264 results on '"Menard, M"'
Search Results
2. Statewide assessment of telehealth use for obstetrical care during the COVID-19 pandemic
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Mallampati, Divya P, Talati, Asha N, Fitzhugh, Chelsea, Enayet, Nafiah, Vladutiu, Catherine J, and Menard, M Kathryn
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Biomedical and Clinical Sciences ,Clinical Sciences ,Clinical Research ,Rural Health ,Health Services ,Good Health and Well Being ,Pregnancy ,Female ,Humans ,COVID-19 ,Pandemics ,Telemedicine ,Obstetrics ,Surveys and Questionnaires ,access ,policy ,remote monitoring ,rural health ,SARS-CoV-2 ,technology ,telemedicine ,Reproductive medicine - Abstract
BackgroundThe COVID-19 pandemic started a period of rapid transition to telehealth in obstetrical care delivery to maintain social distancing and curb the spread of the virus. The use of telehealth, such as telephone and video visits, remote imaging interpretation, and provider-to-provider consultations, increased in the early months of the pandemic to maintain access to prenatal and postpartum care. Although there is considerable literature on the use of telehealth in obstetrical care, there are limited data on widespread telehealth use among different practice types and patient populations during the pandemic and whether these are preferred technologies.ObjectiveThis study aimed to describe variations in telehealth use for obstetrical care among practices in North Carolina during the COVID-19 pandemic and to outline future preferences and needs for continued telehealth use. This study also aimed to delineate telehealth use among rural and micropolitan and metropolitan practices to better understand if telehealth use varied by practice location.Study designA web-based survey was distributed to practice managers of obstetrical practices in North Carolina from June 14, 2020 to September 14, 2020. Practice managers were contacted through assistance of the Community Care of North Carolina Pregnancy Medical Home program. Practice location was defined as rural, micropolitan, or metropolitan based on the county population. The survey assessed telehealth use before and during the COVID-19 pandemic, types of modalities used, and preferences for future use. Descriptive statistics were performed to describe survey responses and compare them by practice location.ResultsA total of 295 practice managers were sent a web-based survey and 98 practice managers responded. Responding practices represented 66 of 100 counties in North Carolina with 50 practices from rural and micropolitan counties and 48 practices from metropolitan counties. The most common type of provider reported by practice managers were general obstetrician and gynecologists (85%), and the most common practice type was county health departments (38%). Overall, 9% of practices reported telehealth use before the pandemic and 60% reported telehealth use during the pandemic. The most common type of telehealth modality was telephone visits. There were no significant differences in the uptake of telehealth or in the modalities used by practice location. A total of 40% of practices endorsed a preference for continued telehealth use beyond the COVID-19 pandemic. The most commonly reported need for continuation of telehealth use was assistance with patient access to telehealth technologies (54%). There were no significant differences in the preferences for telehealth continuation or future needs by practice location.ConclusionTelehealth use increased among a variety of practice types during the pandemic with no variation observed by practice location in terms of modalities used, future preferences, or needs. This study assessed statewide uptake of and differences in obstetrical telehealth use during the early COVID-19 pandemic. With telehealth becoming an integral part of obstetrical care delivery, this survey has implications for anticipating the needs of practices and designing innovative solutions for providers and pregnant people beyond the COVID-19 pandemic.
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- 2023
3. Care Levels for Fetal Therapy Centers.
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Baschat, Ahmet, Blackwell, Sean, Chatterjee, Debnath, Cummings, James, Emery, Stephen, Hollier, Lisa, Johnson, Anthony, Kilpatrick, Sarah, Luks, Francois, Menard, M, McCullough, Lawrence, Moldenhauer, Julie, Mychaliska, George, Narvey, Michael, Rollins, Mark, Skarsgard, Eric, Tsao, KuoJen, Warner, Barbara, Wilpers, Abigail, Ryan, Greg, Moon-Grady, Anita, Norton, Mary, and Hirose, Shinjiro
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Child ,Female ,Fetal Membranes ,Premature Rupture ,Fetal Therapies ,Humans ,Infant ,Newborn ,Infant ,Premature ,Pregnancy ,Premature Birth ,Prenatal Care - Abstract
Fetal therapies undertaken to improve fetal outcome or to optimize transition to neonate life often entail some level of maternal, fetal, or neonatal risk. A fetal therapy center needs access to resources to carry out such therapies and to manage maternal, fetal, and neonatal complications that might arise, either related to the therapy per se or as part of the underlying fetal or maternal condition. Accordingly, a fetal therapy center requires a dedicated operational infrastructure and necessary resources to allow for appropriate oversight and monitoring of clinical performance and to facilitate multidisciplinary collaboration between the relevant specialties. Three care levels for fetal therapy centers are proposed to match the anticipated care complexity, with appropriate resources to achieve an optimal outcome at an institutional and regional level. A level I fetal therapy center should be capable of offering fetal interventions that may be associated with obstetric risks of preterm birth or membrane rupture but that would be very unlikely to require maternal medical subspecialty or intensive care, with neonatal risks not exceeding those of moderate prematurity. A level II center should have the incremental capacity to provide maternal intensive care and to manage extreme neonatal prematurity. A level III therapy center should offer the full range of fetal interventions (including open fetal surgery) and be able manage any of the associated maternal complications and comorbidities, as well as have access to neonatal and pediatric surgical intervention including indicated surgery for neonates with congenital anomalies.
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- 2022
4. High-Frequency GaAs Optomechanical Bullseye Resonator
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Carvalho, N. C., Benevides, R., Ménard, M., Wiederhecker, G. S., Frateschi, N. C., and Alegre, T. P. Mayer
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Physics - Optics ,Physics - Applied Physics - Abstract
The integration of optomechanics and optoelectronics in a single device opens new possibilities for developing information technologies and exploring fundamental phenomena. Gallium arsenide (GaAs) is a well-known material that can bridge the gap between the functionalities of optomechanical devices and optical gain media. Here, we experimentally demonstrate a high-frequency GaAs optomechanical resonator with a ring-type bullseye geometry that is unprecedented in this platform. We measured mechanical modes up to 3.4 GHz with quality factors of 4000 (at 77 K) and optomechanical coupling rates up to 39 kHz at telecom wavelengths. Moreover, we investigated the material symmetry break due to elastic anisotropy and its impact on the mechanical mode spectrum. Finally, we assessed the temperature dependence of the mechanical losses and demonstrated the efficiency and anisotropy resilience of the bullseye anchor loss suppression, indicating that lower temperature operation may allow mechanical quality factors over $10^4$. Such characteristics are valuable for active optomechanics, coherent microwave-to-optics conversion via piezo-mechanics and other implementations of high-frequency oscillators in III-V materials., Comment: 8 pages, 5 figures
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- 2020
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5. Birth journeys: Multi-level facilitators and barriers to birthing parents access to and use of reproductive health care
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Gibson, Amelia N., Harper, Kimberly D., McClain, Erin, Kathryn Menard, M., and Tully, Kristin P.
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- 2024
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6. Microbial Profile and Clinical Outcomes of Fungal Keratitis at a Single-Center Tertiary Care Hospital
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Menard M, Shah YS, Stroh IG, Zafar S, Sriparna M, Zhang N, Agarwal AA, Shekhawat N, Srikumaran D, and Woreta F
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fungal keratitis ,mycotic keratitis ,filamentous keratitis ,corneal culture ,Ophthalmology ,RE1-994 - Abstract
Maylander Menard,1 Yesha S Shah,2 Inna G Stroh,2 Sidra Zafar,2 Manjari Sriparna,2 Nancy Zhang,2 Ank A Agarwal,2 Nakul Shekhawat,2 Divya Srikumaran,2 Fasika Woreta2 1School of Medicine, Meharry Medical College, Nashville, TN, USA; 2Division of Cornea and External Disease, Department of Ophthalmology, The Wilmer Eye Institute, The Johns Hopkins University School of Medicine, Baltimore, MD, USACorrespondence: Fasika Woreta, Division of Cornea and External Disease, Wilmer Eye Institute, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Wilmer B20, Baltimore, MD, 21287, USA, Tel +410 955-5650, Email fworeta1@jhmi.eduPurpose: To evaluate baseline characteristics, microbiological spectrum, management, and outcomes of patients with culture-proven fungal keratitis.Methods: Retrospective review of all patients with culture-proven fungal keratitis seen over 6 years at a tertiary referral center.Results: The present study included 62 eyes from 62 patients. Infection with filamentous organisms was more common than with yeast (66.1% vs 27.4%). The most common filamentous organisms were Fusarium (17.7%) and Aspergillus (16.1%), while the most common yeast was Candida (24.2%). The main predisposing factor for filamentous keratitis was contact lens use. Yeast keratitis is most associated with an immunocompromised host and ocular surface disease. Corneal perforation (20.0%) and surgical interventions (46.8%) were common, with 27.4% of eyes requiring at least one penetrating keratoplasty. Filamentous keratitis is more likely than yeast keratitis to require urgent penetrating keratoplasty or enucleation and to receive more than one topical and systemic antifungal agent. Visual outcomes were poor with nearly half of the eyes remaining at 20/200 or worse upon resolution of infection. Worse visual outcomes were associated with poor vision at presentation and a history of ocular surface disease. Antifungal susceptibility testing was not routinely performed, but it demonstrated a relatively high minimum inhibitory concentration for at least one antifungal drug in 90% of cases when performed (16.1%) and guided the direction of treatment for 80% of the cases.Conclusion: Fungal keratitis is visually devastating. Infections with filamentous fungi predominated over yeast and were generally treated more aggressively both medically and surgically. Filamentous and yeast keratitis had similar durations of infections and visual outcomes. Antifungal susceptibility testing influenced treatment in 80% of cases in which it was performed.Keywords: fungal keratitis, mycotic keratitis, filamentous keratitis, corneal culture
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- 2022
7. A renormalization group analysis of the one-dimensional extended Hubbard model
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Ménard, M. and Bourbonnais, C.
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Condensed Matter - Strongly Correlated Electrons ,Condensed Matter - Statistical Mechanics - Abstract
The phase diagram of the one-dimensional extended Hubbard model at half-filling is investigated by a weak coupling renormalization group method applicable beyond the usual continuum limit for the electron spectrum and coupling constants. We analyze the influence of irrelevant momentum dependent interactions on asymptotic properties of the correlation functions and the nature of dominant phases for the lattice model under study., Comment: 10 pages, 10 figures
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- 2010
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8. Recruitment and inclusion procedures as “pain killers” in clinical trials?
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Nothnagel H, Brown Menard M, Kvarstein G, Norheim AJ, Weiss T, Puta C, Mist SD, and Musial F
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Chronic neck pain ,Hawthorne effect ,Natural course of the disease ,Regression to the mean ,Clinical trial ,Recruitment ,Medicine (General) ,R5-920 - Abstract
H Nothnagel,1–3 M Brown Menard4,5, G Kvarstein,6 AJ Norheim,3 T Weiss,7 C Puta1,8, SD Mist,9 F Musial31Department of Sports Medicine and Health Promotion, Friedrich Schiller University Jena, Jena, Germany; 2Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; 3Department of Community Medicine, The National Research Center for Complementary and Alternative Medicine (NAFKAM), UiT The Arctic University of Norway, Tromsø, Norway; 4Crocker Institute, Kiawah Island, SC, USA; 5School of Integrative Medicine and Health Sciences, Saybrook University, Oakland, CA, USA; 6Pain clinic, University Hospital of Northern Norway, UiT The Arctic University of Norway, Tromsø, Norway; 7Department of Biological and Clinical Psychology, Friedrich Schiller University, Jena, Germany; 8Center for Interdisciplinary Prevention of Diseases related to Professional Activities, Friedrich Schiller University, Jena, Germany; 9Oregon Health & Science University, Portland, OR, USABackground: Recruitment and inclusion procedures in clinical trials are time critical. This holds particularly true for studies investigating patients with fluctuating symptom patterns, like those with chronic neck pain. In a feasibility study on neck pain, we found a clinically relevant decrease in pain ratings within the recruitment period. This paper analyses the phenomenon and gives recommendations for recruitment procedures in clinical trials on pain.Methods: Changes in pain intensity scores of 44 chronic neck pain patients (6 males and 36 females; mean age: 45.3±13.2 years) between the first telephone contact and baseline assessment were analyzed. Inclusion criterion was a mean pain intensity of ≥40 on a 0–100 numerical rating scale during the last three months. Statistical analyses were performed using ANOVA and parametric/non-parametric correlation coefficients.Results: Average pain intensity score decreased significantly from 60.3±13.3 at telephone interview to 38.1±21.7 at baseline assessment. This represents a relative change of 36.8%. A weak but significant negative correlation was found between number of days between assessments and pain rating differences. There was a positive correlation between change of pain intensity and the pain level at the first contact, indicating that the decreased pain ratings over time were also dependent on the initial pain rating.Conclusions: The clinically significant changes in pain intensity were weakly related to waiting time and moderately dependent on initial pain intensity, suggesting regression to the mean. The natural course of the disease and the Hawthorne effect are also discussed as contributing factors.Keywords: chronic neck pain, Hawthorne effect, natural course of the disease, regression to the mean, clinical trial, recruitment
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- 2019
9. Improved Ascertainment of Pregnancy-Associated Suicides and Homicides in North Carolina
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Austin, Anna E., Vladutiu, Catherine J., Jones-Vessey, Kathleen A., Norwood, Tammy S., Proescholdbell, Scott K., and Menard, M. Kathryn
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- 2016
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10. Value and limits of non-robotic transoral oropharyngectomy for local control of T1-2 invasive squamous cell carcinoma of the tonsillar fossa
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Laccourreye, O., Malinvaud, D., Holostenco, V., Ménard, M., Garcia, D., and Bonfils, P.
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- 2015
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11. Otorhinolaryngologists’ personal treatment preferences (total laryngectomy or laryngeal preservation) when faced with advanced stage laryngeal cancer
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Laccourreye, O., Malinvaud, D., Ménard, M., Consoli, S., Giraud, P., and Bonfils, P.
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- 2014
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12. Total laryngectomy or laryngeal preservation for advanced laryngeal cancer. Impact of the functional risk upon the patient's preferences
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Laccourreye, O., Malinvaud, D., Ménard, M., Consoli, S., Giraud, P., and Bonfils, P.
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- 2014
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13. National Partnership for Maternal Safety: Consensus Bundle on Severe Hypertension During Pregnancy and the Postpartum Period
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Bernstein, Peter S., Martin, James N., Jr, Barton, John R., Shields, Laurence E., Druzin, Maurice L., Scavone, Barbara M., Frost, Jennifer, Morton, Christine H., Ruhl, Catherine, Slager, Joan, Tsigas, Eleni Z., Jaffer, Sara, and Menard, M. Kathryn
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- 2017
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14. Transoral robotic surgery in head and neck cancer
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Hans, S., Delas, B., Gorphe, P., Ménard, M., and Brasnu, D.
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- 2012
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15. On a class of ill-posed minimization problems in image processing
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Aubert, G., El Hamidi, A., Ghannam, C., and Ménard, M.
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- 2009
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16. 17-Hydroxyprogesterone caproate (17OHP-C) coverage among eligible women delivering at 2 North Carolina hospitals in 2012 and 2013: A retrospective cohort study
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Stringer, Elizabeth M., Vladutiu, Catherine J., Manuck, Tracy, Verbiest, Sarah, Ollendorff, Arthur, Stringer, Jeffrey S.A., and Menard, M. Kathryn
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- 2016
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17. Understanding barriers and facilitators to implementation of a patient safety bundle for pregnancy-related severe hypertension in 3 North Carolina outpatient clinics: a qualitative study
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Kachoria, Aparna G., Fatima, Hiba, Lightfoot, Alexandra F., Tawfik, Linda, Healy, Joan, Carter, Asia, Farahi, Narges, Teal, E. Nicole, Haidar, Joumana K., Peterson, Herbert B., and Menard, M. Kathryn
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- 2025
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18. Anti-angiogenic factors and pre-eclampsia in type 1 diabetic women
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Yu, Y., Jenkins, A. J., Nankervis, A. J., Hanssen, K. F., Scholz, H., Henriksen, T., Lorentzen, B., Clausen, T., Garg, S. K., Menard, M. K., Hammad, S. M., Scardo, J. C., Stanley, J. R., Dashti, A., May, K., Lu, K., Aston, C. E., Wang, J. J., Zhang, S. X., Ma, J.-X., and Lyons, T. J.
