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For Authors
Submit a ProposalMachine Learning in Nanoelectronics
One Line Description
Bridge the gap between advanced algorithms and hardware innovation with this essential book, which details how machine learning is being used to overcome challenges in nanoelectronics while laying the critical groundwork for the future of neuromorphic computing hardware.
Audience
Research scholars, students, and industry professionals who are working in semiconductor design, nanotechnology, microelectronics, and material science.
Research scholars, students, and industry professionals who are working in semiconductor design, nanotechnology, microelectronics, and material science.
Description
New techniques for obtaining insights from enormous amounts of data and efficiently acquiring smaller data sets are provided by recent developments in machine learning. Researchers in nanoscience and nanoelectronics are experimenting with these tools to tackle challenges across many fields. Nanoscience and nanoelectronics not only advance machine learning but also lay the groundwork for neuromorphic computing hardware to broaden machine learning algorithm implementation. This book is a collection of possibilities for machine learning in nanoelectronics, semiconductor devices, and based circuits. With an easy-to-understand approach, this book explores the latest in machine learning in nanoelectronics materials and nanoscale devices through insights and analysis of recent developments in nanoelectronics.
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New techniques for obtaining insights from enormous amounts of data and efficiently acquiring smaller data sets are provided by recent developments in machine learning. Researchers in nanoscience and nanoelectronics are experimenting with these tools to tackle challenges across many fields. Nanoscience and nanoelectronics not only advance machine learning but also lay the groundwork for neuromorphic computing hardware to broaden machine learning algorithm implementation. This book is a collection of possibilities for machine learning in nanoelectronics, semiconductor devices, and based circuits. With an easy-to-understand approach, this book explores the latest in machine learning in nanoelectronics materials and nanoscale devices through insights and analysis of recent developments in nanoelectronics.
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Author / Editor Details
Ashish Maurya, PhD is an Assistant Professor in the Electronics and Communication Engineering Department and Assistant Dean of Research and Development at the Kanpur Institute of Technology. He has published nine journal articles and seven international conference proceedings. His current research interests include machine learning in semiconductor physics, nanoelectronics, and emerging semiconductor materials and their applications in various analog and digital circuits.
Ashish Maurya, PhD is an Assistant Professor in the Electronics and Communication Engineering Department and Assistant Dean of Research and Development at the Kanpur Institute of Technology. He has published nine journal articles and seven international conference proceedings. His current research interests include machine learning in semiconductor physics, nanoelectronics, and emerging semiconductor materials and their applications in various analog and digital circuits.
Mandeep Singh is a Professor in the Electronics and Communication Engineering Department at the Indian Institute of Information Technology. He has published three books, five book chapters, and various research papers in international journals. His areas of research include semiconductor device modeling, memory design, and low-power VLSI design.
Balwinder Raj, PhD is an Associate Professor at the National Institute of Technology Jalandhar. He has authored and co-authored ten books, 15 book chapters, and more than 150 research papers in peer-reviewed national and international journals and conferences. His areas of interest include classical and non-classical nanoscale semiconductor device modeling, nanoelectronics, FinFET-based memory design, and low-power VLSI design.
