Entwicklung eines automatisierten Wolkendetektions- und Wolkenklassifizierungsverfahrens mit Hilfe von Support Vector Machines angewendet auf METEOSAT-SEVIRI-Daten für den Raum Deutschland

Wolken stellen im Klimasystem der Erde einen zentralen Faktor dar, nicht nur in Bezug auf die Niederschlagsverteilung, sondern auch in Verbindung mit Effekten auf Strahlungsvorgänge innerhalb der Atmosphäre. Eine genaue Abschätzung der komplexen Wechselwirkungen innerhalb des Systems Erde-Atmosphäre ist jedoch immer noch mit Problemen verbunden. Dies betrifft vor allem den vielfach diskutierten Klimawandel. Gerade durch die hohe zeitliche und räumliche Variabilität von Wolken ist ein differenzierter Informationsgewinn über diese von enormer Relevanz. Satellitendaten haben sich hier als Mittel der ersten Wahl herausgestellt. Dies gilt insbesondere für geostationäre Satelliten, die im Gegensatz zu polarumlaufenden Satelliten, bei vergleichbarer spektraler Information, eine höhere zeitliche Auflösung zur Verfügung stellen. Gängige Wolkenerkennungsverfahren beziehen sich vor allem auf Schwellenwertansätze und benötigen in der Regel Zusatzdaten über den Zustand der Atmosphäre. Die Schwierigkeit dieser Verfahren liegt bei der exakten Bestimmung des jeweiligen Schwellenwertes. Andere Bildverarbeitungsverfahren wie Neuronale Netze, Cluster-Analysen oder Support Vector Machines (SVM) wurden zwar schon experimentell durchgeführt, jedoch meist nicht im Sinne einer automatisierten Anwendung auf zeitlich hoch aufgelöste Datenreihen. Ziel dieser Arbeit war demnach die Entwicklung einer Wolkendetektion bzw. Wolkenklassifizierung mit Hilfe von Support Vector Machines, angewendet auf METEOSAT-SEVIRI-Daten für den Raum Deutschland. Die Umsetzung sollte hierbei auf der reinen Bildinformation (wolkenrelevante METEOSAT-Kanäle), ergänzt durch notwendige Trainingsdaten (Ground Truth), basieren. Support Vector Machines stellen in diesem Zusammenhang einen relativ modernen Klassifikator dar, der auch mit wenigen Trainingsdaten effektiv Klassifizierungsprobleme lösen kann. Die Anwendung auf das Gebiet Deutschland ist mit den vielfältigen Wolkengegebenheiten in dieser Region begründet. Eine effektive Wolkendetektion stellt durch die relativ nördliche Lage und den Einfluss des Nordatlantiks eine besondere Anforderung dar. Die größte Herausforderung bei einem Verfahren wie Support Vector Machines ist die exakte Auswahl repräsentativer Trainingsdaten auf deren Basis der Klassifikator lernt. Eine erste Idee war, zu diesem Zweck sogenannte Wetter-Kameras einzusetzen, die an vielen Standorten Teile des Himmels aufnehmen. Rückwirkend stellte sich jedoch die Zusammenstellung eines aussagekräftigen Datensatzes als schwierig heraus. Vielerorts werden die Aufnahmen nicht archiviert, wodurch letztendlich nur sechs Standorte für das Jahr 2008 zur Verfügung standen, die aber dennoch innerhalb des Prozessierungsverfahrens Verwendung finden. Insgesamt wird der Trainingsdatenumfang durch die Anwendung bekannter Schwellenwertalgorithmen erweitert in dem Sinne, dass nur solche Pixel, die mit hoher Sicherheit einer Klasse angehören, einsetzbar sind. Neben der Erstellung einer Wolkenmaske erfolgt die weitere Abtrennung in vier Wolkenklassen, wobei zwischen Höhe und Strahlungsdurchlässigkeit der Wolken unterschieden wird. Zur Überprüfung der Detektion bzw. Klassifizierung wurde, neben dem Vergleich mit den Wolkenabtrennungsverfahren der Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting (SAFNWC), eine Validierung mit synoptischen Beobachtungsdaten durchgeführt. Dies bezieht sich jedoch nur auf die Wolkenmaske mit Hilfe der sogenannten Achtel-Skala. Anhand der Validierungsergebnisse und der Betrachtung des Wolkenbedeckungsgrades für das Jahr 2008 wurde deutlich, dass mit einem automatisierten SVM-Wolkendetektionsverfahren bzw. -Wolkenklassifizierungsverfahren gute Ergebnisse erzielt werden können. Dies betrifft vor allem Situationen, in denen Wolken ausgeprägt bzw. im Vergleich mit wolkenfreien Flächen ausgeglichen vorkommen. Hochdruckwetterlagen mit umfangreichen wolkenfreien Flächen führen dagegen zu einer verstärkten Wolkenüberschätzung durch den SVM-Klassifikator. Demgegenüber kommt es bei der Anwendung der SAFNWC-Cloudmask (CMa) ohne zusätzliche Informationen aus Wettervorhersagemodellen zu einer Unterschätzung, deren Ausprägung sich jedoch nicht auf bestimmte Wetterlagen zurückführen lässt. Zudem konnte durch das Verfahren eine zeitliche und räumliche Differenzierung dargestellt werden, die sich in das allgemeine Wettergeschehen des Jahres 2008 einordnen lässt. Beispielsweise ließ sich anhand der objektiven Großwetterlagen-Klassifikation des Deutschen Wetterdienstes (DWD) eine zeitliche Verknüpfung mit dem Wolkenbedeckungsgrad darstellen. Regionale Unterschiede zeigten sich vor allem in Bezug auf die Höhenlagen der Mittelgebirge, die im Allgemeinen auch höhere Bedeckungsgrade aufweisen. Im Gegensatz dazu sind Teile Nord- und Süddeutschlands, sowie das Rheintal häufig mit geringerer Bedeckung gekennzeichnet. Anhand der vorliegenden Arbeit konnte das Potential von Support Vector Machines, bezogen auf die automatisierte Anwendung einer Wolkendetektion, dargelegt werden. Obwohl es sich um ein sehr rechenintensives Verfahren handelt, lässt sich die gesamte Prozessierung inklusive Training für jeden METEOSAT-Aufnahmezeitpunkt in einem adäquaten zeitlichen Rahmen