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- 2009
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19. Anti-GD2 antibody disrupts GD2:Siglec-7 interactions and synergizes with CD47 blockade to mediate tumor eradication
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Heike E. Daldrup-Link, John Lattin, Menard M, Pons J, Peng Xu, Kristopher D. Marjon, Aidan Tousley, Louise Kiru, John L. Silberstein, Alberto Delaidelli, Payton L. Marshall, Nasholm N, William A. Weiss, Allison Banuelos, Amira A. Barkal, Poul H. Sorensen, Garry L. Coles, Irving L. Weissman, Sabine Heitzeneder, Benjamin A. H. Smith, Robbie G. Majzner, Ricardo A. Fernandes, Jonathan Huang, Julien Sage, Miles H. Linde, Shaurya Dhingra, Elena Sotillo, Johanna Theruvath, Rachel E. Brewer, Ravi Majeti, Cochran, Sangalang Er, Crystal L. Mackall, Dalton Gn, Wei Wu, Carolyn R. Bertozzi, Kuo Tc, and Andy He
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biology ,business.industry ,CD47 ,SIGLEC ,medicine.disease ,Blockade ,Neuroblastoma ,biology.protein ,Cancer research ,Macrophage ,Medicine ,Cytotoxic T cell ,Osteosarcoma ,Antibody ,business - Abstract
The disialoganglioside GD2 is consistently overexpressed in neuroblastoma and osteosarcoma, and is variably expressed in other sarcomas, gliomas, neuroendocrine tumors, and epithelial cancers. Anti-GD2 antibodies have improved the survival rates of patients with neuroblastoma only when administered as part of intense chemotherapy-based cytotoxic regimens, which are associated with debilitating late effects including hearing loss, growth retardation, and secondary leukemias. Despite broad expression of GD2 on osteosarcoma, anti-GD2 antibody has not mediated significant antitumor activity in that disease or any other GD2+ cancers. CD47 is a checkpoint molecule overexpressed on tumor cells that inhibits macrophage activity, and CD47 blockade has demonstrated promising clinical activity in early human trials. We investigated whether anti-CD47 antibody could enhance the efficacy of anti-GD2 antibody in neuroblastoma and other GD2+ malignancies. We demonstrate substantial synergy of these two agents, resulting in the recruitment of tumor associated macrophages (TAMs) to mediate robust and durable anti-tumor responses. The responses are driven by GD2-specific factors that reorient the balance of macrophage activity towards phagocytosis of tumor cells, including disruption of a newly described GD2:Siglec-7 axis. These results demonstrate the unique synergy of combining anti-GD2 with anti-CD47, which has the potential to significantly enhance outcomes for children with neuroblastoma and osteosarcoma and will soon be investigated in a first-in-human clinical trial.
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- 2021
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20. In situ bypass and extra-anatomic bypass procedures result in similar survival in patients with secondary aortoenteric fistulas
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Janko, M.R. Woo, K. Hacker, R.I. Baril, D. Bath, J. Smeds, M.R. Kashyap, V.S. Szeberin, Z. Magee, G.A. Elsayed, R. Wishy, A. St. John, R. Beck, A. Farber, M. Motta, F. Zhou, W. Lemmon, G. Coleman, D. Behrendt, C.-A. Aziz, F. Black, J. Shutze, W. Garrett, H.E., Jr. De Caridi, G. Liapis, C.D. Geroulakos, G. Kakisis, J. Moulakakis, K. Kakkos, S.K. Obara, H. Wang, G. Rhéaume, P. Davila, V. Ravin, R. DeMartino, R. Milner, R. Shalhub, S. Jim, J. Lee, J. Dubuis, C. Ricco, J.-B. Coselli, J. Lemaire, S. Fatima, J. Sanford, J. Yoshida, W. Schermerhorn, M.L. Menard, M. Belkin, M. Blackwood, S. Conrad, M. Wang, L. Crofts, S. Nixon, T. Wu, T. Chiesa, R. Bose, S. Turner, J. Moore, R. Smith, J. Ciocca, R. Hsu, J. Czerny, M. Cullen, J. Kahlberg, A. Setacci, C. Joh, J.H. Senneville, E. Garrido, P. Sarac, T.P. Rizzo, A. Go, M.R. Bjorck, M. Gavali, H. Wanhainen, A. Lawrence, P.F. Chung, J.
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Objective: The optimal revascularization modality in secondary aortoenteric fistula (SAEF) remains unclear in the literature. The purpose of this investigation was to determine the revascularization approach associated with the lowest morbidity and mortality using real-world data in patients with SAEF. Methods: A retrospective, multi-institutional study of SAEF from 2002 to 2014 was performed using a standardized database. Baseline demographics, comorbidities, and operative and postoperative variables were recorded. The primary outcome was long-term mortality. Descriptive statistics, Kaplan-Meier survival analysis, and univariate and multivariate analyses were performed. Results: During the study period, 182 patients at 34 institutions from 11 countries presented with SAEF (median age, 72 years; 79% male). The initial aortic procedures that resulted in SAEF were 138 surgical grafts (76%) and 42 endografts (23%), with 2 unknown; 102 of the SAEFs (56%) underwent complete excision of infected aortic graft material, followed by in situ (in-line) bypass (ISB), including antibiotic-soaked prosthetic graft (53), autogenous femoral vein (neoaortoiliac surgery; 17), cryopreserved allograft (28), and untreated prosthetic grafts (4). There were 80 patients (44%) who underwent extra-anatomic bypass (EAB) with infected graft excision. Overall median Kaplan-Meier estimated survival was 319 days (interquartile range, 20-2410 days). Stratified by EAB vs ISB, there was no significant difference in Kaplan-Meier estimated survival (P =.82). In comparing EAB vs ISB, EAB patients were older (74 vs 70 years; P =.01), had less operative hemorrhage (1200 mL vs 2000 mL; P =.04), were more likely to initiate dialysis within 30 days postoperatively (15% vs 5%; P =.02), and were less likely to experience aorta-related hemorrhage within 30 days postoperatively (3% aortic stump dehiscence vs 11% anastomotic rupture; P =.03). There were otherwise no significant differences in presentation, comorbidities, and intraoperative or postoperative variables. Multivariable Cox regression showed that the duration of antibiotic use (hazard ratio, 0.92; 95% confidence interval, 0.86-0.98; P =.01) and rifampin use at time of discharge (hazard ratio, 0.20; 95% confidence interval, 0.05-0.86; P =.03) independently decreased mortality. Conclusions: These data suggest that ISB does not offer a survival advantage compared with EAB and does not decrease the risk of postoperative aorta-related hemorrhage. After repair
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- 2021
21. Opposing vaccine hesitancy during the COVID-19 pandemic- A critical commentary and united statement of an international osteopathic research community
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Vaucher, P, Menard, M, Vaughan, B, Morin, C, Alvarez, G, Sampath, KK, Cerritelli, F, Shaw, R, Cymet, TC, Bright, P, Hohenschurz-Schmidt, D, Vogel, S, Thomson, OP, MacMillan, A, Draper-Rodi, J, Vaucher, P, Menard, M, Vaughan, B, Morin, C, Alvarez, G, Sampath, KK, Cerritelli, F, Shaw, R, Cymet, TC, Bright, P, Hohenschurz-Schmidt, D, Vogel, S, Thomson, OP, MacMillan, A, and Draper-Rodi, J
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- 2021
22. P02.190. Neural structural/functional and physiological correlates of massage therapy in response to physical stress
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Zhang M, Menard M, VanMeter J, Lozier L, Browning S, Renton R, Breeden A, Brar J, Savani R, Seales P, Basdag B, Kenmore S, Mena T, Dutton M, and Amri H
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Other systems of medicine ,RZ201-999 - Published
- 2012
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23. Fast Track hERG phenotyping to evaluate the pathogenicity of KCNH2 genetic variants
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Ribeiro de Oliveira Mendes, B.B., primary, Feliciangeli, S., additional, Menard, M., additional, Chatelain, F., additional, Montnach, J., additional, Nicolas, S., additional, Ollivier, B., additional, Baro, I., additional, Schott, J., additional, Probst, V., additional, Kyndt, F., additional, Denjoy, I., additional, Lesage, F., additional, Loussouarn, G., additional, and De Waard, M., additional
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- 2021
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24. In-situ Bypass and Extra-anatomic Bypass Procedures Result in Similar Survival in Patients with Secondary Aorto-Enteric Fistulae
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Janko, M. R., Woo, K., Hacker, R. I., Baril, D., Bath, J., Smeds, M. R., Kashyap, V. S., Szeberin, Z., Magee, G. A., Elsayed, R., Wishy, A., John, St., Beck, R., Farber, A., Motta, M., Zhou, F., Lemmon, W. k., Coleman, G. l., Behrendt, D., C. -A., Aziz, Black, F., Shutze, J., Garrett, W., H. E. Jr., De Caridi, G., Liapis, C. D., Geroulakos, G., Kakisis, Moulakakis, J., Kakkos, K., Obara, S. K., Wang, H. v., Rhéaume, G., Davila, P., Ravin, V. y., Demartino, R. z., Milner, R., Shalhub, R., Jim, S., Lee, J., Dubuis, J., Ricco, C., J. -B., Coselli, Lemaire, J. a., Fatima, S., Sanford, J., Yoshida, J., Schermerhorn, W., Menard, M. L., Belkin, M., Blackwood, M., Conrad, S., Wang, Ml, Crofts, L., Nixon, S., Wu, T., Chiesa, T., Bose, R., Turner, S., Moore, J., Smith, R., Ciocca, J., Hsu, R, Czerny, J., Cullen, M., Kahlberg, J., Setacci, A., C. J., Senneville, J. H., Garrido, E., Sarac, P., Rizzo, T. P., Go, A., Bjorck, M. R., Gavali, M., Wanhainen, H., Lawrence, A., Chung, P. F., Janko, Matthew R, Woo, Karen, Hacker, Robert I, Baril, Donald, Bath, Jonathan, Smeds, Matthew R, Kashyap, Vikram S, Szeberin, Zoltan, Magee, Gregory, Elsayed, Ramsey, Wishy, Andrew, St John, Rebecca, Beck, Adam, Farber, Mark, Motta, Fernando, Zhou, Wei, Lemmon, Gary, Coleman, Dawn, Behrendt, Christian-Alexander, Aziz, Faisal, Black, Jame, Shutze, William, Garrett, H Edward, de Caridi, Giovanni, Liapis, Christo, Kakkos, Stavro, Obara, Hideaki, Wang, Grace, Rhéaume, Pascal, Davila, Victor, Ravin, Reid, Demartino, Randall, Milner, Ro, Shalhub, Sherene, Jim, Jeffrey, Lee, Jason, Dubuis, Celine, Ricco, Jean-Baptiste, Coselli, Joseph, Lemaire, Scott, Fatima, Jahvairiah, Sanford, Jennifer, Yoshida, Winston, Schermerhorn, Marc, Menard, Matthew, Belkin, Michael, Blackwood, Stuart, Conrad, Mark, Wang, Linda, Crofts, Sara, Nixon, Thoma, Wu, Timothy, Chiesa, Roberto, Bose, Saideep, Turner, Jason, Moore, Ryan, Smith, Justin, Ciocca, Rocco, Hsu, Jeffrey, Czerny, Martin, Cullen, Jonathan, Kahlberg, Andrea, Setacci, Carlo, Joh, Jin Hyun, Senneville, Eric, Garrido, Pedro, Sarac, Timur, Rizzo, Anthony, Go, Michael, Bjorck, Martin, Gavali, Hamid, Wanhainen, Ander, Lawrence, Peter F, and Chung, Jayer
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Male ,medicine.medical_specialty ,medicine.medical_treatment ,Aortoenteric fistula ,Aorta Aortoenteric fistula ,030204 cardiovascular system & hematology ,Anastomosis ,Revascularization ,03 medical and health sciences ,Blood Vessel Prosthesis Implantation ,0302 clinical medicine ,Interquartile range ,medicine ,Intestinal Fistula ,Humans ,030212 general & internal medicine ,Survival analysis ,Aged ,Retrospective Studies ,Vascular Fistula ,business.industry ,Proportional hazards model ,Hazard ratio ,Middle Aged ,Confidence interval ,United States ,Surgery ,Survival Rate ,Female ,Stents ,Cardiology and Cardiovascular Medicine ,business ,Follow-Up Studies - Abstract
The optimal revascularization modality in secondary aortoenteric fistula (SAEF) remains unclear in the literature. The purpose of this investigation was to determine the revascularization approach associated with the lowest morbidity and mortality using real-world data in patients with SAEF.A retrospective, multi-institutional study of SAEF from 2002 to 2014 was performed using a standardized database. Baseline demographics, comorbidities, and operative and postoperative variables were recorded. The primary outcome was long-term mortality. Descriptive statistics, Kaplan-Meier survival analysis, and univariate and multivariate analyses were performed.During the study period, 182 patients at 34 institutions from 11 countries presented with SAEF (median age, 72 years; 79% male). The initial aortic procedures that resulted in SAEF were 138 surgical grafts (76%) and 42 endografts (23%), with 2 unknown; 102 of the SAEFs (56%) underwent complete excision of infected aortic graft material, followed by in situ (in-line) bypass (ISB), including antibiotic-soaked prosthetic graft (53), autogenous femoral vein (neoaortoiliac surgery; 17), cryopreserved allograft (28), and untreated prosthetic grafts (4). There were 80 patients (44%) who underwent extra-anatomic bypass (EAB) with infected graft excision. Overall median Kaplan-Meier estimated survival was 319 days (interquartile range, 20-2410 days). Stratified by EAB vs ISB, there was no significant difference in Kaplan-Meier estimated survival (P = .82). In comparing EAB vs ISB, EAB patients were older (74 vs 70 years; P = .01), had less operative hemorrhage (1200 mL vs 2000 mL; P = .04), were more likely to initiate dialysis within 30 days postoperatively (15% vs 5%; P = .02), and were less likely to experience aorta-related hemorrhage within 30 days postoperatively (3% aortic stump dehiscence vs 11% anastomotic rupture; P = .03). There were otherwise no significant differences in presentation, comorbidities, and intraoperative or postoperative variables. Multivariable Cox regression showed that the duration of antibiotic use (hazard ratio, 0.92; 95% confidence interval, 0.86-0.98; P = .01) and rifampin use at time of discharge (hazard ratio, 0.20; 95% confidence interval, 0.05-0.86; P = .03) independently decreased mortality.These data suggest that ISB does not offer a survival advantage compared with EAB and does not decrease the risk of postoperative aorta-related hemorrhage. After repair, 50% of SAEF patients survive 10 months. Each week of antibiotic use decreases mortality by 8%. Further study with risk modeling is imperative for this population.
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- 2020
25. Madreporic coral for cranial base reconstruction 8 years experience
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Roux, F. X., Brasnu, D., Menard, M., Devaux, B., Nohra, G., and Loty, B.
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- 1995
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26. Lipid Interrelationship in Health and in Coronary Artery Disease
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Gertler, Menard M. and Garn, Stanley M.
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- 1950
27. Differences in the Rate of Reduction-Oxidation Potentials in Salivary Samples of Certain Groups of Individuals
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Turner, Naomi C., Gertler, Menard M., and Garn, Stanley M.
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- 1954
28. INTEGRATION OF A GENERALISED BUILDING MODEL INTO THE POSE ESTIMATION OF UAS IMAGES
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Unger, Jakob, Rottensteiner, Franz, Heipke, Christian, Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., and Li, Z.
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lcsh:Applied optics. Photonics ,010504 meteorology & atmospheric sciences ,Relation (database) ,Unmanned aerial system ,0211 other engineering and technologies ,Building model ,Bundle adjustments ,Bundle adjustment ,02 engineering and technology ,Unmanned aerial vehicles (UAV) ,lcsh:Technology ,01 natural sciences ,Image (mathematics) ,Unmanned aerial systems ,Simple (abstract algebra) ,Hybrid bundle adjustment ,Computer vision ,Pose ,Dewey Decimal Classification::500 | Naturwissenschaften ,Pose estimation ,Real image sequences ,Konferenzschrift ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Sequence ,lcsh:T ,business.industry ,lcsh:TA1501-1820 ,Object coordinates ,Remote sensing ,Real image ,Dewey Decimal Classification::500 | Naturwissenschaften::520 | Astronomie, Kartographie ,Image orientation ,Distance criterion ,Geography ,lcsh:TA1-2040 ,ddc:520 ,ddc:500 ,UAS ,Artificial intelligence ,lcsh:Engineering (General). Civil engineering (General) ,business - Abstract
A hybrid bundle adjustment is presented that allows for the integration of a generalised building model into the pose estimation of image sequences. These images are captured by an Unmanned Aerial System (UAS) equipped with a camera flying in between the buildings. The relation between the building model and the images is described by distances between the object coordinates of the tie points and building model planes. Relations are found by a simple 3D distance criterion and are modelled as fictitious observations in a Gauss-Markov adjustment. The coordinates of model vertices are part of the adjustment as directly observed unknowns which allows for changes in the model. Results of first experiments using a synthetic and a real image sequence demonstrate improvements of the image orientation in comparison to an adjustment without the building model, but also reveal limitations of the current state of the method.
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- 2016
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29. 3D FEATURE POINT EXTRACTION FROM LIDAR DATA USING A NEURAL NETWORK
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Feng, Yu, Schlichting, Alexander, Brenner, Claus, Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., and Li, Z.
