia M.

    1998-01-01

    A real-time system for validating sensor health has been developed in support of the reusable launch vehicle program. This system was designed for use in a propulsion testbed as part of an overall effort to improve the safety, diagnostic capability, and cost of operation of the testbed. The sensor validation system was designed and developed at the NASA Lewis Research Center and integrated into a propulsion checkout and control system as part of an industry-NASA partnership, led by Rockwell International for the Marshall Space Flight Center. The system includes modules for sensor validation, signal reconstruction, and feature detection and was designed to maximize portability to other applications. Review of test data from initial integration testing verified real-time operation and showed the system to perform correctly on both hard and soft sensor failure test cases. This paper discusses the design of the sensor validation and supporting modules developed at LeRC and reviews results obtained from initial test cases.

  13. Effective Detection of Sub-Surface Archeological Features from Laser Scanning Point Clouds and Imagery Data

    NASA Astrophysics Data System (ADS)

    Fryskowska, A.; Kedzierski, M.; Walczykowski, P.; Wierzbicki, D.; Delis, P.; Lada, A.

    2017-08-01

    The archaeological heritage is non-renewable, and any invasive research or other actions leading to the intervention of mechanical or chemical into the ground lead to the destruction of the archaeological site in whole or in part. For this reason, modern archeology is looking for alternative methods of non-destructive and non-invasive methods of new objects identification. The concept of aerial archeology is relation between the presence of the archaeological site in the particular localization, and the phenomena that in the same place can be observed on the terrain surface form airborne platform. One of the most appreciated, moreover, extremely precise, methods of such measurements is airborne laser scanning. In research airborne laser scanning point cloud with a density of 5 points/sq. m was used. Additionally unmanned aerial vehicle imagery data was acquired. Test area is located in central Europe. The preliminary verification of potentially microstructures localization was the creation of digital terrain and surface models. These models gave an information about the differences in elevation, as well as regular shapes and sizes that can be related to the former settlement/sub-surface feature. The paper presents the results of the detection of potentially sub-surface microstructure fields in the forestry area.

  14. A Feature-Free 30-Disease Pathological Brain Detection System by Linear Regression Classifier.

    PubMed

    Chen, Yi; Shao, Ying; Yan, Jie; Yuan, Ti-Fei; Qu, Yanwen; Lee, Elizabeth; Wang, Shuihua

    2017-01-01

    Alzheimer's disease patients are increasing rapidly every year. Scholars tend to use computer vision methods to develop automatic diagnosis system. (Background) In 2015, Gorji et al. proposed a novel method using pseudo Zernike moment. They tested four classifiers: learning vector quantization neural network, pattern recognition neural network trained by Levenberg-Marquardt, by resilient backpropagation, and by scaled conjugate gradient. This study presents an improved method by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Therefore, it can be used to detect Alzheimer's disease. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  15. Detection of silver nanoparticles in parsley by solid sampling high-resolution-continuum source atomic absorption spectrometry.

    PubMed

    Feichtmeier, Nadine S; Leopold, Kerstin

    2014-06-01

    In this work, we present a fast and simple approach for detection of silver nanoparticles (AgNPs) in biological material (parsley) by solid sampling high-resolution-continuum source atomic absorption spectrometry (HR-CS AAS). A novel evaluation strategy was developed in order to distinguish AgNPs from ionic silver and for sizing of AgNPs. For this purpose, atomisation delay was introduced as significant indication of AgNPs, whereas atomisation rates allow distinction of 20-, 60-, and 80-nm AgNPs. Atomisation delays were found to be higher for samples containing silver ions than for samples containing silver nanoparticles. A maximum difference in atomisation delay normalised by the sample weight of 6.27 ± 0.96 s mg(-1) was obtained after optimisation of the furnace program of the AAS. For this purpose, a multivariate experimental design was used varying atomisation temperature, atomisation heating rate and pyrolysis temperature. Atomisation rates were calculated as the slope of the first inflection point of the absorbance signals and correlated with the size of the AgNPs in the biological sample. Hence, solid sampling HR-CS AAS was proved to be a promising tool for identifying and distinguishing silver nanoparticles from ionic silver directly in solid biological samples.