realisieren. Nach weiterer Optimierung, wäre eine operationelle Anwendung durchaus vorstellbar.Development of an automated cloud detection and cloud classification algorithm using Support Vector Machines applied to METEOSAT SEVIRI data for the area of Germany Clouds are an important part of the earth's climate system, not only because of their connection to precipitation, but also due to their effects on radiation. The accurate estimation of the complex interactions within the earth-atmosphere-system is still a major challenge. Particularly if related to the issues of the continuously discussed climate change. Because of the high temporal and spatial variability in conjunction with clouds, gaining additional information is absolutely necessary. Satellite data presents itself as the first choice. Especially geostationary satellites embodying high temporal resolution and comparable spectral information, can meet the demands of effective cloud detection. Methods of continuous cloud detection are primarily based on threshold techniques and typically require additional data on the atmospheric state. Regarding these methods, determinating an appropriate threshold accurately, is still a problem to be solved. Other approaches of image analysis, like Neural Networks, Cluster-Analysis or Support Vector Machines (SVM), have indeed been carried out experimentally but mostly not in terms of an automated application for high temporal resolution data series. Therefore this study's aim is to develop a cloud detection and cloud classification, applied by using Support Vector Machines on METEOSAT-SEVIRI-data covering the area of Germany. The implementation should only rely on genuine image information (cloud-related METEOSAT-channels), supplemented by the required training data (ground truth). In this context, Support Vector Machines represent a comparatively modern classifier, which is able to solve classification problems effectively, even with only a small amount of training data. The choice of Germany as the main investigation area has been based on the multiple cloud variations and conditions found here. Especially the relatively northern location and the influence of the North Atlantic are challenging aspects regarding effective cloud detection. The main difficulty using Support Vector Machines is the precise selection of representative training data, by which the classifier learns. A first idea for this purpose has been the application of so-called webcams or weather cameras taking pictures of the sky in many places. Retrospectively, the compilation of a meaningful data set turned out to be difficult. In many places, images will not be stored, thus in the end, only six locations for the year 2008 were available. Although this is not enough for a proper classification, the data is used in the processing scheme. To get additional training data, the data set has been expanded through the use of known threshold algorithms. But only those pixels, having a high probability to belong to a certain class, were selected for training. After creating a cloud mask, the clouds were subdivided into four classes, distinguishing between height and radiation transparency. Besides comparing the results to the cloud products of the Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting (SAFNWC), an accuracy assessment has been carried out. This validation procedure has been applied with the help of synoptic observations. However, this has only been done with the cloud mask, using the so-called octa-scale. Based on the validation results and the additional observation of the cloud cover for the year 2008, it was possible to show that accurate results can be achieved with an automated SVM-cloud-detection, respectively SVM-cloud-classification. This refers mainly to situations where you have a lot of clouds, or were clouds are equally distributed, compared to cloud free areas. In contrast, high-pressure weather conditions with extensive cloud free areas lead to increased overestimation of clouds by the SVM-classifier. But on the other side, the results of the SAFNWC-Cloudmask used without additional information from numerical weather prediction (NWP) reveal an underestimation of clouds. This underestimation cannot be reduced to certain weather conditions. In addition, a temporal and spatial differentiation of cloud cover could be shown for the year 2008. In this context, it was possible to link the results with the general weather patterns of 2008. For example, temporal variations have been presented by the connection between the cloud amount and the results of an objective weather type classification established by the German Meteorological Service (DWD). Regional differences were mainly in conjunction to the low mountain ranges, which are generally connected to high cloud coverage. On the other hand, parts of northern and southern Germany and the Rhine Valley are often combined with less coverage. With this work it was possible to demonstrate the potential of Support Vector Machines as an automated application of cloud detection on temporal high resolution data. Although it is a very computationally intensive procedure, the entire processing, including training of the classifier for each METEOSAT-timeslot, can be realized in an appropriate timeframe. After further optimization, an operational application would be quite conceivable

Collateral-flow measurements in humans by myocardial contrast echocardiography: validation of coronary pressure-derived collateral-flow assessment

Aims Myocardial blood flow (MBF) is the gold standard to assess myocardial blood supply and, as recently shown, can be obtained by myocardial contrast echocardiography (MCE). The aims of this human study are (i) to test whether measurements of collateral-derived MBF by MCE are feasible during elective angioplasty and (ii) to validate the concept of pressure-derived collateral-flow assessment. Methods and results Thirty patients with stable coronary artery disease underwent MCE of the collateral-receiving territory during and after angioplasty of 37 stenoses. MCE perfusion analysis was successful in 32 cases. MBF during and after angioplasty varied between 0.060-0.876 mL min−1 g−1 (0.304±0.196 mL min−1 g−1) and 0.676-1.773 mL min−1 g−1 (1.207±0.327 mL min−1 g−1), respectively. Collateral-perfusion index (CPI) is defined as the rate of MBF during and after angioplasty varied between 0.05 and 0.67 (0.26±0.15). During angioplasty, simultaneous measurements of mean aortic pressure, coronary wedge pressure, and central venous pressure determined the pressure-derived collateral-flow index (CFIp), which varied between 0.04 and 0.61 (0.23±0.14). Linear-regression analysis demonstrated an excellent agreement between CFIp and CPI (y=0.88x+0.01; r2=0.92; P<0.0001). Conclusion Collateral-derived MBF measurements by MCE during angioplasty are feasible and proved that the pressure-derived CFI exactly reflects collateral relative to normal myocardial perfusion in human

A general purpose HyperTransport-based Application Accelerator Framework

HyperTransport provides a flexible, low latency and high bandwidth interconnection between processors and also between processors and peripheral omponents. Therefore, the interconnection is no longer a performance bottleneck when integrating application specific accelerators in modern computing systems. Current FPGAs providing huge computational power and permit the acceleration of compute-intensive kernels. We therefore present a general purpose architecture based on HyperTransport and modern FPGAs to accelerate time-consuming computations. Further, we present a prototypical implementation of our architecture. Here we used an AMD Opteron-based system with the HTX Board [6] to demonstrate that common applications can benefit from available hardware accelerators. A cryptographic example showed that the encryption of files, larger then 50 kByte, can be successfully accelerated

Noble gas elemental abundances in three solar wind regimes as recorded by the Genesis mission

