{"product_id":"machine-learning-in-nanoelectronics-devices-circuits-and-systems-hardback-9781394336173","title":"Machine Learning in Nanoelectronics; Devices, Circuits and Systems (Hardback) 9781394336173","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMachine Learning in Nanoelectronics\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eDevices, Circuits and Systems\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAshish Maurya (Edited by), Maurya (Author), Mandeep Singh (Edited by), Balwinder Raj (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394336173, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 March 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e480 pages\u003cbr\u003e22.9 x 15.2 x 2.9 cm, 0.885 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eBridge 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eNew 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e \u003c\/p\u003e \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Machine Learning in Nanoelectronics 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBandi Srinivasa Rao, Rangana Bhanu Meher Srinivas, Kenguva Sai Chandar Rao, Mandeep Singh, Anil Kumar Yadav, Balwinder Raj and Tarun Chaudhary\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.1.1 The Need for Advanced Modeling in Nanoelectronics 2\u003c\/p\u003e \u003cp\u003e1.1.2 Scope of Machine Learning Applications in Semiconductors 4\u003c\/p\u003e \u003cp\u003e1.2 Evolution of Nanoelectronics: From Macroscale to Nanoscale 4\u003c\/p\u003e \u003cp\u003e1.2.1 Moore’s Law, Transistor Scaling Challenges 4\u003c\/p\u003e \u003cp\u003e1.2.2 Physical Scaling Limits in Nanoscale Devices 7\u003c\/p\u003e \u003cp\u003e1.2.3 Various Nanoscale Device Technologies 9\u003c\/p\u003e \u003cp\u003e1.2.4 Machine Learning’s Role in Overcoming Scaling Barriers 11\u003c\/p\u003e \u003cp\u003e1.3 Machine Learning in Nanoscale Device Simulation 11\u003c\/p\u003e \u003cp\u003e1.3.1 Traditional Simulation Techniques 12\u003c\/p\u003e \u003cp\u003e1.3.1.1 Drift-Diffusion Model (DDM) 12\u003c\/p\u003e \u003cp\u003e1.3.1.2 Monte Carlo (MC) Simulations 13\u003c\/p\u003e \u003cp\u003e1.3.1.3 Non-Equilibrium Green’s Function (NEGF) Method 15\u003c\/p\u003e \u003cp\u003e1.3.1.4 Molecular Dynamics (MD) 16\u003c\/p\u003e \u003cp\u003e1.3.1.5 Quantum Mechanical Models: Density Functional Theory (DFT) and Tight-Binding (TB) Models 17\u003c\/p\u003e \u003cp\u003e1.3.2 Surrogate Modeling for Device Behaviour 18\u003c\/p\u003e \u003cp\u003e1.3.2.1 Acceleration of Quantum Simulations 18\u003c\/p\u003e \u003cp\u003e1.3.2.2 Design Space Exploration and Optimization 19\u003c\/p\u003e \u003cp\u003e1.3.2.3 Handling Variability and Defects 19\u003c\/p\u003e \u003cp\u003e1.3.2.4 Transfer Learning for New Materials and Devices 19\u003c\/p\u003e \u003cp\u003e1.3.2.5 Real-Time Parameter Tuning 20\u003c\/p\u003e \u003cp\u003e1.4 Process Optimization in Semiconductor Manufacturing 21\u003c\/p\u003e \u003cp\u003e1.4.1 Variability and Yield in Nanoscale Manufacturing 21\u003c\/p\u003e \u003cp\u003e1.4.2 Real-Time Process Control with ml 22\u003c\/p\u003e \u003cp\u003e1.4.3 Case Study: Graph-Based Yield Prediction in IC Manufacturing 24\u003c\/p\u003e \u003cp\u003e1.4.4 Reliability, Fault Detection and Self-Heating Systems 24\u003c\/p\u003e \u003cp\u003e1.5 Case Study: Machine Learning in Nanowire Tunnel FET Design 25\u003c\/p\u003e \u003cp\u003e1.5.1 Device Structure 25\u003c\/p\u003e \u003cp\u003e1.5.2 Machine Learning Approach 27\u003c\/p\u003e \u003cp\u003e1.5.3 Design Space Exploration 28\u003c\/p\u003e \u003cp\u003e1.5.4 Predictive Modeling 28\u003c\/p\u003e \u003cp\u003e1.5.5 Process Variation Mitigation 28\u003c\/p\u003e \u003cp\u003e1.6 Future Directions and Challenges 29\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 31\u003c\/p\u003e \u003cp\u003eSummary 32\u003c\/p\u003e \u003cp\u003eReferences 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Machine Learning to Explore Opportunities in Quantum 43\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJyoti Khandelwal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction to Quantum Opportunities 44\u003c\/p\u003e \u003cp\u003e2.2 Understanding Quantum Data 46\u003c\/p\u003e \u003cp\u003e2.3 Machine Learning Techniques for Quantum Applications 49\u003c\/p\u003e \u003cp\u003e2.4 Case Studies and Applications 57\u003c\/p\u003e \u003cp\u003e2.5 Tools and Frameworks for Implementation 60\u003c\/p\u003e \u003cp\u003e2.6 Challenges and Opportunities in QML 63\u003c\/p\u003e \u003cp\u003e2.7 Conclusion 63\u003c\/p\u003e \u003cp\u003eReferences 64\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Machine Learning (ML) and Nanotechnology to Heal Cancer: A Review 67\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnshu Srivastava and Shakun Srivastava\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 69\u003c\/p\u003e \u003cp\u003e3.2 Predictive Modelling and Machine Learning’s Application in Cancer Diagnostics 69\u003c\/p\u003e \u003cp\u003e3.2.1 Diagnosis of Cancer 69\u003c\/p\u003e \u003cp\u003e3.2.2 Treatment Planning 71\u003c\/p\u003e \u003cp\u003e3.3 Customized Medical Care 72\u003c\/p\u003e \u003cp\u003e3.3.1 Overview of Machine Learning in Healthcare 73\u003c\/p\u003e \u003cp\u003e3.3.2 Machine Learning Applications in Cancer Therapy 74\u003c\/p\u003e \u003cp\u003e3.3.3 Nanotechnology Applications in Cancer Therapy 76\u003c\/p\u003e \u003cp\u003e3.4 Result and Future Perspective 77\u003c\/p\u003e \u003cp\u003eReferences 79\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Multiplexing the Brain Signals for Low Power Robust Electrode Sensing in Medical Diagnosis 89\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSarin Vijay Mythry, Dinesh N., Asha V Thalange, Chakradhar Adupa, Nanditha Krishna, Praveen Kumar Reddy and Madhuri Gummineni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 90\u003c\/p\u003e \u003cp\u003e4.2 Methodology 94\u003c\/p\u003e \u003cp\u003e4.3 Simulation Results 96\u003c\/p\u003e \u003cp\u003e4.4 Conclusion 104\u003c\/p\u003e \u003cp\u003eReferences 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Hardware Architectures and Optimization Techniques for Convolutional Neural Network Accelerators 