The Interface Region Imaging Spectrograph (IRIS)

The Interface Region Imaging Spectrograph (IRIS) small explorer spacecraft provides simultaneous spectra and images of the photosphere, chromosphere, transition region, and corona with 0.33-0.4 arcsec spatial resolution, 2 s temporal resolution and 1 km/s velocity resolution over a field-of-view of up to 175 arcsec x 175 arcsec. IRIS was launched into a Sun-synchronous orbit on 27 June 2013 using a Pegasus-XL rocket and consists of a 19-cm UV telescope that feeds a slit-based dual-bandpass imaging spectrograph. IRIS obtains spectra in passbands from 1332-1358, 1389-1407 and 2783-2834 Angstrom including bright spectral lines formed in the chromosphere (Mg II h 2803 Angstrom and Mg II k 2796 Angstrom) and transition region (C II 1334/1335 Angstrom and Si IV 1394/1403 Angstrom). Slit-jaw images in four different passbands (C II 1330, Si IV 1400, Mg II k 2796 and Mg II wing 2830 Angstrom) can be taken simultaneously with spectral rasters that sample regions up to 130 arcsec x 175 arcsec at a variety of spatial samplings (from 0.33 arcsec and up). IRIS is sensitive to emission from plasma at temperatures between 5000 K and 10 MK and will advance our understanding of the flow of mass and energy through an interface region, formed by the chromosphere and transition region, between the photosphere and corona. This highly structured and dynamic region not only acts as the conduit of all mass and energy feeding into the corona and solar wind, it also requires an order of magnitude more energy to heat than the corona and solar wind combined. The IRIS investigation includes a strong numerical modeling component based on advanced radiative-MHD codes to facilitate interpretation of observations of this complex region. Approximately eight Gbytes of data (after compression) are acquired by IRIS each day and made available for unrestricted use within a few days of the observation.Comment: 53 pages, 15 figure

A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images

[EN] This work describes a new hybrid method for accurate iris segmentation from full-face images independently of the ethnicity of the subject. It is based on a combination of three methods: facial key-point detection, integro-differential operator (IDO) and mathematical morphology. First, facial landmarks are extracted by means of the Chehra algorithm in order to obtain the eye location. Then, the IDO is applied to the extracted sub-image containing only the eye in order to locate the iris. Once the iris is located, a series of mathematical morphological operations is performed in order to accurately segment it. Results are obtained and compared among four different ethnicities (Asian, Black, Latino and White) as well as with two other iris segmentation algorithms. In addition, robustness against rotation, blurring and noise is also assessed. Our method obtains state-of-the-art performance and shows itself robust with small amounts of blur, noise and/or rotation. Furthermore, it is fast, accurate, and its code is publicly available.Fuentes-Hurtado, FJ.; Naranjo Ornedo, V.; Diego-Mas, JA.; Alcañiz Raya, ML. (2019). A hybrid method for accurate iris segmentation on at-a-distance visible-wavelength images. EURASIP Journal on Image and Video Processing (Online). 2019(1):1-14. https://doi.org/10.1186/s13640-019-0473-0S11420191A. Radman, K. Jumari, N. Zainal, Fast and reliable iris segmentation algorithm. IET Image Process.7(1), 42–49 (2013).M. Erbilek, M. Fairhurst, M. C. D. C Abreu, in 5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013). Age prediction from iris biometrics (London, 2013), pp. 1–5. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6913712&isnumber=6867223 .A. Abbasi, M. Khan, Iris-pupil thickness based method for determining age group of a person. Int. Arab J. Inf. Technol. (IAJIT). 13(6) (2016).G. Mabuza-Hocquet, F. Nelwamondo, T. Marwala, in Intelligent Information and Database Systems. ed. by N. Nguyen, S. Tojo, L. Nguyen, B. Trawiński. Ethnicity Distinctiveness Through Iris Texture Features Using Gabor Filters. ACIIDS 2017. Lecture Notes in Computer Science, vol. 10192 (Springer, Cham, 2017).S. Lagree, K. W. Bowyer, in 2011 IEEE International Conference on Technologies for Homeland Security (HST). Predicting ethnicity and gender from iris texture (IEEEWaltham, 2011). p. 440–445. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6107909&isnumber=6107829 .J. G. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell.15(11), 1148–1161 (1993).N. Kourkoumelis, M. Tzaphlidou. Medical Safety Issues Concerning the Use of Incoherent Infrared Light in Biometrics, eds. A. Kumar, D. Zhang. Ethics and Policy of Biometrics. ICEB 2010. Lecture Notes in Computer Science, vol 6005 (Springer, Berlin, Heidelberg, 2010).R. P. Wildes, Iris recognition: an emerging biometric technology. Proc. IEEE. 85(9), 1348–1363 (1997).M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active contour models. Int. J. Comput. Vision. 1(4), 321–331 (1988).S. J. Pundlik, D. L. Woodard, S. T. Birchfield, in 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Non-ideal iris segmentation using graph cuts (IEEEAnchorage, 2008). p. 1–6. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4563108&isnumber=4562948 .H. Proença, Iris recognition: On the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell.32(8), 1502–1516 (2010). http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5156505&isnumber=5487331 .T. Tan, Z. He, Z. Sun, Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vision Comput.28(2), 223–230 (2010).C. -W. Tan, A. Kumar, in CVPR 2011 WORKSHOPS. Automated segmentation of iris images using visible wavelength face images (Colorado Springs, 2011). p. 9–14. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5981682&isnumber=5981671 .Y. -H. Li, M. Savvides, An automatic iris occlusion estimation method based on high-dimensional density estimation. IEEE Trans. Pattern Anal. Mach. Intell.35(4), 784–796 (2013).M. Yahiaoui, E. Monfrini, B. Dorizzi, Markov chains for unsupervised segmentation of degraded nir iris images for person recognition. Pattern Recogn. Lett.82:, 116–123 (2016).A. Radman, N. Zainal, S. A. Suandi, Automated segmentation of iris images acquired in an unconstrained environment using hog-svm and growcut. Digit. Signal Proc.64:, 60–70 (2017).N. Liu, H. Li, M. Zhang, J. Liu, Z. Sun, T. Tan, in 2016 International Conference on Biometrics (ICB). Accurate iris segmentation in non-cooperative environments using fully convolutional networks (Halmstad, 2016). p. 1–8. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7550055&isnumber=7550036 .Z. Zhao, A. Kumar, in 2017 IEEE International Conference on Computer Vision (ICCV). Towards more accurate iris recognition using deeply learned spatially corresponding features (Venice, 2017). p. 3829–3838. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8237673&isnumber=8237262 .P. Li, X. Liu, L. Xiao, Q. Song, Robust and accurate iris segmentation in very noisy iris images. Image Vision Comput.28(2), 246–253 (2010).D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D. -K. Park, J. Kim, A new iris segmentation method for non-ideal iris images. Image Vision Comput.28(2), 254–260 (2010).Y. Chen, M. Adjouadi, C. Han, J. Wang, A. Barreto, N. Rishe, J. Andrian, A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vision Comput. 28(2), 261–269 (2010).Z. Zhao, A. Kumar, in 2015 IEEE International Conference on Computer Vision (ICCV). An accurate iris segmentation framework under relaxed imaging constraints using total variation model (Santiago, 2015). p. 3828–3836. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7410793&isnumber=7410356 .Y. Hu, K. Sirlantzis, G. Howells, Improving colour iris segmentation using a model selection technique. Pattern Recogn. Lett.57:, 24–32 (2015).E. Ouabida, A. Essadique, A. Bouzid, Vander lugt correlator based active contours for iris segmentation and tracking. Expert Systems Appl.71:, 383–395 (2017).C. -W. Tan, A. Kumar, Unified framework for automated iris segmentation using distantly acquired face images. IEEE Trans. Image Proc.21(9), 4068–4079 (2012).C. -W. Tan, A. Kumar, in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). Human identification from at-a-distance images by simultaneously exploiting iris and periocular features (Tsukuba, 2012). p. 553–556. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460194&isnumber=6460043 .C. -W. Tan, A. Kumar, Towards online iris and periocular recognition under relaxed imaging constraints. IEEE Trans. Image Proc.22(10), 3751–3765 (2013).K. Y. Shin, Y. G. Kim, K. R. Park, Enhanced iris recognition method based on multi-unit iris images. Opt. Eng.52(4), 047201–047201 (2013).CASIA iris databases. http://biometrics.idealtest.org/ . Accessed 06 Sept 2017.WVU iris databases. hhttp://biic.wvu.edu/data-sets/synthetic-iris-dataset . Accessed 06 Sept 2017.UBIRIS iris database. http://iris.di.ubi.pt . Accessed 06 Sept 2017.MICHE iris database. http://biplab.unisa.it/MICHE/ . Accessed 06 Sept 2017.P. J. Phillips, et al, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 1. Overview of the face recognition grand challenge (San Diego, 2005). p. 947–954. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1467368&isnumber=31472 .D. S. Ma, J. Correll, B. Wittenbrink, The chicago face database: A free stimulus set of faces and norming data. Behav. Res. Methods. 47(4), 1122–1135 (2015).P. Soille, Morphological Image Analysis: Principles and Applications (Springer, 2013).A. K. Jain, Fundamentals of Digital Image Processing (Prentice-Hall, Inc., Englewood Cliffs, 1989).J. Daugman, How iris recognition works. IEEE Trans. Circ. Syst. Video Technol.14(1), 21–30 (2004).A. Asthana, S. Zafeiriou, S. Cheng, M. Pantic, in 2014 IEEE Conference on Computer Vision and Pattern Recognition. Incremental face alignment in the wild (Columbus, 2014). p. 1859–1866. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6909636&isnumber=6909393 .T. Baltrusaitis, P. Robinson, L. -P. Morency, in 2013 IEEE International Conference on Computer Vision Workshops. Constrained local neural fields for robust facial landmark detection in the wild (Sydney, 2013). p. 354–361. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6755919&isnumber=6755862 .X. Zhu, D. Ramanan, in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference On. Face detection, pose estimation, and landmark localization in the wild (IEEEBerlin Heidelberg, 2012), pp. 2879–2886.G. Tzimiropoulos, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Project-out cascaded regression with an application to face alignment (Boston, 2015). p. 3659–3667. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298989&isnumber=7298593 .H. Hofbauer, F. Alonso-Fernandez, P. Wild, J. Bigun, A. Uhl, in 2014 22nd International Conference on Pattern Recognition. A ground truth for iris segmentation (Stockholm, 2014). p. 527–532. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6976811&isnumber=6976709 .H. Proença, L. A. Alexandre, in 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems. The NICE.I: Noisy Iris Challenge Evaluation - Part I (Crystal City, 2007). p. 1–4. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4401910&isnumber=4401902 .J. Daugman, in European Convention on Security and Detection. High confidence recognition of persons by rapid video analysis of iris texture, (1995). p. 244–251. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=491729&isnumber=10615 .Code of Matlab implementation of Daugman’s integro-differential operator (IDO). https://es.mathworks.com/matlabcentral/fileexchange/15652-iris-segmentation-using-daugman-s-integrodifferential-operator/ . Accessed 06 Sept 2017.Code of Matlab implementation of Zhao and Kumar’s iris segmentation framework under relaxed imaging constraints using total variation model. http://www4.comp.polyu.edu.hk/~csajaykr/tvmiris.htm/ . Accessed 06 Sept 2017.Code of Matlab implementation of presented work. https://gitlab.com/ffuentes/hybrid_iris_segmentation/ . Accessed 06 Sept 2017.Face and eye detection with OpenCV. https://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html . Accessed 07 Sept 2018.A. K. Boyat, B. K. Joshi, 6. A review paper:noise models in digital image processing signal & image processing. An International Journal (SIPIJ), (2015), pp. 63–75. https://doi.org/10.5121/sipij.2015.6206 .A. Buades, Y. Lou, J. M. Morel, Z. Tang, Multi image noise estimation and denoising (2010). Available: https://hal.archives-ouvertes.fr/hal-00510866/

