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Titlebook: High Performance Computing in Science and Engineering 2000; Transactions of the Egon Krause,Willi Jäger Conference proceedings 2001 Spring

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M. Kinateder,G. Wellein,A. Basermann,H. Fehskes levels are all assessed using vegetation indices, which provide vital information for optimal crop management. This chapter explains how unmanned aerial vehicles (UAVs) and high-end computing devices may automate the process of vegetation index computation, enabling precise and rapid agricultural
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d upon the machine learning paradigm and deep learning network namely Extreme Learning Machine with Auto-encoder (ELM-AE) has been proposed. The efficacy and effectiveness of ELM-AE has been compared with few existing forecasting methods like Generalized Autoregressive Conditional Heteroskedastcity
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E. Grote,F. H. Bussere the eHealth services might be sequentialized with psychological attentions fact that would potential the impact of the ehealth services. However we have identified that quality of service of the eHealth system might be limited seriously in those peripheral zones belonging to large cities because.
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Erik Bitzek,Franz Gähler,Jutta Hahn,Christopher Kohler,Galib Krdzalic,Johannes Roth,Christoph Rudhars. The application of integrated computational and biological approaches may help to achieve a better system-based understanding of biological processes in different environments. This will help to fully access valuable information regarding the evolution of genes and genomes in the wide diversity of organisms.
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Wolfgang Rieger,André Buchau,Günther Lehner,Wolfgang M. Ruckerreby smoothening the voltage waveform. A PI controller is used to tune up the error. In proposed system, analysis of the system is done with a three-phase fault using MATLAB 16a Simulink software. The simulated results are presented.
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Christopher Dahnken,Robert Eder,Enrico Arrigoni,Werner Hankeued state space model. The trend and cyclical components of the endogenous vector are extracted and analyzed. The content of the cyclical component is analyzed by spectral analysis. Finally, the error processes are subjected to some statistical tests and used as signal variables in nonlinear model building.
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