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Titlebook: Electromagnetic Field Computation by Network Methods; Peter Russer,Mauro Mongiardo,Leopold B. Felsen Book 2009 Springer-Verlag Berlin Heid

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發(fā)表于 2025-3-25 05:53:22 | 只看該作者
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發(fā)表于 2025-3-25 12:39:33 | 只看該作者
Book 2009e subcircuits representing the subdomains. The connection networks are lossless, don‘t store energy and represent the overall problem topology. This is similar to what is done in circuit theory and permits a phrasing of the solution of EM field problems in complex structures by Network-oriented methods..
24#
發(fā)表于 2025-3-25 19:47:43 | 只看該作者
Introduction,s, dielectric inhomogeneities, etc., no single method is best suited for handling all possible cases; instead, a combination of methods (hybridization) is needed to attain the greatest flexibility and efficiency.
25#
發(fā)表于 2025-3-25 21:22:57 | 只看該作者
Introduction,1]. Wireless communication systems, for example, pose challenging problems with respect to field propagation prediction, microwave hardware design, compatibility issues, biological hazards, etc. Because different problems have their own combination of geometrical features and scales, frequency range
26#
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27#
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28#
發(fā)表于 2025-3-26 11:10:52 | 只看該作者
,Two—Dimensional Problems, with a modest analytical effort allows to introduce several concepts for open and closed waveguides [1–3] and can be extended to dielectric waveguides [4] or stratified media. Also relevant techniques of applied mathematics find, in this case, immediate application [5–7]. The axially invariant wave
29#
發(fā)表于 2025-3-26 16:00:30 | 只看該作者
Conference proceedings 2024ations, e-governance, e-agriculture, e-education and computing technologies, the Internet of Things (IoT), and e-mining. Written by respected experts and researchers working on ICT, the book offers an asset for young researchers involved in advanced studies. The work is presented in ten volumes..
30#
發(fā)表于 2025-3-26 18:30:58 | 只看該作者
Deep Learning Approach for Pathogen Detection Through Shotgun Metagenomics Sequence Classificationcale datasets. The purpose of this work is to explore how a long short-term memory (LSTM) network can be used to learn sequential genome patterns through pathogen detection from metagenome data. Our experimental result showed that we can obtain similar accuracy to the conventional BLAST method, but at a speed that is about 36 times faster.
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