Preface |
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xv | |
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xvii | |
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1 An Overview of the Intelligent Green Technologies for Sustainable Smart Cities |
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1 | (18) |
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2 | (3) |
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1.2 Case Study 1: Oslo--A Smart City |
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5 | (1) |
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1.3 Case Study 2: Chandigarh--A Smart City |
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5 | (1) |
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1.4 Features of the Smart Cities |
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6 | (1) |
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1.5 Well-Planned Public Spaces and Streets |
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6 | (3) |
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6 | (1) |
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7 | (1) |
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7 | (1) |
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8 | (1) |
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8 | (1) |
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8 | (1) |
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1.6 Intelligent Green Technologies |
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9 | (4) |
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1.7 Global and National Acceptance Scenarios |
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13 | (2) |
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15 | (4) |
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15 | (4) |
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2 Artificial Intelligence for Green Energy Technology |
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19 | (14) |
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19 | (1) |
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20 | (3) |
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2.3 AI Transforms Renewable Energy |
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23 | (1) |
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2.4 IBM Solution Using AI |
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24 | (1) |
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24 | (1) |
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25 | (4) |
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2.7 Renewable Energy Industry in India |
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29 | (1) |
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30 | (3) |
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30 | (1) |
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31 | (1) |
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31 | (2) |
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3 Effective Waste Management System for Smart Cities |
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33 | (20) |
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34 | (2) |
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36 | (1) |
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3.3 Waste Management in India |
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37 | (3) |
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40 | (2) |
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3.4.1 IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System |
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40 | (1) |
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3.4.2 IoT Enabled Solid Waste Management System |
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41 | (1) |
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3.4.3 Smart Garbage Management System |
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41 | (1) |
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42 | (2) |
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42 | (2) |
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3.6 Functionality of the Proposed System |
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44 | (4) |
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44 | (2) |
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46 | (1) |
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47 | (1) |
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3.7 Workflow of the Proposed Framework |
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48 | (1) |
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3.8 Conclusion and Future Scope |
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49 | (4) |
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50 | (3) |
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4 Municipal Solid Waste Energy: An Option for Green Technology for Smart Cities |
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53 | (20) |
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4.1 Unavoidable Impacts of Nonrenewable Energy |
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53 | (2) |
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4.2 Municipal Solid Waste Energy as Clean Energy for Smart Cities |
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55 | (4) |
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4.2.1 Renewable Energy Options |
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55 | (1) |
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4.2.2 Municipal Solid Waste as Renewable Energy Option for Smart Cities |
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56 | (2) |
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4.2.3 Why Is MSW Energy Renewable? |
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58 | (1) |
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4.2.4 Various Waste to Energy Technologies |
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58 | (1) |
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4.3 Waste to Energy Technologies (WTE-T) |
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59 | (10) |
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59 | (2) |
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61 | (2) |
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63 | (2) |
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4.3.4 Anaerobic Digestion |
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65 | (1) |
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4.3.5 Landfill with Gas Capture |
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66 | (2) |
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4.3.6 Microbial Fuel Cell (MFC) |
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68 | (1) |
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4.4 Integrated Solid Waste Management Systems (ISWM-S) for Smart Cities |
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69 | (1) |
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70 | (3) |
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70 | (3) |
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5 E-Waste Management and Recycling Issues: An Overview |
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73 | (16) |
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73 | (2) |
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5.2 Global Status of E-Waste Management |
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75 | (2) |
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5.3 Industrial Practices in E-Waste Management |
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77 | (2) |
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79 | (2) |
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5.5 E-Waste Management Benchmarking |
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81 | (1) |
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5.6 Future of E-Waste Management |
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82 | (1) |
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83 | (6) |
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84 | (5) |
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6 Energy Audit and Management for Green Energy |
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89 | (22) |
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89 | (2) |
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6.2 Types of Renewable Energy |
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91 | (2) |
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91 | (1) |
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91 | (1) |
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92 | (1) |
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92 | (1) |
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93 | (1) |
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93 | (4) |
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6.3.1 Types of Energy Management |
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94 | (1) |
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6.3.1.1 Demand Side Management |
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94 | (1) |
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6.3.1.2 Implementation of DSM |
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95 | (1) |
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6.3.1.3 Supply Side Management |
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96 | (1) |
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6.3.2 Ways to Improve Energy Management |
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97 | (1) |
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97 | (4) |
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6.4.1 Types of Energy Audit |
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98 | (1) |
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6.4.2 Preliminary Energy Audit |
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98 | (1) |
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6.4.3 Detailed Energy Audit |
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98 | (2) |
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100 | (1) |
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6.4.5 Detailed Steps in Energy Audit |
