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Carbon-Based Nanomaterials for Green Applications
Carbon-Based Nanomaterials for Green Applications
Autore Kumar Upendra
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (642 pages)
Disciplina 620.193
Altri autori (Persone) SonkarPiyush Kumar
TripathiSuman Lata
Soggetto topico Carbon compounds
Nanostructured materials
ISBN 9781394243426
1394243421
9781394243402
1394243405
9781394243419
1394243413
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgments -- Chapter 1 Green Energy: An Introduction, Present, and Future Prospective -- 1.1 Introduction -- 1.2 Present Status of Green Energy -- 1.3 Global Renewable Energy Capacity -- 1.4 Leading Green Energy Technologies -- 1.5 Challenges in Green Energy Adoption -- 1.6 Prospects of Green Energy -- 1.7 Sustainable Practices in Green Energy -- 1.8 Case Studies of Successful Green Energy Projects -- 1.9 Policy and Regulatory Framework for Green Energy -- 1.10 Opportunities and Challenges in the Evolution to a Green Energy Future -- 1.10.1 Opportunities -- 1.10.2 Challenges -- 1.11 Conclusion -- References -- Chapter 2 Properties of Carbon-Based Nanomaterials and Techniques for Characterization -- 2.1 Introduction -- 2.1.1 Carbon Nanotubes -- 2.1.2 Graphene -- 2.1.3 Graphene Oxide -- 2.1.4 Fullerenes -- 2.2 Significance in Green Energy -- 2.2.1 Energy Storage -- 2.2.2 Solar Energy -- 2.2.3 Catalysis and Fuel Cells -- 2.2.4 Thermal Management -- 2.2.5 Environmental Remediation -- 2.3 Techniques for Characterization of Properties of Carbon Nanomaterials -- 2.3.1 Electrical Conductivity -- 2.3.2 Thermal Conductivity -- 2.3.3 Mechanical Strength -- 2.3.4 Surface Area Characterization -- 2.3.5 Scanning Electron Microscopy -- 2.3.6 Energy Dispersive X-ray Spectroscopy -- 2.3.7 Transmission Electron Microscopy -- 2.3.8 Electron Energy Loss Spectroscopy -- 2.3.9 Atomic Force Microscopy -- 2.3.10 Raman Spectroscopy -- 2.3.11 Photoluminescence -- 2.3.12 Time-Resolved Photoluminescence -- 2.3.13 Thermal Gravimetric Analysis and Differential Scanning Calorimetry -- 2.3.14 Fourier Transform Infrared Spectroscopy -- 2.3.15 UV-Vis-NIR Spectroscopy -- 2.3.16 X-ray Photoelectron Spectroscopy.
2.3.17 Small Angle X-ray Scattering -- 2.3.18 X-ray Diffraction Analysis -- 2.3.19 Scanning Electrochemical Microscopy -- 2.3.20 Electrochemical Impedance Spectroscopy -- 2.4 Conclusion -- References -- Chapter 3 Green Energy: Present and Future Prospectives -- 3.1 Introduction -- 3.1.1 Systematic Review Survey Reports -- 3.2 Sustainable Energy Resources -- 3.2.1 Wind Energy -- 3.2.1.1 Applications of Wind Turbine Systems -- 3.2.1.2 Advantages of Wind Energy -- 3.2.1.3 Disadvantages of Wind Energy -- 3.2.1.4 Future Prospectives and Challenges -- 3.2.2 Solar Energy -- 3.2.2.1 Applications of Solar Energy -- 3.2.2.2 Advantages of Solar Energy -- 3.2.2.3 Disadvantages of Solar Energy -- 3.2.2.4 Future Prospectives and Challenges -- 3.2.3 Biomass -- 3.2.3.1 Applications of Biomass -- 3.2.3.2 Benefits and Disadvantages of Biomass -- 3.2.3.3 Future Prospectives and Challenges -- 3.2.4 Geothermal Energy -- 3.2.4.1 Applications and Future Prospectives -- 3.2.5 Hydropower -- 3.2.6 Tidal and Wave Energy -- 3.2.6.1 Tidal Power -- 3.2.6.2 Wave Power -- 3.2.6.3 Benefits of Tidal and Wave Energy Systems -- 3.2.6.4 Challenges of Tidal and Wave Energy Systems -- 3.3 Non-Sustainable Energy Resources -- 3.3.1 Fossil Fuels -- 3.3.2 Atomic Energy -- 3.4 Existing Green Energy Models -- 3.5 Conclusions -- References -- Chapter 4 Carbon-Based 2D Materials: Synthesis, Characterization, and Their Green Energy Applications -- 4.1 Introduction -- 4.2 Synthesis of Graphene and Its Derivatives -- 4.2.1 Graphene-Based 2D Materials -- 4.2.2 Graphene -- 4.2.3 Graphene Oxide -- 4.2.4 Reduced Graphene Oxide -- 4.2.5 Graphitic Carbon Nitride -- 4.2.5.1 g-CN-ThinFilm -- 4.2.5.2 Graphitic Carbon Nitride (g-CN)-PowderForm -- 4.2.5.3 Thin Film of g-CN -- 4.3 Properties of g-CN -- 4.3.1 Morphologvical Properties -- 4.3.2 Band Gap -- 4.3.3 Other Properties -- 4.4 Applications of g-CN.
4.4.1 g-CN Role in Organic Solar Cells -- 4.4.2 g-CN Role in Perovskite Solar Cells -- 4.4.3 g-CN Role in Dye-Sensitized Solar Cells -- 4.4.4 g-CN Role as a Photocatalyst -- 4.4.5 g-CN-Sensing Applications -- 4.4.6 g-CN Environmental Applications -- 4.5 Conclusion -- References -- Chapter 5 Exploring the Potential of Graphene in Sustainable Energy Solutions -- 5.1 Introduction -- 5.2 Usage of Graphene in Various Sectors -- 5.3 Implicit Operations of Graphene in the Renewable Energy Sector -- 5.3.1 Battery Technology -- 5.3.2 Touchscreen -- 5.3.3 Integrated Circuits -- 5.3.4 Flexible Memory -- 5.3.5 Solar Power Generation -- 5.3.6 Photovoltaic Cells -- 5.3.7 Solar Cells -- 5.3.8 Lithium-Ion Batteries -- 5.3.9 Supercapacitors -- 5.3.10 Graphene Transistors -- 5.3.11 Graphene Semiconductors -- 5.3.12 Graphene Sensors -- 5.4 Catalysis -- 5.5 Renewable Energies -- 5.6 Nanotechnology -- 5.7 Conclusion -- Chapter 6 Fullerene for Green Hydrogen Energy Application -- 6.1 Introduction -- 6.2 Green Hydrogen Energy -- 6.3 Fullerene as a Hydrogen Storage Material -- 6.4 Size Effect of Fullerene and Hydrogen Storage Efficiency -- 6.5 Functionalized Fullerene, Chemical Structure, and Its Hydrogen Storage Performance -- 6.5.1 Boron -- 6.5.2 Phosphorene or Black Phosphorus -- 6.5.3 Hexagonal Boron Nitride -- 6.5.4 Silicene -- 6.5.5 Carbon Nanotubes -- 6.5.6 Graphene -- 6.5.7 Ferrocene -- 6.5.8 MoS2 -- 6.5.9 Organometallic Framework -- 6.6 Charged Fullerene as Hydrogen Storage System -- 6.7 Hydrogen Storage in Hydro- or Hydrogenated Fullerene -- 6.8 Conclusions and Future Outlook -- Acknowledgments -- References -- Chapter 7 Graphyne-Based Carbon Nanomaterials for Green Energy Applications -- 7.1 Introduction -- 7.1.1 Structural Aspects of Graphyne -- 7.2 Graphyne-Based Carbon Nanomaterials for Green Energy Applications.
7.2.1 Mechanisms Involved in Growth, Doping, Energy Storage, and Conversion Involving Graphyne -- 7.3 Fuel Cells -- 7.3.1 Oxygen Reduction Reaction (ORR) Catalyst for Hydrogen Fuel Cells or Metal-Air Batteries (MABs) -- 7.3.2 Lithium-Ion and Lithium-Metal Batteries -- 7.3.3 Supercapacitors -- 7.3.4 Wind Energy -- 7.4 Solar Energy -- 7.5 Wastewater Treatment -- 7.6 Perspectives and Conclusion -- Acknowledgments -- References -- Chapter 8 Mesoporous Carbon for Green Energy Applications -- 8.1 Introduction -- 8.2 Recent Advances in Synthetic Techniques -- 8.2.1 Hard Template Technique -- 8.2.1.1 Carbon Precursors -- 8.2.2 Soft Template Technique -- 8.3 Applications of Mesoporous Carbon -- 8.3.1 Applications in Lithium Batteries -- 8.3.2 Applications in Supercapacitors -- 8.3.3 Applications in Fuel Cells -- 8.4 Further Directions, Opportunities, and Challenges -- 8.5 Conclusions -- References -- Chapter 9 Green Synthesis of Carbon Dots and Its Application in Hydrogen Generation Through Water Splitting -- 9.1 Introduction -- 9.2 Carbon Dots -- 9.3 Processes Used for Synthesis of CDs -- 9.3.1 Bottom-Up Synthesis Processes -- 9.3.1.1 Solvothermal/Hydrothermal Method -- 9.3.1.2 Sol-GelMethod -- 9.3.1.3 Microwave Irradiation -- 9.3.1.4 Carbonization Route -- 9.3.2 Top-Down Synthesis Processes -- 9.3.2.1 Laser Ablation -- 9.3.2.2 Arc Discharge -- 9.3.2.3 Chemical and Electrochemical Oxidation Methods -- 9.3.2.4 Ultrasonic Treatment -- 9.4 Green Synthesis of Carbon Dots -- 9.4.1 Biomass-Based Green Synthesis of CDs -- 9.4.1.1 Plant Waste-BasedGreen Synthesis of Carbon Dots -- 9.4.1.2 Animal Waste-BasedGreen Synthesis of CDs -- 9.5 Application of CDs in Water Splitting -- 9.5.1 Hydrogen Generation via Water Splitting (Photoreduction) -- 9.5.2 Photocatalytic Degradation of Organic Pollutants.
9.6 Factors Affecting Characteristics of Nanomaterials of Carbon in Photocatalytic H2 Production -- 9.6.1 Doping -- 9.6.2 Defects -- 9.6.3 Dimensions -- 9.7 Conclusion -- References -- Chapter 10 Carbon-Based Nanomaterials in Energy Storage Devices: Solar Cells -- 10.1 Introduction -- 10.2 Carbon Nanotubes -- 10.2.1 Synthesis Techniques Concerning Carbon Nanotubes -- 10.2.2 Carbon Nanotube Applications in Solar Cell Technology -- 10.2.2.1 Transparent Conductive Electrodes -- 10.2.2.2 Charge Transport Materials -- 10.2.2.3 Enhanced Electron Transport -- 10.2.2.4 Improved Charge Collection -- 10.2.2.5 Transparency and Flexibility -- 10.2.2.6 Lightweight and Flexible Design -- 10.2.2.7 Tunable Aspects of Optics -- 10.2.2.8 Durability and Longevity -- 10.2.2.9 Compatibility with Other Materials -- 10.2.2.10 Scalability -- 10.2.3 Recent Advancements and Challenges -- 10.2.3.1 Recent Advancements -- 10.2.3.2 Challenges -- 10.3 Graphene -- 10.3.1 Synthesis Techniques -- 10.3.2 Utilizing Graphene in Solar Cell Applications -- 10.3.2.1 Transparent Conductive Electrodes -- 10.3.2.2 Charge Transport Layers -- 10.3.2.3 Light-HarvestingEnhancements -- 10.3.3 Recent Advancements and Challenges -- 10.3.3.1 Recent Advancements -- 10.3.3.2 Challenges -- 10.4 Carbon Dots -- 10.4.1 Synthesis Techniques -- 10.4.2 Applications of Solar Cell Carbon Dots -- 10.4.2.1 Light Harvesting and Sensitization -- 10.4.2.2 Charge Separation and Transport of Electrons -- 10.4.2.3 Energy Storage and Electrochemical Applications -- 10.4.3 Recent Advancements and Challenges -- 10.4.3.1 Recent Advancements -- 10.4.3.2 Challenges -- 10.5 The Future of Carbon-Based Nanomaterials in Solar Cell Technology -- 10.5.1 Enhanced Light Harvesting and Absorption -- 10.5.2 Improved Charge Transport and Collection -- 10.5.3 Enhanced Stability and Durability.