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Table of Contents
Preface
1. Introduction to Machine Learning in Nanoelectronics
Bandi Srinivasa Rao, Rangana Bhanu Meher Srinivas, Kenguva Sai Chandar Rao, Mandeep Singh, Anil Kumar Yadav, Balwinder Raj and Tarun Chaudhary
1.1 Introduction
1.1.1 The Need for Advanced Modeling in Nanoelectronics
1.1.2 Scope of Machine Learning Applications in Semiconductors
1.2 Evolution of Nanoelectronics: From Macroscale to Nanoscale
1.2.1 Moore’s Law, Transistor Scaling Challenges
1.2.2 Physical Scaling Limits in Nanoscale Devices
1.2.3 Various Nanoscale Device Technologies
1.2.4 Machine Learning’s Role in Overcoming Scaling Barriers
1.3 Machine Learning in Nanoscale Device Simulation
1.3.1 Traditional Simulation Techniques
1.3.1.1 Drift-Diffusion Model (DDM)
1.3.1.2 Monte Carlo (MC) Simulations
1.3.1.3 Non-Equilibrium Green’s Function (NEGF) Method
1.3.1.4 Molecular Dynamics (MD)
1.3.1.5 Quantum Mechanical Models: Density Functional Theory (DFT) and Tight-Binding (TB) Models
1.3.2 Surrogate Modeling for Device Behaviour
1.3.2.1 Acceleration of Quantum Simulations
1.3.2.2 Design Space Exploration and Optimization
1.3.2.3 Handling Variability and Defects
1.3.2.4 Transfer Learning for New Materials and Devices
1.3.2.5 Real-Time Parameter Tuning
1.4 Process Optimization in Semiconductor Manufacturing
1.4.1 Variability and Yield in Nanoscale Manufacturing
1.4.2 Real-Time Process Control with ML
1.4.3 Case Study: Graph-Based Yield Prediction in IC Manufacturing
1.4.4 Reliability, Fault Detection and Self-Heating Systems
1.5 Case Study: Machine Learning in Nanowire Tunnel FET Design
1.5.1 Device Structure
1.5.2 Machine Learning Approach
1.5.3 Design Space Exploration
1.5.4 Predictive Modeling
1.5.5 Process Variation Mitigation
1.6 Future Directions and Challenges
1.7 Conclusion
Summary
References
2. Machine Learning to Explore Opportunities in Quantum
Jyoti Khandelwal
2.1 Introduction to Quantum Opportunities
2.2 Understanding Quantum Data
2.3 Machine Learning Techniques for Quantum Applications
2.4 Case Studies and Applications
2.5 Tools and Frameworks for Implementation
2.6 Challenges and Opportunities in QML
2.7 Conclusion
References
3. Machine Learning (ML) and Nanotechnology to Heal Cancer: A Review
Anshu Srivastava and Shakun Srivastava
3.1 Introduction
3.2 Predictive Modelling and Machine Learning’s Application in Cancer Diagnostics
3.2.1 Diagnosis of Cancer
3.2.2 Treatment Planning
3.3 Customized Medical Care
3.3.1 Overview of Machine Learning in Healthcare
3.3.2 Machine Learning Applications in Cancer Therapy
3.3.3 Nanotechnology Applications in Cancer Therapy
3.4 Result and Future Perspective
References
4. Multiplexing the Brain Signals for Low Power Robust Electrode Sensing in Medical Diagnosis
Sarin Vijay Mythry, Dinesh N., Asha V Thalange, Chakradhar Adupa, Nanditha Krishna, Praveen Kumar Reddy and Madhuri Gummineni
4.1 Introduction
4.2 Methodology
4.3 Simulation Results
4.4 Conclusion
References
5. Hardware Architectures and Optimization Techniques for Convolutional Neural Network Accelerators
Hemkant Nehete, Gaurav Verma, Amit Monga, Alok Kumar Shukla, Shailendra Yadav and Brajesh Kumar Kaushik
5.1 Introduction
5.2 Computational Complexities of Convolutional Neural Networks
5.3 Evolution of CNN Accelerators
5.4 Model Compression Approaches
5.5 Hardware Optimization Techniques
5.6 Design Space Exploration
5.7 Hardware Platforms for Implementing CNNs
5.8 Sparse Neural Networks
5.9 Future Scope and Summary
References
6. Flexible Energy Storage Devices
Tanya Singh, Akriti Dewangan, Puja Kumari, Balwinder Raj, Tarun Chaudhary Mandeep Singh and Yogesh Thakur
6.1 Introduction
