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Series: Engineering Systems Design for Sustainable Development

Series Editor(s): Suman Lata Tripathi, PhD

Scope:
A multidisciplinary approach is the foundation of this series, meant to intensify related research focuses and to achieve the desired goal of sustainability through different techniques and intelligent approaches. This series will cover the information from the ground level of engineering fundamentals leading to smart products related to design and manufacturing technologies including maintenance, reliability, security aspects, and waste management. The series will provide the opportunity for the academician and industry professional alike to share their knowledge and experiences with practitioners and students. This will result in sustainable developments and support to a good and healthy environment.

About the Series Editor(s):
Dr. Suman Lata Tripathi
Professor, VLSI Design, School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India
Post-doc Researcher at NTU, UK

Submission to the series:
All the Book Proposal should be submitted through email:
Email: tripathisumanlata78@gmail.com

Published and Forthcoming Titles

1. Explainable machine learning models & architectures for real-time system implementation

2. Nano-Devices for IC Design

 		Nanodevices for Integrated Circuit Design
Edited by Suman Lata Tripathi, Abhishek Kumar, K. Srinivasa Rao, and Prasantha R. Mudimela
Copyright: 2024   |   Status: Published   |   Hardcover
 
Written and edited by a team of experts in the field, this important new volume broadly covers the design of nano-devices and their integrated applications in digital and analog integrated circuits (IC) design.


 		Explainable Machine Learning Models and Architectures
Real-Time System Implementation
Edited by Suman Lata Tripathi and Mufti Mahmud
Copyright: 2024   |   Status: Published   |   Hardcover
 
This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications.