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Submit a ProposalIntegrated Devices for Artificial Intelligence and VLSI
 | VLSI Design, Simulation and Applications Edited by Balwinder Raj, Suman Lata Tripathi, Tarun Chaudhary, K. Srinivasa Rao, and Mandeep Singh Copyright: 2024 | Status: Published ISBN: 9781394204359 | Hardcover | 369 pages Price: $225 USD  |
One Line Description
With its in-depth exploration of the close connection between microelectronics, AI, and VLSI technology, this book offers valuable insights into the cutting-edge techniques and tools used in VLSI design automation, making it an essential resource for anyone seeking to stay ahead in the rapidly evolving field of VLSI design.
Audience
Graduate and post-graduate students and researchers of electronics engineering and computer engineering, academicians interested in VLSI and Artifical Intelligence, and industry professionals working in electrical engineering and computer science
Graduate and post-graduate students and researchers of electronics engineering and computer engineering, academicians interested in VLSI and Artifical Intelligence, and industry professionals working in electrical engineering and computer science
Description
Very large-scale integration (VLSI) is the inter-disciplinary science of utilizing advanced semiconductor technology to create various functions of computer system. This book addresses the close link of microelectronics and artificial intelligence (AI). By combining VLSI technology, a very powerful computer architecture confinement is possible. To overcome problems at different design stages, researchers introduced artificial intelligent (AI) techniques in VLSI design automation. AI techniques, such as knowledge-based and expert systems, first try to define the problem and then choose the best solution from the domain of possible solutions.
These days, several CAD technologies, such as Synopsys and Mentor Graphics, are specifically created to increase the automation of VLSI design. When a task is completed using the appropriate tool, each stage of the task design produces outcomes that are more productive than typical. However, combining all of these tools into a single package offer has drawbacks. We cannot really use every outlook without sacrificing the efficiency and usefulness of our output. The researchers decided to include AI approaches into VLSI design automation in order to get around these obstacles. AI is one of the fastest growing tools in the world of technology and innovation that helps to make computers more reliable and easy to use. Artificial Intelligence in VLSI design has provided high-end and more feasible solutions to the difficulties faced by the VLSI industry. Physical design, RTL design, STA, etc. are some of the most in-demand courses to enter the VLSI industry. These courses help develop a better understanding of the many tools like Synopsis. With each new dawn, artificial intelligence in VLSI design is continually evolving, and new opportunities are being investigated.
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Very large-scale integration (VLSI) is the inter-disciplinary science of utilizing advanced semiconductor technology to create various functions of computer system. This book addresses the close link of microelectronics and artificial intelligence (AI). By combining VLSI technology, a very powerful computer architecture confinement is possible. To overcome problems at different design stages, researchers introduced artificial intelligent (AI) techniques in VLSI design automation. AI techniques, such as knowledge-based and expert systems, first try to define the problem and then choose the best solution from the domain of possible solutions.
These days, several CAD technologies, such as Synopsys and Mentor Graphics, are specifically created to increase the automation of VLSI design. When a task is completed using the appropriate tool, each stage of the task design produces outcomes that are more productive than typical. However, combining all of these tools into a single package offer has drawbacks. We cannot really use every outlook without sacrificing the efficiency and usefulness of our output. The researchers decided to include AI approaches into VLSI design automation in order to get around these obstacles. AI is one of the fastest growing tools in the world of technology and innovation that helps to make computers more reliable and easy to use. Artificial Intelligence in VLSI design has provided high-end and more feasible solutions to the difficulties faced by the VLSI industry. Physical design, RTL design, STA, etc. are some of the most in-demand courses to enter the VLSI industry. These courses help develop a better understanding of the many tools like Synopsis. With each new dawn, artificial intelligence in VLSI design is continually evolving, and new opportunities are being investigated.
