Auswertung von funktionellen Gruppen des Phytoplanktons aus hyperspektralen Satellitendaten und ihre Anwendung für die Untersuchung der Dynamik des Phytoplanktons in ausgewählten Meeresregionen.

Phytoplankton play a unique role in the marine ecosystem as the basis of the marine food-web. They are the main drivers of the biogeochemical cycles in the ocean, as well as influencing the ocean-atmosphere exchanges of carbon dioxide and particular gases and particles. Based on these exchanges, phytoplankton influence the chemistry of atmosphere and the balance of global climate. Moreover, through interaction with light (absorption and scattering), phytoplankton have a significant impact on the underwater optics, being also responsible for the variations in ocean color. However, performing all these roles depends significantly on the type of phytoplankton, as indeed they comprise of a wide range of species and groups, with different capabilities and different distribution patterns in the World Ocean. Therefore, distinguishing between different types of phytoplankton is important to improve the knowledge of their actual roles in the ocean and climate system. As the spectral patterns of light absorption (essential for photosynthesis) vary among different groups of phytoplankton, the backscatter light from ocean preserves the spectral fingerprints of the inhabitant groups of phytoplankton. This feature can be used to determine remotely different types of phytoplankton. The purpose of this PhD-work was to improve a phytoplankton retrieval method, which was established to distinguish quantitatively major phytoplankton groups based on their absorption characteristics. The method, called PhytoDOAS, uses high spectrally resolved satellite data, provided by SCIAMACHY sensor. So far, by applying PhytoDOAS method to SCIAMACHY data, two main phytoplankton groups, diatoms and cyanobacteria, have been successfully distinguished. Through this work the method was improved to detect additionally coccolithophores, another important taxonomic group with significant biogeochemical functions. In this improvement, instead of the usual approach of the PhytoDOAS, which was based on single-target fitting, the simultaneous fitting of a certain set of phytoplankton groups was implemented within a wider wavelength window, thereby the new approach is called multi-target fit. Selection of the set of phytoplankton targets was according to the spectral analysis of absorption features of those groups that are most important with respect to the principal biogeochemical impacts, based on which marine microalgae are grouped as phytoplankton functional types, PFTs. The improved method was successfully tested through detecting independently reported blooms of coccolithophores, as well as by comparison of PhytoDOAS coccolithophores with global distributions of Particulate Inorganic Carbon (PIC), which is used as a proxy of coccolithophores. As the next step of this PhD-work, the results of the improved PhytoDOAS method were used to investigate temporal variations of coccolithophore blooms in selected regions. Eight years of SCIAMACHY data, from 2003 to 2010, were processed by the PhytoDOAS triple-target mode to monitor the biomass of coccolithophores in three oceanic regions, characterized by the frequent occurrence of large blooms. Then the PhytoDOAS results, as monthly mean time-series, were compared to appropriate satellite products, including the total phytoplankton biomass (total chl-a) from GlobColour data-set and the PIC distribution from MODIS-Aqua. To study the dynamics of coccolithophore blooms, the variations of coccolithophores, overall chl-a and PIC, as monthly mean time series, were investigated in the context of variations in the main oceanic geophysical parameters: sea-surface temperature (SST), mixed-layer depth (MLD) and surface wind speed. As a general result, it was observed that the inter-annual variations of the coccolithophore bloom cycles followed well the respective variations in the mentioned geophysical parameters, as they have been reported being associated with coccolithophore blooms. Observed anomalies were investigated based on the specific regional features of the geophysical conditions. Using the results of regional time series, the hypothesis that close coccolithophore blooms succeed the diatom blooms was roughly approved, suggesting, however, a weekly-based averaging of coccolithophores and diatoms for a more precise analysis. It has been frequently reported that high reflectance from surface waters in coccolithophore rich areas affects the performance of standard chl-a algorithms. The regional time series studies of this thesis indicated an underestimation of total chl-a by the standard algorithm during the time of occolithophore blooms. However, a comprehensive validation of the ocean color algorithms with in-situ phytoplankton data is needed to reach the final assessment of the short-comings

Analysis of Statistical QoS in Half Duplex and Full Duplex Dense Heterogeneous Cellular Networks

Statistical QoS provisioning as an important performance metric in analyzing next generation mobile cellular network, aka 5G, is investigated. In this context, by quantifying the performance in terms of the effective capacity, we introduce a lower bound for the system performance that facilitates an efficient analysis. Based on the proposed lower bound, which is mainly built on a per resource block analysis, we build a basic mathematical framework to analyze effective capacity in an ultra dense heterogeneous cellular network. We use our proposed scalable approach to give insights about the possible enhancements of the statistical QoS experienced by the end users if heterogeneous cellular networks migrate from a conventional half duplex to an imperfect full duplex mode of operation. Numerical results and analysis are provided, where the network is modeled as a Matern point process. The results demonstrate the accuracy and computational efficiency of the proposed scheme, especially in large scale wireless systems. Moreover, the minimum level of self interference cancellation for the full duplex system to start outperforming its half duplex counterpart is investigated.Comment: arXiv admin note: substantial text overlap with arXiv:1604.0058

Deep conv-attention model for diagnosing left bundle branch block from 12-lead electrocardiograms

Cardiac resynchronization therapy (CRT) is a treatment that is used to compensate for irregularities in the heartbeat. Studies have shown that this treatment is more effective in heart patients with left bundle branch block (LBBB) arrhythmia. Therefore, identifying this arrhythmia is an important initial step in determining whether or not to use CRT. On the other hand, traditional methods for detecting LBBB on electrocardiograms (ECG) are often associated with errors. Thus, there is a need for an accurate method to diagnose this arrhythmia from ECG data. Machine learning, as a new field of study, has helped to increase human systems' performance. Deep learning, as a newer subfield of machine learning, has more power to analyze data and increase systems accuracy. This study presents a deep learning model for the detection of LBBB arrhythmia from 12-lead ECG data. This model consists of 1D dilated convolutional layers. Attention mechanism has also been used to identify important input data features and classify inputs more accurately. The proposed model is trained and validated on a database containing 10344 12-lead ECG samples using the 10-fold cross-validation method. The final results obtained by the model on the 12-lead ECG data are as follows. Accuracy: 98.80+-0.08%, specificity: 99.33+-0.11 %, F1 score: 73.97+-1.8%, and area under the receiver operating characteristics curve (AUC): 0.875+-0.0192. These results indicate that the proposed model in this study can effectively diagnose LBBB with good efficiency and, if used in medical centers, will greatly help diagnose this arrhythmia and early treatment

An accurate model of the high-temperature superconducting cable by using stochastic methods

Modeling of high-temperature superconducting (HTS) cables as key elements of future power grids is a remarkable step at the beginning of projects on superconducting cables. Many projects utilize finite element methods (FEMs) to better understand the cable loss mechanism and its value. These methods are unable to evaluate the behavior of cables while connecting to a real grid. Therefore, equivalent circuit models (ECMs) are introduced as variants to provide a suitable environment for testing capabilities of high-temperature superconducting cables under different contingencies of power grids. This advantage has raised interest in the utilization of ECMs to predict the behavior of HTS cables. The accuracy of modeling by ECMs depends on many factors and considerations, among which twisting effect is a vital factor that is able to highly impress the accuracy of simulations. Thus, the Weibull distribution function (WDF) is utilized in this paper as a stochastic solution to increase the accuracy of the model. By applying WDF and sectionizing tapes, the twisting effect on the critical current of cable is accessible. Investigations on different conditions have shown that an ECM with 100,000 sections has high accuracy and acceptable speed