6.1.1 Flexible Devices
6.1.2 History and Origins of Flexible Devices
6.1.3 The Evolution of Flexible Devices
6.2 Energy Storage
6.2.1 Energy Storage Technologies and Their History
6.2.1.1 Batteries
6.2.1.2 Supercapacitor Storage Systems (SSSs)
6.3 Criteria for a Device to Store Energy
6.3.1 The Critical Role of Energy Storage in Modern Energy Systems
6.4 Need of Flexible Energy Storage Devices
6.4.1 Advantages of Flexible Energy Storage Devices
6.4.2 Disadvantages of Flexible Energy Storage Devices
6.5 Different Structures That are Being Used in Flexible Energy Storage
6.5.1 Fiber Structures
6.5.2 Island Bridge Structure
6.5.3 Interdigital Structure
6.6 Emergence of Micro-Supercapacitors
6.7 Materials for Energy Storage Devices
6.8 Electrode Materials
6.8.1 Carbon-Based Electrode
6.8.2 Graphene‐Based Flexible Electrodes
6.9 Comparison Sheet of Different Materials
References
7. VLSI Design for AI Applications
Mandeep Singh, Tarun Chaudhary, Balwinder Raj, Ravi Teja, Akku Naidu and Sivaram
7.1 Introduction
7.2 Specialized Neural Networks Accelerators
7.3 Memory Hierarchy Optimization
7.4 High Speed Interconnects
7.5 Power Optimization
7.6 Scalability
7.7 Key Components of VLSI Design for AI
7.7.1 Field Programmable Gate Array (FPGA)
7.7.2 Application-Specific Integrated Circuit (ASIC)
7.8 Accelerating Chip Design Using ML
7.9 Future Trends in VLSI Design for AI
7.10 Industrial Application of VLSI Design
References
8. Ultra Low Power Adiabatic Logic Circuits at Nanometer Scale
Jitendra Kanungo, Jitendra Raghuwanshi and Sudeb Dasgupta
8.1 Introduction
8.2 Adiabatic Charging Principle
8.3 Adiabatic Logic Family
8.4 Comparative Simulation Results
8.5 Key Challenges
8.6 Comparative Analysis of Energy Recovery Logic and Conventional CMOS Logic
Summary
References
9. High-Frequency Laminate Material-Based Antennas: Deploying Bridge-Coupled Antenna Arrays for mm Wave 5G and IoT V2X Telemetry Systems in Smart Cities
Arun Raj and Durbadal Mandal
9.1 Introduction
9.2 Antenna Design Equations
9.3 Design and Simulation
9.4 Conclusions
References
10. Layout Dependent Effects
Kirti and Deepti Kakkar
10.1 Overview of Layout Considerations
10.1.1 Design Rules
10.2 Analog Layout Techniques
10.2.1 Multifinger Transistors
10.2.2 Symmetry
10.2.3 Shallow Trench Isolation Issues
10.3 Effects of Layout in Deep Nanoscale CMOS
10.3.1 Types of LDEs
10.4 Mismatch of Devices
10.4.1 Impact of Mismatch
10.4.2 Types of Matching
10.4.3 Advantages and Limitations of CC
References
11. Study of FIR Filter Hardware Architecture for Real-Time Multimedia Applications
Anuraj V. and Dhandapani Vaithiyanathan
11.1 Introduction
11.2 Digital Filtering Techniques
11.3 Hardware Architecture
11.3.1 Direct Form and Transposed Form
11.3.2 Hardware Analysis of an FIR Filter
11.3.3 Adder Logic
11.3.4 Multiplier Technique
11.3.5 Multiplier-Accumulator (MAC) Unit
11.3.6 FIR Filter Design without Using Multiplier
11.4 Simulation Setup and Results Analysis
11.5 Summary
References
12. Recent Trends in Deep Neural Networks and Their Hardware Implementation for Biomedical Applications
Amit Monga, Hemkant Nehete, Seema Dhull, Arshid Nisar, Shailendra Yadav and Brajesh Kumar Kaushik
12.1 Introduction
12.2 Neural Network Architectures
12.3 Deep Learning Algorithms for Medical Images
12.4 Recent Trends in Hardware Architectures of DNN
12.5 Challenges and Opportunities
12.6 Summary
Acknowledgements
References
13. Integration with IoT for Smart Homes
Akash Kumar Prajapati, Shubham Patel, Suramya Kumar Rawat, Mandeep Singh, Tarun Chaudhary and Balwinder Raj
13.1 Introduction
13.2 Sensors for Smart Homes
13.2.1 Motion Detection
13.2.2 Flame-Gas Detection Sensor
13.2.3 Toxic Gas Detection
13.2.4 Moisture Leak Detection