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Author / Editor Details
Balwinder Raj, PhD is an associate professor in the Electronics and Communication Engineering Department, at the National Institute of Technical Teachers Training and Research, Chandigarh. He has published more than 100 research papers in national and international journals and conferences. Additionally, the European Commission awarded him a Mobility of Life research fellowship for postdoc research work at the University of Rome, Tor Vergata, Italy in 2010-2011. His areas of interest include nanoscale semiconductor device modeling, sensors design, FinFET-based memory design, and low-power VLSI design.
Balwinder Raj, PhD is an associate professor in the Electronics and Communication Engineering Department, at the National Institute of Technical Teachers Training and Research, Chandigarh. He has published more than 100 research papers in national and international journals and conferences. Additionally, the European Commission awarded him a Mobility of Life research fellowship for postdoc research work at the University of Rome, Tor Vergata, Italy in 2010-2011. His areas of interest include nanoscale semiconductor device modeling, sensors design, FinFET-based memory design, and low-power VLSI design.
Suman Lata Tripathi, PhD is a professor at the Lovely Professional University with more than 20 years of experience in academics. She is also a remote post-doctoral researcher at Nottingham Trent University, London, UK. She has published more than 74 research papers in refereed science journals and conferences, as well as 13 Indian patents and two copyrights. Additionally, she has edited and authored more than 17 books in different areas of electronics and electrical engineering.
Tarun Chaudhary, PhD is an assistant professor in the Electronics and Communication Engineering Department at the Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India. During her PhD, she worked on the design and mathematical modeling of the vertical field effect transistor. She has five book chapters and more than 25 research papers in peer-reviewed national and international journals and conferences.
K Srinivasa Rao, PhD is a professor and the head of the Microelectronics Research Group in the Department of Electronics and Communication Engineering at the Koneru Lakshmaiah Education Foundation, Andhra Pradesh, India. His areas of research include MEMS-based reconfigurable antennas, MEMS actuators, piezoresistive, and VLSI circuts. He is a reviewer for many SCI-indexed journals and an external reviewer for many universities.
Mandeep Singh is a professor at the Indian Institute of Information Technology, Surat Gujarat. He has five years of teaching experience with undergraduate and master students. He has published various research papers in the domain of VLSI design and circuits.
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Table of Contents
Preface
1. Comparative Analysis of MOSFET and FinFET
Mandeep Singh, Tarun Chaudhary, Balwinder Raj, Girish Wadhwa and Suman Lata Tripathi
1.1 Introduction
1.1.1 Scaling Issue
1.1.2 Problems in MOSFET
1.2 Double Gate
1.3 Advantages and Disadvantage of MOSFET
1.4 MOSFET Drawbacks
1.5 FinFET
1.6 SOI-FinFET
1.7 Issues with FinFET-Based Technology