  16. Detectors for the gamma-ray resonant absorption (GRA) method of explosives detection in cargo: a comparative study

    NASA Astrophysics Data System (ADS)

    Vartsky, David; Goldberg, Mark B.; Engler, Gideon; Shor, Asher; Goldschmidt, Aharon; Feldman, Gennady; Bar, Doron; Orion, Itzhak; Wielopolski, Lucian

    2004-01-01

    Gamma-Ray Resonant Absorption (GRA) is an automatic-decision radiographic screening technique that combines high radiation penetration with very good sensitivity and specificity to nitrogenous explosives. The method is particularly well-suited to inspection of large, massive objects (since the resonant γ-ray probe is at 9.17 MeV) such as aviation and marine containers, heavy vehicles and railroad cars. Two kinds of γ-ray detectors have been employed to date in GRA systems: 1) Resonant-response nitrogen-rich liquid scintillators and 2) BGO detectors. This paper analyses and compares the response of these detector-types to the resonant radiation, in terms of single-pixel figures of merit. The latter are sensitive not only to detector response, but also to accelerator-beam quality, via the properties of the nuclear reaction that produces the resonant-γ-rays. Generally, resonant detectors give rise to much higher nitrogen-contrast sensitivity in the radiographic image than their non-resonant detector counterparts and furthermore, do not require proton beams of high energy-resolution. By comparison, the non-resonant detectors have higher γ-detection efficiency, but their contrast sensitivity is very sensitive to the quality of the accelerator beam. Implications of these detector/accelerator characteristics for eventual GRA field systems are discussed.

  17. Sample preparation for arsenic speciation analysis in baby food by generation of substituted arsines with atomic absorption spectrometry detection.

    PubMed

    Huber, Charles S; Vale, Maria Goreti R; Dessuy, Morgana B; Svoboda, Milan; Musil, Stanislav; Dědina, Jiři

    2017-12-01

    A slurry sampling procedure for arsenic speciation analysis in baby food by arsane generation, cryogenic trapping and detection with atomic absorption spectrometry is presented. Several procedures were tested for slurry preparation, including different reagents (HNO 3 , HCl and tetramethylammonium hydroxide - TMAH) and their concentrations, water bath heating and ultrasound-assisted agitation. The best results for inorganic arsenic (iAs) and dimethylarsinate (DMA) were reached when using 3molL -1 HCl under heating and ultrasound-assisted agitation. The developed method was applied for the analysis of five porridge powder and six baby meal samples. The trueness of the method was checked with a certified reference material (CRM) of total arsenic (tAs), iAs and DMA in rice (ERM-BC211). Arsenic recoveries (mass balance) for all samples and CRM were performed by the determination of the tAs by inductively coupled plasma mass spectrometry (ICP-MS) after microwave-assisted digestion and its comparison against the sum of the results from the speciation analysis. The relative limits of detection were 0.44, 0.24 and 0.16µgkg -1 for iAs, methylarsonate and DMA, respectively. The concentrations of the most toxic arsenic species (iAs) in the analyzed baby food samples ranged between 4.2 and 99µgkg -1 which were below the limits of 300, 200 and 100µgkg -1 set by the Brazilian, Chinese and European legislation, respectively. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Regression-Based Approach For Feature Selection In Classification Issues. Application To Breast Cancer Detection And Recurrence

    NASA Astrophysics Data System (ADS)

    Belciug, Smaranda; Serbanescu, Mircea-Sebastian

    2015-09-01

    Feature selection is considered a key factor in classifications/decision problems. It is currently used in designing intelligent decision systems to choose the best features which allow the best performance. This paper proposes a regression-based approach to select the most important predictors to significantly increase the classification performance. Application to breast cancer detection and recurrence using publically available datasets proved the efficiency of this technique.

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

    PubMed

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

    2014-10-01

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

  20. Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers.

    PubMed

    Galpert, Deborah; Fernández, Alberto; Herrera, Francisco; Antunes, Agostinho; Molina-Ruiz, Reinaldo; Agüero-Chapin, Guillermin

