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1. |
Record Nr. |
UNINA9910810243403321 |
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Titolo |
Power line communications : theory and applications for narrowband and broadband communications over power lines / / editors, H.C. Ferreira ... [et al.] |
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Pubbl/distr/stampa |
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Chichester, West Sussex, UK ; ; Hoboken, NJ, : Wiley, 2010 |
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ISBN |
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9786612689239 |
9781119956280 |
1119956285 |
9781282689237 |
1282689231 |
9780470661291 |
0470661291 |
9780470661284 |
0470661283 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (537 p.) |
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Altri autori (Persone) |
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FerreiraH. C (Hendrik C.) |
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Disciplina |
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Soggetti |
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Electric lines - Carrier transmission |
Broadband communication systems |
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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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Contents; List of Contributors; Preface; List of Acronyms; 1 Introduction; 2 Channel Characterization; 3 Electromagnetic Compatibility; 4 Coupling; 5 Digital Transmission Techniques; 6 Protocols for PLC Systems; 7 Industrial and International Standards on PLC-based Networking Technologies; 8 Systems and Implementations; 9 Conclusions; Index |
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Sommario/riassunto |
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Power Line Communications (PLC) is a promising emerging technology, which has attracted much attention due to the wide availability of power distribution lines. This book provides a thorough introduction to the use of power lines for communication purposes, ranging from channel characterization, communications on the physical layer and electromagnetic interference, through to protocols, networks, |
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standards and up to systems and implementations. With contributions from many of the most prominent international PLC experts from academia and industry, Power Line Communications brings togeth |
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2. |
Record Nr. |
UNINA9910993940403321 |
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Titolo |
Materials Informatics I : Methods / / edited by Kunal Roy, Arkaprava Banerjee |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (XVII, 288 p. 66 illus., 53 illus. in color.) |
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Collana |
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Challenges and Advances in Computational Chemistry and Physics, , 2542-4483 ; ; 39 |
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Disciplina |
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Soggetti |
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Cheminformatics |
Materials |
Chemistry |
Computer simulation |
Machine learning |
Artificial intelligence |
Computational Design Of Materials |
Machine Learning |
Artificial 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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Part 1. Introduction -- Introduction to Materials Informatics -- Introduction to Cheminformatics for Predictive Modeling -- Introduction to machine learning for predictive modeling of organic materials -- Quantitative Structure-Property Relationships (QSPR) for Materials Science -- Part 2. Methods and Tools -- Quantitative Structure-Property Relationships (QSPR) and Machine Learning (ML) Models for Materials Science -- Optimising Materials Properties with Minimal Data: Lessons from Vanadium Catalyst Modelling -- In silico QSPR studies based on CDFT and IT descriptors -- Applications of |
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quantitative read-across structure-property relationship (q-RASPR) modeling in the field of materials science -- Machine Learning algorithms for applications in Materials Science I -- Machine Learning algorithms for applications in Materials Science II -- Structure-property modeling of quantum-theoretic properties of benzenoid hydrocarbons by means of connection-related graphical descriptors -- Machine learning tools and Web services for Materials Science modelling. |
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Sommario/riassunto |
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This contributed volume explores the integration of machine learning and cheminformatics within materials science, focusing on predictive modeling techniques. It begins with foundational concepts in materials informatics and cheminformatics, emphasizing quantitative structure-property relationships (QSPR). The volume then presents various methods and tools, including advanced QSPR models, quantitative read-across structure-property relationship (q-RASPR) models, optimization strategies with minimal data, and in silico studies using different descriptors. Additionally, it explores machine learning algorithms and their applications in materials science, alongside innovative modeling approaches for quantum-theoretic properties. Overall, the book serves as a comprehensive resource for understanding and applying machine learning in the study and development of advanced materials and is a useful tool for students, researchers and professionals working in these areas. |
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