10.5.4 Scalable Synthesis and Manufacturing.
Record Nr. UNINA-9911018907203321
Kumar Upendra  
Newark : , : John Wiley & Sons, Incorporated, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Decentralized Systems and Distributed Computing
Decentralized Systems and Distributed Computing
Autore Avasthi Sandhya
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (401 pages)
Disciplina 005.75/8
Altri autori (Persone) TripathiSuman Lata
DhandaNamrata
VermaSatya Bhushan
Collana Decentralized systems and next generation internet
Soggetto topico Electronic data processing - Distributed processing
ISBN 9781394205127
1394205120
9781394205110
1394205112
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Next-Generation Internet and Distributed Systems -- 1.1 Introduction -- 1.2 Traditional Network -- 1.3 Next-Generation Internet -- 1.3.1 Next-Generation Internet Protocol -- 1.3.1.1 IPv6 -- 1.3.1.2 Tunneling -- 1.3.2 Quality of Service (QoS) -- 1.4 Network Middleware -- 1.5 Software-Defined Network (SDN) -- 1.5.1 Necessity of Software-Defined Network -- 1.6 Edge Cloud-Based Next-Generation Internet -- 1.6.1 Edge Cloud -- 1.6.2 Content Distribution Networks (CDNs) -- 1.7 Network Architecture -- 1.7.1 Some Proposed Architectures -- 1.7.1.1 Security Architecture for Networked Enterprises (SANE) -- 1.7.1.2 Swarming Architecture -- 1.7.1.3 Flexible Internet Architecture (FlexNGIA) -- 1.7.1.4 Architecture for Mobility Support -- 1.7.1.5 The Data-Oriented Network Architecture (DONA) -- 1.7.1.6 MILSA (Mobility and Multihoming Supporting Identifier-Locator Split Architecture) -- 1.7.1.7 Delay/Disruption Tolerant Networks (DTN) and Related Architectures -- 1.7.2 Challenges for Architecture -- 1.7.2.1 Network Services -- 1.7.2.2 Network Management -- 1.7.2.3 Network Performance -- 1.7.2.4 Diversity and Change -- 1.8 Security and Safety -- 1.9 Distributed Systems -- 1.9.1 Concept of Distributed System -- 1.9.2 Terminologies -- 1.9.2.1 Grouping of Autonomous Computer Components -- 1.9.2.2 Single Coherent System -- 1.10 Distributed System Design -- 1.10.1 Significance of a Certain Design -- 1.10.2 Scale and Scalability -- 1.10.2.1 Definition of Scale -- 1.10.2.2 Scalability Analysis -- 1.11 Distributed System Monitoring -- 1.11.1 Idea of Monitoring System -- 1.12 Security in Distributed Systems -- 1.12.1 Reasons for Not Using Encryption -- 1.12.2 Authorization -- 1.13 Blockchain and Distributed Systems -- 1.13.1 Decentralization.
1.13.2 Peer-to-Peer Networks (P2P) -- 1.14 Blockchain-Based Distributed Control Systems -- 1.14.1 Control System -- 1.14.1.1 To Handle Complicated Processes -- 1.14.1.2 Pre-Defined Function Blocks -- 1.14.1.3 Scalable Platform -- 1.15 Blockchain for Distributed System Security -- 1.15.1 Fault-Tolerant Consensus in a Distributed System -- 1.15.2 Complications while Using Blockchain for Distributed Computing -- 1.16 Conclusion and Future Scope -- References -- Chapter 2 Decentralized System in Education and Research -- 2.1 Introduction -- 2.2 History of Decentralization in the Education System -- 2.3 Advantages and Challenges of Decentralization of the Education System -- 2.3.1 Advantages -- 2.3.2 Challenges -- 2.4 Impact of Decentralization in Education and Research -- 2.5 Current Approaches -- 2.5.1 World Scenario -- 2.5.1.1 Finland Education System -- 2.5.1.2 Brazil, Federal Universities -- 2.5.1.3 The United States, National Science Foundation -- 2.5.1.4 Mexico, CONACYT -- 2.5.2 Indian Scenario -- 2.5.2.1 Kerala Model of Decentralization Implementation in Education and Research -- 2.6 Development Trend Towards Education and Research -- 2.7 Future Scope and Recommendations -- 2.8 Conclusion -- References -- Chapter 3 Architecture of Blockchain-Enabled Decentralized Systems -- 3.1 Introduction -- 3.2 What is a Decentralized System? -- 3.2.1 Centralized System vs. Decentralized System -- 3.2.2 Merits and Demerits of a Decentralized System -- 3.3 Blockchain -- 3.3.1 What is Blockchain? -- 3.3.2 Structure of Blockchain -- 3.3.3 Requirements of Blockchain -- 3.3.4 Merits and Demerits of Blockchain -- 3.4 Smart Contracts and Its Examples -- 3.5 Blockchain-Enabled Decentralized System -- 3.5.1 Role of Blockchain -- 3.5.2 Types of Decentralization in Blockchain -- 3.5.3 Types of Blockchain Architecture -- 3.5.4 Requirements -- 3.5.5 Pros and Cons.
3.6 Architecture for the Blockchain-Enabled Decentralized System -- 3.6.1 The Network -- 3.6.2 The Consensus Protocol -- 3.6.3 The Data Structure -- 3.7 Decentralized Todo App with Blockchain -- 3.8 Blockchain-Enabled Decentralized System Development and Challenges -- 3.9 Future of the Blockchain-Enabled Decentralized System -- 3.10 Conclusion -- References -- Chapter 4 Mobile Edge Computing for Decentralized Systems -- 4.1 Introduction -- 4.2 Edge Computing -- 4.3 Benefits of Edge Computing -- 4.4 Classification of Attacks in a 5G IoT System -- 4.5 Decentralized Dynamic Computation Offloading Method -- 4.6 Conclusion -- References -- Chapter 5 Blockchain in Education -- 5.1 Introduction -- 5.2 Benefits of Blockchain in Education -- 5.2.1 Blockchain Can Enhance Learner Data Privacy and Security, Allowing Learners to Maintain Control Over Their Personal Information -- 5.2.2 Blockchain Can Enable Lifelong Learning Tracking, Creating a Comprehensive and Immutable Record of a Learner's Achievements and Skills -- 5.2.2.1 Blockchain as a Distributed and Immutable Record-Keeping System -- 5.2.2.2 Benefits of Blockchain-Enabled Lifelong Learning Tracking -- 5.3 Challenges of Blockchain in Education -- 5.3.1 Concerns Related to Data Privacy, Security, and Compliance with Data Protection Regulations -- 5.3.1.1 Data Privacy Concerns -- 5.3.1.2 Compliance with Data Protection Regulations -- 5.3.1.3 Security Concerns -- 5.3.1.4 Collaboration with Industry Experts and Auditors -- 5.3.2 Potential Resistance to Change and Adoption Barriers in the Education Sector -- 5.4 Use Cases of Blockchain in Education -- 5.5 Conclusion -- References -- Chapter 6 Pattern Recognition Applications in Distributed Systems and Distributed Machine Learning -- 6.1 Introduction -- 6.1.1 What is Pattern Recognition? -- 6.1.2 Distributed Computing Environment.
6.1.3 Basic Model of the Pattern Recognition System -- 6.2 Data Generation and Preprocessing -- 6.3 Feature Selection -- 6.3.1 What is Feature Selection? -- 6.3.2 Filter Models of Feature Selection -- 6.3.3 Wrapper Models of Feature Selection -- 6.4 Design of Classifier -- 6.4.1 Conventional Classifiers in Distributed Environment -- 6.4.2 Neural Network Classifiers in a Distributed Environment -- 6.5 Designing Machine Learning Models for Distributed Environment -- 6.6 Role of Distributed Computing in Pattern Recognition -- 6.7 Conclusion -- References -- Chapter 7 Next-Generation Distributed Computing for Cancer Detection -- 7.1 Introduction -- 7.2 Research Motivation -- 7.3 Cancer Statistics -- 7.4 Modern Cancer Diagnosis and Treatment Methods -- 7.5 Methodology -- 7.6 Technical Challenges -- 7.7 Future Directions -- 7.8 Conclusion -- References -- Chapter 8 Benefits and Challenges of Decentralization in Education for Resource Optimization and Improved Performance -- 8.1 Introduction -- 8.1.1 Decentralized Systems in Education and Research -- 8.1.2 Connect Between Democracy and Education -- 8.2 The Centralization and Decentralization Concepts -- 8.3 Decentralized Systems in Education and Research: Policies and Practices -- 8.3.1 Open Educational Resources (OER) Policy -- 8.3.2 Collaborative Research Networks -- 8.3.3 Self-Directed Learning -- 8.3.4 Peer Review -- 8.3.5 Open Data -- 8.3.6 Equity and Inclusion -- 8.3.7 Quality Assurance -- 8.4 Building Capacity Across and Between Levels Within Education Systems -- 8.5 Developing Accountability Measures and Systems in Implementing a Decentralized Education Policy -- 8.5.1 Establish Clear and Quantifiable Policy Objectives -- 8.5.2 Create Performance Indicators and Benchmarks -- 8.5.3 Assign Roles and Responsibilities -- 8.5.4 Build Effective Monitoring and Evaluation Systems.
8.5.5 Encourage Transparency and Involvement -- 8.5.6 Establish Redressal Mechanisms -- 8.6 Developing Local-Level Capacity Across All Education System Levels and Sectors -- 8.7 Education Policies in India to Achieve Decentralization in Education Systems -- 8.7.1 National Education Policy (NEP) 2020 -- 8.7.2 Rashtriya Madhyamik Shiksha Abhiyan (RMSA) -- 8.7.3 RUSA (Rashtriya Uchchatar Shiksha Abhiyan) -- 8.8 Research and Development Policies in India and Promoting Decentralization in R& -- D -- 8.8.1 Atal Innovation Mission (AIM) -- 8.8.2 The National Initiative for Developing and Harnessing Innovations (NIDHI) -- 8.8.3 National Science, Technology, and Innovation Policy (STI) 2020 -- 8.8.4 Council of Scientific and Industrial Research (CSIR) -- 8.8.5 Department of Science and Technology (DST) -- References -- Chapter 9 Blockchain in Data Security and Transparency in Business Transactions -- 9.1 Introduction -- 9.2 Dimensions and Requirements of Data Security -- 9.3 Security Issues in Conventional Data Security -- 9.4 Diversity of Attacks in Conventional Data Security -- 9.5 Blockchain: The Complete Solution -- 9.6 The Decentralized and Immutable Approach -- 9.7 Comparison of ECC and RSA -- 9.8 Digital Signature Algorithm -- 9.9 Blockchain Technology Ensures Transparency -- 9.10 Blockchain Solutions for Traditional Data Security -- 9.11 Blockchain Types -- 9.12 Conclusion -- References -- Chapter 10 A Comparative Study of Ad Hoc and Wireless Sensor Networks -- 10.1 Introduction -- 10.2 Ad Hoc Network -- 10.2.1 Ad Hoc Network Types -- 10.2.2 Ad Hoc Routing Protocol -- 10.2.3 Ad Hoc Security Attack -- 10.3 Wireless Sensor and Network -- 10.3.1 Types of Wireless Network -- 10.3.2 Mobility in WSN -- 10.3.3 Routing Protocol -- 10.4 Challenges, Applications, and Limitations -- 10.5 Conclusion -- References.
Chapter 11 Content Filtering-Based Movie Recommendation System Using Deep Learning.
Record Nr. UNINA-9911019759603321
Avasthi Sandhya  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Design and development of efficient energy systems / / editors, Suman Lata Tripathi [et al.]
Design and development of efficient energy systems / / editors, Suman Lata Tripathi [et al.]
Pubbl/distr/stampa Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]
Descrizione fisica 1 online resource (384 pages)
Disciplina 621.317
Collana Artificial Intelligence and Soft Computing for Industrial Transformation
Soggetto topico Artificial intelligence - Industrial applications
Electric power supplies to apparatus - Energy conservation - Data processing
Soggetto genere / forma Electronic books.
ISBN 1-5231-4332-0
1-119-76178-6
1-119-76177-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Design of Low Power Junction Less Double-Gate MOSFET -- 1.1 Introduction -- 1.2 MOSFET Performance Parameters -- 1.3 Comparison of Existing MOSFET Architectures -- 1.4 Proposed Heavily Doped Junction-Less Double Gate MOSFET (AJ-DGMOSFET) -- 1.5 Heavily Doped JL-DG MOSFET for Biomedical Application -- 1.6 Conclusion -- References -- 2 VLSI Implementation of Vedic Multiplier -- 2.1 Introduction -- 2.2 8x8 Vedic Multiplier -- 2.3 The Architecture of 8x8 Vedic Multiplier (VM) -- 2.3.1 Compressor Architecture -- 2.4 Results and Discussion -- 2.4.1 Instance Power -- 2.4.2 Net Power -- 2.4.3 8-Bit Multiplier -- 2.4.4 16-Bit Multiplier -- 2.4.5 Applications of Multiplier -- 2.5 Conclusion -- References -- 3 Gas Leakage Detection from Drainage to Offer Safety for Sanitary Workers -- 3.1 Introduction -- 3.1.1 IOT-Based Sewer Gas Detection -- 3.1.2 Objective -- 3.1.3 Contribution of this Chapter -- 3.1.4 Outline of the Chapter -- 3.2 Related Works -- 3.2.1 Sewer Gas Leakage Detection -- 3.2.2 Crack Detection -- 3.3 Methodology -- 3.3.1 Sewer Gas Detection -- 3.3.2 Crack Detection -- 3.3.3 Experimental Setup -- 3.4 Experimental Results -- 3.5 Conclusion -- References -- 4 Machine Learning for Smart Healthcare Energy-Efficient System -- 4.1 Introduction -- 4.1.1 IoT in the Digital Age -- 4.1.2 Using IoT to Enhance Healthcare Services -- 4.1.3 Edge Computing -- 4.1.4 Machine Learning -- 4.1.5 Application in Healthcare -- 4.2 Related Works -- 4.3 Edge Computing -- 4.3.1 Architecture -- 4.3.2 Advantages of Edge Computing over Cloud Computing -- 4.3.3 Applications of Edge Computing in Healthcare -- 4.3.4 Edge Computing Advantages -- 4.3.5 Challenges -- 4.4 Smart Healthcare System -- 4.4.1 Methodology -- 4.4.2 Data Acquisition and IoT End Device.
