1.

Record Nr.

UNISALENTO991003813709707536

Autore

Gabrielli, Claudia

Titolo

Apprendere con il museo / a cura di Claudia Gabrielli

Pubbl/distr/stampa

Milano : Angeli, c2001

ISBN

8846436717

Descrizione fisica

160 p. ; 23 cm.

Collana

Collana dell'IRRSAE del Lazio ; 12

Disciplina

069.15

Soggetti

Musei - Attività didattica

Musei - Organizzazione

Lingua di pubblicazione

Italiano

Formato

Materiale a stampa

Livello bibliografico

Monografia

2.

Record Nr.

UNINA9911020005303321

Autore

Matin Mohammad A

Titolo

Artificial Intelligence for Future Networks

Pubbl/distr/stampa

Newark : , : John Wiley & Sons, Incorporated, , 2025

©2025

ISBN

9781394227945

1394227949

9781394227938

1394227930

9781394227952

1394227957

Edizione

[1st ed.]

Descrizione fisica

1 online resource (414 pages)

Altri autori (Persone)

GoudosSotirios K

KaragiannidisGeorge K

Disciplina

621.384

Soggetti

5G mobile communication systems - Technological innovations

Artificial intelligence - Industrial applications

Lingua di pubblicazione

Inglese



Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

Intelligent beam prediction and tracking -- Signal detection with machine learning -- AI-aided channel prediction -- Semantic communications -- Federated learning for wireless communications -- Federated learning in mesh networks -- Antenna design using artificial intelligence -- AI-driven approaches for solving electromagnetic inverse problems -- Reflectarray-based RIS-1 design using support vector machine to enhance mm-wave 5G coverage -- AI at the physical layer for wireless network security and privacy.

Sommario/riassunto

An exploration of connected intelligent edge, artificial intelligence, and machine learning for B5G/6G architecture   Artificial Intelligence for Future Networks illuminates how artificial intelligence (AI) and machine learning (ML) influence the general architecture and improve the usability of future networks like B5G and 6G through increased system capacity, low latency, high reliability, greater spectrum efficiency, and support of massive internet of things (mIoT).   The book reviews network design and management, offering an in-depth treatment of AI oriented future networks infrastructure. Providing up-to-date materials for AI empowered resource management and extensive discussion on energy-efficient communications, this book incorporates a thorough analysis of the recent advancement and potential applications of ML and AI in future networks.   Each chapter is written by an expert at the forefront of AI and ML research, highlighting current design and engineering practices and emphasizing challenging issues related to future wireless applications.   Some of the topics include:    * Signal processing and detection, covering preprocess and level signals, transform signals and extract features, and training and deploying AI models and systems  * Channel estimation and prediction, covering channel characteristics, modeling, and classic learning-aided and AI-aided estimation techniques  * Resource allocation, covering resource allocation optimization and efficient power consumption for different computing paradigms such as Cloud, Edge, Fog, IoT, and MEC  * Antenna design using AI, covering basics of antennas, EM simulator/optimization algorithms, and surrogate modeling  Identifying technical roadblocks and sharing cutting-edge research on developing methodologies, Artificial Intelligence for Future Networks is an essential reference on the subject for professionals and researchers involved in the field of wireless communications and networks, along with graduate and PhD students in electrical and computer engineering programs of study.