13.2.5 Proximity Sensors
13.2.6 Temperature Sensors
13.2.7 Humidity Sensors
13.2.8 Light Sensors
13.2.9 Smart Thermostat Sensor
13.2.10 Intercom/Hub
13.3 Connectivity Protocols for IoT Smart Homes
13.3.1 Zigbee
13.3.2 Z-Wave
13.3.3 Wi-Fi
13.3.4 Bluetooth and Bluetooth Low Energy (BLE)
13.3.5 MQTT (Message Queuing Telemetry Transport)
13.3.6 CoAP (Constrained Application Protocol)
13.3.7 LoRa WAN (Long Range Wide Area Network)
13.3.8 NFC (Near Field Communication)
13.3.9 Cellular(4G/5G)
13.4 Smart Appliances for Smart Homes
13.4.1 Smart Kitchen Appliances
13.4.2 Smart Laundry Appliances
13.4.3 Smart Cleaning Devices
13.4.4 Smart Security Devices
13.4.5 Smart Lighting
13.4.6 Smart Speaker and Hubs
13.4.7 Smart Energy Monitors
13.4.8 Integration and Automation
13.4.9 Benefits of Smart Devices
13.5 Voice Assistants
13.5.1 Amazon Alexa
13.5.2 Google Assistant
13.5.3 Apple Siri
13.5.4 Microsoft Cortana
13.5.5 Samsung Bixby
13.5.6 Raspberry Pi and Custom Assistants
13.6 Security and Surveillance
13.7 Home Healthcare System
13.7.1 Features for Healthcare in Smart Home
13.7.2 User Safety
13.7.3 Patient Health
13.7.4 Design Flexibility
13.7.5 Information and User Engagement
13.8 User Interfaces and Experiences
13.8.1 Mobile Apps and Dashboards
13.8.2 Wearable and Voice Interaction
13.8.3 Intuitive Design for Usability
13.8.4 Remote and In-Home Control Panels
13.9 Sustainability and Smart Homes
13.9.1 Energy Management
13.9.2 Sustainable Appliances
13.9.3 Smart Grids and Renewable Integration
13.9.4 Automated Water and Climate Control
13.10 Future Trends in Smart Home IoT
13.10.1 AI and Machine Learning
13.10.2 Edge Computing
13.10.3 5G and the Future of Connectivity
13.10.4 Interoperability and Universal Standards
13.10.5 Sustainability and Green Energy Solutions
13.11 Conclusions
References
About the Editors
Index
Back to Top
Preface
1. Introduction to Machine Learning in Nanoelectronics
Bandi Srinivasa Rao, Rangana Bhanu Meher Srinivas, Kenguva Sai Chandar Rao, Mandeep Singh, Anil Kumar Yadav, Balwinder Raj and Tarun Chaudhary
1.1 Introduction
1.1.1 The Need for Advanced Modeling in Nanoelectronics
1.1.2 Scope of Machine Learning Applications in Semiconductors
1.2 Evolution of Nanoelectronics: From Macroscale to Nanoscale
1.2.1 Moore’s Law, Transistor Scaling Challenges
1.2.2 Physical Scaling Limits in Nanoscale Devices
1.2.3 Various Nanoscale Device Technologies
1.2.4 Machine Learning’s Role in Overcoming Scaling Barriers
1.3 Machine Learning in Nanoscale Device Simulation
1.3.1 Traditional Simulation Techniques
1.3.1.1 Drift-Diffusion Model (DDM)
1.3.1.2 Monte Carlo (MC) Simulations
1.3.1.3 Non-Equilibrium Green’s Function (NEGF) Method
1.3.1.4 Molecular Dynamics (MD)
1.3.1.5 Quantum Mechanical Models: Density Functional Theory (DFT) and Tight-Binding (TB) Models
1.3.2 Surrogate Modeling for Device Behaviour
1.3.2.1 Acceleration of Quantum Simulations
1.3.2.2 Design Space Exploration and Optimization
1.3.2.3 Handling Variability and Defects
1.3.2.4 Transfer Learning for New Materials and Devices
1.3.2.5 Real-Time Parameter Tuning
1.4 Process Optimization in Semiconductor Manufacturing
1.4.1 Variability and Yield in Nanoscale Manufacturing
1.4.2 Real-Time Process Control with ML
1.4.3 Case Study: Graph-Based Yield Prediction in IC Manufacturing
1.4.4 Reliability, Fault Detection and Self-Heating Systems
1.5 Case Study: Machine Learning in Nanowire Tunnel FET Design
1.5.1 Device Structure
1.5.2 Machine Learning Approach
1.5.3 Design Space Exploration
1.5.4 Predictive Modeling
1.5.5 Process Variation Mitigation
1.6 Future Directions and Challenges
1.7 Conclusion
Summary
References
2. Machine Learning to Explore Opportunities in Quantum