    2018-05-03

    The development of new ortholog detection algorithms and the improvement of existing ones are of major importance in functional genomics. We have previously introduced a successful supervised pairwise ortholog classification approach implemented in a big data platform that considered several pairwise protein features and the low ortholog pair ratios found between two annotated proteomes (Galpert, D et al., BioMed Research International, 2015). The supervised models were built and tested using a Saccharomycete yeast benchmark dataset proposed by Salichos and Rokas (2011). Despite several pairwise protein features being combined in a supervised big data approach; they all, to some extent were alignment-based features and the proposed algorithms were evaluated on a unique test set. Here, we aim to evaluate the impact of alignment-free features on the performance of supervised models implemented in the Spark big data platform for pairwise ortholog detection in several related yeast proteomes. The Spark Random Forest and Decision Trees with oversampling and undersampling techniques, and built with only alignment-based similarity measures or combined with several alignment-free pairwise protein features showed the highest classification performance for ortholog detection in three yeast proteome pairs. Although such supervised approaches outperformed traditional methods, there were no significant differences between the exclusive use of alignment-based similarity measures and their combination with alignment-free features, even within the twilight zone of the studied proteomes. Just when alignment-based and alignment-free features were combined in Spark Decision Trees with imbalance management, a higher success rate (98.71%) within the twilight zone could be achieved for a yeast proteome pair that underwent a whole genome duplication. The feature selection study showed that alignment-based features were top-ranked for the best classifiers while the runners-up were

  1. Optically detected X-ray absorption spectroscopy measurements as a means of monitoring corrosion layers on copper.

    PubMed

    Dowsett, Mark G; Adriaens, Annemie; Jones, Gareth K C; Poolton, Nigel; Fiddy, Steven; Nikitenko, Sergé

    2008-11-15

    XANES and EXAFS information is conventionally measured in transmission through the energy-dependent absorption of X-rays or by observing X-ray fluorescence, but secondary fluorescence processes, such as the emission of electrons and optical photons (e.g., 200-1000 nm), can also be used as a carrier of the XAS signatures, providing complementary information such as improved surface specificity. Where the near-visible photons have a shorter range in a material, the data will be more surface specific. Moreover, optical radiation may escape more readily than X-rays through liquid in an environmental cell. Here, we describe a first test of optically detected X-ray absorption spectroscopy (ODXAS) for monitoring electrochemical treatments on copper-based alloys, for example, heritage metals. Artificially made corrosion products deposited on a copper substrate were analyzed in air and in a 1% (w/v) sodium sesquicarbonate solution to simulate typical conservation methods for copper-based objects recovered from marine environments. The measurements were made on stations 7.1 and 9.2 MF (SRS Daresbury, UK) using the mobile luminescence end station (MoLES), supplemented by XAS measurements taken on DUBBLE (BM26 A) at the ESRF. The ODXAS spectra usually contain fine structure similar to that of XAS spectra measured in X-ray fluorescence. Importantly, for the compounds examined, the ODXAS is significantly more surface specific, and >98% characteristic of thin surface layers of 0.5-1.5-microm thickness in cases where X-ray measurements are dominated by the substrate. However, EXAFS and XANES from broadband optical measurements are superimposed on a high background due to other optical emission modes. This produces statistical fluctuations up to double what would be expected from normal counting statistics because the data retain the absolute statistical fluctuation in the original raw count, while losing up to 70% of their magnitude when background is removed. The problem may be

  2. Indirect ultraviolet detection of alkaline earth metal ions using an imidazolium ionic liquid as an ultraviolet absorption reagent in ion chromatography.

    PubMed

    Liu, Yong-Qiang; Yu, Hong

    2017-04-01

    A convenient and versatile method was developed for the separation and detection of alkaline earth metal ions by ion chromatography with indirect UV detection. The chromatographic separation of Mg 2+ , Ca 2+ , and Sr 2+ was performed on a carboxylic acid base cation exchange column using imidazolium ionic liquid/acid as the mobile phase, in which the imidazolium ionic liquid acted as an UV-absorption reagent. The effects of imidazolium ionic liquids, detection wavelength, acids in the mobile phase, and column temperature on the retention of Mg 2+ , Ca 2+ , and Sr 2+ were investigated. The main factors influencing the separation and detection were the background UV absorption reagent and the concentration of hydrogen ion in ion chromatography with indirect UV detection. The successful separation and detection of Mg 2+ , Ca 2+ , and Sr 2+ within 14 min were achieved using the selected chromatographic conditions, and the detection limits (S/N = 3) were 0.06, 0.12, and 0.23 mg/L, respectively. A new separation and detection method of alkaline earth metal ions by ion chromatography with indirect UV detection was developed, and the application range of ionic liquids was expanded. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Neural Detection of Malicious Network Activities Using a New Direct Parsing and Feature Extraction Technique