4.4.3 IoT End Device and Backend Server -- 4.5 Conclusion and Future Directions -- References -- 5 Review of Machine Learning Techniques Used for Intrusion and Malware Detection in WSNs and IoT Devices -- 5.1 Introduction -- 5.2 Types of Attacks -- 5.3 Some Countermeasures for the Attacks -- 5.4 Machine Learning Solutions -- 5.5 Machine Learning Algorithms -- 5.6 Authentication Process Based on Machine Learning -- 5.7 Internet of Things (IoT) -- 5.8 IoT-Based Attacks -- 5.8.1 Botnets -- 5.8.2 Man-in-the-Middle -- 5.9 Information and Identity Theft -- 5.10 Social Engineering -- 5.11 Denial of Service -- 5.12 Concerns -- 5.13 Conclusion -- References -- 6 Smart Energy-Efficient Techniques for Large-Scale Process Industries -- 6.1 Pumps Operation -- 6.1.1 Parts in a Centrifugal Pump -- 6.1.2 Pump Efficiency -- 6.1.3 VFD -- 6.1.4 VFD and Pump Motor -- 6.1.5 Large HT Motors -- 6.1.6 Smart Pumps -- 6.2 Vapour Absorption Refrigeration System -- 6.2.1 Vapour Compression Refrigeration -- 6.2.2 Vapour Absorption Refrigeration -- 6.3 Heat Recovery Equipment -- 6.3.1 Case Study -- 6.3.2 Advantages of Heat Recovery -- 6.4 Lighting System -- 6.4.1 Technical Terms -- 6.4.2 Introduction -- 6.4.3 LED Lighting -- 6.4.4 Energy-Efficiency Techniques -- 6.4.5 Light Control with IoT -- 6.4.6 EU Practices -- 6.5 Air Conditioners -- 6.5.1 Technical Terms -- 6.5.2 Types of Air Conditioners -- 6.5.3 Star Rating of BEE -- 6.5.4 EU Practices -- 6.5.5 Energy-Efficiency Tips -- 6.5.6 Inverter Air Conditioners -- 6.5.7 IoT-Based Air Conditioners -- 6.6 Fans and Other Smart Appliances -- 6.6.1 BLDC Fan Motors -- 6.6.2 Star Ratings -- 6.6.3 Group Drive of Fans -- 6.6.4 Other Smart Appliances -- 6.7 Motors -- 6.7.1 Motor Efficiency -- 6.7.2 Underrated Operation -- 6.7.3 Energy-Efficient Motors -- 6.7.4 Retrofit of Standard Motors with Energy-Efficient Motors.
6.7.5 Other Salient Points -- 6.7.6 Use of Star-Delta Starter Motor -- 6.8 Energy-Efficient Transformers -- 6.8.1 IEC Recommendation -- 6.8.2 Super Conducting Transformers -- References -- 7 Link Restoration and Relay Node Placement in Partitioned Wireless Sensor Network -- 7.1 Introduction -- 7.2 Related Work -- 7.2.1 Existing Techniques -- 7.3 Proposed K-Means Clustering Algorithm -- 7.3.1 Homogenous and Heterogeneous Network Clustering Algorithms -- 7.3.2 Dynamic and Static Clustering -- 7.3.3 Flow Diagram -- 7.3.4 Objective Function -- 7.4 System Model and Assumption -- 7.4.1 Simulation Parameters -- 7.5 Results and Discussion -- 7.6 Conclusions -- References -- 8 Frequency Modulated PV Powered MLI Fed Induction Motor Drive for Water Pumping Applications -- 8.1 Introduction -- 8.2 PV Panel as Energy Source -- 8.2.1 Solar Cell -- 8.3 Multi-Level Inverter Topologies -- 8.3.1 Types of Inverters Used for Drives -- 8.3.2 Multi-Level Inverters -- 8.4 Experimental Results and Discussion -- 8.4.1 PV Powered H Bridge Inverter-Fed Drive -- 8.4.2 PV Powered DCMLI Fed Drive -- 8.5 Conclusion and Future Scope -- References -- 9 Analysis and Design of Bidirectional Circuits for Energy Storage Application -- 9.1 Introduction -- 9.2 Modes of Operation Based on Main Converters -- 9.2.1 Single-Stage Rectification -- 9.2.2 Single-Stage Inversion -- 9.2.3 Double-Stage Rectification -- 9.2.4 Double-Stage Inversion -- 9.3 Proposed Methodology for Three-Phase System -- 9.3.1 Control Block of Overall System -- 9.3.2 Proposed Carrier-Based PWM Strategy -- 9.3.3 Experiment Results -- 9.4 Conclusion -- References -- 10 Low-Power IOT-Enabled Energy Systems -- 10.1 Overview -- 10.1.1 Conceptions -- 10.1.2 Motivation -- 10.1.3 Methodology -- 10.2 Empowering Tools -- 10.2.1 Sensing Components -- 10.2.2 Movers -- 10.2.3 Telecommunication Technology.
10.2.4 Internet of Things Information and Evaluation -- 10.3 Internet of Things within Power Region -- 10.3.1 Internet of Things along with Vitality Production -- 10.3.2 Smart Metropolises -- 10.3.3 Intelligent Lattice Network -- 10.3.4 Smart Buildings Structures -- 10.3.5 Powerful Usage of Vitality in Production -- 10.3.6 Insightful Transport -- 10.4 Difficulties Relating Internet of Things -- 10.4.1 Vitality Ingestion -- 10.4.2 Synchronization via Internet of Things through Sub-Units -- 10.4.3 Client Confidentiality -- 10.4.4 Safety Challenges -- 10.4.5 IoT Standardization and Architectural Concept -- 10.5 Upcoming Developments -- 10.5.1 IoT and Block Chain -- 10.5.2 Artificial Intelligence and IoT -- 10.5.3 Green IoT -- 10.6 Conclusion -- References -- 11 Efficient Renewable Energy Systems -- Introduction -- 11.1 Renewable-Based Available Technologies -- 11.1.1 Wind Power -- 11.1.2 Solar Power -- 11.1.3 Tidal Energy -- 11.1.4 Battery Storage System -- 11.1.5 Solid Oxide Energy Units for Enhancing Power Life -- 11.2 Adaptability Frameworks -- 11.2.1 Distributed Energy Resources (DER) -- 11.2.2 New Age Grid Connection -- 11.3 Conclusion -- References -- 12 Efficient Renewable Energy Systems -- 12.1 Introduction -- 12.1.1 World Energy Scenario -- 12.2 Sources of Energy: Classification -- 12.3 Renewable Energy Systems -- 12.3.1 Solar Energy -- 12.3.2 Wind -- 12.3.3 Geothermal -- 12.3.4 Biomass -- 12.3.5 Ocean -- 12.3.6 Hydrogen -- 12.4 Solar Energy -- 12.5 Wind Energy -- 12.6 Geothermal Energy -- 12.7 Biomass -- 12.7.1 Forms of Biomass -- 12.8 Ocean Power -- 12.9 Hydrogen -- 12.10 Hydro Power -- 12.11 Conclusion -- References -- 13 Agriculture-IoT-Based Sprinkler System for Water and Fertilizer Conservation and Management -- 13.1 Introduction -- 13.1.1 Novelty of the Work -- 13.1.2 Benefit to Society -- 13.2 Development of the Proposed System.
13.3 System Description -- 13.3.1 Study of the Crop Under Experiment -- 13.3.2 Hardware of the System -- 13.3.3 Software of the System -- 13.4 Layers of the System Architecture -- 13.4.1 Application Layer -- 13.4.2 Cloud Layer -- 13.4.3 Network Layer -- 13.4.4 Physical Layer -- 13.5 Calibration -- 13.6 Layout of the Sprinkler System -- 13.7 Testing -- 13.8 Results and Discussion -- 13.9 Conclusion -- References -- 14 A Behaviour-Based Authentication to Internet of Things Using Machine Learning -- 14.1 Introduction -- 14.2 Basics of Internet of Things (IoT) -- 14.2.1 The IoT Reference Model -- 14.2.2 Working of IoT -- 14.2.3 Utilization of Internet of Things (IoT) -- 14.3 Authentication in IoT -- 14.3.1 Methods of Authentication -- 14.4 User Authentication Based on Behavioral-Biometric -- 14.4.1 Machine Learning -- 14.4.2 Machine Learning Algorithms -- 14.5 Threats and Challenges in the Current Security Solution for IoT -- 14.6 Proposed Methodology -- 14.6.1 Collection of Gait Dataset -- 14.6.2 Gait Data Preprocessing -- 14.6.3 Reduction in Data Size -- 14.6.4 Gaits Feature -- 14.6.5 Classification -- 14.7 Conclusion and Future Work -- References -- 15 A Fuzzy Goal Programming Model for Quality Monitoring of Fruits during Shipment Overseas -- 15.1 Introduction -- 15.2 Proposed System -- 15.2.1 Problem Statement -- 15.2.2 Overview -- 15.2.3 System Components -- 15.3 Work Process -- 15.3.1 System Hardware -- 15.3.2 Connections and Circuitry -- 15.4 Optimization Framework -- 15.4.1 Fuzzy Goal Description -- 15.4.2 Characterizing Fuzzy Membership Function -- 15.4.3 Construction of FGP Model -- 15.4.4 Definition of Variables and Parameters -- 15.4.5 Fuzzy Goal Description -- 15.5 Creation of Database and Website -- 15.5.1 Hosting PHP Application and Creation of MySQL Database -- 15.5.2 Creation of API (Application Programming Interfaces) Key.
15.6 Libraries Used and Code Snipped.
Record Nr. UNINA-9910555117803321
Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Design and development of efficient energy systems / / editors, Suman Lata Tripathi [et al.]
Design and development of efficient energy systems / / editors, Suman Lata Tripathi [et al.]
Pubbl/distr/stampa Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]
Descrizione fisica 1 online resource (384 pages)
Disciplina 621.317
Collana Artificial Intelligence and Soft Computing for Industrial Transformation
Soggetto topico Artificial intelligence - Industrial applications
Electric power supplies to apparatus - Energy conservation - Data processing
ISBN 1-5231-4332-0
1-119-76178-6
1-119-76177-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Design of Low Power Junction Less Double-Gate MOSFET -- 1.1 Introduction -- 1.2 MOSFET Performance Parameters -- 1.3 Comparison of Existing MOSFET Architectures -- 1.4 Proposed Heavily Doped Junction-Less Double Gate MOSFET (AJ-DGMOSFET) -- 1.5 Heavily Doped JL-DG MOSFET for Biomedical Application -- 1.6 Conclusion -- References -- 2 VLSI Implementation of Vedic Multiplier -- 2.1 Introduction -- 2.2 8x8 Vedic Multiplier -- 2.3 The Architecture of 8x8 Vedic Multiplier (VM) -- 2.3.1 Compressor Architecture -- 2.4 Results and Discussion -- 2.4.1 Instance Power -- 2.4.2 Net Power -- 2.4.3 8-Bit Multiplier -- 2.4.4 16-Bit Multiplier -- 2.4.5 Applications of Multiplier -- 2.5 Conclusion -- References -- 3 Gas Leakage Detection from Drainage to Offer Safety for Sanitary Workers -- 3.1 Introduction -- 3.1.1 IOT-Based Sewer Gas Detection -- 3.1.2 Objective -- 3.1.3 Contribution of this Chapter -- 3.1.4 Outline of the Chapter -- 3.2 Related Works -- 3.2.1 Sewer Gas Leakage Detection -- 3.2.2 Crack Detection -- 3.3 Methodology -- 3.3.1 Sewer Gas Detection -- 3.3.2 Crack Detection -- 3.3.3 Experimental Setup -- 3.4 Experimental Results -- 3.5 Conclusion -- References -- 4 Machine Learning for Smart Healthcare Energy-Efficient System -- 4.1 Introduction -- 4.1.1 IoT in the Digital Age -- 4.1.2 Using IoT to Enhance Healthcare Services -- 4.1.3 Edge Computing -- 4.1.4 Machine Learning -- 4.1.5 Application in Healthcare -- 4.2 Related Works -- 4.3 Edge Computing -- 4.3.1 Architecture -- 4.3.2 Advantages of Edge Computing over Cloud Computing -- 4.3.3 Applications of Edge Computing in Healthcare -- 4.3.4 Edge Computing Advantages -- 4.3.5 Challenges -- 4.4 Smart Healthcare System -- 4.4.1 Methodology -- 4.4.2 Data Acquisition and IoT End Device.
4.4.3 IoT End Device and Backend Server -- 4.5 Conclusion and Future Directions -- References -- 5 Review of Machine Learning Techniques Used for Intrusion and Malware Detection in WSNs and IoT Devices -- 5.1 Introduction -- 5.2 Types of Attacks -- 5.3 Some Countermeasures for the Attacks -- 5.4 Machine Learning Solutions -- 5.5 Machine Learning Algorithms -- 5.6 Authentication Process Based on Machine Learning -- 5.7 Internet of Things (IoT) -- 5.8 IoT-Based Attacks -- 5.8.1 Botnets -- 5.8.2 Man-in-the-Middle -- 5.9 Information and Identity Theft -- 5.10 Social Engineering -- 5.11 Denial of Service -- 5.12 Concerns -- 5.13 Conclusion -- References -- 6 Smart Energy-Efficient Techniques for Large-Scale Process Industries -- 6.1 Pumps Operation -- 6.1.1 Parts in a Centrifugal Pump -- 6.1.2 Pump Efficiency -- 6.1.3 VFD -- 6.1.4 VFD and Pump Motor -- 6.1.5 Large HT Motors -- 6.1.6 Smart Pumps -- 6.2 Vapour Absorption Refrigeration System -- 6.2.1 Vapour Compression Refrigeration -- 6.2.2 Vapour Absorption Refrigeration -- 6.3 Heat Recovery Equipment -- 6.3.1 Case Study -- 6.3.2 Advantages of Heat Recovery -- 6.4 Lighting System -- 6.4.1 Technical Terms -- 6.4.2 Introduction -- 6.4.3 LED Lighting -- 6.4.4 Energy-Efficiency Techniques -- 6.4.5 Light Control with IoT -- 6.4.6 EU Practices -- 6.5 Air Conditioners -- 6.5.1 Technical Terms -- 6.5.2 Types of Air Conditioners -- 6.5.3 Star Rating of BEE -- 6.5.4 EU Practices -- 6.5.5 Energy-Efficiency Tips -- 6.5.6 Inverter Air Conditioners -- 6.5.7 IoT-Based Air Conditioners -- 6.6 Fans and Other Smart Appliances -- 6.6.1 BLDC Fan Motors -- 6.6.2 Star Ratings -- 6.6.3 Group Drive of Fans -- 6.6.4 Other Smart Appliances -- 6.7 Motors -- 6.7.1 Motor Efficiency -- 6.7.2 Underrated Operation -- 6.7.3 Energy-Efficient Motors -- 6.7.4 Retrofit of Standard Motors with Energy-Efficient Motors.
