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1. |
Record Nr. |
UNINA9910698302003321 |
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Titolo |
Modeling water-surface elevations and virtual shorelines for the Colorado River in Grand Canyon, Arizona [[electronic resource] /] / by Christopher S. Magirl ... [and others] ; prepared in cooperation with the Grand Canyon Monitoring and Research Center |
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Pubbl/distr/stampa |
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Reston, Va. : , : U.S. Dept. of the Interior, U.S. Geological Survey, , 2008 |
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Descrizione fisica |
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v, 32 pages : digital, PDF file |
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Collana |
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Scientific investigations report ; ; 2008-5075 |
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Altri autori (Persone) |
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Soggetti |
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Water levels - Arizona - Grand Canyon - Computer simulation |
Water levels - Colorado River (Colo.-Mexico) - Computer simulation |
Shorelines - Arizona - Grand Canyon - Computer simulation |
Shorelines - Colorado River (Colo.-Mexico) - Computer simulation |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Title from PDF title screen (viewed on May 19, 2008). |
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Nota di bibliografia |
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Includes bibliographical references (pages 30-32). |
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2. |
Record Nr. |
UNINA9910366610703321 |
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Autore |
Rosa João P. S |
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Titolo |
Using Artificial Neural Networks for Analog Integrated Circuit Design Automation / / by João P. S. Rosa, Daniel J. D. Guerra, Nuno C. G. Horta, Ricardo M. F. Martins, Nuno C. C. Lourenço |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (117 pages) |
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Collana |
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SpringerBriefs in Applied Sciences and Technology, , 2191-530X |
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Disciplina |
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Soggetti |
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Electronic circuits |
Signal processing |
Image processing |
Speech processing systems |
Computational intelligence |
Circuits and Systems |
Signal, Image and Speech Processing |
Computational Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- Related Work -- Overview of Artificial Neural Networks (ANNs) -- On the Exploration of Promising Analog IC Designs via ANNs -- ANNs as an Alternative for Automatic Analog IC Placement -- Conclusions. . |
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Sommario/riassunto |
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This book addresses the automatic sizing and layout of analog integrated circuits (ICs) using deep learning (DL) and artificial neural networks (ANN). It explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. In opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices’ sizes to circuits’ performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog IC sizing as a direct map from specifications to the devices’ sizes. Two separate ANN architectures are |
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proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit’s performances as input features and devices’ sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies. . |
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