Intelligent Technologies for Research and Engineering / edited by S. Kannadhasan [and four others].

Format:

Book

Contributor:

Kannadhasan, S., editor.

Series:

Advanced Technologies for Science and Engineering Series

Advanced Technologies for Science and Engineering Series ; Volume 3

Language:

English

Subjects (All):

Signal processing--Digital techniques.

Signal processing.

Physical Description:

1 online resource (228 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Science Publishers Pte. Ltd., 2024.

Summary:

This volume explores diverse applications for automated machine learning and predictive analytics. The content provides use cases for machine learning in different industries such as healthcare, agriculture, cybersecurity, computing and transportation. Key highlights of this volume include topics on engineering for underwater navigation, and computer vision for healthcare and biometric applications. Chapters 1-4 delve into innovative signal detection, biometric authentication, underwater AUV localization, and COVID-19 face mask detection. Chapters 5-9 focus on wireless pH sensing, differential pattern identification, economic considerations in off-grid hybrid power, high optimization of image transmission, and ANN-based IoT-bot traffic detection. Chapters 10-12 cover mixed-signal VLSI design, pre-placement 3D floor planning, and bio-mimic robotic fish. Finally, Chapters 13 and 14 explore underwater robotic fish and IoT-based automatic irrigation systems, providing a comprehensive overview of cutting-edge technological advancements. The book is a resource for academics, researchers, educators and professionals in the technology sector who want to learn about current trends in intelligent technologies.

Contents:

Cover

Title

Copyright

End User License Agreement

Contents

Preface

List of Contributors

A Fuzzy Based High-Performance Decision-Making Model for Signal Detection in Smart Antenna Through Preference Leveled Evaluation Functions

Seema Khanum1, M. Gunasekaran2, Rajiga S.V.1 and Firos A.3,*

INTRODUCTION

Antenna Engineering

Machine Learning in Antenna Design

Deep Learning Versus Machine Learning

Application of Machine Learning Algorithm in Smart Antenna

THE BACKGROUND

The Smart Antenna

The Difference between Conventional Array and Beamforming Array

Comparison between Switched and the Adaptive Array Antenna

Preference Leveled Evaluation Functions Method to Construct Fuzzy Measures

Construction Method Using Preference Leveled Evaluation Functions

PROPOSED MODEL

Signal Range Detection

Parameterized Fuzzy Measures Decision-Making Model clustering method (PFMDMM) for Signal Detection

Deep Learning Model for Best Signal Selection

EXPERIMENTAL RESULTS

Data Source

Illustrative Example

Experiments with Laboratory Data

CONCLUSION

REFERENCES Generated by AI.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

Description based on print version record.

Includes bibliographical references.

ISBN:

9789815196269

981519626X

OCLC:

1451806845