113\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHemkant Nehete, Gaurav Verma, Amit Monga, Alok Kumar Shukla, Shailendra Yadav and Brajesh Kumar Kaushik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 114\u003c\/p\u003e \u003cp\u003e5.2 Computational Complexities of Convolutional Neural Networks 115\u003c\/p\u003e \u003cp\u003e5.3 Evolution of CNN Accelerators 119\u003c\/p\u003e \u003cp\u003e5.4 Model Compression Approaches 121\u003c\/p\u003e \u003cp\u003e5.5 Hardware Optimization Techniques 124\u003c\/p\u003e \u003cp\u003e5.6 Design Space Exploration 129\u003c\/p\u003e \u003cp\u003e5.7 Hardware Platforms for Implementing CNNs 134\u003c\/p\u003e \u003cp\u003e5.8 Sparse Neural Networks 141\u003c\/p\u003e \u003cp\u003e5.9 Future Scope and Summary 145\u003c\/p\u003e \u003cp\u003eReferences 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Flexible Energy Storage Devices 155\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTanya Singh, Akriti Dewangan, Puja Kumari, Balwinder Raj, Tarun Chaudhary Mandeep Singh and Yogesh Thakur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 155\u003c\/p\u003e \u003cp\u003e6.1.1 Flexible Devices 156\u003c\/p\u003e \u003cp\u003e6.1.2 History and Origins of Flexible Devices 156\u003c\/p\u003e \u003cp\u003e6.1.3 The Evolution of Flexible Devices 158\u003c\/p\u003e \u003cp\u003e6.2 Energy Storage 159\u003c\/p\u003e \u003cp\u003e6.2.1 Energy Storage Technologies and Their History 160\u003c\/p\u003e \u003cp\u003e6.2.1.1 Batteries 160\u003c\/p\u003e \u003cp\u003e6.2.1.2 Supercapacitor Storage Systems (SSSs) 166\u003c\/p\u003e \u003cp\u003e6.3 Criteria for a Device to Store Energy 167\u003c\/p\u003e \u003cp\u003e6.3.1 The Critical Role of Energy Storage in Modern Energy Systems 168\u003c\/p\u003e \u003cp\u003e6.4 Need of Flexible Energy Storage Devices 169\u003c\/p\u003e \u003cp\u003e6.4.1 Advantages of Flexible Energy Storage Devices 170\u003c\/p\u003e \u003cp\u003e6.4.2 Disadvantages of Flexible Energy Storage Devices 171\u003c\/p\u003e \u003cp\u003e6.5 Different Structures That are Being Used in Flexible Energy Storage 172\u003c\/p\u003e \u003cp\u003e6.5.1 Fiber Structures 173\u003c\/p\u003e \u003cp\u003e6.5.2 Island Bridge Structure 177\u003c\/p\u003e \u003cp\u003e6.5.3 Interdigital Structure 178\u003c\/p\u003e \u003cp\u003e6.6 Emergence of Micro-Supercapacitors 179\u003c\/p\u003e \u003cp\u003e6.7 Materials for Energy Storage Devices 180\u003c\/p\u003e \u003cp\u003e6.8 Electrode Materials 180\u003c\/p\u003e \u003cp\u003e6.8.1 Carbon-Based Electrode 181\u003c\/p\u003e \u003cp\u003e6.8.2 Graphene‐Based Flexible Electrodes 184\u003c\/p\u003e \u003cp\u003e6.9 Comparison Sheet of Different Materials 187\u003c\/p\u003e \u003cp\u003eReferences 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 VLSI Design for AI Applications 197\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMandeep Singh, Tarun Chaudhary, Balwinder Raj, Ravi Teja, Akku Naidu and Sivaram\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 198\u003c\/p\u003e \u003cp\u003e7.2 Specialized Neural Networks Accelerators 201\u003c\/p\u003e \u003cp\u003e7.3 Memory Hierarchy Optimization 