Jyoti Khandelwal
2.1 Introduction to Quantum Opportunities
2.2 Understanding Quantum Data
2.3 Machine Learning Techniques for Quantum Applications
2.4 Case Studies and Applications
2.5 Tools and Frameworks for Implementation
2.6 Challenges and Opportunities in QML
2.7 Conclusion
References
3. Machine Learning (ML) and Nanotechnology to Heal Cancer: A Review
Anshu Srivastava and Shakun Srivastava
3.1 Introduction
3.2 Predictive Modelling and Machine Learning’s Application in Cancer Diagnostics
3.2.1 Diagnosis of Cancer
3.2.2 Treatment Planning
3.3 Customized Medical Care
3.3.1 Overview of Machine Learning in Healthcare
3.3.2 Machine Learning Applications in Cancer Therapy
3.3.3 Nanotechnology Applications in Cancer Therapy
3.4 Result and Future Perspective
References
4. Multiplexing the Brain Signals for Low Power Robust Electrode Sensing in Medical Diagnosis
Sarin Vijay Mythry, Dinesh N., Asha V Thalange, Chakradhar Adupa, Nanditha Krishna, Praveen Kumar Reddy and Madhuri Gummineni
4.1 Introduction
4.2 Methodology
4.3 Simulation Results
4.4 Conclusion
References
5. Hardware Architectures and Optimization Techniques for Convolutional Neural Network Accelerators
Hemkant Nehete, Gaurav Verma, Amit Monga, Alok Kumar Shukla, Shailendra Yadav and Brajesh Kumar Kaushik
5.1 Introduction
5.2 Computational Complexities of Convolutional Neural Networks
5.3 Evolution of CNN Accelerators
5.4 Model Compression Approaches
5.5 Hardware Optimization Techniques
5.6 Design Space Exploration
5.7 Hardware Platforms for Implementing CNNs
5.8 Sparse Neural Networks
5.9 Future Scope and Summary
References
6. Flexible Energy Storage Devices
Tanya Singh, Akriti Dewangan, Puja Kumari, Balwinder Raj, Tarun Chaudhary Mandeep Singh and Yogesh Thakur
6.1 Introduction
6.1.1 Flexible Devices
6.1.2 History and Origins of Flexible Devices
6.1.3 The Evolution of Flexible Devices
6.2 Energy Storage
6.2.1 Energy Storage Technologies and Their History
6.2.1.1 Batteries
6.2.1.2 Supercapacitor Storage Systems (SSSs)
6.3 Criteria for a Device to Store Energy
6.3.1 The Critical Role of Energy Storage in Modern Energy Systems
6.4 Need of Flexible Energy Storage Devices
6.4.1 Advantages of Flexible Energy Storage Devices
6.4.2 Disadvantages of Flexible Energy Storage Devices
6.5 Different Structures That are Being Used in Flexible Energy Storage
6.5.1 Fiber Structures
6.5.2 Island Bridge Structure
6.5.3 Interdigital Structure
6.6 Emergence of Micro-Supercapacitors
6.7 Materials for Energy Storage Devices
6.8 Electrode Materials
6.8.1 Carbon-Based Electrode
6.8.2 Graphene‐Based Flexible Electrodes
6.9 Comparison Sheet of Different Materials
References
7. VLSI Design for AI Applications
Mandeep Singh, Tarun Chaudhary, Balwinder Raj, Ravi Teja, Akku Naidu and Sivaram
7.1 Introduction
7.2 Specialized Neural Networks Accelerators
7.3 Memory Hierarchy Optimization
7.4 High Speed Interconnects
7.5 Power Optimization
7.6 Scalability
7.7 Key Components of VLSI Design for AI
7.7.1 Field Programmable Gate Array (FPGA)
7.7.2 Application-Specific Integrated Circuit (ASIC)
7.8 Accelerating Chip Design Using ML
7.9 Future Trends in VLSI Design for AI
7.10 Industrial Application of VLSI Design
References
8. Ultra Low Power Adiabatic Logic Circuits at Nanometer Scale
Jitendra Kanungo, Jitendra Raghuwanshi and Sudeb Dasgupta
8.1 Introduction
8.2 Adiabatic Charging Principle
8.3 Adiabatic Logic Family
8.4 Comparative Simulation Results
8.5 Key Challenges
8.6 Comparative Analysis of Energy Recovery Logic and Conventional CMOS Logic
Summary
References
9. High-Frequency Laminate Material-Based Antennas: Deploying Bridge-Coupled Antenna Arrays for mm Wave 5G and IoT V2X Telemetry Systems in Smart Cities