6.7.5 Other Salient Points -- 6.7.6 Use of Star-Delta Starter Motor -- 6.8 Energy-Efficient Transformers -- 6.8.1 IEC Recommendation -- 6.8.2 Super Conducting Transformers -- References -- 7 Link Restoration and Relay Node Placement in Partitioned Wireless Sensor Network -- 7.1 Introduction -- 7.2 Related Work -- 7.2.1 Existing Techniques -- 7.3 Proposed K-Means Clustering Algorithm -- 7.3.1 Homogenous and Heterogeneous Network Clustering Algorithms -- 7.3.2 Dynamic and Static Clustering -- 7.3.3 Flow Diagram -- 7.3.4 Objective Function -- 7.4 System Model and Assumption -- 7.4.1 Simulation Parameters -- 7.5 Results and Discussion -- 7.6 Conclusions -- References -- 8 Frequency Modulated PV Powered MLI Fed Induction Motor Drive for Water Pumping Applications -- 8.1 Introduction -- 8.2 PV Panel as Energy Source -- 8.2.1 Solar Cell -- 8.3 Multi-Level Inverter Topologies -- 8.3.1 Types of Inverters Used for Drives -- 8.3.2 Multi-Level Inverters -- 8.4 Experimental Results and Discussion -- 8.4.1 PV Powered H Bridge Inverter-Fed Drive -- 8.4.2 PV Powered DCMLI Fed Drive -- 8.5 Conclusion and Future Scope -- References -- 9 Analysis and Design of Bidirectional Circuits for Energy Storage Application -- 9.1 Introduction -- 9.2 Modes of Operation Based on Main Converters -- 9.2.1 Single-Stage Rectification -- 9.2.2 Single-Stage Inversion -- 9.2.3 Double-Stage Rectification -- 9.2.4 Double-Stage Inversion -- 9.3 Proposed Methodology for Three-Phase System -- 9.3.1 Control Block of Overall System -- 9.3.2 Proposed Carrier-Based PWM Strategy -- 9.3.3 Experiment Results -- 9.4 Conclusion -- References -- 10 Low-Power IOT-Enabled Energy Systems -- 10.1 Overview -- 10.1.1 Conceptions -- 10.1.2 Motivation -- 10.1.3 Methodology -- 10.2 Empowering Tools -- 10.2.1 Sensing Components -- 10.2.2 Movers -- 10.2.3 Telecommunication Technology.
10.2.4 Internet of Things Information and Evaluation -- 10.3 Internet of Things within Power Region -- 10.3.1 Internet of Things along with Vitality Production -- 10.3.2 Smart Metropolises -- 10.3.3 Intelligent Lattice Network -- 10.3.4 Smart Buildings Structures -- 10.3.5 Powerful Usage of Vitality in Production -- 10.3.6 Insightful Transport -- 10.4 Difficulties Relating Internet of Things -- 10.4.1 Vitality Ingestion -- 10.4.2 Synchronization via Internet of Things through Sub-Units -- 10.4.3 Client Confidentiality -- 10.4.4 Safety Challenges -- 10.4.5 IoT Standardization and Architectural Concept -- 10.5 Upcoming Developments -- 10.5.1 IoT and Block Chain -- 10.5.2 Artificial Intelligence and IoT -- 10.5.3 Green IoT -- 10.6 Conclusion -- References -- 11 Efficient Renewable Energy Systems -- Introduction -- 11.1 Renewable-Based Available Technologies -- 11.1.1 Wind Power -- 11.1.2 Solar Power -- 11.1.3 Tidal Energy -- 11.1.4 Battery Storage System -- 11.1.5 Solid Oxide Energy Units for Enhancing Power Life -- 11.2 Adaptability Frameworks -- 11.2.1 Distributed Energy Resources (DER) -- 11.2.2 New Age Grid Connection -- 11.3 Conclusion -- References -- 12 Efficient Renewable Energy Systems -- 12.1 Introduction -- 12.1.1 World Energy Scenario -- 12.2 Sources of Energy: Classification -- 12.3 Renewable Energy Systems -- 12.3.1 Solar Energy -- 12.3.2 Wind -- 12.3.3 Geothermal -- 12.3.4 Biomass -- 12.3.5 Ocean -- 12.3.6 Hydrogen -- 12.4 Solar Energy -- 12.5 Wind Energy -- 12.6 Geothermal Energy -- 12.7 Biomass -- 12.7.1 Forms of Biomass -- 12.8 Ocean Power -- 12.9 Hydrogen -- 12.10 Hydro Power -- 12.11 Conclusion -- References -- 13 Agriculture-IoT-Based Sprinkler System for Water and Fertilizer Conservation and Management -- 13.1 Introduction -- 13.1.1 Novelty of the Work -- 13.1.2 Benefit to Society -- 13.2 Development of the Proposed System.
13.3 System Description -- 13.3.1 Study of the Crop Under Experiment -- 13.3.2 Hardware of the System -- 13.3.3 Software of the System -- 13.4 Layers of the System Architecture -- 13.4.1 Application Layer -- 13.4.2 Cloud Layer -- 13.4.3 Network Layer -- 13.4.4 Physical Layer -- 13.5 Calibration -- 13.6 Layout of the Sprinkler System -- 13.7 Testing -- 13.8 Results and Discussion -- 13.9 Conclusion -- References -- 14 A Behaviour-Based Authentication to Internet of Things Using Machine Learning -- 14.1 Introduction -- 14.2 Basics of Internet of Things (IoT) -- 14.2.1 The IoT Reference Model -- 14.2.2 Working of IoT -- 14.2.3 Utilization of Internet of Things (IoT) -- 14.3 Authentication in IoT -- 14.3.1 Methods of Authentication -- 14.4 User Authentication Based on Behavioral-Biometric -- 14.4.1 Machine Learning -- 14.4.2 Machine Learning Algorithms -- 14.5 Threats and Challenges in the Current Security Solution for IoT -- 14.6 Proposed Methodology -- 14.6.1 Collection of Gait Dataset -- 14.6.2 Gait Data Preprocessing -- 14.6.3 Reduction in Data Size -- 14.6.4 Gaits Feature -- 14.6.5 Classification -- 14.7 Conclusion and Future Work -- References -- 15 A Fuzzy Goal Programming Model for Quality Monitoring of Fruits during Shipment Overseas -- 15.1 Introduction -- 15.2 Proposed System -- 15.2.1 Problem Statement -- 15.2.2 Overview -- 15.2.3 System Components -- 15.3 Work Process -- 15.3.1 System Hardware -- 15.3.2 Connections and Circuitry -- 15.4 Optimization Framework -- 15.4.1 Fuzzy Goal Description -- 15.4.2 Characterizing Fuzzy Membership Function -- 15.4.3 Construction of FGP Model -- 15.4.4 Definition of Variables and Parameters -- 15.4.5 Fuzzy Goal Description -- 15.5 Creation of Database and Website -- 15.5.1 Hosting PHP Application and Creation of MySQL Database -- 15.5.2 Creation of API (Application Programming Interfaces) Key.
15.6 Libraries Used and Code Snipped.
Record Nr. UNINA-9910830773703321
Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Distributed and Parallel Computing
Distributed and Parallel Computing
Autore Avasthi Sandhya
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2026
Descrizione fisica 1 online resource (369 pages)
Altri autori (Persone) TripathiSuman Lata
ISBN 1-394-28803-4
1-394-28802-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Distributed Systems -- 1.1 Introduction -- 1.1.1 Background and Context -- 1.1.2 Objectives of the Study -- 1.1.3 Scope and Limitations -- 1.1.4 Key Characteristics of Distributed Systems -- 1.1.4.1 Decentralization and Concurrency -- 1.1.4.2 Communication and Scalability -- 1.1.4.3 Fault Tolerance and Consistency -- 1.1.4.4 Security and Resource Sharing -- 1.1.5 Types of Distributed Systems -- 1.1.5.1 Systems for Clients and Servers -- 1.1.5.2 Peer-To-Peer Systems -- 1.1.5.3 Middleware-Based Systems -- 1.1.5.4 Three-Tier and N-Tier -- 1.1.5.5 Grid and Cloud Computing -- 1.1.6 Distributed Algorithms for Boolean Equations Over Networks -- 1.1.6.1 System of Boolean Equations Over a Network -- 1.1.6.2 Locally Private Distributed Algorithm -- 1.1.6.3 Consensus Projection for Linear Algebraic Equations -- 1.1.6.4 Affine Subspace Boolean Vector Search -- 1.1.6.5 Distributed SAT Verification -- 1.1.6.6 Distributed Equilibrium Computation of Boolean Networks -- 1.1.7 Effective Communication in Dispersed ML -- 1.1.7.1 Data Parallelism with Gradient Compression -- 1.1.7.2 Scheduling Issues -- 1.1.7.3 Decentralized Analytics -- 1.1.7.4 Federated Learning -- 1.1.7.5 Distributed Security and Privacy in Mobile Networks and IoT Systems -- 1.1.8 DS Applications Across a Wide Range of Domains -- 1.1.8.1 Web Services and Mobile Apps -- 1.1.8.2 Databases and Distributed File Storage Systems -- 1.1.8.3 Blockchain Platforms -- 1.1.8.4 Financial Services -- 1.1.8.5 Scientific Research and Simulation -- 1.1.9 Challenges and Considerations -- 1.1.9.1 Scalability of Distributed Systems in Various Applications -- 1.1.9.2 Integration with Existing Systems -- 1.1.9.3 Regulatory Compliance and Legal Framework -- 1.1.10 Future Prospects and Trends.
1.1.10.1 Distributed Systems Emerging Technologies (Quantum Distributed Computing) -- 1.1.10.2 Research Advancements -- 1.1.10.3 Anticipated Benefits and Challenges -- 1.1.11 Conclusion -- 1.1.11.1 Recap of Key Findings -- 1.1.11.2 Implications for Healthcare Transformation -- References -- Chapter 2 Topology in Network Technologies -- 2.1 Introduction -- 2.2 Related Work -- 2.3 Network Topology Design -- 2.4 Advantages of Network Topologies -- 2.5 Case Studies of Network Topologies -- 2.6 Distributed and Parallel Computing in Network Topology -- 2.6.1 Challenges/Issues in Network Topology in Distributed Computing or Parallel Computing -- 2.7 Conclusion -- References -- Chapter 3 Distributed Processing Technology and Advancements -- 3.1 Introduction -- 3.2 Distributed Processing in Modern Computing -- 3.3 Evolution of Distributed Processing -- 3.4 Key Concepts and Technologies Driving the Evolution of Distributed Processing -- 3.5 Recent Advancements in Distributed Processing -- 3.6 Changing Landscape of Computing -- 3.6.1 New Opportunities and Challenges for Developers and Businesses -- 3.6.2 Use of Distributed and Parallel Computing -- 3.7 Security and Privacy Considerations -- 3.7.1 Case Studies -- 3.7.2 Future Directions -- 3.8 Opportunities and Challenges in Distributed Computing -- 3.8.1 Future Directions and Opportunity -- 3.8.2 Challenges -- 3.9 Conclusion -- References -- Chapter 4 Distributed System Architecture and Computing Models -- 4.1 Introduction -- 4.2 Distributed System Architecture -- 4.2.1 Architecture Style -- 4.2.2 Middleware Organization -- 4.3 Middleware in Distributed Systems -- 4.3.1 Host Infrastructure Middleware -- 4.3.2 Distribution Middleware -- 4.3.3 Domain-Specific Middleware -- 4.3.4 Intelligent Middleware -- 4.4 Distributed Cloud Architecture -- 4.5 Distributed Machine Learning -- 4.6 Conclusion -- References.
Chapter 5 Parallel Computing Models and Architecture -- 5.1 Introduction -- 5.2 Evolution of Parallel Computing Models -- 5.2.1 Basic Approach Parallel Computing -- 5.2.2 Architecture -- 5.2.3 Flowchart Analysis -- 5.3 Parallel Computing Models -- 5.3.1 Complexity -- 5.3.2 Advantages -- 5.3.3 Disadvantages -- 5.3.4 Algorithmic Problem -- 5.3.4.1 Shared Memory Cache -- 5.3.4.2 Distributed Memory Model -- 5.3.4.3 Data Parallel Model -- 5.3.4.4 Task Parallel Model -- 5.3.5 Pipeline Model -- 5.3.6 Hybrid Models -- References -- Chapter 6 Network Issues and High-Level Communication Tools in Distributed Computing -- 6.1 Introduction -- 6.1.1 Common Network Issues in Distributed Computing -- 6.1.2 Group Communication in Distributed Systems -- 6.2 Latency in Distributed and Parallel Computing -- 6.3 Straggler Effect -- 6.4 Packet Loss in the Distributed Network -- 6.4.1 Implications of Packet Loss -- 6.4.2 Identifying Packet Loss -- 6.4.3 Tools to Measure Packet Loss -- 6.4.4 Techniques to Prevent Packet Loss -- 6.5 Network Congestion -- 6.5.1 Causes of Network Congestion and Implications -- 6.5.2 Network Congestion Implication -- 6.6 Communication Load -- 6.6.1 A Deterministic Strategy -- 6.6.2 Bidding Strategy -- 6.6.3 Drafting Strategy -- 6.6.4 Greedy Strategy -- 6.6.5 Threshold Strategy -- 6.7 Conclusion -- References -- Chapter 7 Infinite Horizons: Empowering Business Education Through Metaverse -- 7.1 Introduction -- 7.2 Challenges and Concerns -- 7.3 Rules of Law-Based Governance -- 7.4 Possible Implementations in Educational Framework of Metaverse Technology -- 7.5 Understanding Metaverse -- 7.6 Explore the Transformational Potential of Metaverse for Business Education -- 7.7 Innovation and Design Ideas Forum -- 7.8 Continuing Education and Career Development -- 7.9 Takeaways -- 7.10 Ethical and Legal Issues -- 7.11 Future Prospects and Innovations.