1.8 Advantage of FinFET
1.9 Drawbacks of FinFET
1.10 Applications of FinFET Technology
1.11 Conclusion
References
2. Nanosheet FET for Future Technology Scaling
Aruru Sai Kumar, V. Bharath Sreenivasulu, M. Deekshana, G. Shanthi and K. Srinivasa Rao
2.1 Introduction
2.2 Device Description and Simulation Parameters
2.2.1 Analysis of the Results Obtained
2.2.2 Impact of Variation in Width Across Various Thickness Values on Device Characteristics
2.2.3 Transfer Characteristics
2.2.4 Impact of Geometrical Variations on ON Current
2.2.5 Impact of Geometrical Variations on OFF-Current
2.2.6 Impact of Geometrical Variations on Switching Ratio
2.2.7 Impact of Geometrical Variations on Threshold Voltage
2.2.8 Impact of Geometrical Variations on Subthreshold Swing
2.2.9 Impact of Geometrical Variations on DIBL
2.2.10 Comparison with Previous Works
2.3 Conclusions
References
3. Comparison of Different TFETs: An Overview
Rama Satya, Nageswara Rao and K. Srinivasa Rao
3.1 Introduction
3.2 Tunnel FET
3.3 Gate Engineering
3.3.1 Oxide-Thickness and Dielectric-Constant of Gateoxide
3.3.2 Multiple Gates
3.3.3 Spacer Engineering
3.4 Tunneling-Junction Engineering
3.4.1 Doping of Source
3.4.2 Heterojunctions
3.5 Materials Engineering
3.5.1 Germanium
3.5.2 III-V Semiconductors
3.5.3 Nanowires
3.6 Conclusion
References
4. GaAs Nanowire Field Effect Transistor
Shailendra Yadav, Mandeep Singh, Tarun Chaudhary, Balwinder Raj, Alok Kumar Shukla and Brajesh Kumar Kaushik
4.1 Introduction
4.1.1 Semiconductor Nanowires
4.1.2 Metal Nanowires
4.1.3 Oxide Nanowires
4.1.4 Hybrid Nanowires
4.1.5 Biological Nanowires
4.2 Properties of Nanowires
4.2.1 Electrical Properties of Nanowire
4.2.2 Mechanical Properties
4.2.3 Optical Properties of Nanowire
4.2.4 Nonlinear Optical Properties
4.2.5 Photovoltaic Properties
4.3 Nanowire-FET
4.4 Proposed Work (GaAs Nanowire-FET)
4.5 Conclusion
References
5. Graphene Nanoribbon for Future VLSI Applications: A Review
Himanshu Sharma
5.1 Introduction
5.1.1 Significance of Nano-Scale Reign
5.1.2 Importance of Repeaters
5.1.3 Interconnect Models
5.1.4 Lumped Model
5.1.5 Distributed Model
5.1.6 Aluminum and Copper as Interconnects
5.1.7 Graphene Nanoribbon as Interconnects
5.1.8 Classification of GNRs
5.1.9 Fundamental Physics
5.1.10 According to Structure and Conductivity
5.1.11 GNR Field Effect Transistor (GNRFET)
5.1.12 Model Development of GNRFET
5.1.13 Pros and Cons of GNRFET
5.2 Future Applications of Graphene and Graphene-Based FETs
References
6. Ferroelectric Random Access Memory (FeRAM)
B. Vimala Reddy, Tarun Chaudhary, Mandeep Singh and Balwinder Raj
6.1 Introduction
6.1.1 Basic Characteristics of Ferroelectric Capacitors
6.1.2 FRAM Fabrication Process
6.2 Structure of Ferroelectric Memory Cells in Capacitor-Type FRAM Devices
6.2.1 A Capacitor-Type FRAM with a Memory Cell Resembling DRAM
6.3 Write/Read Operations in the FRAM Using a Capacitor-Type Memory Cell that Resembles a DRAM
6.4 Other Capacitor-Type FRAM
6.5 FRAM of FET Type
6.6 Memory Utilizing a Ferroelectric Tunnel Junction
6.6.1 Previous Ferroelectric Memory Designs
6.7 Cross Point Matrix Array
6.8 Ferroelectric Shadow RAMs
6.9 2T2C Ferroelectric RAM Architecture
6.9.1 Evaluation of FRAM Devices’ Reliability
6.9.2 Comparative Analysis of FeRAM to Other Memory Technologies