Arun Raj and Durbadal Mandal
9.1 Introduction
9.2 Antenna Design Equations
9.3 Design and Simulation
9.4 Conclusions
References
10. Layout Dependent Effects
Kirti and Deepti Kakkar
10.1 Overview of Layout Considerations
10.1.1 Design Rules
10.2 Analog Layout Techniques
10.2.1 Multifinger Transistors
10.2.2 Symmetry
10.2.3 Shallow Trench Isolation Issues
10.3 Effects of Layout in Deep Nanoscale CMOS
10.3.1 Types of LDEs
10.4 Mismatch of Devices
10.4.1 Impact of Mismatch
10.4.2 Types of Matching
10.4.3 Advantages and Limitations of CC
References
11. Study of FIR Filter Hardware Architecture for Real-Time Multimedia Applications
Anuraj V. and Dhandapani Vaithiyanathan
11.1 Introduction
11.2 Digital Filtering Techniques
11.3 Hardware Architecture
11.3.1 Direct Form and Transposed Form
11.3.2 Hardware Analysis of an FIR Filter
11.3.3 Adder Logic
11.3.4 Multiplier Technique
11.3.5 Multiplier-Accumulator (MAC) Unit
11.3.6 FIR Filter Design without Using Multiplier
11.4 Simulation Setup and Results Analysis
11.5 Summary
References
12. Recent Trends in Deep Neural Networks and Their Hardware Implementation for Biomedical Applications
Amit Monga, Hemkant Nehete, Seema Dhull, Arshid Nisar, Shailendra Yadav and Brajesh Kumar Kaushik
12.1 Introduction
12.2 Neural Network Architectures
12.3 Deep Learning Algorithms for Medical Images
12.4 Recent Trends in Hardware Architectures of DNN
12.5 Challenges and Opportunities
12.6 Summary
Acknowledgements
References
13. Integration with IoT for Smart Homes
Akash Kumar Prajapati, Shubham Patel, Suramya Kumar Rawat, Mandeep Singh, Tarun Chaudhary and Balwinder Raj
13.1 Introduction
13.2 Sensors for Smart Homes
13.2.1 Motion Detection
13.2.2 Flame-Gas Detection Sensor
13.2.3 Toxic Gas Detection
13.2.4 Moisture Leak Detection
13.2.5 Proximity Sensors
13.2.6 Temperature Sensors
13.2.7 Humidity Sensors
13.2.8 Light Sensors
13.2.9 Smart Thermostat Sensor
13.2.10 Intercom/Hub
13.3 Connectivity Protocols for IoT Smart Homes
13.3.1 Zigbee
13.3.2 Z-Wave
13.3.3 Wi-Fi
13.3.4 Bluetooth and Bluetooth Low Energy (BLE)
13.3.5 MQTT (Message Queuing Telemetry Transport)
13.3.6 CoAP (Constrained Application Protocol)
13.3.7 LoRa WAN (Long Range Wide Area Network)
13.3.8 NFC (Near Field Communication)
13.3.9 Cellular(4G/5G)
13.4 Smart Appliances for Smart Homes
13.4.1 Smart Kitchen Appliances
13.4.2 Smart Laundry Appliances
13.4.3 Smart Cleaning Devices
13.4.4 Smart Security Devices
13.4.5 Smart Lighting
13.4.6 Smart Speaker and Hubs
13.4.7 Smart Energy Monitors
13.4.8 Integration and Automation
13.4.9 Benefits of Smart Devices
13.5 Voice Assistants
13.5.1 Amazon Alexa
13.5.2 Google Assistant
13.5.3 Apple Siri
13.5.4 Microsoft Cortana
13.5.5 Samsung Bixby
13.5.6 Raspberry Pi and Custom Assistants
13.6 Security and Surveillance
13.7 Home Healthcare System
13.7.1 Features for Healthcare in Smart Home
13.7.2 User Safety
13.7.3 Patient Health
13.7.4 Design Flexibility
13.7.5 Information and User Engagement
13.8 User Interfaces and Experiences
13.8.1 Mobile Apps and Dashboards
13.8.2 Wearable and Voice Interaction
13.8.3 Intuitive Design for Usability
13.8.4 Remote and In-Home Control Panels
13.9 Sustainability and Smart Homes
13.9.1 Energy Management
13.9.2 Sustainable Appliances
13.9.3 Smart Grids and Renewable Integration
13.9.4 Automated Water and Climate Control
13.10 Future Trends in Smart Home IoT
13.10.1 AI and Machine Learning
13.10.2 Edge Computing
13.10.3 5G and the Future of Connectivity
13.10.4 Interoperability and Universal Standards
13.10.5 Sustainability and Green Energy Solutions
13.11 Conclusions
References
About the Editors
Index
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