Titlebook: Simulation of Power Electronics Circuits with MATLAB®/Simulink®; Design, Analyze, and Farzin Asadi Book 2022 Farzin Asadi 2022 MATLAB.Simul

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F. J. Espinosa-Garcia,M. Ceccarelli,M. Arias-Montiel Process Mining.Deals with conformance checking, one of the .Process mining techniques can be used to discover, analyze and improve real processes, by extracting models from observed behavior. The aim of this book is conformance checking, one of the main areas of process mining. In conformance check

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lassification method by using multiple kernel sparse representation (MKSR) is proposed in this paper. Kernel sparse representation (KSR) behaves good robust and occlusion like as sparse representation (SR) methods. Especially, KSR behaves better classification property than common sparse representat

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Marek Rewizorskianni.Il grande impulso a questa branca della chirurgia èlegato allo sforzo combinato di molti chirurghi pediatripionieri nel campo delle tecniche chirurgiche dedicateesclusivamente all’età pediatrica e allo sviluppotecnologico, che ha permesso la miniaturizzazione deglistrumenti laparoscopici nati i

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echt herzlich danken, die mich immer vielfältig unterstützt und mich zum Durchhalten ermutigt haben; ohne ihre Hilfe wäre die vorliegende Arbeit nicht möglich geworden. Der Hans-Böckler-Stiftung danke ich dafiir, daß sie mir ein Promotionssti­ pendium gewährt hat. Folgenden Personen möchte ich beson

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DPDudoNet: Deep-Prior Based Dual-Domain Network for Low-Dose Computed Tomography Reconstructiond incredible success in this field, most of the existing DL-based reconstruction models lack interpretability and generalizability. In this paper, we propose a novel deep prior-based dual-domain network (DPDudoNet) by unrolling the model-based algorithm using iteratively-cascaded DenseNet and deconv

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HAPmamba: Linear-Time Sequence Modeling for Terrain Classification by Legged Robotsoposed model is based on the latest architecture for Linear-Time Sequence Modeling using Selective State Spaces, called Mamba. We obtained lightweight models with very low inference times, which are two times faster than comparable transformer-based solutions. We evaluated HAPmamba alongside other s

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