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lcsh:Applied optics. Photonics ,Corner detector ,Reference data (financial markets) ,Backpropagation ,Extraction ,02 engineering and technology ,010501 environmental sciences ,01 natural sciences ,lcsh:Technology ,0202 electrical engineering, electronic engineering, information engineering ,Computer vision ,Edge detection ,Dewey Decimal Classification::500 | Naturwissenschaften ,Artificial neural network ,Backpropagation algorithms ,Remote sensing ,Lidar ,Geography ,Feature (computer vision) ,Lidar point clouds ,020201 artificial intelligence & image processing ,ddc:500 ,Poles ,Feature point extraction ,Neural networks ,Feature points extraction ,LiDAR ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Mobile mapping system ,Point (geometry) ,Konferenzschrift ,0105 earth and related environmental sciences ,Landmark ,Image matching ,business.industry ,lcsh:T ,lcsh:TA1501-1820 ,Pattern recognition ,Vehicles ,3D feature points extraction ,Neural network ,Dewey Decimal Classification::500 | Naturwissenschaften::520 | Astronomie, Kartographie ,lcsh:TA1-2040 ,Autonomous driving ,ddc:520 ,Artificial intelligence ,business ,lcsh:Engineering (General). Civil engineering (General) - Abstract
Accurate positioning of vehicles plays an important role in autonomous driving. In our previous research on landmark-based positioning, poles were extracted both from reference data and online sensor data, which were then matched to improve the positioning accuracy of the vehicles. However, there are environments which contain only a limited number of poles. 3D feature points are one of the proper alternatives to be used as landmarks. They can be assumed to be present in the environment, independent of certain object classes. To match the LiDAR data online to another LiDAR derived reference dataset, the extraction of 3D feature points is an essential step. In this paper, we address the problem of 3D feature point extraction from LiDAR datasets. Instead of hand-crafting a 3D feature point extractor, we propose to train it using a neural network. In this approach, a set of candidates for the 3D feature points is firstly detected by the Shi-Tomasi corner detector on the range images of the LiDAR point cloud. Using a back propagation algorithm for the training, the artificial neural network is capable of predicting feature points from these corner candidates. The training considers not only the shape of each corner candidate on 2D range images, but also their 3D features such as the curvature value and surface normal value in z axis, which are calculated directly based on the LiDAR point cloud. Subsequently the extracted feature points on the 2D range images are retrieved in the 3D scene. The 3D feature points extracted by this approach are generally distinctive in the 3D space. Our test shows that the proposed method is capable of providing a sufficient number of repeatable 3D feature points for the matching task. The feature points extracted by this approach have great potential to be used as landmarks for a better localization of vehicles.
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- 2016
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30. ACCURACY ASSESSMENT OF MOBILE MAPPING POINT CLOUDS USING THE EXISTING ENVIRONMENT AS TERRESTRIAL REFERENCE
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Hofmann, Sabine, Brenner, Claus, Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., and Li, Z.
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lcsh:Applied optics. Photonics ,010504 meteorology & atmospheric sciences ,Reference data (financial markets) ,Coordinate system ,0211 other engineering and technologies ,Point cloud ,02 engineering and technology ,Mobile mapping ,Surveys ,01 natural sciences ,lcsh:Technology ,Reference data ,Point (geometry) ,3D test field ,Konferenzschrift ,Dewey Decimal Classification::500 | Naturwissenschaften ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing ,lcsh:T ,Traffic signs ,Orthophoto ,Total station ,lcsh:TA1501-1820 ,Street profile ,Dewey Decimal Classification::500 | Naturwissenschaften::520 | Astronomie, Kartographie ,Geography ,3d tests ,Accuracy assessment ,Mapping ,GNSS applications ,lcsh:TA1-2040 ,ddc:520 ,ddc:500 ,lcsh:Engineering (General). Civil engineering (General) ,Facades ,Geodesy - Abstract
Mobile mapping data is widely used in various applications, what makes it especially important for data users to get a statistically verified quality statement on the geometric accuracy of the acquired point clouds or its processed products. The accuracy of point clouds can be divided into an absolute and a relative quality, where the absolute quality describes the position of the point cloud in a world coordinate system such as WGS84 or UTM, whereas the relative accuracy describes the accuracy within the point cloud itself. Furthermore, the quality of processed products such as segmented features depends on the global accuracy of the point cloud but mainly on the quality of the processing steps. Several data sources with different characteristics and quality can be thought of as potential reference data, such as cadastral maps, orthophoto, artificial control objects or terrestrial surveys using a total station. In this work a test field in a selected residential area was acquired as reference data in a terrestrial survey using a total station. In order to reach high accuracy the stationing of the total station was based on a newly made geodetic network with a local accuracy of less than 3 mm. The global position of the network was determined using a long time GNSS survey reaching an accuracy of 8 mm. Based on this geodetic network a 3D test field with facades and street profiles was measured with a total station, each point with a two-dimensional position and altitude. In addition, the surface of poles of street lights, traffic signs and trees was acquired using the scanning mode of the total station. Comparing this reference data to the acquired mobile mapping point clouds of several measurement campaigns a detailed quality statement on the accuracy of the point cloud data is made. Additionally, the advantages and disadvantages of the described reference data source concerning availability, cost, accuracy and applicability are discussed.
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- 2018
31. VEHICLE LOCALIZATION BY LIDAR POINT CORRELATION IMPROVED BY CHANGE DETECTION
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Schlichting, Alexander, Brenner, Claus, Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., and Li, Z.
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lcsh:Applied optics. Photonics ,LiDAR ,010504 meteorology & atmospheric sciences ,Decision trees ,0211 other engineering and technologies ,Decision tree ,Chemical detection ,Mobile mapping ,Optical radar ,02 engineering and technology ,lcsh:Technology ,01 natural sciences ,Classification algorithm ,Standard deviation ,Detection theory ,Computer vision ,Dewey Decimal Classification::500 | Naturwissenschaften ,Konferenzschrift ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Dynamic Scan ,Classification (of information) ,Image matching ,lcsh:T ,business.industry ,lcsh:TA1501-1820 ,Vehicles ,Remote sensing ,Change detection algorithms ,Classification ,Dewey Decimal Classification::500 | Naturwissenschaften::520 | Astronomie, Kartographie ,Correlation ,Random forest ,Lidar ,Geography ,Mapping ,lcsh:TA1-2040 ,Correlation methods ,Localization ,ddc:520 ,Change detection ,ddc:500 ,Artificial intelligence ,lcsh:Engineering (General). Civil engineering (General) ,business ,Signal detection - Abstract
LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.
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- 2018
32. PLÉIADES PROJECT: ASSESSMENT OF GEOREFERENCING ACCURACY, IMAGE QUALITY, PANSHARPENING PERFORMENCE AND DSM/DTM QUALITY
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Topan, H., Cam, A., Özendi, M., Oruç, M., Jacobsen, Karsten, Taşkanat, T., Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Halounova, L, Safar, V, Toth, CK, and Zonguldak Bülent Ecevit Üniversitesi
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lcsh:Applied optics. Photonics ,010504 meteorology & atmospheric sciences ,Image quality ,0208 environmental biotechnology ,0211 other engineering and technologies ,02 engineering and technology ,Pansharpening ,01 natural sciences ,lcsh:Technology ,Standard deviation ,Rational polynomial coefficient ,Georeferencing ,Computer vision ,Pan-sharpening ,020701 environmental engineering ,Dewey Decimal Classification::500 | Naturwissenschaften ,Signal to noise ratio ,Pleiades ,Orientation (computer vision) ,Statistics ,Ground sample distance ,Remote sensing ,Geography ,ddc:500 ,Ground control points ,0207 environmental engineering ,DSM/DTM ,Georeferencing Accuracy ,Digital surface models ,FOS: Mathematics ,Ground sampling distances ,Konferenzschrift ,0105 earth and related environmental sciences ,021101 geological & geomatics engineering ,Application programs ,business.industry ,lcsh:T ,lcsh:TA1501-1820 ,Pléiades ,Dewey Decimal Classification::500 | Naturwissenschaften::520 | Astronomie, Kartographie ,020801 environmental engineering ,Panchromatic film ,Image Quality ,Signal-to-noise ratio (imaging) ,lcsh:TA1-2040 ,Rock mechanics ,ddc:520 ,Rational polynomial coefficients ,Artificial intelligence ,business ,lcsh:Engineering (General). Civil engineering (General) - Abstract
23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS) -- JUL 12-19, 2016 -- Prague, CZECH REPUBLIC, WOS: 000392750100082, Pleiades 1A and 1B are twin optical satellites of Optical and Radar Federated Earth Observation (ORFEO) program jointly miming by France and Italy. They are the first satellites of Europe with sub-meter resolution. Airbus DS (formerly Astrium Geo) runs a MyGIC (formerly Pleiades Users Group) program to validate Pleiades images worldwide for various application purposes. The authors conduct three projects, one is within this program, the second is supported by BEU Scientific Research Project Program, and the third is supported by TUBITAK. Assessment of georeferencing accuracy, image quality, pansharpening performance and Digital Surface Model/Digital Terrain Model (DSM/DTM) quality subjects are investigated in these projects. For these purposes, triplet panchromatic (50 cm Ground Sampling Distance (GSD)) and VNIR (2 m GSD) Pleiades 1A images were investigated over Zonguldak test site (Turkey) which is urbanised, mountainous and covered by dense forest. The georeferencing accuracy was estimated with a standard deviation in X and Y (SX, SY) in the range of 0.45m by bias corrected Rational Polynomial Coefficient (RPC) orientation, using 170 Ground Control Points (GCPs). 3D standard deviation of +/- 0.44m in X, +/- 0.51m in Y, and +/- 1.82m in Z directions have been reached in spite of the very narrow angle of convergence by bias corrected RPC orientation. The image quality was also investigated with respect to effective resolution, Signal to Noise Ratio (SNR) and blur coefficient. The effective resolution was estimated with factor slightly below 1.0, meaning that the image quality corresponds to the nominal resolution of 50cm. The blur coefficients were achieved between 0.39-0.46 for triplet panchromatic images, indicating a satisfying image quality. SNR is in the range of other comparable space borne images which may be caused by de-noising of Pleiades images. The pansharpened images were generated by various methods, and are validated by most common statistical metrics and also visual interpretation. The generated DSM and DTM were achieved with +/- 1.6m standard deviation in Z (SZ) in relation to a reference DTM., Int Soc Photogrammetry & Remote Sensing, Airbus Defence and Space; BEU [2014-47912266-01]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [114Y380], Authors would like to thank to Airbus Defence and Space, BEU (Project ID: 2014-47912266-01), and TUBITAK (Project ID: 114Y380) for their support on the Pleiades projects.
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- 2018
33. Response to Comment on Kelly et al. Subclinical First Trimester Renal Abnormalities Are Associated With Preeclampsia in Normoalbuminuric Women With Type 1 Diabetes. Diabetes Care 2018;41:120–127
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Kelly, Clare B., primary, Hookham, Michelle B., additional, Yu, Jeremy Y., additional, Jenkins, Alicia J., additional, Nankervis, Alison J., additional, Hanssen, Kristian F., additional, Garg, Satish K., additional, Scardo, James A., additional, Hammad, Samar M., additional, Menard, M. Kathryn, additional, Aston, Christopher E., additional, and Lyons, Timothy J., additional
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- 2018
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34. Overview of the JET results with the ITER-like wall