We discuss elemental abundances of noble gases in targets exposed to the solar wind (SW) onboard the “Genesis” mission during the three different SW “regimes”: “Slow” (interstream, IS) wind, “Fast” (coronal hole, CH) wind and solar wind related to coronal mass ejections (CME). To this end we first present new Ar, Kr, and Xe elemental abundance data in Si targets sampling the different regimes. We also discuss He, Ne, and Ar elemental and isotopic abundances obtained on Genesis regime targets partly published previously. Average Kr/Ar ratios for all three regimes are identical to each other within their uncertainties of about 1% with one exception: the Fast SW has a 12% lower Xe/Ar ratio than do the other two regimes. In contrast, the He/Ar and Ne/Ar ratios in the CME targets are higher by more than 20% and 10%, respectively, than the corresponding Fast and Slow SW values, which among themselves vary by no more than 2–4%. Earlier observations on lunar samples and Genesis targets sampling bulk SW wind had shown that Xe, with a first ionisation potential (FIP) of ∼12 eV, is enriched by about a factor of two in the bulk solar wind over Ar and Kr compared to photospheric abundances, similar to many “low FIP” elements with a FIP less than ∼10 eV. This behaviour of the “high FIP” element Xe was not easily explained, also because it has a Coulomb drag factor suggesting a relatively inefficient feeding into the SW acceleration region and hence a depletion relative to other high FIP elements such as Kr and Ar. The about 12% lower enrichment of Xe in Genesis’ Fast SW regime observed here is, however, in line with the hypothesis that the depletion of Xe in the SW due to the Coulomb drag effect is overcompensated as a result of the relatively short ionisation time of Xe in the ion-neutral separation region in the solar chromosphere. We will also discuss the rather surprising fact that He and Ne in CME targets are quite substantially enriched (by 20% and 10%, respectively) relative to the other solar wind regimes, but that this enrichment is not accompanied by an isotopic fractionation. The Ne isotopic data in CMEs are consistent with a previous hypothesis that isotopic fractionation in the solar wind is mass-dependent

Numerical Analysis of Ca2+ Depletion in the Transverse Tubular System of Mammalian Muscle

AbstractCalcium currents were recorded in contracting and actively shortening mammalian muscle fibers. In order to characterize the influence of extracellular calcium concentration changes in the small unstirred lumina of the transverse tubular system (TTS) on the time course of the slow L-type calcium current (ICa), we have combined experimental measurements of ICa with quantitative numerical simulations of Ca2+ depletion. ICa was recorded both in calcium-buffered and unbuffered external solutions using the two-microelectrode voltage clamp technique (2-MVC) on short murine toe muscle fibers. A simulation program based on a distributed TTS model was used to calculate the effect of ion depletion in the TTS. The experimental data obtained in a solution where ion depletion is suppressed by a high amount of a calcium buffering agent were used as input data for the simulation. The simulation output was then compared with experimental data from the same fiber obtained in unbuffered solution. Taking this approach, we could quantitatively show that the calculated Ca2+ depletion in the transverse tubular system of contracting mammalian muscle fibers significantly affects the time-dependent decline of Ca2+ currents. From our findings, we conclude that ion depletion in the tubular system may be one of the major effects for the ICa decline measured in isotonic physiological solution under voltage clamp conditions

On the Distribution of Salient Objects in Web Images and its Influence on Salient Object Detection

It has become apparent that a Gaussian center bias can serve as an important prior for visual saliency detection, which has been demonstrated for predicting human eye fixations and salient object detection. Tseng et al. have shown that the photographer's tendency to place interesting objects in the center is a likely cause for the center bias of eye fixations. We investigate the influence of the photographer's center bias on salient object detection, extending our previous work. We show that the centroid locations of salient objects in photographs of Achanta and Liu's data set in fact correlate strongly with a Gaussian model. This is an important insight, because it provides an empirical motivation and justification for the integration of such a center bias in salient object detection algorithms and helps to understand why Gaussian models are so effective. To assess the influence of the center bias on salient object detection, we integrate an explicit Gaussian center bias model into two state-of-the-art salient object detection algorithms. This way, first, we quantify the influence of the Gaussian center bias on pixel- and segment-based salient object detection. Second, we improve the performance in terms of F1 score, Fb score, area under the recall-precision curve, area under the receiver operating characteristic curve, and hit-rate on the well-known data set by Achanta and Liu. Third, by debiasing Cheng et al.'s region contrast model, we exemplarily demonstrate that implicit center biases are partially responsible for the outstanding performance of state-of-the-art algorithms. Last but not least, as a result of debiasing Cheng et al.'s algorithm, we introduce a non-biased salient object detection method, which is of interest for applications in which the image data is not likely to have a photographer's center bias (e.g., image data of surveillance cameras or autonomous robots)