    DTIC Science & Technology

    2015-09-01

    intrusion detection systems , neural networks 15. NUMBER OF PAGES 75 16. PRICE CODE 17. SECURITY CLASSIFICATION OF... detection system (IDS) software, which learns to detect and classify network attacks and intrusions through prior training data. With the added criteria of...BACKGROUND The growing threat of malicious network activities and intrusion attempts makes intrusion detection systems (IDS) a

  4. A Detection of the Baryon Acoustic Oscillation Features in the SDSS BOSS DR12 Galaxy Bispectrum

    NASA Astrophysics Data System (ADS)

    Pearson, David W.; Samushia, Lado

    2018-05-01

    We present the first high significance detection (4.1σ) of the Baryon Acoustic Oscillations (BAO) feature in the galaxy bispectrum of the twelfth data release (DR12) of the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS sample (0.43 ≤ z ≤ 0.7). We measured the scale dilation parameter, α, using the power spectrum, bispectrum, and both simultaneously for DR12, plus 2048 MultiDark-PATCHY mocks in the North and South Galactic Caps (NGC and SGC, respectively), and the volume weighted averages of those two samples (N+SGC). The fitting to the mocks validated our analysis pipeline, yielding values consistent with the mock cosmology. By fitting to the power spectrum and bispectrum separately, we tested the robustness of our results, finding consistent values from the NGC, SGC and N+SGC in all cases. We found DV = 2032 ± 24(stat.) ± 15(sys.) Mpc, DV = 2038 ± 55(stat.) ± 15(sys.) Mpc, and DV = 2031 ± 22(stat.) ± 10(sys.) Mpc from the N+SGC power spectrum, bispectrum and simultaneous fitting, respectively. Our bispectrum measurement precision was mainly limited by the size of the covariance matrix. Based on the fits to the mocks, we showed that if a less noisy estimator of the covariance were available, from either a theoretical computation or a larger suite of mocks, the constraints from the bispectrum and simultaneous fits would improve to 1.1 per cent (1.3 per cent with systematics) and 0.7 per cent (0.9 per cent with systematics), respectively, with the latter being slightly more precise than the power spectrum only constraints from the reconstructed field.

  5. Linear feature detection algorithm for astronomical surveys – II. Defocusing effects on meteor tracks

    SciTech Connect

    Bektešević, Dino; Vinković, Dejan; Rasmussen, Andrew

    Given the current limited knowledge of meteor plasma micro-physics and its interaction with the surrounding atmosphere and ionosphere, meteors are a highly interesting observational target for high-resolution wide-field astronomical surveys. Such surveys are capable of resolving the physical size of meteor plasma heads, but they produce large volumes of images that need to be automatically inspected for possible existence of long linear features produced by meteors. Here in this paper, we show how big aperture sky survey telescopes detect meteors as defocused tracks with a central brightness depression. We derive an analytic expression for a defocused point source meteor trackmore » and use it to calculate brightness profiles of meteors modelled as uniform brightness discs. We apply our modelling to meteor images as seen by the Sloan Digital Sky Survey and Large Synoptic Survey Telescope telescopes. The expression is validated by Monte Carlo ray-tracing simulations of photons travelling through the atmosphere and the Large Synoptic Survey Telescope telescope optics. We show that estimates of the meteor distance and size can be extracted from the measured full width at half-maximum and the strength of the central dip in the observed brightness profile. However, this extraction becomes difficult when the defocused meteor track is distorted by the atmospheric seeing or contaminated by a long-lasting glowing meteor trail. The full width at half-maximum of satellite tracks is distinctly narrower than meteor values, which enables removal of a possible confusion between satellites and meteors.« less

  6. Linear feature detection algorithm for astronomical surveys – II. Defocusing effects on meteor tracks

    DOE PAGES

    Bektešević, Dino; Vinković, Dejan; Rasmussen, Andrew; ...