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Romanelli, F., Abel, I., Afanesyev, V., Aftanas, M., Agarici, G., Aggarwal, K. M., Aho-Mantila, L., Ahonen, E., Aints, M., Airila, M., Akers, R., Alarcon, Th., Albanese, R., Alexeev, A., Alfier, A., Allan, P., Almaviva, S., Alonso, A., Alper, B., Altmann, H., Alves, D., Ambrosino, G., Amosov, V., Andersson, F., Andersson Sundén, E., Andreev, V., Andrew, Y., Angelone, M., Anghel, M., Anghel, A., Angioni, C., Apruzzese, G., Arcis, N., Arena, P., Argouarch, A., Ariola, M., Armitano, A., Armstrong, R., Arnoux, G., Arshad, S., Artaserse, G., Artaud, J. F., Ash, A., Asp, E., Asunta, O., Atanasiu, C. V., Atkins, G., Avotina, L., Axton, M. D., Ayres, C., Baciero, A., Bailescu, V., Baiocchi, B., Baker, R. A., Balboa, I., Balden, M., Balorin, C., Balshaw, N., Banks, J. W., Baranov, Y. F., Barbier, D., Barlow, I. L., Barnard, M. A., Barnsley, R., Barrena, L., Barrera, L., Baruzzo, M., Basiuk, V., Bateman, G., Batistoni, P., Baumgarten, N., Baylor, L., Bazylev, B., Beaumont, P. S., Beausang, K., Bã©coulet, M., Bekris, N., Beldishevski, M., Bell, A. C., Belli, F., Bellinger, M., Bellizio, T., Belo, P. S. A., Belonohy, Ã., Bennett, P. E., Benterman, N. A., Berger-By, G., Bergsã¥ker, H., Berk, H., Bernardo, J., Bernert, M., Bertrand, B., Beurskens, M. N. A., Bieg, B., Bienkowska, B., Biewer, T. M., Bigi, M., Bãlkovã¡, P., Bin, W., Bird, J., Bizarro, J., Bjã¶rkas, C., Blackman, T. R., Blanchard, P., Blanco, E., Blum, J., Bobkov, V., Boboc, A., Boilson, D., Bolshakova, I., Bolzonella, T., Boncagni, L., Bonheure, G., Bonnin, X., Borba, D., Borthwick, A., Botrugno, A., Boulbe, C., Bouquey, F., Bourdelle, C., Bovert, K. v., Bowden, M., Boyce, T., Boyer, H. J., Bozhenkov, A., Brade, R. J., Bradshaw, J. M. A., Braet, J., Braic, V., Braithwaite, G. C., Brault, C., Breizman, B., Bremond, S., Brennan, P. D., Brett, A., Breue, J., Brezinsek, S., Bright, M. D. J., Briscoe, F., Brix, M., Brombin, M., Brown, B. C., Brown, D. P. D., Brzozowski, J., Bucalossi, J., Buckley, M. A., Budd, T., Budny, R. V., Bunting, P., Buratti, P., Burcea, G., Burckhart, A., Butcher, P. R., Buttery, R. J., Cahyna, P., Calabrã², G., Callaghan, C. P., Caminade, J. P., Camp, P. G., Campling, D. C., Caniello, R., Canik, J., Cannas, B., Capel, A. J., Carannante, G., Card, P. J., Cardinali, A., Carlstrom, T., Carman, P., Carralero, D., Carraro, L., Carter, T., Carvalho, B. B., Carvalho, I., Carvalho, P., Casati, A., Castaldo, C., Caughman, J., Cavazzana, R., Cavinato, M., Cecconello, M., Cecil, E., Cecil, F. E., Cenedese, A., Centioli, C., Cesario, R., Challis, C. D., Chandler, M., Chang, C., Chankin, A., Chapman, I. T., Chektybayev, B., Chernyshova, M., Child, D. J., Chiru, P., Chitarin, G., Chugonov, I., Chugunov, I., Ciric, D., Clairet, F., Clarke, R. H., Clay, R., Clever, M., Coad, J. P., Coates, P. A., Cocilovo, V., Coda, S., Coelho, R., Coenen, J., Coffey, I., Colas, L., Cole, M., Collins, S., Combs, S., Compan, J., Conboy, J. E., Conroy, S., Cook, N., Cook, S. P., Coombs, D., Cooper, S. R., Corre, Y., Corrigan, G., Cortes, S., Coster, D., Counsell, G. F., Courtois, X., Cox, M., Craciunescu, T., Cramp, S., Crisanti, F., Croci, G., Croft, O., Crombe, K., Crombã©, K., Crowley, B. J., Cruz, N., Cseh, G., Cupido, L., Curuia, M., Cusack, R. A., Czarnecka, A., Czarski, T., Dalley, S., Daly, E. T., Dalziel, A., Daniel, R., Darrow, D., David, O., Davies, N., Davies, W., Davis, J. J., Day, I. E., Day, C., De Angelis, R., De Arcas, G., De Baar, M. R., De La Cal, E., De La Luna, E., De Pablos, J. L., De Tommasi, G., De Vries, P. C., De-Angelis, R., Degli Agostini, F., Delabie, E., Del-Castillo-Negrete, D., Delpech, L., Denisov, G., Denyer, A. J., Denyer, R. F., Devaux, S., Devynck, P., Di Matteo, L., Di Pace, L., Dirken, P. J., Dittmar, T., Dodt, D., Dnestrovskiy, A., Doerner, R., Doldatov, S., Dominiczak, K., Dooley, P., Dorling, S. E., Douai, D., Down, A. P., Doyle, P. T., Drake, J. R., Dreischuh, T., Drozdov, V., Dumortier, P., Dunai, D., Duran, I., Durodiã©, F., Dutta, P., Dux, R., Dylst, K., Eaton, R., Edlington, T., Edwards, A. M., Edwards, D. T., Edwards, P. K., Eich, Th., Ekedahl, A., Elevant, T., Ellingboe, B., Elsmore, C. G., Emmoth, B., Erdei, G., Ericsson, G., Eriksson, L. G., Eriksson, A., Esposito, B., Esser, H. G., Estrada, T., Evangelidis, E. A., Evans, G. E., Ewart, G. D., Ewers, D. T., Falchetto, G., Falie, D., Fanthome, J. G. A., Farthing, J. W., Fasoli, A., Faugeras, B., Fedorczak, N., Felton, R. C., Fenzi, C., Fernades, A., Fernandes, H., Ferreira, J. A., Ferreira, J., Ferron, J., Fessey, J. A., Figini, L., Figueiredo, J., Figueiredo, A., Finburg, P., Finken, K. H., Fischer, U., Fitzgerald, N., Flanagan, J., Fleming, C., Forbes, A. D., Ford, O., Formisano, A., Fraboulet, D., Francis, R. J., Frassinetti, L., Fresa, R., Friconneau, J. P., Frigione, D., Fullard, K., Fundamenski, W., Furno Palumbo, M., Gã¡l, K., Gao, X., Garavaglia, S., Garbet, X., Garcia, J., Garcia Munoz, M., Gardner, W., Garibaldi, P., Garnier, D., Garzotti, L., Gatu Johnson, M., Gaudio, P., Gauthier, E., Gaze, J. W., Gear, D. F., Gedney, J., Gee, S. J., Gelfusa, M., Genangeli, E., Gerasimov, S., Geraud, A., Gerbaud, T., Gherendi, M., Ghirelli, N., Giacalone, J. C., Giacomelli, L., Gibson, C. S., Gil, C., Gilligan, S. J., Gimblett, C. G., Gin, D., Giovannozzi, E., Giroud, C., Giruzzi, G., Godwin, J., Goff, J. K., Gohil, P., Gã³jska, A., Goloborod'Ko, V., Gonã§alves, B., Goniche, M., Gonzales, S., González De Vicente, S. M., Goodyear, A., Gorelenkov, N., Gorini, G., Goulding, R., Graham, B., Graham, D., Graham, M. E., Graves, J., Green, N. R., Greuner, H., Grigore, E., Griph, F. S., Grisolia, C., Gros, G., Groth, M., Grã¼nhagen, S., Gryaznevich, M. P., Guirlet, R., Gunn, J., Gupta, A., Guzdar, P., Hackett, L. J., Hacquin, S., Haist, B., Hakola, A., Halitovs, M., Hall, S. J., Hallworth Cook, S. P., Hamilton, D. T., Han, H., Handley, R. C., Harding, S., Harling, J. D. W., Harting, D., Harvey, M. J., Haupt, T. D. V., Hawkes, N. C., Hawryluk, R., Hay, J. H., Hayashi, N., Haydon, P. W., Hayward, I. R., Hazel, S., Heesterman, P. J. L., Heidbrink, W., Heinola, K., Hellesen, C., Hellsten, T., Hemming, O. N., Hender, T. C., Henderson, M., Hennion, V., Hidalgo, C., Higashijima, S., Hill, J. W., Hill, M., Hill, K., Hillairet, J., Hillis, D., Hirai, T., Hitchin, M., Hobirk, J., Hogan, C., Hogben, C. H. A., Hogeweij, G. M. D., Hollingham, I. C., Holyaka, R., Homfray, D. A., Honeyands, G., Hong, S. H., Hong, J. H., Horã¡cek, J., Horn, B. A., Horton, A. R., Horton, L. D., Hotchin, S. P., Hough, M. R., Houlberg, W., Howell, D. F., Huber, A., Huddleston, T. M., Hudson, Z., Hughes, M., Hã¼hnerbein, M., Hume, C. C., Hunt, A. J., Hunter, C. L., Hutchinson, T. S., Huygen, S., Huysmans, G., Ide, S., Illescas, C., Imbeaux, F., Ivanova, D., Ivanova-Stanik, I., Ivings, E., Jachmich, S., Jackson, G., Jacquet, P., Jakubowska, K., James, P. V., Janky, F., Jã¤rvinen, A., Jednorog, S., Jenkins, I., Jennison, M. A. C., Jeskins, C., Jin Kwon, O., Joffrin, E., Johnson, M. F., Johnson, R., Johnson, T., Jolovic, D., Jonauskas, V., Jones, E. M., Jones, G., Jones, H. D., Jones, T. T. C., Jouvet, M., Jupã©n, C., Kachtchouk, I., Kaczmarczyk, J., Kallenbach, A., Kã¤llne, J., Kalupin, D., Kã¡lvin, S., Kamelander, G., Kamendje, R., Kamiya, K., Kappatou, A., Kasparek, W., Kasprowicz, G., Katramados, I., Kaveney, G., Kaye, A. S., Kear, M. J., Keeling, D. L., Kelliher, D., Kempenaars, M., Khilar, P., Khilkevich, E., Kidd, N. G., Kiisk, M., Kim, K. M., Kim, H., King, R. F., Kinna, D. 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- Subjects
Nuclear and High Energy Physics ,Materials science ,REGIME ,Nuclear engineering ,chemistry.chemical_element ,Condensed Matter Physic ,Effective radiated power ,Tungsten ,Condensed Matter Physics ,Pedestal ,PLASMA-FACING COMPONENTS ,TOKAMAK PLASMAS ,Jet (fluid) ,TUNGSTEN ,Divertor ,perfomance ,Plasma ,PERFORMANCE ,Settore FIS/07 - Fisica Applicata(Beni Culturali, Ambientali, Biol.e Medicin) ,chemistry ,Beta (plasma physics) ,DIVERTOR ,Beryllium ,Atomic physics - Abstract
Following the completion in May 2011 of the shutdown for the installation of the beryllium wall and the tungsten divertor, the first set of JET campaigns have addressed the investigation of the retention properties and the development of operational scenarios with the new plasma-facing materials. The large reduction in the carbon content (more than a factor ten) led to a much lower Zeff (1.2-1.4) during L- and H-mode plasmas, and radiation during the burn-through phase of the plasma initiation with the consequence that breakdown failures are almost absent. Gas balance experiments have shown that the fuel retention rate with the new wall is substantially reduced with respect to the C wall. The re-establishment of the baseline H-mode and hybrid scenarios compatible with the new wall has required an optimization of the control of metallic impurity sources and heat loads. Stable type-I ELMy H-mode regimes with H98,y2 close to 1 and βN ∼ 1.6 have been achieved using gas injection. ELM frequency is a key factor for the control of the metallic impurity accumulation. Pedestal temperatures tend to be lower with the new wall, leading to reduced confinement, but nitrogen seeding restores high pedestal temperatures and confinement. Compared with the carbon wall, major disruptions with the new wall show a lower radiated power and a slower current quench. The higher heat loads on Be wall plasma-facing components due to lower radiation made the routine use of massive gas injection for disruption mitigation essential. © 2013 IAEA, Vienna.
- Published
- 2013
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35. Overview of the JET results with the ITER-like wall
- Author
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Romanelli, F, Abel, I, Afanesyev, V, Aftanas, M, Agarici, G, Aggarwal, K, Aho Mantila, L, Ahonen, E, Aints, M, Airila, M, Akers, R, Alarcon, T, Albanese, R, Alexeev, A, Alfier, A, Allan, P, Almaviva, S, Alonso, A, Alper, B, Altmann, H, Alves, D, Ambrosino, G, Amosov, V, Andersson, F, Sunden, E, Andreev, V, Andrew, Y, Angelone, M, Anghel, M, Anghel, A, Angioni, C, Apruzzese, G, Arcis, N, Arena, P, Argouarch, A, Ariola, M, Armitano, A, Arnoux, G, Arshad, S, Artaserse, G, Artaud, J, Ash, A, Asp, E, Asunta, O, Atanasiu, C, Atkins, G, Avotina, L, Axton, M, Ayres, C, Baciero, A, Bailescu, V, Baiocchi, B, Baker, R, Balboa, I, Balden, M, Balorin, C, Balshaw, N, Banks, J, Baranov, Y, Barbier, D, Barlow, I, Barnard, M, Barnsley, R, Barrena, L, Barrera, L, Baruzzo, M, Basiuk, V, Bateman, G, Batistoni, P, Baumgarten, N, Baylor, L, Bazylev, B, Beaumont, P, Beausang, K, Becoulet, M, Bekris, N, Beldishevski, M, Bell, A, Belli, F, Bellinger, M, Bellizio, T, Belo, P, Belonohy, E, Bennett, P, Benterman, N, Berger By, G, Bergsaker, H, Berk, H, Bernardo, J, Bernert, M, Bertrand, B, Beurskens, M, Bieg, B, Bienkowska, B, Biewer, T, Bigi, M, Bilkova, P, Bin, W, Bird, J, Bizarro, J, Bjorkas, C, Blackman, T, Blanchard, P, Blanco, E, Blum, J, Bobkov, V, Boboc, A, Boilson, D, Bolshakova, I, Bolzonella, T, Boncagni, L, Bonheure, G, Bonnin, X, Borba, D, Borthwick, A, Botrugno, A, Boulbe, C, Bouquey, F, Bourdelle, C, Von, B, Bowden, M, Boyce, T, Boyer, H, Bozhenkov, A, Brade, R, Bradshaw, J, Braet, J, Braic, V, Braithwaite, G, Brault, C, Breizman, B, Bremond, S, Brennan, P, Brett, A, Breue, J, Brezinsek, S, Bright, M, Briscoe, F, Brix, M, Brombin, M, Brown, B, Brown, D, Brzozowski, J, Bucalossi, J, Buckley, M, Budd, T, Budny, R, Bunting, P, Buratti, P, Burcea, G, Burckhart, A, Butcher, P, Buttery, R, Cahyna, P, Calabro, G, Callaghan, C, Caminade, J, Camp, P, Campling, D, Caniello, R, Canik, J, Cannas, B, Capel, A, Carannante, G, Card, P, Cardinali, A, Carlstrom, T, Carman, P, Carralero, D, Carraro, L, Carter, T, Carvalho, B, Carvalho, I, Carvalho, P, Casati, A, Castaldo, C, Caughman, J, Cavazzana, R, Cavinato, M, Cecconello, M, Cecil, E, Cecil, F, Cenedese, A, Centioli, C, Cesario, R, Challis, C, Chandler, M, Chang, C, Chankin, A, Chapman, I, Chektybayev, B, Chernyshova, M, Child, D, Chiru, P, Chitarin, G, Chugonov, I, Ciric, D, Clairet, F, Clarke, R, Clay, R, Clever, M, Coad, J, Coates, P, Cocilovo, V, Coda, S, Coelho, R, Coenen, J, Coffey, I, Colas, L, Cole, M, Collins, S, Combs, S, Compan, J, Conboy, J, Conroy, S, Cook, N, Cook, S, Coombs, D, Cooper, S, Corre, Y, Corrigan, G, Cortes, S, Coster, D, Counsell, G, Courtois, X, Cox, M, Craciunescu, T, Cramp, S, Crisanti, F, Croci, G, Croft, O, Crombe, K, Crowley, B, Cruz, N, Cseh, G, Cupido, L, Curuia, M, Cusack, R, Czarnecka, A, Czarski, T, Dalley, S, Daly, E, Dalziel, A, Daniel, R, Darrow, D, David, O, Davies, N, Davies, W, Davies, J, Day, I, Day, C, De, A, De, B, De, L, De, P, De, T, De, V, De Angelis, R, Degli, A, Delabie, E, del Castillo Negrete, D, Delpech, L, Denisov, G, Denyer, A, Denyer, R, Devaux, S, Devynck, P, Di, M, Di, P, Dirken, P, Dittmar, T, Dnestrovskiy, A, Dodt, D, Doerner, R, Doldatov, S, Dominiczak, K, Dooley, P, Dorling, S, Douai, D, Down, A, Doyle, P, Drake, J, Dreischuh, T, Drozdov, V, Dumortier, P, Dunai, D, Duran, I, Durodie, F, Dutta, P, Dux, R, Dylst, K, Eaton, R, Edlington, T, Edwards, A, Edwards, D, Edwards, P, Eich, T, Ekedahl, A, Elevant, T, Ellingboe, B, Elsmore, C, Emmoth, B, Erdei, G, Ericsson, G, Eriksson, L, Eriksson, A, Esposito, B, Esser, H, Estrada, T, Evangelidis, E, Evans, G, Ewart, G, Ewers, D, Falchetto, G, Falie, D, Fanthome, J, Farthing, J, Fasoli, A, Faugeras, B, Fedorczak, N, Felton, R, Fenzi, C, Fernades, A, Fernandes, H, Ferreira, J, Ferron, J, Fessey, J, Figini, L, Figueiredo, J, Figueiredo, A, Finburg, P, Finken, K, Fischer, U, Fitzgerald, N, Flanagan, J, Fleming, C, Forbes, A, Ford, O, Formisano, A, Fraboulet, D, Francis, R, Frassinetti, L, Fresa, R, Friconneau, J, Frigione, D, Fullard, K, Fundamenski, W, Palumbo, M, Gal, K, Gao, X, Garavaglia, S, Garbet, X, Garcia, J, Munoz, M, Gardner, W, Garibaldi, P, Garnier, D, Garzotti, L, Johnson, M, Gaudio, P, Gauthier, E, Gaze, J, Gear, D, Gedney, J, Gee, S, Gelfusa, M, Genangeli, E, Gerasimov, S, Geraud, A, Gerbaud, T, Gherendi, M, Ghirelli, N, Giacalone, J, Giacomelli, L, Gibson, C, Gil, C, Gilligan, S, Gimblett, C, Gin, D, Giovannozzi, E, Giroud, C, Giruzzi, G, Godwin, J, Goff, J, Gohil, P, Gojska, A, Goloborod'Ko, V, Goncalves, B, Goniche, M, Gonzales, S, Goodyear, A, Gorelenkov, N, Gorini, G, Goulding, R, Graham, B, Graham, D, Graham, M, Graves, J, Green, N, Greuner, H, Grigore, E, Griph, F, Grisolia, C, Gros, G, Groth, M, Grunhagen, S, Gryaznevich, M, Guirlet, R, Gunn, J, Gupta, A, Guzdar, P, Hackett, L, Hacquin, S, Haist, B, Hakola, A, Halitovs, M, Hall, S, Hamilton, D, Han, H, Handley, R, Harding, S, Harling, J, Harting, D, Harvey, M, Haupt, T, Hawkes, N, Hawryluk, R, Hay, J, Hayashi, N, Haydon, P, Hayward, I, Hazel, S, Heesterman, P, Heidbrink, W, Heinola, K, Hellesen, C, Hellsten, T, Hemming, O, Hender, T, Henderson, M, Hennion, V, Hidalgo, C, Higashijima, S, Hill, J, Hill, M, Hill, K, Hillairet, J, Hillis, D, Hirai, T, Hitchin, M, Hobirk, J, Hogan, C, Hogben, C, Hogeweij, G, Hollingham, I, Holyaka, R, Homfray, D, Honeyands, G, Hong, S, Hong, J, Horacek, J, Horn, B, Horton, A, Horton, L, Hotchin, S, Hough, M, Houlberg, W, Howell, D, Huber, A, Huddleston, T, Hudson, Z, Hughes, M, Huehnerbein, M, Hume, C, Hunt, A, Hunter, C, Hutchinson, T, Huygen, S, Huysmans, G, Ide, S, Illescas, C, Imbeaux, F, Ivanova, D, Ivanova