Name: Wireless Communication Using Deep Learning Techniques for Neuromorphic Vlsi Computing (Synthesis Lectures on Engineering, Science, and Technology)
SKU: 63664660
Price: 1865.00 TWD
Availability: InStock
Author: Ziad El-Khatib; Sherif Moussa
ISBN: 9783031737992

portada Wireless Communication Using Deep Learning Techniques for Neuromorphic Vlsi Computing (Synthesis Lectures on Engineering, Science, and Technology)

Wireless Communication Using Deep Learning Techniques for Neuromorphic Vlsi Computing (Synthesis Lectures on Engineering, Science, and Technology)

Type

Physical Book

Publisher

Springer

Author

Ziad El-Khatib; Sherif Moussa

Language

English

Pages

114

Format

Hardcover

ISBN13

9783031737992

Edition No.

2024th

Wireless Communication Using Deep Learning Techniques for Neuromorphic Vlsi Computing (Synthesis Lectures on Engineering, Science, and Technology)

Ziad El-Khatib; Sherif Moussa (Author) · Springer · Hardcover

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Synopsis "Wireless Communication Using Deep Learning Techniques for Neuromorphic Vlsi Computing (Synthesis Lectures on Engineering, Science, and Technology)"

This book describes Deep Learning-based architecture design for intelligent wireless communication systems and specifically for Deep Learning-based receiver design. Deep Learning-based architecture design utilizes Deep Learning (DL) techniques to reformulate the traditional block-based wireless communication architecture. Deep Learning-based algorithm design utilizes Deep Learning methods to speed up the processing at a guaranteed high accuracy performance. Automatic signal modulation classification in AI-based wireless communication can be done using deep learning techniques to improve dynamic spectrum allocation. Automatic signal modulation recognition in wireless communication is described using Deep Learning techniques to improve resource shortage and spectrum utilization efficiency. Moreover, using deep learning neural network circuit methods and doing parallel computations on hardware can reduce costs. Spiking neural network (SNN) provides a promising solution for low-power hardware for neuromorphic computing. Spiking Neural Networks circuit functions with a pre-trained network's weights consume less power. Spiking neural network is more promising than other neural networks that can pave a new way for low-power computing applications. Analog VLSI is utilized to design spiking neural networks circuits such as silicon synapse and CMOS neuron.

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Wireless Communication Using Deep Learning Techniques for Neuromorphic Vlsi Computing (Synthesis Lectures on Engineering, Science, and Technology) - Ziad El-Khatib; Sherif Moussa

Synopsis "Wireless Communication Using Deep Learning Techniques for Neuromorphic Vlsi Computing (Synthesis Lectures on Engineering, Science, and Technology)"

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