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ABOUT THE BOOK

Human–Machine Interface Technology Advancements and Applications focuses on analysis, design, and evaluation perspectives in HMI technological breakthroughs and applications. It covers a wide range of ideas, methodologies, approaches, and instruments to give the reader a thorough understanding of the field's current academic and industry practice and debate. Physical, cognitive, social, and emotional factors are all considered in the work, which is exemplified by key application fields such as aerospace, automobile, medicine, and defense. This book covers AI and machine learning methodologies as well as biological signals and HMI applications. Nanotechnology, user interface design, and interactive systems are also featured. The MATLAB approach to signal processing applications is also included.

This book discusses advances in the field of human–machine interfaces and provides practical knowledge in biomedical signal processing, AI, and machine learning. It discusses augmented reality/virtual reality-based HMI applications. It examines advances in nanotechnology, user interface design, and interactive systems.

This book is intended to serve as a research guide that will both inform readers about the fundamentals of HMI from academic and industrial perspectives and provide a glimpse into how human-centered designers, such as engineers and human factors specialists, will attempt to design and develop human–machine systems in the future.

TABLE OF CONTENTS

Chapter 1. Neural Networks for Human-Machine Interface

Deepthi Dayanand, K. Sutejas , Vrishank Shishir, Shobana Padmanabhan, and Murthy Chavali

Chapter 2. An Overview of Recent Approaches in Brain-Computer Interface Systems using Electroencephalography

Shishir Maheswari, Kandala N. V. P. S. Rajesh, Usha Desai, and T. Sunil Kumar

Chapter 3. Automatic Detection of Motor Imagery EEG Signals Using Swarm Decomposition For Robust BCI System

Shailesh Vitthalrao Bhalerao and Ram Bilas Pachori

Chapter 4. Analysis of Mental Stress during Public Speaking Using Feature Optimization Techniques on EEG Signals

Dr. T. Ananda Babu, Dr. D. Krishna, G. Sridevi, Y. Mamillu, Dr. S. Ravi, and Dr. P. Rajesh Kumar

Chapter 5. Classification of Human Emotions Based on Electroencephalogram (EEG) Using Two-Layer Gated Recurrent Units (GRUs)

Ch.Rajendra Prasad, Sreedhar Kollem, Ravichander Janapati, Srinivas Samala, and Sandip Bhattacharya

Chapter 6. Epileptic Seizure Classification Using Machine Learning Algorithms with the Techniques of PCA Feature Reduction

Dr. B. Shadaksharappa and P.Ramkumar

Chapter 7. Nanotechnology Advancements in HMI

Vivek P. Chavda, Mital Patel, Ritu Patel, Kunjal Thakkar, Disha Patel, Pankti C. Balar, Piyush Patel, and Usha Desai

Chapter 8. An Engineering Approach of Noninvasive Detection and Analysis of Human Heart Sound for Alarm Generation

Iti Saha Misra

Chapter 9. Image Denoising: An Overview of Important Methods in Spatial and Transform Domain

B. N. Aravind, K. V. Suresh, H.D. Nataraj Urs, N. Yashwanth, and Usha Desai

Chapter 10. Diabetic Retinopathy Diagnosis System Based on Artificial Intelligence

Nayana Hegde, Savitha Krishna, and Sunilkumar S.Manvi

Chapter 11. Usability to User Experience with Interactive Systems in Biomedical Applications and Devices

Chitharanjan Billa and Murthy Chavali

Chapter 12. Dynamic Energy Management of Hybrid Electric Vehicles (HEVs) Using Fuzzy Logic Controller

Sohan Das, Souvik Biswas, Sukrit Sarkar, Tamal Maji, and Dr. Syamasree Biswas Raha

Chapter 13. Predictive Maintenance and Anomaly Detection in Smart Manufacturing

Trisiladevi C. Nagavi, Sanchith Hegde, V. Prajith, M. S. Sudeep and B. Rajesh

Chapter 14. Impact of Digitalization and Artificial Intelligence (AI) on Diagnostic Pathology: Are We Looking at a Renaissance?

Asitava Deb Roy

Chapter 15. University Chatbot System Using Natural Language Processing

P Pramod Kumar, Parupally Sindhu, Avula Jahnavi, Bokka Prudvi Charan, and Sukka Prashanth