Stanik, I, Ivings, E, Jachmich, S, Jackson, G, Jacquet, P, Jakubowska, K, James, P, Janky, F, Jaervinen, A, Jednorog, S, Jenkins, I, Jennison, M, Jeskins, C, Kwon, O, Joffrin, E, Johnson, R, Johnson, T, Jolovic, D, Jonauskas, V, Jones, E, Jones, G, Jones, H, Jones, T, Jouvet, M, Jupen, C, Kachtchouk, I, Kaczmarczyk, J, Kallenbach, A, Kaellne, J, Kalupin, D, Kalvin, S, Kamelander, G, Kamendje, R, Kamiya, K, Kappatou, A, Kasparek, W, Kasprowicz, G, Katramados, I, Kaveney, G, Kaye, A, Kear, M, Keeling, D, Kelliher, D, Kempenaars, M, Khilar, P, Khilkevich, E, Kidd, N, Kiisk, M, Kim, K, Kim, H, King, R, Kinna, D, Kiptily, V, Kirnev, G, Kirneva, N, Kirov, K, Kirschner, A, Kisielius, R, Kislov, D, Kiss, G, Kizane, G, Klein, A, Klepper, C, Klimov, N, Klix, A, Knaup, M, Kneuper, K, Kneupner, H, Knight, P, Knipe, S, Kocan, M, Koch, R, Kochl, F, Kocsis, G, Koivuranta, S, Koppitz, T, Korotkov, A, Koskela, T, Koslowski, H, Kotov, V, Kovari, M, Kramer, G, Krasilnikov, A, Krasilnikov, V, Kraus, S, Kreter, A, Krieger, K, Kritz, A, Krivchenkov, Y, Kruezi, U, Krylov, S, Ksiazek, I, Kuhn, S, Kuhnlein, W, Kukushkin, A, Kundu, A, Kurki Suonio, T, Kurowski, A, Kuteev, B, Kuyanov, A, Kyrytsya, V, La, H, Laan, M, Labate, C, Lachichi, A, Laguardia, L, Lam, N, Lang, P, Large, M, Lasa, A, Lassiwe, I, Last, J, Lawson, K, Laxaback, M, Layne, R, Le, G, Leblanc, B, Lee, S, Lee, J, Leggate, H, Lehnen, M, Leigheb, M, Lengar, I, Lennholm, M, Lerche, E, Lescure, C, Li, Y, Li, P, Liang, Y, Likonen, J, Lin, Y, Lindholm, V, Linke, J, Linstead, S, Lipshultz, B, Litaudon, X, Litvak, A, Liu, Y, Loarer, T, Loarte, A, Lobel, R, Lomas, P, Long, F, Lonnroth, J, Looker, D, Lopez, J, Lotte, P, Louche, F, Loughlin, M, Loving, A, Lowry, C, Luce, T, Lucock, R, Lukanitsa, A, Lukin, A, Lungu, A, Lungu, C, Lyssoivan, A, Macheta, P, Mackenzie, A, Macrae, M, Maddaluno, G, Maddison, G, Madsen, J, Magesh, B, Maget, P, Maggi, C, Maier, H, Mailloux, J, Makkonen, T, Makowski, M, Malaquias, A, Manning, C, Mansfield, M, Manso, M, Mantica, P, Marcenko, N, Marchitti, M, Mardenfeld, M, Marechal, J, Marinelli, M, Marinucci, M, Marocco, D, Marren, C, Marsen, S, Martin, D, Martin, G, Martin, Y, Martin Solis, J, Masaki, K, Masiello, A, Maszl, C, Matejcik, S, Matilal, A, Mattei, M, Matthews, G, Mattoo, S, Matveev, D, Maviglia, F, May, C, Mayer, M, Mayoral, M, Mazon, D, Mazzotta, C, Mazzucato, E, Mccarthy, P, Mcclements, K, Mccormick, K, Mccullen, P, Mccune, D, Mcdonald, D, Mcgregor, R, Mckivitt, J, Meakins, A, Medina, F, Meigs, A, Menard, M, Meneses, L, Menmuir, S, Merrigan, I, Mertens, P, Messiaen, A, Meszaros, B, Meyer, H, Miano, G, Michling, R, Miele, M, Miettunen, J, Migliucci, P, Miller, A, Mills, S, Milnes, J, Mindham, T, Miorin, E, Mirizzi, F, Mirones, E, Mironov, M, Mitteau, R, Mlynar, J, Mollard, P, Monakhov, I, Monier Garbet, P, Mooney, R, Moreau, D, Moreau, P, Moreira, L, Morgan, A, Morgan, P, Morlock, C, Morris, A, Mort, G, Murakami, M, Murari, A, Mustata, I, Nabais, F, Nakano, T, Nardon, E, Nash, G, Naulin, V, Nave, M, Nazikian, R, Nedzelski, I, Negus, C, Neilson, J, Nemtsev, G, Neto, A, Neu, R, Neubauer, O, Newbert, G, Newman, M, Nicholls, K, Nicolai, A, Nicolas, L, Nieckchen, P, Nielsen, A, Nielsen, S, Nielsen, P, Nielsen, G, Nieto, J, Nightingale, M, Nishijima, D, Noble, C, Nocente, M, Nordman, H, Norman, M, Nowak, S, Nunes, I, Oberkofler, M, Odstrcil, M, O'Gorman, T, Ohsako, T, Okabayashi, M, Olariu, S, Oleynikov, A, O'Mullane, M, Ongena, J, Orsitto, F, Oswuigwe, O, Ottaviani, M, Oyama, N, Pacella, D, Paget, K, Pajuste, E, Palazzo, S, Palenic, J, Pamela, J, Pamela, S, Pangione, L, Panin, A, Panja, S, Pankin, A, Pantea, A, Parail, V, Paris, P, Parisot, T, Park, M, Parkin, A, Parsloe, A, Parsons, B, Pasqualotto, R, Pastor, P, Paterson, R, Paul, M, Peach, D, Pearce, R, Pearson, B, Pearson, I, Pedrick, L, Pedrosa, M, Pegourie, B, Pereira, R, Pereslavtsev, P, Perevezentsev, A, Von, T, Pericoli Ridolfini, V, Perona, A, Perrot, Y, Peruzzo, S, Peschanyy, S, Petravich, G, Petrizzi, L, Petrov, V, Petrzilka, V, Philipps, V, Piccolo, F, Pietropaolo, A, Pillon, M, Pinches, S, Pinna, T, Pintsuk, G, Piovesan, P, Pironti, A, Pisano, F, Pitts, R, Plaum, B, Plyusnin, V, Polasik, M, Poli, F, Pomaro, N, Pompilian, O, Poncet, L, Pool, P, Popovichev, S, Porcelli, F, Porfiri, M, Portafaix, C, Pospieszczyk, A, Possnert, G, Pozniak, K, Pradhan, S, Pragash, R, Prajapati, V, Prestopino, G, Prior, P, Prokopowicz, R, Puiatti, M, Purahoo, K, Pustovitov, V, Putterich, T, Puttmann Kneupner, D, Quercia, A, Rachlew, E, Rademaker, R, Rafiq, T, Rainford, M, Ramogida, G, Rapp, J, Rasmussen, J, Rathod, K, Ratta, G, Ravera, G, Refy, D, Reichle, R, Reinelt, M, Reiser, D, Reiss, R, Reiter, D, Rendell, D, Reux, C, Rewoldt, G, Ribeiro, T, Riccardo, V, Richards, D, Rigollet, F, Rimini, F, Rios, L, Riva, M, Roberts, J, Robins, R, Robinson, D, Robinson, S, Robson, D, Roche, H, Roedig, M, Rodionov, N, Rohde, V, Rolfe, A, Romanelli, M, Romano, A, Romero, J, Ronchi, E, Rosanvallon, S, Roux, C, Rowe, S, Rubel, M, Rubinacci, G, Ruiz, M, Ruset, C, Russell, M, Ruth, A, Ryc, L, Rydzy, A, Rzadkiewicz, J, Saarelma, S, Sabathier, F, Sabot, R, Sadakov, S, Sadvakassova, A, Sadykov, A, Sagar, P, Saibene, G, Saille, A, Saint Laurent, F, Salewski, M, Salmi, A, Salzedas, F, Samm, U, Sanchez, P, Sanders, S, Sandford, G, Sandland, K, Sandquist, P, Sands, D, Santala, M, Santra, P, Sartori, F, Sartori, R, Sauter, O, Savelyev, A, Savtchkov, A, Scales, S, Scarabosio, A, Schaefer, N, Schmidt, V, Schmidt, A, Schmitz, O, Schmuck, S, Schneider, M, Scholz, M, Schoepf, K, Schweer, B, Schweinzer, J, Seki, M, Semeraro, L, Semerok, A, Sergienko, G, Sertoli, M, Shannon, M, Sharapov, S, Shaw, S, Shevelev, A, Sieglin, B, Sievering, R, Silva, C, Simmons, P, Simonetto, A, Simpson, D, Sipilae, S, Sips, A, Siren, P, Sirinelli, A, Sjoestrand, H, Skopintsev, D, Slabkowska, K, Smith, P, Snipes, J, Snoj, L, Snyder, S, Soare, S, Solano, E, Soleto, A, Solomon, W, Soltane, C, Sonato, P, Sopplesa, A, Sorrentino, A, Sousa, J, Sowden, C, Sozzi, C, Spaeh, P, Spelzini, T, Spence, J, Spineanu, F, Spuig, P, Stagg, R, Stamp, M, Stancalie, V, Stangeby, P, Stankiewicz, R, Stan Sion, C, Starkey, D, Stead, M, Stejner, M, Stephen, A, Stephen, M, Stevens, A, Stokes, R, Stork, D, Stoyanov, D, Strachan, J, Strand, P, Stransky, M, Strauss, D, Strintzi, D, Studholme, W, Na, Y, Subba, F, Summers, H, Sun, Y, Surdu Bob, C, Surrey, E, Sutton, D, Svensson, J, Swain, D, Syme, B, Symonds, I, Szabolics, T, Szepesi, T, Szydlowski, A, Tabares, F, Takalo, V, Takenaga, H, Tala, T, Talbot, A, Taliercio, C, Tame, C, Tardocchi, M, Taroni, L, Telesca, G, Terra, A, Terrington, A, Testa, D, Theis, J, Thomas, J, Thomas, P, Thompson, V, Thomser, C, Thyagaraja, A, Tigwell, P, Tiseanu, I, Tivey, R, Todd, J, Todd, T, Tokar, M, Tosti, S, Trabuc, P, Travere, J, Trimble, P, Trkov, A, Trukhina, E, Tsalas, M, Tsitrone, E, Jun, D, Tudisco, O, Tugarinov, S, Turner, M, Tyrrell, S, Umeda, N, Unterberg, B, Urano, H, Urquhart, A, Uytdenhouwen, I, Vaccaro, A, Vadgama, A, Vagliasindi, G, Valcarcel, D, Valisa, M, Vallory, J, Valovic, M, Van, E, Van, M, Van, R, Varandas, C, Vartanian, S, Vasava, K, Vdovin, V, Vega, J, Verdoolaege, G, Verger, J, Vermare, L, Verona, C, Versloot, T, Vervier, M, Vicente, J, Villari, S, Villedieu, E, Villone, F, Vince, J, Vine, G, Vinyar, I, Viola, B, Vitale, E, Vitelli, R, Vitins, A, Vlad, M, Voitsekhovitch, I, Vrancken, M, Vulliez, K, Waldon, C, Walker, M, Walsh, M, Waterhouse, J, Watkins, M, Watson, M, Wauters, T, Way, M, Webb, C, Weiland, J, Weisen, H, Weiszflog, M, Wenninger, R, West, A, Weulersse, J, Wheatley, M, Whiteford, A, Whitehead, A, Whitehurst, A, Widdowson, A, Wiegmann, C, Wiesen, S, Wilson, A, Wilson, D, Wilson, H, Wischmeier, M, Witts, D, Wolf, R, Wolowski, J, Woscov, P, Wright, J, Xu, G, Yavorskij, V, Yerashok, V, Yoo, M, Yorkshades, J, Young, C, Young, D, Young, I, Yuhong, X, Yun, S, Zabeo, L, Zabolotny, W, Zaccarian, L, Zagorski, R, Zaitsev, F, Zakharov, L, Zanino, R, Zaroschi, V, Zastrow, K, Zatz, I, Zefran, B, Zeidner, W, Zerbini, M, Zhang, T, Zhitlukin, A, Zhu, Y, Zimmermann, O, Zoita, V, Zoletnik, S, Zwingman, W, Romanelli, F., Abel, I., Afanesyev, V., Aftanas, M., Agarici, G., Aggarwal, K., Aho Mantila, L., Ahonen, E., Aints, M., Airila, M., Akers, R., Alarcon, T.h., Albanese, R., Alexeev, A., Alfier, A., Allan, P., Almaviva, S., Alonso, A., Alper, B., Altmann, H., Alves, D., Ambrosino, G., Amosov, V., Andersson, F., Sunden, E., Andreev, V., Andrew, Y., Angelone, M., Anghel, M., Anghel, A., Angioni, C., Apruzzese, G., Arcis, N., Arena, P., Argouarch, A., Ariola, M., Armitano, A., Arnoux, G., Arshad, S., Artaserse, G., Artaud, J., Ash, A., Asp, E., Asunta, O., Atanasiu, C., Atkins, G., Avotina, L., Axton, M., Ayres, C., Baciero, A., Bailescu, V., Baiocchi, B., Baker, R., Balboa, I., Balden, M., Balorin, C., Balshaw, N., Banks, J., Baranov, Y., Barbier, D., Barlow, I., Barnard, M., Barnsley, R., Barrena, L., Barrera, L., Baruzzo, M., Basiuk, V., Bateman, G., Batistoni, P., Baumgarten, N., Baylor, L., Bazylev, B., Beaumont, P., Beausang, K., Becoulet, M., Bekris, N., Beldishevski, M., Bell, A., Belli, F., Bellinger, M., Bellizio, T., Belo, P., Belonohy, E., Bennett, P., Benterman, N., Berger By, G., Bergsaker, H., Berk, H., Bernardo, J., Bernert, M., Bertrand, B., Beurskens, M., Bieg, B., Bienkowska, B., Biewer, T., Bigi, M., Bilkova, P., Bin, W., Bird, J., Bizarro, J., Bjorkas, C., Blackman, T., Blanchard, P., Blanco, E., Blum, J., Bobkov, V., Boboc, A., Boilson, D., Bolshakova, I., Bolzonella, T., Boncagni, L., Bonheure, G., Bonnin, X., Borba, D., Borthwick, A., Botrugno, A., Boulbe, C., Bouquey, F., Bourdelle, C., Von Bovert, Bowden, M., Boyce, T., Boyer, H., Bozhenkov, A., Brade, R., Bradshaw, J., Braet, J., Braic, V., Braithwaite, G., Brault, C., Breizman, B., Bremond, S., Brennan, P., Brett, A., Breue, J., Brezinsek, S., Bright, M., Briscoe, F., Brix, M., Brombin, M., Brown, B., Brown, D., Brzozowski, J., Bucalossi, J., Buckley, M., Budd, T., Budny, R., Bunting, P., Buratti, P., Burcea, G., Burckhart, A., Butcher, P., Buttery, R., Cahyna, P., Calabro, G., Callaghan, C., Caminade, J., Camp, P., Campling, D., Caniello, R., Canik, J., Cannas, B., Capel, A., Carannante, G., Card, P., Cardinali, A., Carlstrom, T., Carman, P., Carralero, D., Carraro, L., Carter, T., Carvalho, B., Carvalho, I., Carvalho, P., Casati, A., Castaldo, C., Caughman, J., Cavazzana, R., Cavinato, M., Cecconello, M., Cecil, E., Cecil, F., Cenedese, A., Centioli, C., Cesario, R., Challis, C., Chandler, M., Chang, C., Chankin, A., Chapman, I., Chektybayev, B., Chernyshova, M., Child, D., Chiru, P., Chitarin, G., Chugonov, I., Ciric, D., Clairet, F., Clarke, R., Clay, R., Clever, M., Coad, J., Coates, P., Cocilovo, V., Coda, S., Coelho, R., Coenen, J., Coffey, I., Colas, L., Cole, M., Collins, S., Combs, S., Compan, J., Conboy, J., Conroy, S., Cook, N., Cook, S., Coombs, D., Cooper, S., Corre, Y., Corrigan, G., Cortes, S., Coster, D., Counsell, G., Courtois, X., Cox, M., Craciunescu, T., Cramp, S., Crisanti, F., Croft, O., Crombe, K., Crowley, B., Cruz, N., Cseh, G., Cupido, L., Curuia, M., Cusack, R., Czarnecka, A., Czarski, T., Dalley, S., Daly, E., Dalziel, A., Daniel, R., Darrow, D., David, O., Davies, N., Davies, W., Davies, J., Day, I., Day, C., De Angelis, de Arcas, de Baar, de la, de Pablos, De Tommasi, de Vries, De Angelis, R., Degli Agostini, Delabie, E., del Castillo Negrete, D., Delpech, L., Denisov, G., Denyer, A., Denyer, R., Devaux, S., Devynck, P., Di Matteo, Di Pace, Dirken, P., Dittmar, T., Dnestrovskiy, A., Dodt, D., Doerner, R., Doldatov, S., Dominiczak, K., Dooley, P., Dorling, S., Douai, D., Down, A., Doyle, P., Drake, J., Dreischuh, T., Drozdov, V., Dumortier, P., Dunai, D., Duran, I., Durodie, F., Dutta, P., Dux, R., Dylst, K., Eaton, R., Edlington, T., Edwards, A., Edwards, D., Edwards, P., Eich, T.h., Ekedahl, A., Elevant, T., Ellingboe, B., Elsmore, C., Emmoth, B., Erdei, G., Ericsson, G., Eriksson, L., Eriksson, A., Esposito, B., Esser, H., Estrada, T., Evangelidis, E., Evans, G., Ewart, G., Ewers, D., Falchetto, G., Falie, D., Fanthome, J., Farthing, J., Fasoli, A., Faugeras, B., Fedorczak, N., Felton, R., Fenzi, C., Fernades, A., Fernandes, H., Ferreira, J., Ferron, J., Fessey, J., Figini, L., Figueiredo, J., Figueiredo, A., Finburg, P., 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Harling, J., Harting, D., Harvey, M., Haupt, T., Hawkes, N., Hawryluk, R., Hay, J., Hayashi, N., Haydon, P., Hayward, I., Hazel, S., Heesterman, P., Heidbrink, W., Heinola, K., Hellesen, C., Hellsten, T., Hemming, O., Hender, T., Henderson, M., Hennion, V., Hidalgo, C., Higashijima, S., Hill, J., Hill, M., Hill, K., Hillairet, J., Hillis, D., Hirai, T., Hitchin, M., Hobirk, J., Hogan, C., Hogben, C., Hogeweij, G., Hollingham, I., Holyaka, R., Homfray, D., Honeyands, G., Hong, S., Hong, J., Horacek, J., Horn, B., Horton, A., Horton, L., Hotchin, S., Hough, M., Houlberg, W., Howell, D., Huber, A., Huddleston, T., Hudson, Z., Hughes, M., Huehnerbein, M., Hume, C., Hunt, A., Hunter, C., Hutchinson, T., Huygen, S., Huysmans, G., Ide, S., Illescas, C., Imbeaux, F., Ivanova, D., Ivanova Stanik, I., Ivings, E., Jachmich, S., Jackson, G., Jacquet, P., Jakubowska, K., James, P., Janky, F., Jaervinen, A., Jednorog, S., Jenkins, I., Jennison, M., Jeskins, C., Kwon, O., Joffrin, E., Johnson, R., Johnson, T., Jolovic, D., Jonauskas, V., Jones, E., Jones, G., Jones, H., Jones, T., Jouvet, M., Jupen, C., Kachtchouk, I., Kaczmarczyk, J., Kallenbach, A., Kaellne, J., Kalupin, D., Kalvin, S., Kamelander, G., Kamendje, R., Kamiya, K., Kappatou, A., Kasparek, W., Kasprowicz, G., Katramados, I., Kaveney, G., Kaye, A., Kear, M., Keeling, D., Kelliher, D., Kempenaars, M., Khilar, P., Khilkevich, E., Kidd, N., Kiisk, M., Kim, K., Kim, H., King, R., Kinna, D., Kiptily, V., Kirnev, G., Kirneva, N., Kirov, K., Kirschner, A., Kisielius, R., Kislov, D., Kiss, G., Kizane, G., Klein, A., Klepper, C., Klimov, N., Klix, A., Knaup, M., Kneuper, K., Kneupner, H., Knight, P., Knipe, S., Kocan, M., Koch, R., Kochl, F., Kocsis, G., Koivuranta, S., Koppitz, T., Korotkov, A., Koskela, T., Koslowski, H., Kotov, V., Kovari, M., Kramer, G., Krasilnikov, A., Krasilnikov, V., Kraus, S., Kreter, A., Krieger, K., Kritz, A., Krivchenkov, Y., Kruezi, U., Krylov, S., Ksiazek, I., Kuhn, S., Kuhnlein, W., Kukushkin, A., Kundu, A., Kurki Suonio, T., Kurowski, A., Kuteev, B., Kuyanov, A., Kyrytsya, V., La Haye, Laan, M., Labate, C., Lachichi, A., Laguardia, L., Lam, N., Lang, P., Large, M., Lasa, A., Lassiwe, I., Last, J., Lawson, K., Laxaback, M., Layne, R., Le Guern, LeBlanc, B., Lee, S., Lee, J., Leggate, H., Lehnen, M., Leigheb, M., Lengar, I., Lennholm, M., Lerche, E., Lescure, C., Li, Y., Li