7.12 Conclusion -- References -- Chapter 8 Paradigm Shifts and Future Directions in Distributed Data Management for Decentralized Networks -- 8.1 Introduction -- 8.2 Paradigm Shifts in Distributed Data Management -- 8.2.1 Evolution of Data Management Paradigms -- 8.2.2 Impact of Decentralized Architectures -- 8.2.3 Key Technological Drivers -- 8.3 Emergent Architectures and Frameworks -- 8.3.1 Overview of Emergent Architectures -- 8.3.2 Domain-Specific Languages in Data Analytics -- 8.4 Integration of IoT -- 8.4.1 IoT Ecosystem and Data Management Challenges -- 8.5 Edge Computing and Big Data Analytics -- 8.5.1 Benefits of Edge Computing -- 8.5.2 Big Data Management in Distributed Systems -- 8.6 Data Aggregation and Summarization Techniques -- 8.6.1 Importance of Data Aggregation -- 8.6.2 Case Studies and Practical Implementations -- 8.7 Advanced Applications and Case Studies -- 8.7.1 Domestic and Industrial Applications -- 8.8 Future Directions in Distributed Data Management -- 8.8.1 Blockchain for Enhanced Data Security -- 8.8.2 AI and ML in Data Analytics -- 8.8.3 Prospective Research Avenues -- 8.9 Conclusion -- References -- Chapter 9 Autonomy and Adaptive Architectures in Distributed Systems -- 9.1 Introduction -- 9.2 Conceptual Framework -- 9.2.1 Definition of Key Concepts -- 9.2.1.1 Autonomy -- 9.2.1.2 Adaptive Architectures -- 9.2.1.3 Multiagent Systems -- 9.2.1.4 The Evolutionary Trajectory of MAS -- 9.2.2 Importance of Decentralized Paradigms -- 9.3 Architectural Models -- 9.3.1 Overview of Emergent Architectures -- 9.3.2 Agent-Based Models -- 9.3.2.1 Agent Architecture and Control Flow -- 9.4 Adaptive Strategies in Distributed Systems -- 9.4.1 Mechanisms for System Resilience and Scalability -- 9.5 Case Studies -- 9.5.1 Smart Grids -- 9.5.2 Geographical and Functional Infrastructure Interdependence -- 9.5.3 Water Distribution Systems.
9.5.3.1 Adaptive Water Management -- 9.5.3.2 Leak Detection and Management -- 9.5.3.3 Water Quality Monitoring -- 9.5.4 Transportation Systems -- 9.5.4.1 Intelligent Transportation Systems -- 9.5.4.2 Traffic Management -- 9.5.4.3 Public Transportation -- 9.6 Application of MASs -- 9.6.1 Autonomous Surface Ships -- 9.6.2 Unmanned Surface Vehicles -- 9.6.3 Swarm Robotics -- 9.6.4 Autonomous Underwater Vehicles -- 9.6.5 Hybrid MASs -- 9.6.6 Common Coordination Models in MAS -- 9.6.6.1 Centralized Coordination -- 9.6.6.2 Decentralized Coordination -- 9.6.6.3 Hierarchical Coordination -- 9.6.6.4 Market-Based Coordination -- 9.6.6.5 Contract Net Protocol -- 9.7 Autonomous Navigation Systems -- 9.7.1 Examples of Recently Developed USVs -- 9.8 Collision Avoidance Algorithms -- 9.8.1 Comparison of Collision Avoidance Algorithms -- 9.8.2 Implementation of TBA -- 9.9 Network Properties in Distributed Systems -- 9.9.1 Small-World Networks -- 9.9.2 Scale-Free Networks -- 9.9.3 Robustness and Fault Tolerance -- 9.9.4 Scalability -- 9.9.5 Application-Specific Design -- 9.10 Challenges and Future Directions -- 9.10.1 Security and Privacy Considerations -- 9.10.2 Integration with Emerging Technologies -- 9.10.2.1 Blockchain -- 9.10.2.2 Artificial Intelligence -- 9.10.2.3 Internet of Things -- 9.11 Conclusion -- References -- Chapter 10 Distributed Consensus Frequency Control in Networked Microgrid -- 10.1 Introduction -- 10.2 System Model -- 10.3 Distributed Control Technique -- 10.3.1 ANN Tuned FOPID Distributed Controller -- 10.4 Results and Discussion -- 10.5 Conclusion -- References -- Appendix -- Chapter 11 Navigating Trust in Distributed Systems -- 11.1 Introduction -- 11.1.1 Trust in Distributed Computing Ecosystem -- 11.1.2 Trust in the Internet of Things -- 11.2 Transparency Basics -- 11.3 Heterogeneous and Homogeneous DSs -- 11.3.1 Trust Concepts.
11.3.2 Inadequacies with Security Mechanisms.
Record Nr. UNINA-9911040927303321
Avasthi Sandhya  
Newark : , : John Wiley & Sons, Incorporated, , 2026
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Electric vehicle design : design, simulation and applications / / edited by Krishan Arora, Suman Lata Tripathi and Himanshu Sharma
Electric vehicle design : design, simulation and applications / / edited by Krishan Arora, Suman Lata Tripathi and Himanshu Sharma
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc.
Descrizione fisica 1 online resource (358 pages)
Disciplina 629.22/93
Altri autori (Persone) TripathiSuman Lata
SharmaHimanshu
Soggetto topico Electric vehicles - Design and construction
ISBN 9781394205080
1394205082
9781394205097
1394205090
9781394205073
1394205074
9781394204373
139420437X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Development of Braking Systems in Fuel Cell Electric Vehicles -- 1.1 Introduction -- 1.2 Historical Background of Fuel Cell -- 1.3 ADVISOR -- 1.4 Why Hydrogen is Preferred -- 1.5 What is a Fuel Cell? -- 1.6 Working of Fuel Cells -- 1.7 Types of Fuel Cells -- 1.7.1 Direct Methanol Fuel Cell (DMFC) -- 1.7.2 Phosphoric Acid Fuel Cell (PAFC) -- 1.7.3 Alkaline Fuel Cell (AFC) -- 1.7.4 Solid Oxide Fuel Cell (SOFC) -- 1.7.5 Molten Carbonate Fuel Cell (MCFC) -- 1.8 Block Diagram of Vehicle on MATLAB/Simulink -- 1.9 Braking System in Vehicle -- 1.10 Regenerative Braking System -- 1.11 Anti-Lock Braking System (ABS) -- 1.11.1 Component of ABS -- 1.11.1.1 Wheel Speed Sensor -- 1.11.1.2 Valves -- 1.11.1.3 Pumps -- 1.11.1.4 Electronic Control Unit -- 1.11.2 Types of the ABS Model -- 1.11.3 Anti-Lock Braking System Plot -- 1.12 Conclusion -- References -- Chapter 2 Design and Applications of Fuel Cells -- 2.1 Introduction -- 2.2 Types of Electric Vehicles -- 2.2.1 Battery Electric Vehicles (BEVs) -- 2.2.2 Hybrid Electric Vehicles (HEVs) -- 2.2.3 Plug-In Hybrid Electric Vehicles (PHEVs) -- 2.2.4 Fuel Cell Electric Vehicles (FCEVs) -- 2.3 Design Equations of Fuel Cells -- 2.3.1 Nernst Equation -- 2.3.2 Ohm's Law -- 2.3.3 Power Output -- 2.3.4 Efficiency Equation -- 2.3.5 Kinetic Current Density Equation -- 2.3.6 Over-Potential Equation -- 2.3.7 Heat Generation Equation -- 2.4 Designing of Fuel Cells -- 2.5 Types of Fuel Cells -- 2.6 Solid Oxide FCs (SOFCs) -- 2.6.1 Working of SOFCs -- 2.6.2 Advantages of SOFCs -- 2.6.3 Disadvantages of SOFCs -- 2.6.4 Applications of SOFCs -- 2.7 Alkaline Fuel Cells (AFCs) -- 2.7.1 Working of AFCs -- 2.7.2 Advantages of AFCs -- 2.7.3 Disadvantages of AFCs -- 2.7.4 Applications of AFCs -- 2.8 Molten Carbonate Fuel Cell (MCFC) -- 2.8.1 Working of MCFC.
2.8.2 Advantages of MCFCs -- 2.8.3 Disadvantages of MCFCs -- 2.8.4 Applications of MCFCs -- 2.9 Phosphoric Acid Fuel Cells (PAFCs) -- 2.9.1 Working of PAFCs -- 2.9.2 Advantages of PAFCs -- 2.9.3 Disadvantages of PAFCs -- 2.9.4 Applications of PAFCs -- 2.10 Polymer Electrolyte Membrane Fuel Cell (PEMFC) -- 2.10.1 Working of PEMFC -- 2.10.2 Advantages of PEMFCs -- 2.10.3 Advantages of PEMFCs -- 2.10.4 Applications of PEMFCs -- 2.11 Direct Methanol Fuel Cells (DMFCs) -- 2.11.1 Working of DFMC -- 2.11.2 Advantages of DMFCs -- 2.11.3 Disadvantages of DMFCs -- 2.11.4 Applications of DMFCs -- 2.12 Parameters Affecting the Performance of FCs -- References -- Chapter 3 Smart Energy Management and Monitoring System for Electric Vehicles with IoT Integration -- 3.1 Introduction -- 3.2 The Control of Electric Vehicles Using IoT -- 3.2.1 Battery Management System -- 3.2.2 Safe and Intelligent Driving -- 3.2.3 System for Fault Alert and Preventative Maintenance -- 3.2.4 Data from Telemetry -- 3.2.4.1 Battery Usage Information -- 3.2.4.2 Report on Charging -- 3.2.4.3 Notify About Nearby Charging Stations -- 3.2.4.4 Data on Driver Behavior -- 3.3 IoT Management Issues with Electric Vehicles -- 3.3.1 Internet Safety -- 3.3.2 Higher Price -- 3.3.3 Considering the Challenges and Advantages -- 3.4 Monitoring and Management Benefits of IoT -- 3.4.1 IoT and Battery Management Systems -- 3.4.2 IoT for Safe and Intelligent Driving -- 3.4.3 Theft Prevention -- 3.4.4 Detection of Falling or Crashes -- 3.4.5 Battery Leasing Made Simple -- 3.5 Predictive Maintenance System with Fault Alerts -- 3.6 IoT Management and Monitoring Issues with Electric Vehicles -- 3.6.1 Threat from Cyber Attacks -- 3.6.2 Electric Car Prices are Quite High -- 3.6.3 Technological Difficulty -- 3.6.4 Connectivity and Reliance on Power -- 3.6.5 Battery Management and Monitoring System.
3.6.6 Prototype of a Battery Charge Control and Monitoring System -- 3.6.7 Scenario for the Battery Monitoring and Management System -- 3.7 Microcontroller -- 3.7.1 DC Current Sensor -- 3.7.2 Fuel Gauge Module for Li-Lon Batteries -- 3.8 IoT-Based Systems for Battery Management and Monitoring -- 3.9 Design of Battery Charge Control and Monitoring System -- 3.10 Results and Discussion -- 3.11 Conclusions -- 3.12 Future Scope of IoT in Electric Vehicles -- References -- Chapter 4 A Review of Electric Vehicles: Technologies and Challenges -- 4.1 Introduction -- 4.2 Electric Motors -- 4.2.1 DC Series Motor -- 4.2.2 Brushless DC Motors -- 4.2.2.1 Out-Runner-Type BLDC Motor -- 4.2.2.2 In-Runner-Type BLDC Motor -- 4.2.3 Permanent Magnet Synchronous Motor -- 4.2.4 Three-Phase AC Induction Motors -- 4.2.5 Switched Reluctance Motors -- 4.3 Power Electronic Converters -- 4.3.1 Bi-Directional DC-DC Converter -- 4.3.1.1 Non-Isolated Converters -- 4.3.1.2 Isolated Converters -- 4.4 Battery in Electric Vehicles -- 4.4.1 Types of Battery in Electric Vehicles -- 4.4.2 Traditional Battery Charging Approach -- 4.4.2.1 Constant Current (CC) Charging Approach -- 4.4.2.2 Constant Voltage (CV) Charging Approach -- 4.4.2.3 Constant Current-Constant Voltage (CC-CV) Charging Approach -- 4.4.2.4 Multi-Stage Constant Current (MCC) Approach -- 4.5 Conclusion -- References -- Chapter 5 Electric Vehicle and Design Using MATLAB -- List of Abbreviations -- 5.1 Introduction -- 5.2 Motivation -- 5.2.1 History of EVs -- 5.3 Basic Fundamentals of EVs -- 5.4 Why Electric Vehicles? -- 5.5 Comparison Between ICV and EV -- 5.6 Classification of EVs -- 5.7 Design and Structure of EV -- 5.7.1 HEV -- 5.7.2 FCEV -- 5.7.3 PHEV -- 5.7.4 Basic Design of EV -- 5.8 Mathematical Model of an Electric Vehicle -- 5.9 Control Strategy of EVs -- 5.10 Design Methodology for Electric Vehicles (EVs).
