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1. |
Record Nr. |
UNINA9910787194503321 |
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Autore |
Röhrich Martina |
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Titolo |
Grundlagen der Investitionsrechnung : Darstellung anhand einer Fallstudie / / Martina Röhrich ; lektorat, Anja Ludwig |
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Pubbl/distr/stampa |
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Munich, [Germany] : , : De Gruyter Oldenbourg, , 2014 |
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©2014 |
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ISBN |
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3-11-039883-4 |
3-486-85492-5 |
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Edizione |
[2. überarbeitete und erweiterte Auflage.] |
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Descrizione fisica |
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1 online resource (212 p.) |
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Classificazione |
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Disciplina |
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Soggetti |
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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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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Frontmatter -- Inhalt -- Vorwort zur zweiten Auflage -- Abkürzungen und Symbole -- Basisdaten der Fahrzeuge -- 1. Einführung in das Wesen von Investitionsentscheidungen -- 2. Statische Verfahren der Investitionsrechnung -- 3. Dynamische Verfahren der Investitionsrechnung -- 4. Investitionsentscheidungen bei Unsicherheit -- 5. Spezialfragen der Investitionsrechnung -- 6. Verzeichnis aller Aufgaben und Lösungen -- 7. Literatur -- 8. Finanzmathematische Faktoren -- 9. Stichwortverzeichnis |
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Sommario/riassunto |
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Drawing on a single case example, this textbook charts a clear trajectory across the diverse methodology used in investment analysis. Exercises with detailed solutions engender further confidence in dealing with the methods of investment analysis. |
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2. |
Record Nr. |
UNINA9910404090703321 |
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Autore |
SunÌeÌ Jordi |
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Titolo |
Memristors for Neuromorphic Circuits and Artificial Intelligence Applications |
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Pubbl/distr/stampa |
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MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
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ISBN |
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Descrizione fisica |
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1 online resource (244 p.) |
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Soggetti |
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History of engineering and technology |
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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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Sommario/riassunto |
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Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications. |
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