Puma, Liang, Y., Likonen, J., Lin, Y., Lindholm, V., Linke, J., Linstead, S., Lipshultz, B., Litaudon, X., Litvak, A., Liu, Y., Loarer, T., Loarte, A., Lobel, R., Lomas, P., Long, F., Lonnroth, J., Looker, D., Lopez, J., Lotte, P.h., Louche, F., Loughlin, M., Loving, A., Lowry, C., Luce, T., Lucock, R., Lukanitsa, A., Lukin, A., Lungu, A., Lungu, C., Lyssoivan, A., Macheta, P., Mackenzie, A., Macrae, M., Maddaluno, G., Maddison, G., Madsen, J., Magesh, B., Maget, P., Maggi, C., Maier, H., Mailloux, J., Makkonen, T., Makowski, M., Malaquias, A., Manning, C., Mansfield, M., Manso, M., Mantica, P., Marcenko, N., Marchitti, M., Mardenfeld, M., Marechal, J., Marinelli, M., Marinucci, M., Marocco, D., Marren, C., Marsen, S., Martin, D., Martin, G., Martin, Y., Martin Solis, J., Masaki, K., Masiello, A., Maszl, C., Matejcik, S., Matilal, A., Mattei, M., Matthews, G., Mattoo, S., Matveev, D., Maviglia, F., May, C., Mayer, M., Mayoral, M., Mazon, D., Mazzotta, C., Mazzucato, E., McCarthy, P., McClements, K., McCormick, K., McCullen, P., McCune, D., McDonald, D., Mcgregor, R., Mckivitt, J., Meakins, A., Medina, F., Meigs, A., Menard, M., Meneses, L., Menmuir, S., Merrigan, I., Mertens, P.h., Messiaen, A., Meszaros, B., Meyer, H., Miano, G., Michling, R., Miele, M., Miettunen, J., Migliucci, P., Miller, A., Mills, S., Milnes, J., Mindham, T., Miorin, E., Mirizzi, F., Mirones, E., Mironov, M., Mitteau, R., Mlynar, J., Mollard, P., Monakhov, I., Monier Garbet, P., Mooney, R., Moreau, D., Moreau, P.h., Moreira, L., Morgan, A., Morgan, P., Morlock, C., Morris, A., Mort, G., Murakami, M., Murari, A., Mustata, I., Nabais, F., Nakano, T., Nardon, E., Nash, G., Naulin, V., Nave, M., Nazikian, R., Nedzelski, I., Negus, C., Neilson, J., Nemtsev, G., Neto, A., Neu, R., Neubauer, O., Newbert, G., Newman, M., Nicholls, K., Nicolai, A., Nicolas, L., Nieckchen, P., Nielsen, A., Nielsen, S., Nielsen, P., Nielsen, G., Nieto, J., Nightingale, M., Nishijima, D., Noble, C., Nordman, H., Norman, M., Nowak, S., Nunes, I., Oberkofler, M., Odstrcil, M., O'Gorman, T., Ohsako, T., Okabayashi, M., Olariu, S., Oleynikov, A., O'Mullane, M., Ongena, J., Orsitto, F., Oswuigwe, O., Ottaviani, M., Oyama, N., Pacella, D., Paget, K., Pajuste, E., Palazzo, S., Palenic, J., Pamela, J., Pamela, S., Pangione, L., Panin, A., Panja, S., Pankin, A., Pantea, A., Parail, V., Paris, P., Parisot, T.h., Park, M., Parkin, A., Parsloe, A., Parsons, B., Pasqualotto, R., Pastor, P., Paterson, R., Paul, M., Peach, D., Pearce, R., Pearson, B., Pearson, I., Pedrick, L., Pedrosa, M., Pegourie, B., Pereira, R., Pereslavtsev, P., Perevezentsev, A., von Thun, Pericoli Ridolfini, V., Perona, A., Perrot, Y., Peruzzo, S., Peschanyy, S., Petravich, G., Petrizzi, L., Petrov, V., Petrzilka, V., Philipps, V., Piccolo, F., Pietropaolo, A., Pillon, M., Pinches, S., Pinna, T., Pintsuk, G., Piovesan, P., Pironti, A., Pisano, F., Pitts, R., Plaum, B., Plyusnin, V., Polasik, M., Poli, F., Pomaro, N., Pompilian, O., Poncet, L., Pool, P., Popovichev, S., Porcelli, F., Porfiri, M., Portafaix, C., Pospieszczyk, A., Possnert, G., Pozniak, K., Pradhan, S., Pragash, R., Prajapati, V., Prestopino, G., Prior, P., Prokopowicz, R., Puiatti, M., Purahoo, K., Pustovitov, V., Putterich, T.h., Puttmann Kneupner, D., Quercia, A., Rachlew, E., Rademaker, R., Rafiq, T., Rainford, M., Ramogida, G., Rapp, J., Rasmussen, J., Rathod, K., Ratta, G., Ravera, G., Refy, D., Reichle, R., Reinelt, M., Reiser, D., Reiss, R., Reiter, D., Rendell, D., Reux, C., Rewoldt, G., Ribeiro, T., Riccardo, V., Richards, D., Rigollet, F., Rimini, F., Rios, L., Riva, M., Roberts, J., Robins, R., Robinson, D., Robinson, S., Robson, D., Roche, H., Roedig, M., Rodionov, N., Rohde, V., Rolfe, A., Romanelli, M., Romano, A., Romero, J., Ronchi, E., Rosanvallon, S., Roux, C.h., Rowe, S., Rubel, M., Rubinacci, G., Ruiz, M., Ruset, C., Russell, M., Ruth, A., Ryc, L., Rydzy, A., Rzadkiewicz, J., Saarelma, S., Sabathier, F., Sabot, R., Sadakov, S., Sadvakassova, A., Sadykov, A., Sagar, P., Saibene, G., Saille, A., Saint Laurent, F., Salewski, M., Salmi, A., Salzedas, F., Samm, U., Sanchez, P., Sanders, S., Sandford, G., Sandland, K., Sandquist, P., Sands, D., Santala, M., Santra, P., Sartori, F., Sartori, R., Sauter, O., Savelyev, A., Savtchkov, A., Scales, S., Scarabosio, A., Schaefer, N., Schmidt, V., Schmidt, A., Schmitz, O., Schmuck, S., Schneider, M., Scholz, M., Schoepf, K., Schweer, B., Schweinzer, J., Seki, M., Semeraro, L., Semerok, A., Sergienko, G., Sertoli, M., Shannon, M., Sharapov, S., Shaw, S., Shevelev, A., Sieglin, B., Sievering, R., Silva, C., Simmons, P., Simonetto, A., Simpson, D., Sipilae, S., Sips, A., Siren, P., Sirinelli, A., Sjoestrand, H., Skopintsev, D., Slabkowska, K., Smith, P., Snipes, J., Snoj, L., Snyder, S., Soare, S., Solano, E., Soleto, A., Solomon, W., Soltane, C., Sonato, P., Sopplesa, A., Sorrentino, A., Sousa, J., Sowden, C., Sozzi, C., Spaeh, P., Spelzini, T., Spence, J., Spineanu, F., Spuig, P., Stagg, R., Stamp, M., Stancalie, V., Stangeby, P., Stankiewicz, R., Stan Sion, C., Starkey, D., Stead, M., Stejner, M., Stephen, A., Stephen, M., Stevens, A., Stokes, R., Stork, D., Stoyanov, D., Strachan, J., Strand, P., Stransky, M., Strauss, D., Strintzi, D., Studholme, W., Na, Y., Subba, F., Summers, H., Sun, Y., Surdu Bob, C., Surrey, E., Sutton, D., Svensson, J., Swain, D., Syme, B., Symonds, I., Szabolics, T., Szepesi, T., Szydlowski, A., Tabares, F., Takalo, V., Takenaga, H., Tala, T., Talbot, A., Taliercio, C., Tame, C., Taroni, L., Telesca, G., Terra, A., Terrington, A., Testa, D., Theis, J., Thomas, J., Thomas, P., Thompson, V., Thomser, C., Thyagaraja, A., Tigwell, P., Tiseanu, I., Tivey, R., Todd, J., Todd, T., Tokar, M., Tosti, S., Trabuc, P., Travere, J., Trimble, P., Trkov, A., Trukhina, E., Tsalas, M., Tsitrone, E., Jun, D., Tudisco, O., Tugarinov, S., Turner, M., Tyrrell, S., Umeda, N., Unterberg, B., Urano, H., Urquhart, A., Uytdenhouwen, I., Vaccaro, A., Vadgama, A., Vagliasindi, G., Valcarcel, D., Valisa, M., Vallory, J., Valovic, M., Van Eester, van Milligen, van Rooij, Varandas, C., Vartanian, S., Vasava, K., Vdovin, V., Vega, J., Verdoolaege, G., Verger, J., Vermare, L., Verona, C., Versloot, T.h., Vervier, M., Vicente, J., Villari, S., Villedieu, E., Villone, F., Vince, J., Vine, G., Vinyar, I., Viola, B., Vitale, E., Vitelli, R., Vitins, A., Vlad, M., Voitsekhovitch, I., Vrancken, M., Vulliez, K., Waldon, C., Walker, M., Walsh, M., Waterhouse, J., Watkins, M., Watson, M., Wauters, T., Way, M., Webb, C., Weiland, J., Weisen, H., Weiszflog, M., Wenninger, R., West, A., Weulersse, J., Wheatley, M., Whiteford, A., Whitehead, A., Whitehurst, A., Widdowson, A., Wiegmann, C., Wiesen, S., Wilson, A., Wilson, D., Wilson, H., Wischmeier, M., Witts, D., Wolf, R., Wolowski, J., Woscov, P., Wright, J., Xu, G., Yavorskij, V., Yerashok, V., Yoo, M., Yorkshades, J., Young, C., Young, D., Young, I., Yuhong, X., Yun, S., Zabeo, L., Zabolotny, W., Zaccarian, L., Zagorski, R., Zaitsev, F., Zakharov, L., Zanino, R., Zaroschi, V., Zastrow, K., Zatz, I., Zefran, B., Zeidner, W., Zerbini, M., Zhang, T., Zhitlukin, A., Zhu, Y., Zimmermann, O., Zoita, V., Zoletnik, S., Zwingman, W., CROCI, GABRIELE, GORINI, GIUSEPPE, NOCENTE, MASSIMO, and TARDOCCHI, MARCO
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- 2013
36. Analysis and correction of systematic height model errors
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Jacobsen, Karsten, Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., and Li, Z.
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lcsh:Applied optics. Photonics ,010504 meteorology & atmospheric sciences ,Satellites ,Errors ,0211 other engineering and technologies ,Base (geometry) ,02 engineering and technology ,Shuttle Radar Topography Mission ,DSM/DTM ,01 natural sciences ,lcsh:Technology ,Orientation information ,Orientation ,Digital surface models ,Calibration ,Optical space images ,Satellite jitter ,Optical satellite images ,Konferenzschrift ,Dewey Decimal Classification::500 | Naturwissenschaften ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Tracking radar ,Orientation (computer vision) ,lcsh:T ,lcsh:TA1501-1820 ,Systematic error ,Remote sensing ,Geodesy ,Deformation ,Dewey Decimal Classification::500 | Naturwissenschaften::520 | Astronomie, Kartographie ,Tilt (optics) ,Geography ,lcsh:TA1-2040 ,Rock mechanics ,Affine transformations ,ddc:520 ,Satellite ,Affine transformation ,Prism ,ddc:500 ,lcsh:Engineering (General). Civil engineering (General) ,Aluminum ,Crystal orientation - Abstract
The geometry of digital height models (DHM) determined with optical satellite stereo combinations depends upon the image orientation, influenced by the satellite camera, the system calibration and attitude registration. As standard these days the image orientation is available in form of rational polynomial coefficients (RPC). Usually a bias correction of the RPC based on ground control points is required. In most cases the bias correction requires affine transformation, sometimes only shifts, in image or object space. For some satellites and some cases, as caused by small base length, such an image orientation does not lead to the possible accuracy of height models. As reported e.g. by Yong-hua et al. 2015 and Zhang et al. 2015, especially the Chinese stereo satellite ZiYuan-3 (ZY-3) has a limited calibration accuracy and just an attitude recording of 4 Hz which may not be satisfying. Zhang et al. 2015 tried to improve the attitude based on the color sensor bands of ZY-3, but the color images are not always available as also detailed satellite orientation information. There is a tendency of systematic deformation at a Pléiades tri-stereo combination with small base length. The small base length enlarges small systematic errors to object space. But also in some other satellite stereo combinations systematic height model errors have been detected. The largest influence is the not satisfying leveling of height models, but also low frequency height deformations can be seen. A tilt of the DHM by theory can be eliminated by ground control points (GCP), but often the GCP accuracy and distribution is not optimal, not allowing a correct leveling of the height model. In addition a model deformation at GCP locations may lead to not optimal DHM leveling. Supported by reference height models better accuracy has been reached. As reference height model the Shuttle Radar Topography Mission (SRTM) digital surface model (DSM) or the new AW3D30 DSM, based on ALOS PRISM images, are satisfying. They allow the leveling and correction of low frequency height errors and lead to satisfying correction of the DSM based on optical satellite images. The potential of DHM generation, influence of systematic model deformation and possibilities of improvement has been investigated.
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- 2016
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37. Orientation of oblique airborne image sets - Experiences from the ISPRS/Eurosdr benchmark on multi-platform photogrammetry
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Gerke, M., Nex, F., Remondino, F., Jacobsen, Karsten, Kremer, J., Karel, W., Huf, H., Ostrowski, W., Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., and Li, Z.
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Bundle block adjustments ,Image matching ,Overlapping images ,Tie point matching ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Bundle adjustments ,Remote sensing ,Tie points ,Commercial packages ,Dewey Decimal Classification::500 | Naturwissenschaften::520 | Astronomie, Kartographie ,Image processing ,Oblique ,Orientation ,Photogrammetry ,Multicamera systems ,ddc:520 ,Bundle adjustment ,Current limitation ,ddc:500 ,Random errors ,Dewey Decimal Classification::500 | Naturwissenschaften ,Konferenzschrift ,Crystal orientation - Abstract
During the last decade the use of airborne multi camera systems increased significantly. The development in digital camera technology allows mounting several mid- or small-format cameras efficiently onto one platform and thus enables image capture under different angles. Those oblique images turn out to be interesting for a number of applications since lateral parts of elevated objects, like buildings or trees, are visible. However, occlusion or illumination differences might challenge image processing. From an image orientation point of view those multi-camera systems bring the advantage of a better ray intersection geometry compared to nadir-only image blocks. On the other hand, varying scale, occlusion and atmospheric influences which are difficult to model impose problems to the image matching and bundle adjustment tasks. In order to understand current limitations of image orientation approaches and the influence of different parameters such as image overlap or GCP distribution, a commonly available dataset was released. The originally captured data comprises of a state-of-the-art image block with very high overlap, but in the first stage of the so-called ISPRS/EUROSDR benchmark on multi-platform photogrammetry only a reduced set of images was released. In this paper some first results obtained with this dataset are presented. They refer to different aspects like tie point matching across the viewing directions, influence of the oblique images onto the bundle adjustment, the role of image overlap and GCP distribution. As far as the tie point matching is concerned we observed that matching of overlapping images pointing to the same cardinal direction, or between nadir and oblique views in general is quite successful. Due to the quite different perspective between images of different viewing directions the standard tie point matching, for instance based on interest points does not work well. How to address occlusion and ambiguities due to different views onto objects is clearly a non-solved research problem so far. In our experiments we also confirm that the obtainable height accuracy is better when all images are used in bundle block adjustment. This was also shown in other research before and is confirmed here. Not surprisingly, the large overlap of 80/80% provides much better object space accuracy – random errors seem to be about 2-3fold smaller compared to the 60/60% overlap. A comparison of different software approaches shows that newly emerged commercial packages, initially intended to work with small frame image blocks, do perform very well.
