Optics for AI and AI for Optics

• Autore: Wei Jinlong
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
• Descrizione fisica: 1 online resource (162 p.)
• Soggetto topico: History of engineering and technology
• Soggetto non controllato: artificial intelligence; artificial neural networks; BCSVM; charge qubit; chromatic dispersion; clustering; coherent communications; coherent detection; coherent optical communication; cryogenic technologies; deep neural network; deep neural networks; digital signal processing; entanglement; equalizer; failure management; fiber optics communications; hybrid signal processing; image classification; Jaynes-Cummings-Hubbard formalism; k-nearest neighbor algorithm; light emitting diode; machine learning; modulation format identification; n/a; neural network; neural networks; nonlinear compensation; nonlinear equalization; nonlinear systems; nonlinearity cancellation; nonlinearity compensation; nonlinearity estimation and compensation; optical communications; optical Fast-OFDM; optical fiber communications; optical networks; optical performance monitoring; optical transmission; optimization; OSNR monitoring; passive optical networks; photonic integrated circuits; photonic neural network; position-based semiconductor qubits; probabilistic Bayesian learning; quality of transmission; semiconductor optical amplifiers; semiconductor photon communication; short-reach communication; support vector machines; tap estimation; visible light communication; volterra equalization
• Persona (resp. second.): Tao LauAlan Pak; YiLilin; GiacoumidisElias; ChengQixiang; WeiJinlong
• Sommario/riassunto: Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today's telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today's optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields.
• Titolo autorizzato: Optics for AI and AI for Optics
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557127903321
• Lo trovi qui: Univ. Federico II