5.11 Latest Emerging Technology in EV -- 5.12 Performance Valuation of BLDC Motor and Induction Motor for Electric Vehicle Propulsion Application -- 5.12.1 A Mathematical Model for a Brushless DC (BLDC) Motor-Driven Electric Vehicle -- 5.12.2 Induction Motor -- 5.12.3 Mathematical Model for an Induction Motor-Driven Electric Vehicle -- 5.12.4 Induction Motor Design with Their Specifications -- 5.13 Conclusion -- References -- Chapter 6 Model Order Reduction of Battery for Smart Battery Management System -- 6.1 Introduction -- 6.2 Problem Formulation -- 6.3 Modeling of Battery -- 6.4 Methodology for Model Order Reduction -- 6.5 Result and Discussion -- 6.6 Conclusion -- Appendix -- References -- Chapter 7 Power Electronic Converters for Electric Vehicle Application -- 7.1 Introduction -- 7.2 Types of Electrical Vehicle and Role of Power Electronic Converter -- 7.2.1 Battery Electric Vehicles (BEVs) -- 7.2.1.1 Power Electronics in BEVs -- 7.2.2 Plug-In Hybrid Electric Vehicles (Plug-In HEVs) -- 7.2.2.1 Power Electronics in Plug-In Hybrid Electric Vehicles (Plug-In HEV) -- 7.2.3 Hybrid Electric Vehicles (HEVs) -- 7.2.3.1 Series Hybrid Electric Vehicles (SHEVs) -- 7.2.3.2 Parallel Hybrid Electric Vehicles (PHEVs) -- 7.2.3.3 Series-Parallel Hybrid Electric Vehicles (SPHEVs) -- 7.2.3.4 Power Electronics in Hybrid Electric Vehicles (HEVs) -- 7.2.4 Fuel Cell Electric Vehicles (FCEVs) -- 7.2.4.1 Power Electronics in Fuel Cell Electric Vehicles (FCEVs) -- 7.2.5 Solar Cell Electric Vehicles (SCEVs) -- 7.2.5.1 Power Electronics in Solar Cell Electric Vehicles (SCEVs) -- 7.3 Recent Development in Power Electronic Converter -- 7.4 Power Electronic Converters in Electric, Hybrid, and Fuel Cell Vehicles -- 7.4.1 Power Electronic Converters in EVs -- 7.4.2 Categorization of Power Electronic Converters -- 7.5 Challenges in Power Electronic Vehicular System.
7.5.1 Efficiency -- 7.5.2 Longevity -- 7.5.3 Performance of EV -- 7.5.4 Luxurious Features -- 7.5.5 Safety -- 7.5.6 Overall Cost -- 7.5.7 Noise -- 7.6 Conclusion -- References -- Chapter 8 Integrating Electric Vehicles Into Smart Grids Through Data Analytics: Challenges and Opportunities -- 8.1 Introduction -- 8.2 Smart Grid and Electric Vehicle -- 8.3 Impact of Electric Vehicle-Based Data Analytics for Smart Grids -- 8.4 Importance of Resource Availability, Price, and Load for EV -- 8.5 Electric-Tariff Design Based on Impact of Electric Vehicle Usage -- 8.6 Data Analytics for Electric Vehicles -- 8.7 Machine Learning for EV Analytics -- 8.8 What are the Different ML Algorithms Used by Authors for EV Analytics? -- 8.9 Importance of Data Analysis in the EV Industry Using an Open Source Data -- 8.10 Description of the Dataset -- 8.11 Features and Factors That Influence the Prices of EVs -- 8.12 Price Prediction of EVs -- 8.13 Random Forest-Based Price Prediction of Electric Vehicles -- 8.14 Machine Learning Model -- 8.15 Electric Vehicle Usage in India -- 8.16 The Challenges of Adopting EV in India -- 8.17 How to Increase Renewable Energy in India to Meet EV Demand -- Conclusion -- References -- Chapter 9 Hybrid Electrical Vehicle Designs -- 9.1 Introduction -- 9.2 Plug-In Hybrid Electric Vehicles -- 9.3 Classification of HEVs -- 9.3.1 Series Hybrid -- 9.3.2 Parallel Hybrid -- 9.3.3 Series-Parallel Hybrid -- 9.4 Fuel Cell Electric Vehicles (FCEVs) -- 9.4.1 Micro-Hybrids -- 9.4.2 Mild Hybrids -- 9.4.3 Full Hybrids -- 9.5 Hybrid Electric Vehicle System Design and Analysis -- 9.6 Control Strategy in Series Hybrid Drivetrain Configuration -- 9.6.1 Modes of Operation -- 9.6.2 Max. SoC-of-PPS Control Strategy -- 9.7 Design of Fuel Cell Electric Vehicles with Fuel Economy -- 9.7.1 Traction Mode -- 9.8 Conclusion -- References.
Chapter 10 EV Battery Charging System.
Record Nr. UNINA-9911020337603321
Hoboken, NJ : , : John Wiley & Sons, Inc.
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Electrical and electronic devices, circuits, and materials : technological challenges and solutions / / editors, Suman Lata Tripathi, Parvej Ahmad Alvi, Umashankar Subramaniam
Electrical and electronic devices, circuits, and materials : technological challenges and solutions / / editors, Suman Lata Tripathi, Parvej Ahmad Alvi, Umashankar Subramaniam
Pubbl/distr/stampa Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]
Descrizione fisica 1 online resource (614 pages)
Disciplina 621.3815
Soggetto topico Electronic apparatus and appliances
Soggetto genere / forma Electronic books.
ISBN 1-119-75510-7
1-119-75509-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: DESIGN AND ANALYSIS -- 1 Strain Engineering in Modern Field Effect Transistors -- 1.1 Introduction -- 1.2 Theory of Strain Technology -- 1.2.1 Stress and Strain -- 1.2.2 Stress Matrix for Biaxial and Uniaxial Stress -- 1.2.3 Impact of Strain on MOSFET Parameters -- 1.3 Simulation Studies in Strain Technology -- 1.4 Experimental Studies on Strain Technology -- 1.5 Summary and Future Scope -- Future Scope -- Acknowledgement -- References -- 2 Design and Optimization of Heterostructure Double Gate Tunneling Field Effect Transistor for Ultra Low Power Circuit and System -- 2.1 Introduction -- 2.2 Fundamental of Device Physics -- 2.2.1 Basic Working Principles of TFET -- 2.2.2 Kane's Model -- 2.3 Analysis Approach and Device Parameters -- 2.4 Switching Behavior of TFET -- 2.5 Results and Discussion -- 2.6 Conclusion -- Acknowledgement -- References -- 3 Polymer Electrolytes: Development and Supercapacitor Application -- 3.1 Introduction -- 3.1.1 The Basic Principle and Types of Supercapacitors -- 3.1.2 Key Characteristics of the Electrolyte -- 3.1.3 Polymer Electrolytes and Types -- 3.1.4 Modification Strategies for Polymer Electrolytes -- 3.2 Preparation and Characterization Techniques -- 3.3 Latest Developments -- 3.4 Summary -- References -- 4 Tunable RF/Microwave Filter with Fractal DGS -- Tunable RF/Microwave Filter with Fractal DGS -- 4.1 Introduction -- 4.2 Literature Review -- 4.2.1 Planar Reconfigurable Filters -- 4.3 Proposed Work -- 4.3.1 Design of Hairpin Bandpass Filter -- 4.3.2 Design of Hairpin Bandpass Filter with Fractal DGS -- 4.3.3 Design of Tunable Hairpin Bandpass Filter with Fractal DGS -- 4.4 Conclusion -- Acknowledgement -- References.
5 GaN High Electron Mobility Transistor Device Technology for RF and High-Power Applications -- 5.1 Introduction -- 5.2 HEMT Structures -- 5.2.1 GaAs-Based HEMTs -- 5.2.2 InP-Based HEMTs -- 5.2.3 GaN-Based HEMTs -- 5.3 Polarization Impact and Creation of 2DEG in GaN HEMT -- 5.3.1 Polarization Effect -- 5.3.2 Formation of 2DEG -- 5.4 GaN-Based HEMT Performance Affecting Factors -- 5.4.1 Surface Passivation -- 5.4.2 Parasitic Effects -- 5.4.3 Field Plate Engineering Technique -- 5.4.4 Impact of Barrier Layer -- 5.5 Conclusion -- References -- 6 Design and Analyses of a Food Protein Sensing System Based on Memristive Properties -- 6.1 Introduction -- 6.2 Background -- 6.2.1 Principle of a Memristor -- 6.2.2 Bio-Memristors -- 6.2.3 Applications of Memristors -- 6.3 Motivation -- 6.4 Experimental Set-Up -- 6.5 Experimental Methodology and Preliminary Validation -- 6.5.1 Experimental Methodology -- 6.5.2 Preliminary Validation -- 6.6 Sensitivity Parameters -- 6.6.1 Resistance-Based Sensitivity (Sr) -- 6.6.2 Point Slope-Based Sensitivity (Sm) -- 6.6.3 Hysteresis-Line Slope Sensitivity -- 6.7 Results and Discussion -- 6.7.1 Category I: Egg Albumin and Milk -- 6.7.2 Category II: Protein Blend -- 6.8 Conclusions and Prospects -- References -- 7 Design of Low-Power DRAM Cell Using Advanced FET Architectures -- 7.1 Introduction -- 7.2 1T-DRAM (MOS) -- 7.3 1T-DRAM (CNT-FET) -- 7.4 1T-DRAM (FinFET) -- 7.5 1-T DRAM (TFET) -- 7.6 Conclusion -- References -- 8 Application of Microwave Radiation in Determination of Quality Sensing of Agricultural Products -- 8.1 Microwave Heating and its Applications to Agricultural Products -- 8.1.1 Principle of Microwave Heating -- 8.1.2 Moisture Sensing -- 8.1.3 Promoting Germination -- 8.1.4 Food Processing -- 8.1.5 Weeds, Insects and Pests Control -- 8.1.6 Product Conditioning -- 8.1.7 Microwave Drying.
8.1.8 Quality Sensing in Fruits and Vegetables -- 8.2 Measurement Techniques -- 8.2.1 Open-Ended Coaxial Probe - Network Analyzer Technique -- 8.2.2 Network Analyzer -- 8.3 Dielectric Spectroscopy of Agricultural Products at Different Temperatures -- 8.4 Correlation of Dielectric Properties with Nutrients -- 8.5 Conclusion -- References -- 9 Solar Cell -- Introduction -- 9.1 History of Solar Cell -- 9.2 Constructional Features of Solar Cell [2] -- 9.3 Criteria for Materials to Be Used in Manufacturing of Solar Cell -- 9.4 Types of Solar Cells [5] -- 9.5 Process of Making Crystals for Solar Cell Manufacturing [2] -- 9.6 Glass -- 9.7 Cell Combinations -- 9.7.1 Series Combination of Solar Cells [4] -- 9.7.2 Parallel Combination of Solar Cells [4] -- 9.7.3 Series-Parallel Combination of Solar Cells [4] -- 9.8 Solar Panels -- 9.9 Working of Solar Cell [3] -- 9.10 Solar Cell Efficiency -- 9.11 Uses/Applications of Solar Cells -- Conclusion -- References -- 10 Fabrication of Copper Indium Gallium Diselenide (Cu(In,Ga)Se2) Thin Film Solar Cell -- 10.1 Introduction -- 10.2 Device Structure of CIGS Thin Film Solar Cell -- 10.3 Fabrication and Characterization of CIGS Thin Film Solar Cell -- 10.3.1 Effect of Thermally Evaporated CdS Film Thickness on the Operation of CIGS Solar Cell -- 10.3.2 Effect of Heat Soaks on CIGS/CdS Hetero-Junction -- 10.3.3 Effect of Flash Evaporated CdS Film Thickness on the Performance of CIGS Solar Cell -- 10.3.4 Effect of i-ZnO Film Thickness on the Performance of CIGS Solar Cell -- 10.4 Conclusion -- References -- 11 Parameter Estimation of Solar Cells: A Multi-Objective Approach -- 11.1 Introduction -- 11.2 Problem Statement -- 11.2.1 SDM -- 11.2.2 DDM -- 11.3 Methodology -- 11.4 Results and Discussions -- 11.4.1 Results for the Single-Diode Model -- 11.4.2 Results for Double-Diode Model -- 11.5 Conclusions -- References.
12 An IoT-Based Smart Monitoring Scheme for Solar PV Applications -- 12.1 Introduction -- 12.2 Solar PV Systems -- 12.2.1 Solar Photovoltaic (PV) Systems -- 12.2.2 Concentrates Solar Power (CSP) -- 12.2.3 Solar Water Heater Systems -- 12.2.4 Passive Solar Design -- 12.2.5 Solar Microgrid System -- 12.2.6 Battery -- 12.2.7 MPPT -- 12.2.8 Inverters & -- Other Electronic Equipment -- 12.2.9 Charge Controller -- 12.2.10 Additional Systems Equipment -- 12.3 IoT -- 12.3.1 Artificial Intelligence (AI) and Machine Learning -- 12.3.2 Big Data and Cloud Computing -- 12.3.3 Smart Sensors -- 12.3.4 Additional Devices for Control and Communication -- 12.3.5 Renewable Energy and IoT in Energy Sector -- 12.3.6 Application of IoT -- 12.4 Remote Monitoring Methods of Solar PV System -- 12.4.1 Wireless Monitoring -- 12.4.2 Physical/Wired Monitoring -- 12.4.3 SCADA Monitoring -- 12.4.4 Monitoring Using Cloud Computing -- 12.4.5 Monitoring Using IOT -- 12.5 Challenges and Issues of Implementation of IoT on Renewable Energy Resources -- 12.5.1 Challenges -- 12.5.2 Solutions -- 12.6 Conclusion -- References -- 13 Design of Low-Power Energy Harvesting System for Biomedical Devices -- 13.1 Introduction -- 13.2 Investigation on Topologies of DC-DC Converter -- 13.2.1 Hybrid Source Architecture Based on Synchronous Boost Converter -- 13.2.2 Hybrid Source Architecture Using Single-Inductor Dual-Input Single-Output Converter -- 13.2.3 Hybrid Source Architecture Employing a Multi-Input DC Chopper -- 13.3 Hardware Results -- 13.4 Conclusion -- References -- 14 Performance Analysis of Some New Hybrid Metaheuristic Algorithms for HighDimensional Optimization Problems -- 14.1 Introduction -- 14.2 An Overview of Proposed Hybrid Methodologies -- 14.3 Experimental Results and Discussion -- 14.4 Conclusions -- References.