    2017-12-05

    Given the current limited knowledge of meteor plasma micro-physics and its interaction with the surrounding atmosphere and ionosphere, meteors are a highly interesting observational target for high-resolution wide-field astronomical surveys. Such surveys are capable of resolving the physical size of meteor plasma heads, but they produce large volumes of images that need to be automatically inspected for possible existence of long linear features produced by meteors. Here in this paper, we show how big aperture sky survey telescopes detect meteors as defocused tracks with a central brightness depression. We derive an analytic expression for a defocused point source meteor trackmore » and use it to calculate brightness profiles of meteors modelled as uniform brightness discs. We apply our modelling to meteor images as seen by the Sloan Digital Sky Survey and Large Synoptic Survey Telescope telescopes. The expression is validated by Monte Carlo ray-tracing simulations of photons travelling through the atmosphere and the Large Synoptic Survey Telescope telescope optics. We show that estimates of the meteor distance and size can be extracted from the measured full width at half-maximum and the strength of the central dip in the observed brightness profile. However, this extraction becomes difficult when the defocused meteor track is distorted by the atmospheric seeing or contaminated by a long-lasting glowing meteor trail. The full width at half-maximum of satellite tracks is distinctly narrower than meteor values, which enables removal of a possible confusion between satellites and meteors.« less

  7. Automated Detection of Geomorphic Features in LiDAR Point Clouds of Various Spatial Density

    NASA Astrophysics Data System (ADS)

    Dorninger, Peter; Székely, Balázs; Zámolyi, András.; Nothegger, Clemens

    2010-05-01

    considerably varying considerably because of the various base points that were needed to cover the whole landslide. The resulting point spacing is approximately 20 cm. The achievable accuracy was about 10 cm. The airborne data was acquired with mean point densities of 2 points per square-meter. The accuracy of this dataset was about 15 cm. The second testing site is an area of the Leithagebirge in Burgenland, Austria. The data was acquired by an airborne Riegl LMS-Q560 laser scanner mounted on a helicopter. The mean point density was 6-8 points per square with an accuracy better than 10 cm. We applied our processing chain on the datasets individually. First, they were transformed to local reference frames and fine adjustments of the individual scans respectively flight strips were applied. Subsequently, the local regression planes were determined for each point of the point clouds and planar features were extracted by means of the proposed approach. It turned out that even small displacements can be detected if the number of points used for the fit is enough to define a parallel but somewhat displaced plane. Smaller cracks and erosional incisions do not disturb the plane fitting, because mostly they are filtered out as outliers. A comparison of the different campaigns of the Doren site showed exciting matches of the detected geomorphic structures. Although the geomorphic structure of the Leithagebirge differs from the Doren landslide, and the scales of the two studies were also different, reliable results were achieved in both cases. Additionally, the approach turned out to be highly robust against points which were not located on the terrain. Hence, no false positives were determined within the dense vegetation above the terrain, while it was possible to cover the investigated areas completely with reliable planes. In some cases, however, some structures in the tree crowns were also recognized, but these small patches could be very well sorted out from the geomorphically

  8. Research on vehicle detection based on background feature analysis in SAR images

    NASA Astrophysics Data System (ADS)

    Zhang, Bochuan; Tang, Bo; Zhang, Cong; Hu, Ruiguang; Yun, Hongquan; Xiao, Liping

    2017-10-01

    Aiming at vehicle detection on the ground through low resolution SAR images, a method is proposed for determining the region of the vehicles first and then detecting the target in the specific region. The experimental results show that this method not only reduces the target detection area, but also reduces the influence of terrain clutter on the detection, which greatly improves the reliability of the target detection.

  9. Application of Cavity Enhanced Absorption Spectroscopy to the Detection of Nitric Oxide, Carbonyl Sulphide, and Ethane—Breath Biomarkers of Serious Diseases

    PubMed Central

    Wojtas, Jacek

    2015-01-01

    The paper presents one of the laser absorption spectroscopy techniques as an effective tool for sensitive analysis of trace gas species in human breath. Characterization of nitric oxide, carbonyl sulphide and ethane, and the selection of their absorption lines are described. Experiments with some biomarkers showed that detection of pathogenic changes at the molecular level is possible using this technique. Thanks to cavity enhanced spectroscopy application, detection limits at the ppb-level and short measurements time (<3 s) were achieved. Absorption lines of reference samples of the selected volatile biomarkers were probed using a distributed feedback quantum cascade laser and a tunable laser system consisting of an optical parametric oscillator and difference frequency generator. Setup using the first source provided a detection limit of 30 ppb for nitric oxide and 250 ppb for carbonyl sulphide. During experiments employing a second laser, detection limits of 0.9 ppb and 0.3 ppb were obtained for carbonyl sulphide and ethane, respectively. The conducted experiments show that this type of diagnosis would significantly increase chances for effective therapy of some diseases. Additionally, it offers non-invasive and real time measurements, high sensitivity and selectivity as well as minimizing discomfort for patients. For that reason, such sensors can be used in screening for early detection of serious diseases. PMID:26091398

  10. Application of Cavity Enhanced Absorption Spectroscopy to the Detection of Nitric Oxide, Carbonyl Sulphide, and Ethane--Breath Biomarkers of Serious Diseases.