204\u003c\/p\u003e \u003cp\u003e7.4 High Speed Interconnects 208\u003c\/p\u003e \u003cp\u003e7.5 Power Optimization 211\u003c\/p\u003e \u003cp\u003e7.6 Scalability 213\u003c\/p\u003e \u003cp\u003e7.7 Key Components of VLSI Design for AI 214\u003c\/p\u003e \u003cp\u003e7.7.1 Field Programmable Gate Array (FPGA) 215\u003c\/p\u003e \u003cp\u003e7.7.2 Application-Specific Integrated Circuit (ASIC) 216\u003c\/p\u003e \u003cp\u003e7.8 Accelerating Chip Design Using ml 217\u003c\/p\u003e \u003cp\u003e7.9 Future Trends in VLSI Design for AI 219\u003c\/p\u003e \u003cp\u003e7.10 Industrial Application of VLSI Design 221\u003c\/p\u003e \u003cp\u003eReferences 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Ultra Low Power Adiabatic Logic Circuits at Nanometer Scale 231\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJitendra Kanungo, Jitendra Raghuwanshi and Sudeb Dasgupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 232\u003c\/p\u003e \u003cp\u003e8.2 Adiabatic Charging Principle 232\u003c\/p\u003e \u003cp\u003e8.3 Adiabatic Logic Family 234\u003c\/p\u003e \u003cp\u003e8.4 Comparative Simulation Results 236\u003c\/p\u003e \u003cp\u003e8.5 Key Challenges 236\u003c\/p\u003e \u003cp\u003e8.6 Comparative Analysis of Energy Recovery Logic and Conventional CMOS Logic 240\u003c\/p\u003e \u003cp\u003eSummary 247\u003c\/p\u003e \u003cp\u003eReferences 248\u003c\/p\u003e \u003cp\u003e9 High-Frequency Laminate Material-Based Antennas: Deploying Bridge-Coupled Antenna Arrays for mm Wave 5G and IoT V2X Telemetry Systems in Smart Cities 257\u003cbr\u003e \u003ci\u003eArun Raj and Durbadal Mandal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 258\u003c\/p\u003e \u003cp\u003e9.2 Antenna Design Equations 260\u003c\/p\u003e \u003cp\u003e9.3 Design and Simulation 262\u003c\/p\u003e \u003cp\u003e9.4 Conclusions 292\u003c\/p\u003e \u003cp\u003eReferences 294\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Layout Dependent Effects 307\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKirti and Deepti Kakkar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Overview of Layout Considerations 308\u003c\/p\u003e \u003cp\u003e10.1.1 Design Rules 308\u003c\/p\u003e \u003cp\u003e10.2 Analog Layout Techniques 312\u003c\/p\u003e \u003cp\u003e10.2.1 Multifinger Transistors 312\u003c\/p\u003e \u003cp\u003e10.2.2 Symmetry 315\u003c\/p\u003e \u003cp\u003e10.2.3 Shallow Trench Isolation Issues 319\u003c\/p\u003e \u003cp\u003e10.3 Effects of Layout in Deep Nanoscale CMOS 320\u003c\/p\u003e \u003cp\u003e10.3.1 Types of LDEs 321\u003c\/p\u003e \u003cp\u003e10.4 Mismatch of Devices 326\u003c\/p\u003e \u003cp\u003e10.4.1 Impact of Mismatch 329\u003c\/p\u003e \u003cp\u003e10.4.2 Types of Matching 329\u003c\/p\u003e \u003cp\u003e10.4.3 Advantages and Limitations of cc 331\u003c\/p\u003e \u003cp\u003eReferences 332\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Study of FIR Filter Hardware Architecture for Real-Time Multimedia Applications 343\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAnuraj V. and Dhandapani Vaithiyanathan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 