Risk of decompression illness among 230 divers in relation to the presence and size of patent foramen ovale

Background The risk of developing decompression illness (DCI) in divers with a patent foramen ovale (PFO) has not been directly determined so far; neither has it been assessed in relation to the PFO's size. Methods In 230 scuba divers (age 39±8 years), contrast trans-oesophageal echocardiography (TEE) was performed for the detection and size grading (0-3) of PFO. Prior to TEE, the study individuals answered a detailed questionnaire about their health status and about their diving habits and accidents. For inclusion into the study, ⩾200 dives and strict adherence to decompression tables were required. Results Sixty-three divers (27%) had a PFO. Overall, the absolute risk of suffering a DCI event was 2.5 per 104 dives. There were 18 divers (29%) with, and 10 divers (6%) without, PFO who had experienced ⩾1 major DCI events \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} $(P=0.016)$ \end{document}. In the group with PFO, the incidence per 104 dives of a major DCI, a DCI lasting longer than 24 h and of being treated in a decompression chamber amounted to 5.1 (median 0, interquartile range [IQR] 0-10.0), 1.9 (median 0, IQR 0-4.0) and 3.6 (median 0, IQR 0-9.8), respectively and was 4.8-12.9-fold higher than in the group without PFO \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} $(P{<}0.001)$ \end{document}. The risk of suffering a major DCI, of a DCI lasting longer than 24 h and of being treated by recompression increased with rising PFO size. Conclusion The presence of a PFO is related to a low absolute risk of suffering five major DCI events per 104 dives, the odds of which is five times as high as in divers without PFO. The risk of suffering a major DCI parallels PFO siz

Supplementary data for the article: Ilić Đurđić, K.; Ece, S.; Ostafe, R.; Vogel, S.; Schillberg, S.; Fischer, R.; Prodanović, R. Improvement in Oxidative Stability of Versatile Peroxidase by Flow Cytometry-Based High-Throughput Screening System. Biochemical Engineering Journal 2020, 157. https://doi.org/10.1016/j.bej.2020.107555

Supplementary material for: [https://doi.org/10.1016/j.bej.2020.107555]Related to published version: [http://cherry.chem.bg.ac.rs/handle/123456789/3888]Related to accepted version: [http://cherry.chem.bg.ac.rs/handle/123456789/3898

Enhancement of fluorescent properties of near-infrared dyes using clickable oligoglycerol dendrons

Near-infrared (NIR) fluorescent dyes are gaining increased attention due to their potential to serve as molecular probes for in vivo imaging. Here, we demonstrate that oligoglycerol dendrons effectively enhance the fluorescence properties of an NIR dye by increasing the solubility in water and the prevention of aggregate formation. First- and second-generation oligoglycerol dendrons were conjugated to an NIR dye via a dipolar-cycloaddition (click) reaction. The two new dye conjugates exhibited enhanced NIR fluorescent emission and considerably higher fluorescent quantum yields than the dye alone. The high photostability measured for one of the oligoglycerol-linked dyes, in comparison to commonly used fluorogenic dyes such as Cy5 and Cy7, was validated using fluorescence microscopy of macrophages

A rare differential diagnosis to occupational neck pain: bilateral stylohyoid syndrome

Chronic neck pain is widely prevalent and a common source of disability in the working-age population. Etiology of chronic neck pain includes neck sprain, mechanical or muscular neck pain, myofascial pain syndrome, postural neck pain as well as pain due to degenerative changes. We report the case of a 42 year old secretary, complaining about a longer history of neck pain and limited movement of the cervical spine. Surprisingly, the adequate radiologic examination revealed a bilateral ossification of the stylohyoid ligament complex. Her symptoms remained intractable from conservative treatment consisting of anti-inflammatory medication as well as physical therapy. Hence the patient was admitted to surgical resection of the ossified stylohyoid ligament complex. Afterwards she was free of any complaints and went back to work. Therefore, ossification of the stylohyoid ligament complex causing severe neck pain and movement disorder should be regarded as a rare differential diagnosis of occupational related neck pain