6.10 FeRAM vs. EEPROM
6.11 FeRAM vs. Static RAM
6.12 FeRAM vs. Dynamic RAM
6.13 FeRAM vs. Flash Memory
6.13.1 Uses of FRAM Devices
6.14 Conclusion and Upcoming Trends
References
7. Applications of AI/ML Algorithms in VLSI Design and Technology
Jaswinder Singh and Damanpreet Singh
7.1 Introduction
7.2 Artificial Intelligence and Machine Learning
7.3 AI/ML Algorithms
7.4 Supervised Machine Learning (SML)
7.5 Classification Techniques
7.6 K-Nearest Neighbors (KNN)
7.7 Support Vector Machine (SVM)
7.8 Linearly Separable Classification
7.9 Decision Tree Classifier (DTC)
7.10 Performance Measures in Classification
7.11 Unsupervised Machine Learning (UML)
7.12 Hierarchical Clustering
7.13 Partitional Clustering
7.14 K-Means
7.15 Fuzzy (soft) Clustering
7.16 Cluster Validation Measures
7.17 Internal Clustering Validation Measures
7.18 External Clustering Validation Criteria
7.19 Limitation and Challenges – VLSI
References
8. Advancement of Neuromorphic Computing Systems with Memristors
Jeetendra Singh, Shailendra Singh, Balwant Raj, Vikas Patel and Balwinder Raj
8.1 Introduction
8.1.1 Evolution in Neural Networks
8.1.2 Study Plan and Difficulties in Exhibiting Effective Neuromorphic Computing Systems
8.1.3 Hardware for Neuromorphic Systems
8.1.4 Device-Level Perspective
8.1.5 Electrical Circuits to Realize Neurons
8.1.6 Broad Applications of Neuromorphic Computing
8.2 Summary
References
9. Neuromorphic Computing and Its Application
Tejasvini Thakral, Lucky Lamba, Manjeet Singh, Tarun Chaudhary and Mandeep Singh
9.1 Introduction
9.2 Evolution of Neuroinspired Computing Chips
9.3 Science Behind Brain Physics
9.4 Limitations of Semiconductor Devices
9.5 Various Combination of Networks
9.5.1 ANN-SNN Hybrid
9.5.2 Convolutional Neural Network (CNN)-Recurrent Neural Network (RNN) Hybrid
9.5.3 Deep Reinforcement Learning (DRL) Hybrid
9.5.4 Ensemble Hybrid
9.5.5 Different Types of Neural Networks
9.6 Artificial Intelligence
9.7 A Summary of Neuromorphic Hardware Methodologies
9.8 Neuromorphic Computing in Robotics
9.8.1 Sensor Processing and Perception
9.8.2 Motor Control and Movement
9.8.3 Neuromorphic Hardware Advances
9.8.4 Brain-Inspired Learning Algorithms
9.9 Challenges in Neuromorphic Computing
9.9.1 Language Understanding and Interpretation
9.9.2 Sentiment Analysis and Emotion Recognition
9.9.3 Natural Language Generation
9.9.4 Language Translation and Multilingual Processing
9.9.5 Dialogue Systems and Conversational Agents
9.9.6 Language Modeling and Prediction
9.9.7 Text Summarization and Information Extraction
9.10 Applications of Neuromorphic Computing
9.10.1 Medicines
9.10.2 Artificial Intelligence [AI]
9.10.3 Imaging
9.10.4 Sensor Processing and Perception
9.10.5 Motor Control and Movement
9.10.6 Autonomous Navigation and Mapping
9.10.7 Human-Robot Interaction and Collaboration
9.10.8 Adaptive and Learning Capabilities
9.10.9 Task Planning and Decision Making
9.10.10 Robustness and Fault Tolerance
9.10.11 Some More Applications
9.11 Conclusion
References
10. Performance Evaluation of Prototype Microstrip Patch Antenna Fabrication Using Microwave Dielectric Ceramic Nanocomposite Materials for X-Band Applications
Srilali Siragam
10.1 Introduction
10.2 Materials and Methods
10.3 Results and Discussion
10.4 Conclusions
References
11. Build and Deploy a Smart Speaker with Biometric Authentication and Advanced Voice Interaction Capabilities
Gur Sharan Kant and Kavi Bhushan
11.1 Introduction
11.2 Cybersecurity Risk as Smart Speakers Don’t Have an Authentication Process
11.3 Related Work
11.4 Overview of Biometric Authentication and the Voice Algorithm-Based Smart Speaker
11.5 Conclusion and Discussion
Acknowledgements
References
12. Boron-Based Nanomaterials for Intelligent Drug Delivery Using Computer-Aided Tools
Jupinder Kaur, Ravinder Kumar and Rajan Vohra
12.1 Introduction
12.2 Computational Details
12.3 Results and Discussion
12.3.1 Interaction of Anisamide with 7-Membered Ring of B40
12.3.2 Interaction of Anisamide with 6-Membered Ring of B40
12.3.3 Interaction of 5F-Uracil with the Heptagonal Ring of B40+7AN Complex (AN on Heptagonal Ring)
12.3.4 Interaction of 5F-Uracil with the Hexagonal Ring of B40+7AN Complex (AN on Heptagonal Ring)
12.3.5 Interaction of 5F-Uracil with the Heptagonal Ring of B40+6AN Complex (AN on Hexagonal Ring)
12.3.6 Interaction of 5F-Uracil with the Hexagonal Ring of B40+6AN Complex (AN on Hexagonal Ring)
12.3.7 Stability in Aqueous Solution
12.3.8 Drug Release
Acknowledgement
Conflict of Interest
References
13. Design and Analysis of Rectangular Wave Guide Using an HFSS Simulator
Srilali Siragam
13.1 Background
13.2 Introduction
13.3 Mathematical Computations
13.4 Numerical Analysis
13.5 Conclusion
References
Index
Back to Top
Preface
1. Comparative Analysis of MOSFET and FinFET
Mandeep Singh, Tarun Chaudhary, Balwinder Raj, Girish Wadhwa and Suman Lata Tripathi
1.1 Introduction
1.1.1 Scaling Issue
1.1.2 Problems in MOSFET
1.2 Double Gate
1.3 Advantages and Disadvantage of MOSFET
1.4 MOSFET Drawbacks
1.5 FinFET
1.6 SOI-FinFET
1.7 Issues with FinFET-Based Technology
1.8 Advantage of FinFET
1.9 Drawbacks of FinFET
1.10 Applications of FinFET Technology
1.11 Conclusion
References
2. Nanosheet FET for Future Technology Scaling
Aruru Sai Kumar, V. Bharath Sreenivasulu, M. Deekshana, G. Shanthi and K. Srinivasa Rao
2.1 Introduction
2.2 Device Description and Simulation Parameters
2.2.1 Analysis of the Results Obtained
2.2.2 Impact of Variation in Width Across Various Thickness Values on Device Characteristics
2.2.3 Transfer Characteristics
2.2.4 Impact of Geometrical Variations on ON Current
2.2.5 Impact of Geometrical Variations on OFF-Current
2.2.6 Impact of Geometrical Variations on Switching Ratio
2.2.7 Impact of Geometrical Variations on Threshold Voltage
2.2.8 Impact of Geometrical Variations on Subthreshold Swing
2.2.9 Impact of Geometrical Variations on DIBL
2.2.10 Comparison with Previous Works
2.3 Conclusions
References
3. Comparison of Different TFETs: An Overview
Rama Satya, Nageswara Rao and K. Srinivasa Rao
3.1 Introduction
3.2 Tunnel FET
3.3 Gate Engineering
3.3.1 Oxide-Thickness and Dielectric-Constant of Gateoxide
3.3.2 Multiple Gates
3.3.3 Spacer Engineering
3.4 Tunneling-Junction Engineering
3.4.1 Doping of Source
3.4.2 Heterojunctions
3.5 Materials Engineering
3.5.1 Germanium
3.5.2 III-V Semiconductors
3.5.3 Nanowires
3.6 Conclusion
References
4. GaAs Nanowire Field Effect Transistor
Shailendra Yadav, Mandeep Singh, Tarun Chaudhary, Balwinder Raj, Alok Kumar Shukla and Brajesh Kumar Kaushik
4.1 Introduction
4.1.1 Semiconductor Nanowires
4.1.2 Metal Nanowires
4.1.3 Oxide Nanowires
4.1.4 Hybrid Nanowires
4.1.5 Biological Nanowires
4.2 Properties of Nanowires
4.2.1 Electrical Properties of Nanowire
4.2.2 Mechanical Properties
4.2.3 Optical Properties of Nanowire
4.2.4 Nonlinear Optical Properties
4.2.5 Photovoltaic Properties
4.3 Nanowire-FET
4.4 Proposed Work (GaAs Nanowire-FET)
4.5 Conclusion
References
5. Graphene Nanoribbon for Future VLSI Applications: A Review
Himanshu Sharma
5.1 Introduction
5.1.1 Significance of Nano-Scale Reign
5.1.2 Importance of Repeaters
5.1.3 Interconnect Models
5.1.4 Lumped Model
5.1.5 Distributed Model
5.1.6 Aluminum and Copper as Interconnects
5.1.7 Graphene Nanoribbon as Interconnects
5.1.8 Classification of GNRs
5.1.9 Fundamental Physics
5.1.10 According to Structure and Conductivity
5.1.11 GNR Field Effect Transistor (GNRFET)
5.1.12 Model Development of GNRFET
5.1.13 Pros and Cons of GNRFET
5.2 Future Applications of Graphene and Graphene-Based FETs
References
6. Ferroelectric Random Access Memory (FeRAM)
B. Vimala Reddy, Tarun Chaudhary, Mandeep Singh and Balwinder Raj
6.1 Introduction
6.1.1 Basic Characteristics of Ferroelectric Capacitors
6.1.2 FRAM Fabrication Process
6.2 Structure of Ferroelectric Memory Cells in Capacitor-Type FRAM Devices
6.2.1 A Capacitor-Type FRAM with a Memory Cell Resembling DRAM
6.3 Write/Read Operations in the FRAM Using a Capacitor-Type Memory Cell that Resembles a DRAM
6.4 Other Capacitor-Type FRAM
6.5 FRAM of FET Type
6.6 Memory Utilizing a Ferroelectric Tunnel Junction
6.6.1 Previous Ferroelectric Memory Designs
6.7 Cross Point Matrix Array
6.8 Ferroelectric Shadow RAMs
6.9 2T2C Ferroelectric RAM Architecture
6.9.1 Evaluation of FRAM Devices’ Reliability
6.9.2 Comparative Analysis of FeRAM to Other Memory Technologies
6.10 FeRAM vs. EEPROM
6.11 FeRAM vs. Static RAM
6.12 FeRAM vs. Dynamic RAM
6.13 FeRAM vs. Flash Memory
6.13.1 Uses of FRAM Devices
6.14 Conclusion and Upcoming Trends
References
7. Applications of AI/ML Algorithms in VLSI Design and Technology
Jaswinder Singh and Damanpreet Singh
7.1 Introduction
7.2 Artificial Intelligence and Machine Learning
7.3 AI/ML Algorithms
7.4 Supervised Machine Learning (SML)
7.5 Classification Techniques
7.6 K-Nearest Neighbors (KNN)
7.7 Support Vector Machine (SVM)
7.8 Linearly Separable Classification
7.9 Decision Tree Classifier (DTC)
7.10 Performance Measures in Classification
7.11 Unsupervised Machine Learning (UML)
7.12 Hierarchical Clustering
7.13 Partitional Clustering
7.14 K-Means
7.15 Fuzzy (soft) Clustering
7.16 Cluster Validation Measures
7.17 Internal Clustering Validation Measures
7.18 External Clustering Validation Criteria
7.19 Limitation and Challenges – VLSI
References
8. Advancement of Neuromorphic Computing Systems with Memristors
Jeetendra Singh, Shailendra Singh, Balwant Raj, Vikas Patel and Balwinder Raj
8.1 Introduction
8.1.1 Evolution in Neural Networks
8.1.2 Study Plan and Difficulties in Exhibiting Effective Neuromorphic Computing Systems
8.1.3 Hardware for Neuromorphic Systems
8.1.4 Device-Level Perspective
8.1.5 Electrical Circuits to Realize Neurons
8.1.6 Broad Applications of Neuromorphic Computing
8.2 Summary
References
9. Neuromorphic Computing and Its Application
Tejasvini Thakral, Lucky Lamba, Manjeet Singh, Tarun Chaudhary and Mandeep Singh
9.1 Introduction
9.2 Evolution of Neuroinspired Computing Chips
9.3 Science Behind Brain Physics
9.4 Limitations of Semiconductor Devices
9.5 Various Combination of Networks
9.5.1 ANN-SNN Hybrid
9.5.2 Convolutional Neural Network (CNN)-Recurrent Neural Network (RNN) Hybrid
9.5.3 Deep Reinforcement Learning (DRL) Hybrid
9.5.4 Ensemble Hybrid
9.5.5 Different Types of Neural Networks
9.6 Artificial Intelligence
9.7 A Summary of Neuromorphic Hardware Methodologies
9.8 Neuromorphic Computing in Robotics
9.8.1 Sensor Processing and Perception
9.8.2 Motor Control and Movement
9.8.3 Neuromorphic Hardware Advances
9.8.4 Brain-Inspired Learning Algorithms
9.9 Challenges in Neuromorphic Computing
9.9.1 Language Understanding and Interpretation
9.9.2 Sentiment Analysis and Emotion Recognition
9.9.3 Natural Language Generation
9.9.4 Language Translation and Multilingual Processing
9.9.5 Dialogue Systems and Conversational Agents
9.9.6 Language Modeling and Prediction
9.9.7 Text Summarization and Information Extraction
9.10 Applications of Neuromorphic Computing
9.10.1 Medicines
9.10.2 Artificial Intelligence [AI]
9.10.3 Imaging
9.10.4 Sensor Processing and Perception
9.10.5 Motor Control and Movement
9.10.6 Autonomous Navigation and Mapping
9.10.7 Human-Robot Interaction and Collaboration
9.10.8 Adaptive and Learning Capabilities
9.10.9 Task Planning and Decision Making
9.10.10 Robustness and Fault Tolerance
9.10.11 Some More Applications
9.11 Conclusion
References
10. Performance Evaluation of Prototype Microstrip Patch Antenna Fabrication Using Microwave Dielectric Ceramic Nanocomposite Materials for X-Band Applications
Srilali Siragam
10.1 Introduction
10.2 Materials and Methods
10.3 Results and Discussion
10.4 Conclusions
References
11. Build and Deploy a Smart Speaker with Biometric Authentication and Advanced Voice Interaction Capabilities
Gur Sharan Kant and Kavi Bhushan
11.1 Introduction
11.2 Cybersecurity Risk as Smart Speakers Don’t Have an Authentication Process
11.3 Related Work
11.4 Overview of Biometric Authentication and the Voice Algorithm-Based Smart Speaker
11.5 Conclusion and Discussion
Acknowledgements
References
12. Boron-Based Nanomaterials for Intelligent Drug Delivery Using Computer-Aided Tools
Jupinder Kaur, Ravinder Kumar and Rajan Vohra
12.1 Introduction
12.2 Computational Details
12.3 Results and Discussion
12.3.1 Interaction of Anisamide with 7-Membered Ring of B40
12.3.2 Interaction of Anisamide with 6-Membered Ring of B40
12.3.3 Interaction of 5F-Uracil with the Heptagonal Ring of B40+7AN Complex (AN on Heptagonal Ring)
12.3.4 Interaction of 5F-Uracil with the Hexagonal Ring of B40+7AN Complex (AN on Heptagonal Ring)
12.3.5 Interaction of 5F-Uracil with the Heptagonal Ring of B40+6AN Complex (AN on Hexagonal Ring)
12.3.6 Interaction of 5F-Uracil with the Hexagonal Ring of B40+6AN Complex (AN on Hexagonal Ring)
12.3.7 Stability in Aqueous Solution
12.3.8 Drug Release
Acknowledgement
Conflict of Interest
References
13. Design and Analysis of Rectangular Wave Guide Using an HFSS Simulator
Srilali Siragam
13.1 Background
13.2 Introduction
13.3 Mathematical Computations
13.4 Numerical Analysis
13.5 Conclusion
References
Index
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