344\u003c\/p\u003e \u003cp\u003e11.2 Digital Filtering Techniques 345\u003c\/p\u003e \u003cp\u003e11.3 Hardware Architecture 347\u003c\/p\u003e \u003cp\u003e11.3.1 Direct Form and Transposed Form 350\u003c\/p\u003e \u003cp\u003e11.3.2 Hardware Analysis of an FIR Filter 353\u003c\/p\u003e \u003cp\u003e11.3.3 Adder Logic 353\u003c\/p\u003e \u003cp\u003e11.3.4 Multiplier Technique 354\u003c\/p\u003e \u003cp\u003e11.3.5 Multiplier-Accumulator (MAC) Unit 354\u003c\/p\u003e \u003cp\u003e11.3.6 FIR Filter Design without Using Multiplier 355\u003c\/p\u003e \u003cp\u003e11.4 Simulation Setup and Results Analysis 356\u003c\/p\u003e \u003cp\u003e11.5 Summary 359\u003c\/p\u003e \u003cp\u003eReferences 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Recent Trends in Deep Neural Networks and Their Hardware Implementation for Biomedical Applications 363\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAmit Monga, Hemkant Nehete, Seema Dhull, Arshid Nisar, Shailendra Yadav and Brajesh Kumar Kaushik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 364\u003c\/p\u003e \u003cp\u003e12.2 Neural Network Architectures 365\u003c\/p\u003e \u003cp\u003e12.3 Deep Learning Algorithms for Medical Images 373\u003c\/p\u003e \u003cp\u003e12.4 Recent Trends in Hardware Architectures of DNN 386\u003c\/p\u003e \u003cp\u003e12.5 Challenges and Opportunities 393\u003c\/p\u003e \u003cp\u003e12.6 Summary 396\u003c\/p\u003e \u003cp\u003eAcknowledgements 397\u003c\/p\u003e \u003cp\u003eReferences 397\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Integration with IoT for Smart Homes 409\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAkash Kumar Prajapati, Shubham Patel, Suramya Kumar Rawat, Mandeep Singh, Tarun Chaudhary and Balwinder Raj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 410\u003c\/p\u003e \u003cp\u003e13.2 Sensors for Smart Homes 413\u003c\/p\u003e \u003cp\u003e13.2.1 Motion Detection 413\u003c\/p\u003e \u003cp\u003e13.2.2 Flame-Gas Detection Sensor 413\u003c\/p\u003e \u003cp\u003e13.2.3 Toxic Gas Detection 414\u003c\/p\u003e \u003cp\u003e13.2.4 Moisture Leak Detection 415\u003c\/p\u003e \u003cp\u003e13.2.5 Proximity Sensors 416\u003c\/p\u003e \u003cp\u003e13.2.6 Temperature Sensors 416\u003c\/p\u003e \u003cp\u003e13.2.7 Humidity Sensors 417\u003c\/p\u003e \u003cp\u003e13.2.8 Light Sensors 418\u003c\/p\u003e \u003cp\u003e13.2.9 Smart Thermostat Sensor 418\u003c\/p\u003e \u003cp\u003e13.2.10 Intercom\/Hub 418\u003c\/p\u003e \u003cp\u003e13.3 Connectivity Protocols for IoT Smart Homes 419\u003c\/p\u003e \u003cp\u003e13.3.1 Zigbee 419\u003c\/p\u003e \u003cp\u003e13.3.2 Z-Wave 419\u003c\/p\u003e \u003cp\u003e13.3.3 Wi-Fi 420\u003c\/p\u003e \u003cp\u003e13.3.4 Bluetooth and Bluetooth Low Energy (BLE) 420\u003c\/p\u003e \u003cp\u003e13.3.5 MQTT (Message Queuing Telemetry Transport) 420\u003c\/p\u003e \u003cp\u003e13.3.6 CoAP (Constrained Application Protocol) 421\u003c\/p\u003e \u003cp\u003e13.3.7 LoRa WAN (Long Range Wide Area Network) 421\u003c\/p\u003e \u003cp\u003e13.3.8 NFC (Near Field Communication) 421\u003c\/p\u003e \u003cp\u003e13.3.9 Cellular(4G\/5G) 422\u003c\/p\u003e \u003cp\u003e13.4 Smart Appliances for Smart Homes 422\u003c\/p\u003e \u003cp\u003e13.4.1 Smart Kitchen Appliances 422\u003c\/p\u003e \u003cp\u003e13.4.2 Smart Laundry Appliances 422\u003c\/p\u003e \u003cp\u003e13.4.3 Smart Cleaning Devices 423\u003c\/p\u003e \u003cp\u003e13.4.4 Smart Security Devices 423\u003c\/p\u003e \u003cp\u003e13.4.5 Smart Lighting 423\u003c\/p\u003e \u003cp\u003e13.4.6 Smart Speaker and Hubs 423\u003c\/p\u003e \u003cp\u003e13.4.7 Smart Energy Monitors 423\u003c\/p\u003e \u003cp\u003e13.4.8 Integration and Automation 424\u003c\/p\u003e \u003cp\u003e13.4.9 Benefits of Smart Devices 424\u003c\/p\u003e \u003cp\u003e13.5 Voice Assistants 424\u003c\/p\u003e \u003cp\u003e13.5.1 Amazon Alexa 425\u003c\/p\u003e \u003cp\u003e13.5.2 Google Assistant 425\u003c\/p\u003e \u003cp\u003e13.5.3 Apple Siri 425\u003c\/p\u003e \u003cp\u003e13.5.4 Microsoft Cortana 426\u003c\/p\u003e \u003cp\u003e13.5.5 Samsung Bixby 426\u003c\/p\u003e \u003cp\u003e13.5.6 Raspberry Pi and Custom Assistants 426\u003c\/p\u003e \u003cp\u003e13.6 Security and Surveillance 426\u003c\/p\u003e \u003cp\u003e13.7 Home Healthcare System 427\u003c\/p\u003e \u003cp\u003e13.7.1 Features for Healthcare in Smart Home 428\u003c\/p\u003e \u003cp\u003e13.7.2 User Safety 428\u003c\/p\u003e \u003cp\u003e13.7.3 Patient Health 429\u003c\/p\u003e \u003cp\u003e13.7.4 Design Flexibility 430\u003c\/p\u003e \u003cp\u003e13.7.5 Information and User Engagement 430\u003c\/p\u003e \u003cp\u003e13.8 User Interfaces and Experiences 430\u003c\/p\u003e \u003cp\u003e13.8.1 Mobile Apps and Dashboards 431\u003c\/p\u003e \u003cp\u003e13.8.2 Wearable and Voice Interaction 431\u003c\/p\u003e \u003cp\u003e13.8.3 Intuitive Design for Usability 432\u003c\/p\u003e \u003cp\u003e13.8.4 Remote and In-Home Control Panels 432\u003c\/p\u003e \u003cp\u003e13.9 Sustainability and Smart Homes 433\u003c\/p\u003e \u003cp\u003e13.9.1 Energy Management 433\u003c\/p\u003e \u003cp\u003e13.9.2 Sustainable Appliances 434\u003c\/p\u003e \u003cp\u003e13.9.3 Smart Grids and Renewable Integration 434\u003c\/p\u003e \u003cp\u003e13.9.4 Automated Water and Climate Control 434\u003c\/p\u003e \u003cp\u003e13.10 Future Trends in Smart Home IoT 435\u003c\/p\u003e \u003cp\u003e13.10.1 AI and Machine Learning 435\u003c\/p\u003e \u003cp\u003e13.10.2 Edge Computing 436\u003c\/p\u003e \u003cp\u003e13.10.3 5G and the Future of Connectivity 436\u003c\/p\u003e \u003cp\u003e13.10.4 Interoperability and Universal Standards 436\u003c\/p\u003e \u003cp\u003e13.10.5 Sustainability and Green Energy Solutions 437\u003c\/p\u003e \u003cp\u003e13.11 Conclusions 437\u003c\/p\u003e \u003cp\u003eReferences 438\u003c\/p\u003e \u003cp\u003eAbout the Editors 449\u003c\/p\u003e \u003cp\u003eIndex 451\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433818190104,"sku":"9781394336173","price":192.95,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394336173.jpg?v=1784853940","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/machine-learning-in-nanoelectronics-devices-circuits-and-systems-hardback-9781394336173","provider":"Freshly Printed Books","version":"1.0","type":"link"}