15 Investigation of Structural, Optical and Wettability Properties of Cadmium Sulphide Thin Films Synthesized by Environment Friendly SILAR Technique -- 15.1 Introduction -- 15.2 Experimental Details -- 15.3 Results and Discussion -- 15.3.1 Film Formation Mechanism -- 15.3.2 Thickness Measurement -- 15.3.3 Structural Studies -- 15.3.4 Raman Spectroscopy -- 15.3.5 Scanning Electron Microscopy -- 15.3.6 Optical Studies -- 15.3.7 Wettability Studies -- 15.4 Conclusion -- 15.5 Acknowledgement -- References -- Part II: DESIGN, IMPLEMENTATION ANDAPPLICATIONS -- 16 Solar Photovoltaic Cells -- 16.1 Introduction -- 16.2 Need for Solar Cells -- 16.3 Structure of Solar Cell -- 16.4 Solar Cell Classification -- 16.4.1 First-Generation Solar Cells -- 16.4.2 Second-Generation Solar Cells -- 16.4.3 Third-Generation Solar Cells -- 16.5 Solar PV Cells -- 16.6 Solar Cell Working -- 16.7 Mathematical Modelling of Solar Cell -- 16.8 Solar Cell Connection Methods -- 16.9 Types of Solar PV System -- 16.10 Conclusion -- References -- 17 An Intelligent Computing Technique for Parameter Extraction of Different Photovoltaic (PV) Models -- 17.1 Introduction -- 17.2 Problem Formulation -- 17.2.1 Single-Diode Model -- 17.2.2 Double-Diode Model -- 17.2.3 Three-Diode Model -- 17.3 Proposed Optimization Technique -- 17.3.1 Various Phases of Optimization of Harris Hawks -- 17.4 Results and Discussions -- 17.5 Conclusions -- References -- 18 Experimental Investigation on Wi-Fi Signal Loss by Scattering Property of Duranta Plant Leaves -- 18.1 Introduction -- 18.1.1 Duranta Golden Plant -- 18.1.2 Foliage Loss -- 18.2 Measurement and Calculation -- 18.2.1 Scattering Feasibility -- 18.2.2 Comparison with Tree Shadowing Effect -- 18.3 Result and Discussion -- 18.4 Conclusions -- References -- 19 Multi-Quantum Well-Based Solar Cell -- 19.1 Introduction.
19.2 Theoretical Aspects of Solar Cell.
Record Nr. UNINA-9910555076403321
Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Electrical and electronic devices, circuits, and materials : technological challenges and solutions / / editors, Suman Lata Tripathi, Parvej Ahmad Alvi, Umashankar Subramaniam
Electrical and electronic devices, circuits, and materials : technological challenges and solutions / / editors, Suman Lata Tripathi, Parvej Ahmad Alvi, Umashankar Subramaniam
Pubbl/distr/stampa Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]
Descrizione fisica 1 online resource (614 pages)
Disciplina 621.3815
Soggetto topico Electronic apparatus and appliances
ISBN 1-119-75510-7
1-119-75509-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: DESIGN AND ANALYSIS -- 1 Strain Engineering in Modern Field Effect Transistors -- 1.1 Introduction -- 1.2 Theory of Strain Technology -- 1.2.1 Stress and Strain -- 1.2.2 Stress Matrix for Biaxial and Uniaxial Stress -- 1.2.3 Impact of Strain on MOSFET Parameters -- 1.3 Simulation Studies in Strain Technology -- 1.4 Experimental Studies on Strain Technology -- 1.5 Summary and Future Scope -- Future Scope -- Acknowledgement -- References -- 2 Design and Optimization of Heterostructure Double Gate Tunneling Field Effect Transistor for Ultra Low Power Circuit and System -- 2.1 Introduction -- 2.2 Fundamental of Device Physics -- 2.2.1 Basic Working Principles of TFET -- 2.2.2 Kane's Model -- 2.3 Analysis Approach and Device Parameters -- 2.4 Switching Behavior of TFET -- 2.5 Results and Discussion -- 2.6 Conclusion -- Acknowledgement -- References -- 3 Polymer Electrolytes: Development and Supercapacitor Application -- 3.1 Introduction -- 3.1.1 The Basic Principle and Types of Supercapacitors -- 3.1.2 Key Characteristics of the Electrolyte -- 3.1.3 Polymer Electrolytes and Types -- 3.1.4 Modification Strategies for Polymer Electrolytes -- 3.2 Preparation and Characterization Techniques -- 3.3 Latest Developments -- 3.4 Summary -- References -- 4 Tunable RF/Microwave Filter with Fractal DGS -- Tunable RF/Microwave Filter with Fractal DGS -- 4.1 Introduction -- 4.2 Literature Review -- 4.2.1 Planar Reconfigurable Filters -- 4.3 Proposed Work -- 4.3.1 Design of Hairpin Bandpass Filter -- 4.3.2 Design of Hairpin Bandpass Filter with Fractal DGS -- 4.3.3 Design of Tunable Hairpin Bandpass Filter with Fractal DGS -- 4.4 Conclusion -- Acknowledgement -- References.
5 GaN High Electron Mobility Transistor Device Technology for RF and High-Power Applications -- 5.1 Introduction -- 5.2 HEMT Structures -- 5.2.1 GaAs-Based HEMTs -- 5.2.2 InP-Based HEMTs -- 5.2.3 GaN-Based HEMTs -- 5.3 Polarization Impact and Creation of 2DEG in GaN HEMT -- 5.3.1 Polarization Effect -- 5.3.2 Formation of 2DEG -- 5.4 GaN-Based HEMT Performance Affecting Factors -- 5.4.1 Surface Passivation -- 5.4.2 Parasitic Effects -- 5.4.3 Field Plate Engineering Technique -- 5.4.4 Impact of Barrier Layer -- 5.5 Conclusion -- References -- 6 Design and Analyses of a Food Protein Sensing System Based on Memristive Properties -- 6.1 Introduction -- 6.2 Background -- 6.2.1 Principle of a Memristor -- 6.2.2 Bio-Memristors -- 6.2.3 Applications of Memristors -- 6.3 Motivation -- 6.4 Experimental Set-Up -- 6.5 Experimental Methodology and Preliminary Validation -- 6.5.1 Experimental Methodology -- 6.5.2 Preliminary Validation -- 6.6 Sensitivity Parameters -- 6.6.1 Resistance-Based Sensitivity (Sr) -- 6.6.2 Point Slope-Based Sensitivity (Sm) -- 6.6.3 Hysteresis-Line Slope Sensitivity -- 6.7 Results and Discussion -- 6.7.1 Category I: Egg Albumin and Milk -- 6.7.2 Category II: Protein Blend -- 6.8 Conclusions and Prospects -- References -- 7 Design of Low-Power DRAM Cell Using Advanced FET Architectures -- 7.1 Introduction -- 7.2 1T-DRAM (MOS) -- 7.3 1T-DRAM (CNT-FET) -- 7.4 1T-DRAM (FinFET) -- 7.5 1-T DRAM (TFET) -- 7.6 Conclusion -- References -- 8 Application of Microwave Radiation in Determination of Quality Sensing of Agricultural Products -- 8.1 Microwave Heating and its Applications to Agricultural Products -- 8.1.1 Principle of Microwave Heating -- 8.1.2 Moisture Sensing -- 8.1.3 Promoting Germination -- 8.1.4 Food Processing -- 8.1.5 Weeds, Insects and Pests Control -- 8.1.6 Product Conditioning -- 8.1.7 Microwave Drying.
8.1.8 Quality Sensing in Fruits and Vegetables -- 8.2 Measurement Techniques -- 8.2.1 Open-Ended Coaxial Probe - Network Analyzer Technique -- 8.2.2 Network Analyzer -- 8.3 Dielectric Spectroscopy of Agricultural Products at Different Temperatures -- 8.4 Correlation of Dielectric Properties with Nutrients -- 8.5 Conclusion -- References -- 9 Solar Cell -- Introduction -- 9.1 History of Solar Cell -- 9.2 Constructional Features of Solar Cell [2] -- 9.3 Criteria for Materials to Be Used in Manufacturing of Solar Cell -- 9.4 Types of Solar Cells [5] -- 9.5 Process of Making Crystals for Solar Cell Manufacturing [2] -- 9.6 Glass -- 9.7 Cell Combinations -- 9.7.1 Series Combination of Solar Cells [4] -- 9.7.2 Parallel Combination of Solar Cells [4] -- 9.7.3 Series-Parallel Combination of Solar Cells [4] -- 9.8 Solar Panels -- 9.9 Working of Solar Cell [3] -- 9.10 Solar Cell Efficiency -- 9.11 Uses/Applications of Solar Cells -- Conclusion -- References -- 10 Fabrication of Copper Indium Gallium Diselenide (Cu(In,Ga)Se2) Thin Film Solar Cell -- 10.1 Introduction -- 10.2 Device Structure of CIGS Thin Film Solar Cell -- 10.3 Fabrication and Characterization of CIGS Thin Film Solar Cell -- 10.3.1 Effect of Thermally Evaporated CdS Film Thickness on the Operation of CIGS Solar Cell -- 10.3.2 Effect of Heat Soaks on CIGS/CdS Hetero-Junction -- 10.3.3 Effect of Flash Evaporated CdS Film Thickness on the Performance of CIGS Solar Cell -- 10.3.4 Effect of i-ZnO Film Thickness on the Performance of CIGS Solar Cell -- 10.4 Conclusion -- References -- 11 Parameter Estimation of Solar Cells: A Multi-Objective Approach -- 11.1 Introduction -- 11.2 Problem Statement -- 11.2.1 SDM -- 11.2.2 DDM -- 11.3 Methodology -- 11.4 Results and Discussions -- 11.4.1 Results for the Single-Diode Model -- 11.4.2 Results for Double-Diode Model -- 11.5 Conclusions -- References.
12 An IoT-Based Smart Monitoring Scheme for Solar PV Applications -- 12.1 Introduction -- 12.2 Solar PV Systems -- 12.2.1 Solar Photovoltaic (PV) Systems -- 12.2.2 Concentrates Solar Power (CSP) -- 12.2.3 Solar Water Heater Systems -- 12.2.4 Passive Solar Design -- 12.2.5 Solar Microgrid System -- 12.2.6 Battery -- 12.2.7 MPPT -- 12.2.8 Inverters & -- Other Electronic Equipment -- 12.2.9 Charge Controller -- 12.2.10 Additional Systems Equipment -- 12.3 IoT -- 12.3.1 Artificial Intelligence (AI) and Machine Learning -- 12.3.2 Big Data and Cloud Computing -- 12.3.3 Smart Sensors -- 12.3.4 Additional Devices for Control and Communication -- 12.3.5 Renewable Energy and IoT in Energy Sector -- 12.3.6 Application of IoT -- 12.4 Remote Monitoring Methods of Solar PV System -- 12.4.1 Wireless Monitoring -- 12.4.2 Physical/Wired Monitoring -- 12.4.3 SCADA Monitoring -- 12.4.4 Monitoring Using Cloud Computing -- 12.4.5 Monitoring Using IOT -- 12.5 Challenges and Issues of Implementation of IoT on Renewable Energy Resources -- 12.5.1 Challenges -- 12.5.2 Solutions -- 12.6 Conclusion -- References -- 13 Design of Low-Power Energy Harvesting System for Biomedical Devices -- 13.1 Introduction -- 13.2 Investigation on Topologies of DC-DC Converter -- 13.2.1 Hybrid Source Architecture Based on Synchronous Boost Converter -- 13.2.2 Hybrid Source Architecture Using Single-Inductor Dual-Input Single-Output Converter -- 13.2.3 Hybrid Source Architecture Employing a Multi-Input DC Chopper -- 13.3 Hardware Results -- 13.4 Conclusion -- References -- 14 Performance Analysis of Some New Hybrid Metaheuristic Algorithms for HighDimensional Optimization Problems -- 14.1 Introduction -- 14.2 An Overview of Proposed Hybrid Methodologies -- 14.3 Experimental Results and Discussion -- 14.4 Conclusions -- References.
15 Investigation of Structural, Optical and Wettability Properties of Cadmium Sulphide Thin Films Synthesized by Environment Friendly SILAR Technique -- 15.1 Introduction -- 15.2 Experimental Details -- 15.3 Results and Discussion -- 15.3.1 Film Formation Mechanism -- 15.3.2 Thickness Measurement -- 15.3.3 Structural Studies -- 15.3.4 Raman Spectroscopy -- 15.3.5 Scanning Electron Microscopy -- 15.3.6 Optical Studies -- 15.3.7 Wettability Studies -- 15.4 Conclusion -- 15.5 Acknowledgement -- References -- Part II: DESIGN, IMPLEMENTATION ANDAPPLICATIONS -- 16 Solar Photovoltaic Cells -- 16.1 Introduction -- 16.2 Need for Solar Cells -- 16.3 Structure of Solar Cell -- 16.4 Solar Cell Classification -- 16.4.1 First-Generation Solar Cells -- 16.4.2 Second-Generation Solar Cells -- 16.4.3 Third-Generation Solar Cells -- 16.5 Solar PV Cells -- 16.6 Solar Cell Working -- 16.7 Mathematical Modelling of Solar Cell -- 16.8 Solar Cell Connection Methods -- 16.9 Types of Solar PV System -- 16.10 Conclusion -- References -- 17 An Intelligent Computing Technique for Parameter Extraction of Different Photovoltaic (PV) Models -- 17.1 Introduction -- 17.2 Problem Formulation -- 17.2.1 Single-Diode Model -- 17.2.2 Double-Diode Model -- 17.2.3 Three-Diode Model -- 17.3 Proposed Optimization Technique -- 17.3.1 Various Phases of Optimization of Harris Hawks -- 17.4 Results and Discussions -- 17.5 Conclusions -- References -- 18 Experimental Investigation on Wi-Fi Signal Loss by Scattering Property of Duranta Plant Leaves -- 18.1 Introduction -- 18.1.1 Duranta Golden Plant -- 18.1.2 Foliage Loss -- 18.2 Measurement and Calculation -- 18.2.1 Scattering Feasibility -- 18.2.2 Comparison with Tree Shadowing Effect -- 18.3 Result and Discussion -- 18.4 Conclusions -- References -- 19 Multi-Quantum Well-Based Solar Cell -- 19.1 Introduction.