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- 2016
38. Subclinical First Trimester Renal Abnormalities Are Associated With Preeclampsia in Normoalbuminuric Women With Type 1 Diabetes
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Kelly, Clare B., primary, Hookham, Michelle B., additional, Yu, Jeremy Y., additional, Jenkins, Alicia J., additional, Nankervis, Alison J., additional, Hanssen, Kristian F., additional, Garg, Satish K., additional, Scardo, James A., additional, Hammad, Samar M., additional, Menard, M. Kathryn, additional, Aston, Christopher E., additional, and Lyons, Timothy J., additional
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- 2017
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39. Textbook of Fetal Abnormalities
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Menard, M Kathryn
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- 2002
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40. Annexin II mediates plasminogen-dependent matrix invasion by human monocytes: enhanced expression by macrophages
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Domenick J. Falcone, K.M. Faisal Khan, Arunkumar B. Deora, Andrew T. Jacovina, Menard M. Gertler, Rebecca Weintraub, Katherine A. Hajjar, and Carrie Brownstein
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Phagocyte ,Plasmin ,Immunology ,In Vitro Techniques ,Biology ,Biochemistry ,Tissue plasminogen activator ,Monocytes ,Cell Movement ,Annexin ,medicine ,Humans ,Macrophage ,Annexin A2 ,Macrophages ,Monocyte ,Cell Differentiation ,Plasminogen ,Cell Biology ,Hematology ,Molecular biology ,Extracellular Matrix ,medicine.anatomical_structure ,Immunoglobulin G ,Thioglycolates ,Tissue Plasminogen Activator ,Annexin A1 ,medicine.drug - Abstract
Monocytes and macrophages participate in a wide variety of host defense mechanisms. Annexin II, a fibrinolytic receptor, binds plasminogen and tissue plasminogen activator (t-PA) independently at the cell surface, thereby enhancing the catalytic efficiency of plasmin production. We demonstrated previously that annexin II on the surface of both cultured monocytoid cells and monocyte-derived macrophages promotes their ability to remodel extracellular matrix. Here, we demonstrate that human peripheral blood monocytes represent the major circulating annexin II–expressing cell. Annexin II supported t-PA–dependent generation of cell surface plasmin and the matrix-penetrating activity of human monocytes. Compared to polymorphonuclear leukocytes, monocytes supported a 12.9-fold greater rate of plasmin generation in the presence of exogenous t-PA, and this activity was largely attributable to annexin II. Likewise, anti–annexin II IgG directed against the t-PA–binding tail domain inhibited plasminogen-dependent, cytokine-directed monocyte migration through extracellular matrix. On differentiation of monocytes to macrophages, there was a 2.4-fold increase in annexin II–specific mRNA, and a 7.9-fold increase in surface annexin II. Thioglycolate-elicited peritoneal macrophages, furthermore, displayed an additional 3.8-fold increase in annexin II surface expression compared with resident cells. Thus, annexin II–mediated assembly of plasminogen and t-PA on monocyte/macrophages contributes to plasmin generation, matrix remodeling, and directed migration.
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- 2004
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41. Overview of JET results
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Romanelli, F, Laxaback, M, Abel, I, Afanesyev, V, Agarici, G, Aggarwal, K, Airila, M, Akers, R, Alarcon, T, Albanese, R, Alexeev, A, Alfier, A, Allan, P, Almaviva, S, Alonso, A, Alonso, M, Alper, B, Altmann, H, Alves, D, Ambrosino, G, Amosov, V, Anda, G, Andersson, F, Andersson Sunden, E, Andreev, V, Andrew, Y, Angelone, M, Anghel, M, Anghel, A, Angioni, C, Apruzzese, G, Arcis, N, Arena, P, Argouarch, A, Ariola, M, Armitano, A, Armstrong, R, Arnoux, G, Arshad, S, Artaserse, G, Artaud, J, Ash, A, Asp, E, Asunta, O, Atanasiu, C, Atkins, G, Axton, M, Ayres, C, Baciero, A, Bailescu, V, Baiocchi, B, Baker, R, Balboa, I, Balorin, C, Balshaw, N, Banks, J, Baranov, Y, Barbier, D, Barlow, I, Barnard, M, Barnsley, R, Barrena, L, Barrera, L, Baruzzo, M, Basiuk, V, Bateman, G, Batistoni, P, Baumgarten, N, Baylor, L, Bazylev, B, Beaumont, P, Beausang, K, Becoulet, M, Bekris, N, Beldishevski, M, Bell, A, Belli, F, Bellinger, M, Bellizio, T, Belo, P, Belonohy, E, Bennett, P, Benterman, N, Berger-By, G, Bergsaker, H, Berk, H, Bernardo, J, Bertrand, B, Beurskens, M, Bieg, B, Bienkowska, B, Biewer, T, Bigi, M, Bilato, R, Bird, J, Bizarro, J, Blackman, T, Blanchard, P, Blanco, E, Blum, J, Bobkov, V, Boboc, A, Boilson, D, Bolshakova, I, Bolzonella, T, Boncagni, L, Bonheure, G, Bonnin, X, Borba, D, Borthwick, A, Botrugno, A, Boulbe, C, Bouquey, F, Bourdelle, C, Bovert, K, Bowden, M, Boyce, T, Boyer, H, Bozhenkov, A, Brade, R, Bradshaw, J, Braet, J, Braic, V, Braithwaite, G, Brault, C, Braune, H, Breizman, B, Bremond, S, Brennan, P, Brett, A, Breue, J, Brezinsek, S, Bright, M, Briscoe, F, Brix, M, Brombin, M, Brown, B, Brown, D, Bruschi, A, Brzozowski, J, Bucalossi, J, Buckley, M, Budd, T, Budny, R, Bunting, P, Buratti, P, Burcea, G, Butcher, P, Buttery, R, Cacao, R, Calabro, G, Callaghan, C, Caminade, J, Camp, P, Campling, D, Canik, J, Cannas, B, Capel, A, Carannante, G, Card, P, Cardinali, A, Carlstrom, T, Carman, P, Carralero, D, Carraro, L, Carter, T, Carvalho, B, Carvalho, P, Casati, A, Castaldo, C, Caughman, J, Cavazzana, R, Cavinato, M, Cecconello, M, Cecil, F, Cenedese, A, Centioli, C, Cesario, R, Challis, C, Chandler, M, Chang, C, Chankin, A, Chapman, I, Child, D, Chiru, P, Chitarin, G, Chugonov, I, Chugunov, I, Ciric, D, Clairet, F, Clarke, R, Clay, R, Clever, M, Coad, J, Coates, P, Coccorese, V, Cocilovo, V, Coda, S, Coelho, R, Coenen, J, Coffey, I, Colas, L, Cole, M, Collins, S, Combs, S, Compan, J, Conboy, J, Conroy, S, Cook, N, Cook, S, Coombs, D, Cooper, S, Corre, Y, Corrigan, G, Cortes, S, Coster, D, Counsell, G, Courtois, X, Cox, M, Craciunescu, T, Cramp, S, Crisanti, F, Croft, O, Crombe, K, Crowley, B, Cruz, N, Cupido, L, Curuia, M, Cusack, R, Czarnecka, A, Dalley, S, Daly, E, Dalziel, A, Darrow, D, David, O, Davies, N, Davis, J, Day, I, Day, C, De Angelis, R, de Arcas, G, de Baar, M, de la Cal, E, de la Luna, E, de Pablos, J, De Temmerman, G, De Tommasi, G, de Vries, P, De-Angelis, R, Degli Agostini, F, Delabie, E, del-Castillo-Negrete, D, 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E., Granucci G., Graves J., Green N. R., Greuner H., Grigore E., Griph F. S., Grisolia C., Gros G., Grossetti G., Groth M., Grunhagen S., Gryaznevich M. P., Guirlet R., Gulejova B., Gunn J., Gupta A., Guzdar P., Hacek P., Hackett L. J., Hacquin S., Haist B., Hakola A., Hall S. J., Hallworth Cook S. P., Hamilton D. T., Han H., Handley R. C., Harding S., Harling J. D. W., Harting D., Harvey M. J., Haupt T. D. V., Havlickova E., Hawkes N. C., Hawryluk R., Hay J. H., Hayashi N., Haydon P. W., Hayward I. R., Hazel S., Heesterman P. J. L., Heidbrink W., Heikkinen J., Hellesen C., Hellsten T., Hemming O. N., Hender T. C., Henderson M., Hennig C., Hennion V., Hidalgo C., Higashijima S., Hill J. W., Hill M., Hill K., Hillairet J., Hillis D., Hirai T., Hitchin M., Hobirk J., Hogan C., Hogben C. H. A., Hogeweij G. M. D., Hollingham I. C., Holyaka R., Homfray D. A., Honeyands G., Hong S. H., Hopf C., Horn B. A., Horton A. R., Horton L. D., Hotchin S. P., Hough M. R., Houlberg W., Howell D. F., Hron M., Huber A., Huddleston T. M., Hudson Z., Hughes M., Huhnerbein M., Hume C. C., Hunt A. J., Hunter C. L., Hutchinson T. S., Huygen S., Huysmans G., Hynonen V., Ide S., Igreja R., Illescas C., Imbeaux F., Ivanova D., Ivings E., Jachmich S., Jackson G., Jacquet P., Jakubowska K., Jakubowski M., James P. V., Jaspers R. J. E., Jednorog S., Jenkins I., Jennison M. A. C., Jeskins C., Jin Kwon O., Joffrin E., Johnson M. F., Johnson R., Johnson T., Jolovic D., Jonauskas V., Jones E. M., Jones G., Jones H. D., Jones T. T. C., Jouvet M., Jupen C., Kachtchouk I., Kaczmarczyk J., Kallenbach A., Kallne J., Kalupin D., Kalvin S., Kamelander G., Kamendje R., Kappatou A., Karttunen S., Kasparek W., Katramados I., Kaufmann M., Kaveney G., Kaye A. S., Kear M. J., Keeling D. L., Kelliher D., Kempenaars M., Khilar P., Kidd N. G., Kiisk M., Kim K. M., King R. F., Kinna D. J., Kiptily V., Kirnev G., Kirneva N., Kirov K., Kirschner A., Kisielius R., Kislov D., Kiss G., Kiviniemi T., Kizane G., Klein A., Klix A., Knaup M., Kneuper K., Kneupner H., Knight P. J., Knipe S. J., Kocan M., Kochl F., Kocsis G., Konz C., Koppitz T., Korotkov A., Koslowski H. R., Kotov V., Kovari M. D., Kovarik K., Kramer G., Krasilnikov A., Krasilnikov V., Kraus S., Kreter A., Krieger K., Kritz A., Krivchenkov Y., Kruezi U., Krychowiak M., Krylov S., Ksiazek I., Kubic M., Kuhn S., Kuhnlein W., Kurki-Suonio T., Kurowski A., Kuteev B., Kuyanov A., La Haye R., Laan M., Labate C., Lachichi A., Lam N., Lang P., Large M. T., Lassiwe I., Last J. R., Lawson K. D., Layne R. A., Lazzaro E., Le Guern F., Le Blanc B., Leggate H. J., Lehnen M., Leigheb M., Lengar I., Lennholm M., Lerche E., Lescure C. N., Li Y., Li Puma A., Liang Y., Likonen J., Lin Y., Linke J., Linstead S. A., Lipshultz B., Litaudon X., Litvak A. G., Liu Y., Loarer T., Loarte A., Lobel R. C., Lomas P. J., Long F. D., Lonnroth J., Looker D. J., Lopez J., Lotte P., Loughlin M. J., Loving A. B., Lowry C., Luce T., Lucock R. M. A., Lukanitsa A., Lungu A. M., Lungu C. P., Lyssoivan A., Macheta P., Mackenzie A. S., Macrae M., Maddaluno G., Maddison G. P., Madsen J., Maget P., Maggi C., Maier H., Mailloux J., Makowski M., Manning C. J., Mansfield M., Manso M. E., Mantica P., Mantsinen M., Maraschek M., Marchetto C., Marchitti M. A., Mardenfeld M., Marechal J. L., Marinelli M., Marinoni A., Marinucci M., Marki J., Marocco D., Marren C. A., Martin D., Martin D. L., Martin G., Martin Y., Martin-Solis J. R., Masaki K., Masiello A., Maslov M., Maszl C., Matilal A., Mattei M., Matthews G. F., Maviglia F., May C. R., Mayer M., Mayoral M. L., Mazon D., Mazzotta C., Mazzucato E., McCarthy P., McClements K. G., McCormick K., McCullen P. A., McCune D., McDonald D. C., Mcgregor R., Mckivitt J. P., Meakins A., Medina F., Meigs A. G., Menard M., Meneses L., Menmuir S., Merrigan I. 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D., Lonnroth J., Looker D. J., Lopez J., Lotte P., Loughlin M. J., Loving A. B., Lowry C., Luce T., Lucock R. M. A., Lukanitsa A., Lungu A. M., Lungu C. P., Lyssoivan A., Macheta P., Mackenzie A. S., Macrae M., Maddaluno G., Maddison G. P., Madsen J., Maget P., Maggi C., Maier H., Mailloux J., Makowski M., Manning C. J., Mansfield M., Manso M. E., Mantica P., Mantsinen M., Maraschek M., Marchetto C., Marchitti M. A., Mardenfeld M., Marechal J. L., Marinelli M., Marinoni A., Marinucci M., Marki J., Marocco D., Marren C. A., Martin D., Martin D. L., Martin G., Martin Y., Martin-Solis J. R., Masaki K., Masiello A., Maslov M., Maszl C., Matilal A., Mattei M., Matthews G. F., Maviglia F., May C. R., Mayer M., Mayoral M. L., Mazon D., Mazzotta C., Mazzucato E., McCarthy P., McClements K. G., McCormick K., McCullen P. A., McCune D., McDonald D. C., Mcgregor R., Mckivitt J. P., Meakins A., Medina F., Meigs A. G., Menard M., Meneses L., Menmuir S., Merrigan I. R., Mertens P., Messiaen A., Meyer H., Miano G., Miele M., Migliucci P., Miller A. G., Mills S. F., Milnes J. J., Min Kim K., Mindham T., Mirizzi F., Mirones E., Mironov M., Mitteau R., Mlynar J., Mollard P., Monakhov I., Monier-Garbet P., Mooney R., Moradi S., Moreau D., Moreau P., Moreira L., Morgan A., Morgan P. D., Morlock C., Moro A., Morris A. W., Mort G. L., Mrozek C., Mueck A., Muller H. W., Murakami M., Murari A., Mustata I., Nabais F., Nardon E., Nash G., Naulin V., Nave M. F. F., Nazikian R., Nedzelski I., Negus C. R., Neilson J. D., Neto A., Neu R., Neubauer O., Newbert G. J., Newman M., Nicholls K. J., Nicolai A., Nicolas L., Nieckchen P., Nielsen P., Nielsen A. H., Nielsen S. K., Nielson G., Nieto J., Nightingale M. P. S., Noble C., Nocente M., Nora M., Nordman H., Norman M., Noterdaeme J-M., Nowak S., Nunes I., Ognissanto F., O'Gorman T., Olariu S., Oleynikov A., O'Mullane M., Ongena J., Orsitto F., Oswuigwe O. I., Ottaviani M., Oyama N., Pacella D., Paget K., Palazzo S., Pamela J., Pamela S., Panek R., Pangione L., Panin A., Panis T., Pankin A., Pantea A., Parail V., Parisot T., Parkin A., Parsloe A., Parsons B. T., Pasqualotto R., Pastor P., Paterson R., Paul M. K., Peach D., Pearce R. J. H., Pearson B. J., Pearson I. J., Pedrick L. C., Pedrosa M. A., Pegourie B., Pereira R., Perelli Cippo E., Pereverzev G., Perevezentsev A., von Thun C. P., Pericoli-Ridolfini V., Perona A., Perrot Y., Peruzzo S., Peschanyy S., Petravich G., Petrizzi L., Petrov V., Petrzilka V., Philipps V., Piazza G., Piccolo F., Pietropaolo A., Pillon M., Pinches S. D., Pinna T., Pintsuk G., Piovesan P., Pironti A., Pisano F., Pitts R., Plaum B., Plyusnin V., Polasik M., Poli F. M., Pomaro N., Pompilian O., Poncet L., Pool P. J., Popovichev S., Porcelli F., Porfiri M. T., Portafaix C., Pospieszczyk A., Possnert G., Prestopino G., Prior P., Prokopowicz R., Proverbio I., Pugno R., Puiatti M. E., Purahoo K., Pustovitov V., Putterich T., Puttmann-Kneupner D., Quercia A., Rachlew E., Rademaker R., Rafiq T., Rainford M. S. J., Ramogida G., Rantamaki K., Rapp J., Rasmussen J. J., Ratta G., Ravera G., Reich M., Reichle R., Reiser D., Reiss R., Reiter D., Rendell D., Reux C., Rewoldt G., Ribeiro T. T., Riccardo V., Richards D., Rigollet F., Rimini F. G., Rios L., Riva M., Roberts J. E. C., Robins R. J., Robinson D. S., Robinson S. A., Robson D. W., Roche H., Rodig M., Rodionov N., Rohde V., Rolfe A., Romanelli M., Romano A., Romero J., Ronchi E., Rosanvallon S., Roux C., Rowe S., Rubel M., Rubinacci G., Ruchko L., Ruiz M., Ruset C., Russell M., Ruth A., Ryc L., Rydzy A., Ryter F., Rzadkiewicz J., Saarelma S., Sabathier F., Sabot R., Sadakov S., Sagar P., Saibene G., Saille A., Saint-Laurent F., Salmi A., Salomaa R., Salzedas F., Samm U., Sanchez P., Sanders S., Sanders S. G., Sandford G., Sandland K., Sandquist P., Sands D. E. G., Santala M. I. K., Sartori F., Sartori R., Sauter O., Savelyev A., Savtchkov A., Scales S. C., Scarabosio A., Schaefer N., Schlatter C., Schmidt V., Schmidt A., Schmitz O., Schmuck S., Schneider M., Scholz M., Schopf K., Schweer B., Schweinzer J., Scott B., Seki M., Semeraro L., Semerok A., Sergienko G., Serra F., Sertoli M., Shannon M. M. J., Sharapov S. E., Shaw S. R., Shevelev A., Sievering R., Silva C. A., Simmons P. A., Simonetto A., Simpson D., Sipila S., Sips A. C. C., Sirinelli A., Sjostrand H., Skopintsev D., Slabkowska K., Smith P. G., Snipes J., Snoj L., Snyder S., Soare S., Solano E. R., Soldatov S., Soleto A., Solomon W., Soltane C., Sonato P., Sopplesa A., Sorrentino A., Sousa J., Sowden C. B. C., Sozzi C., Spah P., Spelzini T., Spence J., Spineanu F., Spuig P., Stabler A., Stagg R. D., Stamp M. F., Stancalie V., Stangeby P., Stan-Sion C., Starkey D. E., Stead M. J., Stephen A. V., Stevens A. L., Stober J., Stokes R. B., Stork D., Stoyanov D., Strachan J., Strand P., Stransky M., Strauss D., Strintzi D., Studholme W., Su Na Y., Subba F., Summers H. P., Sun Y., Surdu-Bob C., Surrey E., Sutton D. J., Svensson J., Swain D., Syme B. D., Symonds I. D., Szepesi T., Szydlowski A., Tabares F., Takalo V., Takenaga H., Tala T., Talbot A. R., Taliercio C., Tame C., Tardini G., Tardocchi M., Taroni L., Telesca G., Terra A., Terrington A. O., Testa D., Theis J. M., Thomas J. D., Thomas P. D., Thomas P. R., Thompson V. K., Thomsen H., Thomser C., Thyagaraja A., Tigwell P. A., Tiseanu I., Tivey R., Todd J. M., Todd T. N., Tokar M. Z., Tosti S., Trabuc P., Travere J. M., Treutterer W., Trimble P., Trkov A., Trukhina E., Tsalas M., Tsige-Tamirat H., Tsitrone E., Tskhakaya jun D., Tudisco O., Tugarinov S., Turner M. M., Turri G., Tyrrell S. G. J., Umeda N., Unterberg B., Urano H., Urquhart A. J., Uytdenhouwen I., Vaccaro A., Vadgama A. P., Vagliasindi G., Valcarcel D., Valisa M., Vallory J., Valovic M., Van Eester D., van Milligen B., van Rooij G. J., Varandas C. A. F., Vartanian S., Vdovin V., Vega J., Verdoolaege G., Verger J. M., Vermare L., Verona C., Versloot T., Vervier M., Vicente J., Villari S., Villedieu E., Villone F., Vince J. E., Vine G. J., Viola B., Vitale E., Vitelli R., Vlad M., Voitsekhovitch I., Vrancken M., Vulliez K., Waldon C. W. F., Walker M., Walsh M. J., Waterhouse J., Watkins M. L., Watson M. J., Wauters T., Way M. W., Webb C. R., Weiland J., Weisen H., Weiszflog M., Wenninger R., West A. T., Weulersse J. M., Weyssow B., Wheatley M. R., Whiteford A. D., Whitehead A. M., Whitehurst A. G., Widdowson A. M., Wieggers R. C., Wiegmann C., Wiesen S., Wilson A., Wilson D., Wilson D. J., Wilson H. R., Wischmeier M., Witts D. M., Wolf R. C., Wolowski J., Woscov P., Wright G. M., Wright J., Xu G. S., Yavorskij V., Yerashok V., Yorkshades J., Young C., Young D., Young I. D., Yuhong X., Zabeo L., Zabolotsky A., Zaccarian L., Zagorski R., Zaitsev F. S., Zajac S., Zakharov L., Zanino R., Zaroschi V., Zastrow K. D., Zatz I., Zefran B., Zeidner W., Zerbini M., Zhang T., Zhu Y., Zilli E., Zimmermann O., Zoita V., Zoletnik S., and Zwingman W.