    PubMed

    Wojtas, Jacek

    2015-06-17

    The paper presents one of the laser absorption spectroscopy techniques as an effective tool for sensitive analysis of trace gas species in human breath. Characterization of nitric oxide, carbonyl sulphide and ethane, and the selection of their absorption lines are described. Experiments with some biomarkers showed that detection of pathogenic changes at the molecular level is possible using this technique. Thanks to cavity enhanced spectroscopy application, detection limits at the ppb-level and short measurements time (<3 s) were achieved. Absorption lines of reference samples of the selected volatile biomarkers were probed using a distributed feedback quantum cascade laser and a tunable laser system consisting of an optical parametric oscillator and difference frequency generator. Setup using the first source provided a detection limit of 30 ppb for nitric oxide and 250 ppb for carbonyl sulphide. During experiments employing a second laser, detection limits of 0.9 ppb and 0.3 ppb were obtained for carbonyl sulphide and ethane, respectively. The conducted experiments show that this type of diagnosis would significantly increase chances for effective therapy of some diseases. Additionally, it offers non-invasive and real time measurements, high sensitivity and selectivity as well as minimizing discomfort for patients. For that reason, such sensors can be used in screening for early detection of serious diseases.

  11. Drogue detection for vision-based autonomous aerial refueling via low rank and sparse decomposition with multiple features

    NASA Astrophysics Data System (ADS)

    Gao, Shibo; Cheng, Yongmei; Song, Chunhua

    2013-09-01

    The technology of vision-based probe-and-drogue autonomous aerial refueling is an amazing task in modern aviation for both manned and unmanned aircraft. A key issue is to determine the relative orientation and position of the drogue and the probe accurately for relative navigation system during the approach phase, which requires locating the drogue precisely. Drogue detection is a challenging task due to disorderly motion of drogue caused by both the tanker wake vortex and atmospheric turbulence. In this paper, the problem of drogue detection is considered as a problem of moving object detection. A drogue detection algorithm based on low rank and sparse decomposition with local multiple features is proposed. The global and local information of drogue is introduced into the detection model in a unified way. The experimental results on real autonomous aerial refueling videos show that the proposed drogue detection algorithm is effective.

  12. Automatic change detection: does the auditory system use representations of individual stimulus features or gestalts?

    PubMed

    Deacon, D; Nousak, J M; Pilotti, M; Ritter, W; Yang, C M

    1998-07-01

    The effects of global and feature-specific probabilities of auditory stimuli were manipulated to determine their effects on the mismatch negativity (MMN) of the human event-related potential. The question of interest was whether the automatic comparison of stimuli indexed by the MMN was performed on representations of individual stimulus features or on gestalt representations of their combined attributes. The design of the study was such that both feature and gestalt representations could have been available to the comparator mechanism generating the MMN. The data were consistent with the interpretation that the MMN was generated following an analysis of stimulus features.

  13. SERS active Ag encapsulated Fe@SiO2 nanorods in electromagnetic wave absorption and crystal violet detection.

    PubMed

    Senapati, Samarpita; Srivastava, Suneel Kumar; Singh, Shiv Brat; Kulkarni, Ajit R

    2014-11-01

    The present work is focused on the preparation of Fe nanorods by the chemical reduction of FeCl3 (aq) using NaBH4 in the presence of glycerol as template followed by annealing of the product at 500°C in the presence of H2 gas flow. Subsequently, its surface has been modified by silica followed by silver nanoparticles to form silica coated Fe (Fe@SiO2) and Ag encapsulated Fe@SiO2 nanostructure employing the Stöber method and silver mirror reaction respectively. XRD pattern of the products confirmed the formation of bcc phase of iron and fcc phase of silver, though silica remained amorphous. FESEM images established the growth of iron nanorods from the annealed product and also formation of silica and silver coating on its surface. The appearance of the characteristics bands in FTIR confirmed the presence of SiO2 on the Fe surface. Magnetic measurements at room temperature indicated the ferromagnetic behavior of as prepared iron nanorods, Fe@SiO2 and silver encapsulated Fe@SiO2 nanostructures. All the samples exhibited strong microwave absorption property in the high frequency range (10GHz),