Bioenergy routes for valorizing constructed wetland vegetation: An overview

Valorizing constructed wetlands vegetation into biofuels can be a way to contribute to mitigating the increasing energy demand, avoiding the use of arable land, freshwater, and fertilizers consumption, while simultaneously treating wastewater with eco-friendly technology. This work shortly overviews the main genera of wetland plants and the main routes of vegetal biomass conversion into biofuels including biochemical and thermochemical processes, and through a cross-search, in the Scopus database, the research intensity in bioenergy application for each genus was assessed. A total of 283 genera of wetland plants were identified and classified into five groups, from very common to very rare genera. The very common group includes 10 genera and contributes to 62% of the literature hits, while the 147 genera classified as very rare contribute to only 3% of the hits. Concerning the bioenergy applications, four genera stand out from the remaining. The plants of the genus Sorghum are the most referred to in bioenergy applications, followed by the genera Brassica, Miscanthus, and Saccharum. Miscanthus is a less common wetland plant, while the other genera are rarely applied in constructed wetlands. The relevance of bioenergy routes depends on the plants' group. For common wetland plants, the most relevant applications are biogas production, followed by bio-ethanol production, and pyrolysis processing. As a recommendation for future research works the genera with high energy potential should be evaluated as wetland vegetation, and it is recommended that the goal to recover wetland vegetation for bioenergy applications be viewed as an integral step of the design and implementation of constructed wetlands facilities.info:eu-repo/semantics/publishedVersio

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 352)

This bibliography lists 147 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during July 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

The InfoSec Handbook

Computer scienc

High throughput proteomic analysis of Mycobacterium tuberculosis associated Immune Reconstitution Inflammatory Syndrome (TB-IRIS)

Includes abstract. Includes bibliographical references