- Published
- 2003
42. Analysing post-seismic deformation of izmit earthquake with insar, gnss and coulomb stress modelling
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Halounova, L, Safar, V, Toth, CK, Karas, J, Huadong, G, Haala, N, Habib, A, Reinartz, P, Tang, X, Li, J, Armenakis, C, Grenzdorffer, G, LeRoux, P, Stylianidis, S, Blasi, R, Menard, M, Dufourmount, H, Li, Z, Barut, RA, Trinder, J ; https://orcid.org/0000-0003-0165-5685, Rizos, C ; https://orcid.org/0000-0002-7276-1488, Halounova, L, Safar, V, Toth, CK, Karas, J, Huadong, G, Haala, N, Habib, A, Reinartz, P, Tang, X, Li, J, Armenakis, C, Grenzdorffer, G, LeRoux, P, Stylianidis, S, Blasi, R, Menard, M, Dufourmount, H, Li, Z, Barut, RA, Trinder, J ; https://orcid.org/0000-0003-0165-5685, and Rizos, C ; https://orcid.org/0000-0002-7276-1488
- Abstract
On August 17th 1999, a Mw 7.4 earthquake struck the city of Izmit in the north-west of Turkey. This event was one of the most devastating earthquakes of the twentieth century. The epicentre of the Izmit earthquake was on the North Anatolian Fault (NAF) which is one of the most active right-lateral strike-slip faults on earth. However, this earthquake offers an opportunity to study how strain is accommodated in an inter-segment region of a large strike slip fault. In order to determine the Izmit earthquake post-seismic effects, the authors modelled Coulomb stress changes of the aftershocks, as well as using the deformation measurement techniques of Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS). The authors have shown that InSAR and GNSS observations over a time period of three months after the earthquake combined with Coulomb Stress Change Modelling can explain the fault zone expansion, as well as the deformation of the northern region of the NAF. It was also found that there is a strong agreement between the InSAR and GNSS results for the post-seismic phases of investigation, with differences less than 2mm, and the standard deviation of the differences is less than 1mm.
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- 2016
43. 3D feature point extraction from LiDAR data using a neural network
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Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Feng, Yu, Schlichting, Alexander, Brenner, Claus, Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Feng, Yu, Schlichting, Alexander, and Brenner, Claus
- Abstract
Accurate positioning of vehicles plays an important role in autonomous driving. In our previous research on landmark-based positioning, poles were extracted both from reference data and online sensor data, which were then matched to improve the positioning accuracy of the vehicles. However, there are environments which contain only a limited number of poles. 3D feature points are one of the proper alternatives to be used as landmarks. They can be assumed to be present in the environment, independent of certain object classes. To match the LiDAR data online to another LiDAR derived reference dataset, the extraction of 3D feature points is an essential step. In this paper, we address the problem of 3D feature point extraction from LiDAR datasets. Instead of hand-crafting a 3D feature point extractor, we propose to train it using a neural network. In this approach, a set of candidates for the 3D feature points is firstly detected by the Shi-Tomasi corner detector on the range images of the LiDAR point cloud. Using a back propagation algorithm for the training, the artificial neural network is capable of predicting feature points from these corner candidates. The training considers not only the shape of each corner candidate on 2D range images, but also their 3D features such as the curvature value and surface normal value in z axis, which are calculated directly based on the LiDAR point cloud. Subsequently the extracted feature points on the 2D range images are retrieved in the 3D scene. The 3D feature points extracted by this approach are generally distinctive in the 3D space. Our test shows that the proposed method is capable of providing a sufficient number of repeatable 3D feature points for the matching task. The feature points extracted by this approach have great potential to be used as landmarks for a better localization of vehicles.
- Published
- 2016
44. Integration of a generalised building model into the pose estimation of UAS images
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Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Unger, Jakob, Rottensteiner, Franz, Heipke, Christian, Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Unger, Jakob, Rottensteiner, Franz, and Heipke, Christian
- Abstract
A hybrid bundle adjustment is presented that allows for the integration of a generalised building model into the pose estimation of image sequences. These images are captured by an Unmanned Aerial System (UAS) equipped with a camera flying in between the buildings. The relation between the building model and the images is described by distances between the object coordinates of the tie points and building model planes. Relations are found by a simple 3D distance criterion and are modelled as fictitious observations in a Gauss-Markov adjustment. The coordinates of model vertices are part of the adjustment as directly observed unknowns which allows for changes in the model. Results of first experiments using a synthetic and a real image sequence demonstrate improvements of the image orientation in comparison to an adjustment without the building model, but also reveal limitations of the current state of the method.
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- 2016
45. Accuracy assessment of mobile mapping point clouds using the existing environment as terrestrial reference
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Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Hofmann, Sabine, Brenner, Claus, Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Hofmann, Sabine, and Brenner, Claus
- Abstract
Mobile mapping data is widely used in various applications, what makes it especially important for data users to get a statistically verified quality statement on the geometric accuracy of the acquired point clouds or its processed products. The accuracy of point clouds can be divided into an absolute and a relative quality, where the absolute quality describes the position of the point cloud in a world coordinate system such as WGS84 or UTM, whereas the relative accuracy describes the accuracy within the point cloud itself. Furthermore, the quality of processed products such as segmented features depends on the global accuracy of the point cloud but mainly on the quality of the processing steps. Several data sources with different characteristics and quality can be thought of as potential reference data, such as cadastral maps, orthophoto, artificial control objects or terrestrial surveys using a total station. In this work a test field in a selected residential area was acquired as reference data in a terrestrial survey using a total station. In order to reach high accuracy the stationing of the total station was based on a newly made geodetic network with a local accuracy of less than 3 mm. The global position of the network was determined using a long time GNSS survey reaching an accuracy of 8 mm. Based on this geodetic network a 3D test field with facades and street profiles was measured with a total station, each point with a two-dimensional position and altitude. In addition, the surface of poles of street lights, traffic signs and trees was acquired using the scanning mode of the total station. Comparing this reference data to the acquired mobile mapping point clouds of several measurement campaigns a detailed quality statement on the accuracy of the point cloud data is made. Additionally, the advantages and disadvantages of the described reference data source concerning availability, cost, accuracy and applicability are discussed.
- Published
- 2016
46. Analysis and correction of systematic height model errors
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Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Jacobsen, Karsten, Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., and Jacobsen, Karsten
- Abstract
The geometry of digital height models (DHM) determined with optical satellite stereo combinations depends upon the image orientation, influenced by the satellite camera, the system calibration and attitude registration. As standard these days the image orientation is available in form of rational polynomial coefficients (RPC). Usually a bias correction of the RPC based on ground control points is required. In most cases the bias correction requires affine transformation, sometimes only shifts, in image or object space. For some satellites and some cases, as caused by small base length, such an image orientation does not lead to the possible accuracy of height models. As reported e.g. by Yong-hua et al. 2015 and Zhang et al. 2015, especially the Chinese stereo satellite ZiYuan-3 (ZY-3) has a limited calibration accuracy and just an attitude recording of 4 Hz which may not be satisfying. Zhang et al. 2015 tried to improve the attitude based on the color sensor bands of ZY-3, but the color images are not always available as also detailed satellite orientation information. There is a tendency of systematic deformation at a Pléiades tri-stereo combination with small base length. The small base length enlarges small systematic errors to object space. But also in some other satellite stereo combinations systematic height model errors have been detected. The largest influence is the not satisfying leveling of height models, but also low frequency height deformations can be seen. A tilt of the DHM by theory can be eliminated by ground control points (GCP), but often the GCP accuracy and distribution is not optimal, not allowing a correct leveling of the height model. In addition a model deformation at GCP locations may lead to not optimal DHM leveling. Supported by reference height models better accuracy has been reached. As reference height model the Shuttle Radar Topography Mission (SRTM) digital surface model (DSM) or the new AW3D30 DSM, based on ALOS PRISM images, are sa
- Published
- 2016
47. Orientation of oblique airborne image sets - Experiences from the ISPRS/Eurosdr benchmark on multi-platform photogrammetry
- Author
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Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Gerke, M., Nex, F., Remondino, F., Jacobsen, Karsten, Kremer, J., Karel, W., Huf, H., Ostrowski, W., Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Gerke, M., Nex, F., Remondino, F., Jacobsen, Karsten, Kremer, J., Karel, W., Huf, H., and Ostrowski, W.
- Abstract
During the last decade the use of airborne multi camera systems increased significantly. The development in digital camera technology allows mounting several mid- or small-format cameras efficiently onto one platform and thus enables image capture under different angles. Those oblique images turn out to be interesting for a number of applications since lateral parts of elevated objects, like buildings or trees, are visible. However, occlusion or illumination differences might challenge image processing. From an image orientation point of view those multi-camera systems bring the advantage of a better ray intersection geometry compared to nadir-only image blocks. On the other hand, varying scale, occlusion and atmospheric influences which are difficult to model impose problems to the image matching and bundle adjustment tasks. In order to understand current limitations of image orientation approaches and the influence of different parameters such as image overlap or GCP distribution, a commonly available dataset was released. The originally captured data comprises of a state-of-the-art image block with very high overlap, but in the first stage of the so-called ISPRS/EUROSDR benchmark on multi-platform photogrammetry only a reduced set of images was released. In this paper some first results obtained with this dataset are presented. They refer to different aspects like tie point matching across the viewing directions, influence of the oblique images onto the bundle adjustment, the role of image overlap and GCP distribution. As far as the tie point matching is concerned we observed that matching of overlapping images pointing to the same cardinal direction, or between nadir and oblique views in general is quite successful. Due to the quite different perspective between images of different viewing directions the standard tie point matching, for instance based on interest points does not work well. How to address occlusion and ambiguities due to different views onto obje
- Published
- 2016
48. Pléiades project: Assessment of georeferencing accuracy, image quality, pansharpening performence and DSM/DTM quality
- Author
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Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Topan, H., Cam, A., Özendi, M., Oruç, M., Jacobsen, Karsten, Taşkanat, T., Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Topan, H., Cam, A., Özendi, M., Oruç, M., Jacobsen, Karsten, and Taşkanat, T.
- Abstract
Pléiades 1A and 1B are twin optical satellites of Optical and Radar Federated Earth Observation (ORFEO) program jointly running by France and Italy. They are the first satellites of Europe with sub-meter resolution. Airbus DS (formerly Astrium Geo) runs a MyGIC (formerly Pléiades Users Group) program to validate Pléiades images worldwide for various application purposes. The authors conduct three projects, one is within this program, the second is supported by BEU Scientific Research Project Program, and the third is supported by TÜBİTAK. Assessment of georeferencing accuracy, image quality, pansharpening performance and Digital Surface Model/Digital Terrain Model (DSM/DTM) quality subjects are investigated in these projects. For these purposes, triplet panchromatic (50 cm Ground Sampling Distance (GSD)) and VNIR (2 m GSD) Pléiades 1A images were investigated over Zonguldak test site (Turkey) which is urbanised, mountainous and covered by dense forest. The georeferencing accuracy was estimated with a standard deviation in X and Y (SX, SY) in the range of 0.45m by bias corrected Rational Polynomial Coefficient (RPC) orientation, using ~170 Ground Control Points (GCPs). 3D standard deviation of ±0.44m in X, ±0.51m in Y, and ±1.82m in Z directions have been reached in spite of the very narrow angle of convergence by bias corrected RPC orientation. The image quality was also investigated with respect to effective resolution, Signal to Noise Ratio (SNR) and blur coefficient. The effective resolution was estimated with factor slightly below 1.0, meaning that the image quality corresponds to the nominal resolution of 50cm. The blur coefficients were achieved between 0.39-0.46 for triplet panchromatic images, indicating a satisfying image quality. SNR is in the range of other comparable space borne images which may be caused by de-noising of Pléiades images. The pansharpened images were generated by various methods, and are validated by most common statistical metrics and a
- Published
- 2016
49. Vehicle localization by lidar point correlation improved by change detection
- Author
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Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Schlichting, Alexander, Brenner, Claus, Halounova, L., Šafář, V., Toth, C.K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, P., Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z., Schlichting, Alexander, and Brenner, Claus
- Abstract
LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.
- Published
- 2016
50. Adenocarcinoma of the Ethmoid sinuses: Results of a new protocol based on inductive chemotherapy combined with surgery. Four years experience
- Author
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Roux, F. X., Brasnu, D., Menard, M., Bassot, V., Nguyen, S., Donadieu, S., Chodkiewicz, J. L., and Laccourreye, H.
- Published
- 1989
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