19.2 Theoretical Aspects of Solar Cell.
Record Nr. UNINA-9910829928203321
Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Energy-Efficient Communication Networks
Energy-Efficient Communication Networks
Autore Chopra Shakti Raj
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (205 pages)
Disciplina 621.382
Altri autori (Persone) AroraKrishan
TripathiSuman Lata
KumarVikram
Soggetto topico Telecommunication - Power supply
ISBN 1-394-27167-0
1-394-27168-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- List of Contributors -- Chapter 1 Efficient Energy Management in Hyperledger Fabric Blockchain Networks: A Proposed Optimized Solution -- 1.1 Introduction -- 1.2 Methodology -- 1.3 Experimental Analysis -- 1.3.1 Existing Problem in the Network -- 1.3.2 Proposed Hyperledger Fabric Network Approach -- 1.4 Results and Discussion -- 1.5 Conclusion -- References -- Chapter 2 Framework for UAV-Based Wireless Power Harvesting -- 2.1 Introduction -- 2.2 Literature Review -- 2.2.1 Proposed Framework -- 2.2.2 Integration with UAV Systems -- 2.2.3 Methodology -- 2.3 Results and Discussion -- 2.4 Conclusion -- References -- Chapter 3 Future Generation Technology and Feasibility Assessment -- 3.1 Introduction -- 3.1.1 Technological Breakthroughs -- 3.1.2 Economic Viability and Feasibility -- 3.1.3 Regulatory Environments -- 3.1.4 Atmospheric Reliability -- 3.1.5 Customer Requirements -- 3.1.6 Societal Acceptability -- 3.2 Next-Generation Electrical Technologies -- 3.2.1 Smart Grids -- 3.2.1.1 Components and Features -- 3.2.1.2 Advantages -- 3.2.1.3 Challenges -- 3.2.2 Renewable Energy Integration -- 3.2.2.1 Grid Integration -- 3.2.2.2 Power Electronics and Control System -- 3.2.2.3 Energy Storage -- 3.2.2.4 Transmission and Distribution -- 3.2.2.5 Challenges -- 3.2.3 Energy Storage -- 3.2.3.1 Types of Energy Storage -- 3.2.3.2 Applications of Energy Storage -- 3.2.3.3 Advancements and Challenges -- 3.2.4 Electric Vehicles -- 3.2.4.1 Types of Electric Vehicles -- 3.2.4.2 Key Components and Systems -- 3.2.4.3 Challenges -- 3.2.5 Power Electronics -- 3.2.5.1 Components and Systems -- 3.2.5.2 Applications -- 3.2.5.3 Challenges and Future Trends -- 3.2.6 Internet of Things (IoT) and Connectivity -- 3.2.6.1 Internet of Things (IoT) -- 3.2.6.2 Connectivity in Electrical Engineering.
3.2.6.3 Advantages and Challenges -- 3.3 Artificial Intelligence -- 3.3.1 Types of Artificial Intelligence -- 3.3.1.1 Type I -- 3.3.1.2 Type II (Based on Functionalities) -- 3.3.2 Applications of AI in Electrical Engineering -- 3.3.2.1 Design and Development -- 3.3.2.2 Predictive Maintenance -- 3.3.2.3 Power System and Grid Management -- 3.3.2.4 Automation and Control Systems -- 3.3.2.5 Energy Efficiency -- 3.4 Machine Learning -- 3.4.1 Types of Machine Learning -- 3.4.1.1 Supervised Machine Learning -- 3.4.1.2 Unsupervised Machine Learning -- 3.4.1.3 Semi-Supervised Learning -- 3.4.1.4 Reinforcement Learning -- 3.4.2 Applications of Machine Learning in Electrical Engineering -- 3.4.2.1 Predictive Maintenance -- 3.4.2.2 Power System Optimization -- 3.4.2.3 Control Systems and Optimization -- 3.4.2.4 Energy Efficiency -- 3.4.2.5 Design and Development -- 3.5 Conclusion -- References -- Chapter 4 IoT-Enabled Weather Forecasting Systems in Future Networks: Constraints and Solutions -- 4.1 Introduction -- 4.2 Need of IoT-Based Weather Forecasting System -- 4.3 Methodology and Results -- 4.4 Conclusion -- References -- Chapter 5 Cognitive Radio-Based NOMA Communication Networks -- 5.1 Introduction to Cognitive Radio and NOMA Networks -- 5.1.1 Motivation for Integrating Cognitive Radio with NOMA -- 5.2 Fundamentals of Cognitive Radio Technology -- 5.2.1 Spectrum Sensing Techniques in Cognitive Radio -- 5.2.2 Dynamic Spectrum Access (DSA) -- 5.2.3 Spectrum Management -- 5.2.4 Cognitive Radio Architectures and Protocols -- 5.3 Principles of Non-Orthogonal Multiple Access (NOMA) -- 5.3.1 Orthogonal Multiple Access versus NOMA -- 5.3.2 NOMA Techniques and Variants -- 5.3.3 Advantages and Challenges of NOMA Networks -- 5.4 Integration of Cognitive Radio with NOMA -- 5.4.1 Cognitive Radio Capabilities and Spectrum Sensing in NOMA Networks.
5.4.2 Spectrum-Sharing Techniques in Cognitive Radio- NOMA Systems -- 5.4.3 Cognitive Radio-NOMA Architecture and Protocol Stack -- 5.4.4 Resource Allocation and Management in Cognitive Radio-NOMA Networks -- 5.4.4.1 Power Allocation and Control Strategies -- 5.4.4.2 Spectrum Sensing and Dynamic Spectrum Access in NOMA-CR Networks -- 5.4.4.3 QoS Provisioning and Optimization Techniques -- 5.5 Performance Evaluation and Analysis -- 5.5.1 Metrics for Assessing Cognitive Radio-NOMA Networks -- 5.5.2 Simulation and Modeling Approaches -- 5.6 Applications and Use Cases -- 5.6.1 Cognitive Radio-NOMA in Next-Generation Wireless Systems -- 5.6.2 Internet of Things (IoT) and Machine-to-Machine (M2M) Communications -- 5.6.3 Vertical Industry Applications -- 5.7 Challenges and Future Directions -- 5.7.1 Interference Management and Coexistence Issues -- 5.7.2 Security and Privacy Concerns in Cognitive Radio- NOMA Systems -- 5.7.3 Emerging Trends and Future Research Directions -- 5.8 Conclusion -- References -- Chapter 6 Cognitive Radio (CR) Based Non-Orthogonal Multiple Access (NOMA) Network -- 6.1 Introduction -- 6.2 Fundamentals of CR -- 6.2.1 Spectrum Hole Approach -- 6.2.2 Physical Layout of CR -- 6.2.3 Characteristics of CR -- 6.2.3.1 Cognitive Capability -- 6.2.3.2 Reconfigurability -- 6.2.4 CR Paradigms -- 6.2.5 Multiple Access Scheme -- 6.3 Spectrum Management System -- 6.3.1 Spectrum Sensing -- 6.3.2 Spectrum Decision -- 6.3.3 Spectrum Sharing -- 6.3.4 Spectrum Mobility -- 6.4 Noma Networks -- 6.4.1 NOMA Classification -- 6.4.1.1 PD-NOMA -- 6.4.1.2 CD-NOMA -- 6.4.2 OMA vs. NOMA -- 6.4.3 Downlink NOMA -- 6.4.4 Uplink NOMA -- 6.4.5 CR-Based NOMA Network -- 6.5 Enabling Technologies -- 6.5.1 Millimeter Wave (mmWave) -- 6.5.2 Intelligent Reflecting Surfaces (IRS) -- 6.5.3 Simultaneous Wireless Information and Power Transfer (SWIPT).
6.5.4 Cooperative CR-Based NOMA Systems -- 6.5.5 Satellite Communication (SatCom) CR-Based NOMA Systems -- 6.6 Conclusion -- References -- Chapter 7 Artificial Intelligence and Machine Learning-Based Network Power Optimization Schemes -- 7.1 Introduction -- 7.2 Network -- 7.2.1 Working of Network -- 7.2.1.1 Client-Server Architecture -- 7.2.1.2 Network Protocols -- 7.2.1.3 Network Addresses -- 7.2.2 Network Methods -- 7.2.2.1 Wireless vs. Wired -- 7.2.2.2 Network Range -- 7.3 Decentralized Connection -- 7.4 Communication Network -- 7.4.1 Types of Communication Networks -- 7.4.2 Components of Communication Networks -- 7.4.3 Communication Protocols -- 7.4.4 Communication Medium -- 7.5 Internet of Things (IoT) -- 7.6 5G and Future Technologies -- 7.7 Network Power and Unstable Power Supply of Computer Networks -- 7.8 Adaption of Optimization Schemes to Enhance Network Power -- 7.9 Related Work -- 7.10 Traditional Evaluation AI and ML-Based Network Energy Optimization Techniques -- 7.11 AI- and ML-Based Systems for Network Energy Optimization Techniques -- 7.11.1 Problem Definition and Objectives -- 7.12 Conclusion -- References -- Chapter 8 Integration of PV Solar Rooftop Technology for Enhanced Performance and Sustainability of Electric Vehicles: A Techno-Analytical Approach -- 8.1 Introduction -- 8.1.1 Electric Vehicle -- 8.2 Literature Review -- 8.2.1 Numerous Challenges Faced by Electric Vehicles -- 8.3 Methods and Methodology -- 8.3.1 Structure of an Electric Vehicle Driven by Induction Motor -- 8.3.1.1 Solar Panel -- 8.3.1.2 Battery System -- 8.3.1.3 Motor Controller -- 8.3.1.4 Induction Motor -- 8.3.1.5 Power Electronics -- 8.3.1.6 Charging System -- 8.3.1.7 Energy Management System -- 8.3.1.8 Regenerative Braking System -- 8.3.1.9 Vehicle Control Unit -- 8.3.1.10 Mechanical Design -- 8.3.2 Contribution -- 8.4 Result and Discussion.
8.4.1 Modeling and Simulation of Induction Motor Used in Electric Vehicles -- 8.4.1.1 Dynamic Equations -- 8.4.1.2 Electric Dynamics -- 8.4.1.3 Magnetic Dynamic -- 8.4.1.4 Mechanical Dynamics -- 8.4.1.5 Equation of Motion -- 8.4.1.6 Electromagnetic Torque Equation -- 8.4.1.7 Synchronous Speed -- 8.4.1.8 Rotor Speed -- 8.4.1.9 Torque-Speed Characteristics -- 8.4.1.10 Load Torque -- 8.4.2 Outcomes and a Comparative Analysis of Our Proposed Photovoltaic (PV)-Based Electric Vehicle (EV) System -- 8.4.2.1 Simulation of an Induction Motor with Inverter -- 8.5 Conclusion -- References -- Chapter 9 The Viability of Advanced Technology for Future Generations -- 9.1 Introduction -- 9.2 Communication Systems -- 9.2.1 5G -- 9.2.2 6G -- 9.2.3 Quantum Communications -- 9.2.4 Satellite Communication -- 9.2.5 Holography -- 9.2.6 Brain Computer Interface (BCI) -- 9.2.7 Artificial Intelligence (AI) -- 9.2.8 Internet of Things (IOT) -- 9.3 Conclusion -- References -- Chapter 10 Power Optimization and Scheduling for Multi-Layer, Multi-Dimensional 6G Communication Networks -- 10.1 Introduction -- 10.1.1 Background -- 10.1.2 Motivation -- 10.2 Literature Review -- 10.2.1 Evolution of Communication Networks -- 10.2.2 Key Features and Requirements of 6G -- 10.2.3 Previous Approaches to Power Optimization and Scheduling -- 10.3 Multi-Layer, Multi-Dimensional 6G Communication Networks -- 10.3.1 Architecture Overview -- 10.3.2 Integration of Multiple Layers -- 10.3.3 Consideration of Various Dimensions -- 10.4 Power Optimization in MLMD 6G Networks -- 10.4.1 Challenges in Power Consumption -- 10.4.2 Machine Learning Approaches -- 10.4.3 Adaptive Power Management -- 10.5 Scheduling Strategies for MLMD 6G Networks -- 10.5.1 Dimensions Considered in Scheduling -- 10.5.2 Resource Allocation Algorithms -- 10.5.3 Interference Mitigation Techniques -- 10.6 Proposed Framework.
10.6.1 Integration of Power Optimization and Scheduling.
Record Nr. UNINA-9911020462803321
Chopra Shakti Raj  
Newark : , : John Wiley & Sons, Incorporated, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Explainable machine learning models and architectures / / edited by Suman Lata Tripathi and Mufti Mahmud
Explainable machine learning models and architectures / / edited by Suman Lata Tripathi and Mufti Mahmud
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, NJ : , : John Wiley & Sons, Inc., , [2023]
Descrizione fisica 1 online resource (273 pages)
Disciplina 006.3
Soggetto topico Computational intelligence
Artificial intelligence
Big data
ISBN 1-394-18657-6
1-394-18656-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910830639403321
Hoboken, NJ : , : John Wiley & Sons, Inc., , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui