Free shipping on all orders over $79* Limited Time Only
Track My Order
Help Centre
Likes
My Wish ListsLogin / Join
Booktopia
Australia's local bookstore
Get Free Shipping on orders over $79
Wireless Communication Using Deep Learning Techniques for Neuromorphic VLSI Computing : Synthesis Lectures on Engineering, Science, and Technology - Ziad El-Khatib

Wireless Communication Using Deep Learning Techniques for Neuromorphic VLSI Computing

By: Ziad El-Khatib, Sherif Moussa

eText | 15 January 2025

At a Glance

eText

$84.99

or 4 interest-free payments of $21.25 with

 or 

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

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.

on
Desktop
Tablet
Mobile

You Can Find This eBook In

Non-FictionEngineering & TechnologyElectronics & Communications EngineeringElectronics EngineeringMicrowave TechnologyCommunications Engineering & TelecommunicationsCircuits & ComponentsComputing & I.T.Computer ScienceDigital Signal Processing (DSP)

More in Circuits & Components

Handbook of Computer Architecture - Anupam Chattopadhyay

eBOOK

Handbook of Computer Architecture

eBook

RRP $969.00

$872.99

10%
OFF

This product is categorised by