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100 | (1) |
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6.5 Energy Audit in Solar Plant |
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101 | (3) |
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6.5.1 Technical Inspection Steps of Solar Power Plant |
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103 | (1) |
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104 | (4) |
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6.6.1 Energy Conservation Methods |
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104 | (1) |
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105 | (3) |
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108 | (3) |
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108 | (3) |
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7 A Smart Energy-Efficient Support System for PV Power Plants |
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111 | (32) |
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112 | (6) |
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118 | (13) |
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7.2.1 Solar Tracking System |
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119 | (1) |
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7.2.2 Solar Cleaning Mechanisms |
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120 | (3) |
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123 | (8) |
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131 | (7) |
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131 | (5) |
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136 | (1) |
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136 | (1) |
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7.3.4 Modeling and Simulation |
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136 | (1) |
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137 | (1) |
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137 | (1) |
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138 | (5) |
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138 | (5) |
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8 A New Hybrid Proposition Based on a Cuckoo Search Algorithm for Parameter Estimation of Solar Cells |
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143 | (22) |
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144 | (1) |
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8.2 Modelling of an Amended Double Diode Model (ADDM) and the Objective Function |
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145 | (4) |
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149 | (1) |
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8.4 Results and Discussions |
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149 | (12) |
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161 | (4) |
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162 | (3) |
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9 Supervisory Digital Feedback Control System for An Effective PV Management and Battery Integration |
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165 | (30) |
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166 | (7) |
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173 | (12) |
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9.2.1 GHI in the Middle East |
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173 | (1) |
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9.2.2 Types of PV Systems |
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173 | (3) |
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9.2.3 Solar Tracking Systems |
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176 | (3) |
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179 | (1) |
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179 | (1) |
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180 | (1) |
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9.2.7 Pulse Width Modulation |
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180 | (1) |
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9.2.8 Maximum Power Point Tracker Charger Controller |
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181 | (1) |
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9.2.9 Reducing the Charging Time |
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182 | (1) |
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183 | (2) |
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185 | (4) |
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9.3.1 Single Axis Solar Tracking System |
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186 | (1) |
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9.3.2 Supervisory Digital Feedback Solar Tracker Control System |
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186 | (1) |
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9.3.3 Database-Based Digital Solar Tracker Control System |
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187 | (1) |
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9.3.4 Soiling Treatment Module |
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187 | (1) |
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9.3.5 PV-to-Battery Switching Module |
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187 | (2) |
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189 | (2) |
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191 | (4) |
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191 | (4) |
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10 Performance Analysis of Tunnel Field Effect Transistor for Low-Power Applications |
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195 | (32) |
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196 | (5) |
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10.1.1 Limitation of Conventional MOSFET |
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199 | (1) |
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10.1.2 Subthreshold Slope Devices |
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199 | (2) |
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10.2 TFET Structure and Simulation Setup |
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201 | (2) |
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10.3 TFET Working Principle |
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203 | (6) |
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10.3.1 Transport Mechanism in TFET |
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205 | (1) |
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10.3.1.1 Band to Band (BTB) Tunneling Transmission |
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205 | (3) |
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208 | (1) |
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10.4 Subthreshold Swing (SS) in Tunnel FETs |
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209 | (5) |
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10.5 Performance of Hetrojunction Tunnel FET |
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214 | (7) |
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10.5.1 Transfer Characteristics Analysis of TFET Devices |
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214 | (5) |
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10.5.2 Frequency Analysis of TFET Devices |
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219 | (2) |
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221 | (6) |
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222 | (5) |
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11 Low-Power Integrated Circuit Smart Device Design |
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227 | (20) |
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228 | (1) |
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229 | (1) |
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11.3 Design Techniques of Low Power |
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230 | (2) |
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11.3.1 Power Optimization by IC System |
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230 | (1) |
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11.3.2 Power Optimization by Algorithm Section |
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231 | (1) |
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11.3.3 Power Optimization by Architecture Design |
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231 | (1) |
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11.3.4 Power Optimization by Circuit Level |
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231 | (1) |
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11.3.5 Power Optimization by Process Technology |
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231 | (1) |
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11.4 VLSI Circuit Design for Low Power |
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232 | (2) |
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11.4.1 Power Dissipation of CMOS Inverter |
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232 | (1) |
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232 | (1) |
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233 | (1) |
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11.4.1.3 Short Circuit Power Dissipation |
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233 | (1) |
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11.4.1.4 Other Power Issue |
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233 | (1) |
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11.4.2 Capacitance Estimation of CMOS Logic Gate |
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234 | (1) |
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11.5 Circuit Techniques for Low Power |
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234 | (2) |
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11.5.1 Static Power Technique |
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234 | (1) |
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11.5.1.1 Self-Reverse Biasing |
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234 | (1) |
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11.5.1.2 Multithreshold Voltage Technique |
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235 | (1) |
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11.5.2 Dynamic Power Technique |
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235 | (1) |
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11.6 Random Access Memory (RAM) Circuits for Low Power |
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236 | (1) |
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11.6.1 Low-Power Techniques for SRAM |
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236 | (1) |
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11.6.2 Low-Power Techniques for DRAM |
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237 | (1) |
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11.7 VLSI Design Methodologies for Low Power |
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237 | (2) |
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11.7.1 Low-Power Physical Design |
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237 | (1) |
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11.7.2 Low-Power Gate Level Design |
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237 | (1) |
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11.7.2.1 Technology Mapping and Logic Minimization |
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238 | (1) |
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11.7.2.2 Reduction of Spurious Transitions |
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238 | (1) |
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11.7.2.3 Power Reduction by Precomputation |
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238 | (1) |
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11.7.3 Low-Power Architecture Level Design |
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238 | (1) |
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11.8 Power Reduction by Algorithmic Level |
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239 | (1) |
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11.8.1 Lowering in Switched Capacitance |
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239 | (1) |
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11.8.2 Lowering in Switching Activities |
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239 | (1) |
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11.9 Power Estimation Technique |
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239 | (1) |
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11.9.1 Circuit Level Tool |
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239 | (1) |
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240 | (1) |
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11.9.3 Architectural Level |
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240 | (1) |
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240 | (1) |
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11.10 Low-Power Flood Sensor Design |
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240 | (1) |
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11.11 Low-Power VCO Design |
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241 | (1) |
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11.12 Low-Power Gilbert Mixer Design |
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241 | (2) |
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243 | (4) |
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243 | (4) |
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12 GaN Technology Analysis as a Greater Mobile Semiconductor: An Overview |
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247 | (22) |
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248 | (2) |
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12.2 Research and Collected Data |
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250 | (5) |
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12.3 Studies Reviewed and Findings |
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255 | (11) |
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266 | (3) |
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266 | (3) |
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13 Multilevel Distributed Energy Efficient Clustering Protocol for Relay Node Selection in Three-Tiered Architecture |
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269 | (22) |
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270 | (6) |
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270 | (1) |
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13.1.2 Routing Challenges and Design Issues |
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271 | (1) |
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13.1.3 Heterogeneous Wireless Sensor Networks (HWSNs) |
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272 | (1) |
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13.1.3.1 Clustering in WSN |
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273 | (1) |
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13.1.4 Relay Node Selection Scheme |
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274 | (1) |
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275 | (1) |
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13.1.6 Problem Definition and Motivation |
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275 | (1) |
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276 | (1) |
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13.2 Implementation of Proposed Relay Node Selection Based on GA |
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276 | (6) |
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276 | (1) |
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13.2.2 Heterogenous Network Model |
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277 | (2) |
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13.2.3 Radio Energy Dissipation Model |
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279 | (1) |
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13.2.4 GA-Based Relay Node Selection |
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279 | (3) |
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13.2.5 Steady State Phase or Data Communication Phase |
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282 | (1) |
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13.3 Results of Simulation For Energy Consumption, Lifetime and Throughput of Network |
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282 | (5) |
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282 | (2) |
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13.3.2 Comparison of Residual Energy Consumption |
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284 | (1) |
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13.3.3 Comparison of Lifetime of Network |
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284 | (2) |
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13.3.4 Comparison of Throughput at BS |
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286 | (1) |
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13.4 Conclusion and Future Scope |
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287 | (4) |
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288 | (3) |
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14 Privacy and Security of Smart Systems |
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291 | (26) |
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14.1 Smart Systems--An Overview |
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291 | (1) |
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14.2 Security and Privacy Challenges in Smart Systems |
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292 | (2) |
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14.2.1 Botnet Activities in Smart Systems |
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294 | (1) |
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14.2.2 Threats of Nonhuman-Operated Cars |
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294 | (1) |
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14.2.3 Privacy Issues of Virtual Reality |
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294 | (1) |
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14.3 Case Studies--Security Breaches in Smart Systems |
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294 | (2) |
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14.3.1 Breaching Smart Surveillance Cameras |
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295 | (1) |
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14.3.2 Hacking Smart Televisions |
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295 | (1) |
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14.3.3 Hacked Smart Bulbs |
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295 | (1) |
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14.3.4 Vulnerable Smart Homes |
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296 | (1) |
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14.3.5 Identity Stealing using Smart Coffee Machines |
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296 | (1) |
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14.4 Existing Security and Privacy Protection Technologies |
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296 | (5) |
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297 | (2) |
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299 | (2) |
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14.4.3 Block Chain Technology |
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301 | (1) |
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14.5 Machine Learning, Deep Learning, and Artificial Intelligence |
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301 | (2) |
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14.5.1 Machine Learning in Smart Systems |
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301 | (1) |
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302 | (1) |
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14.5.3 Deep Learning in Smart Systems |
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303 | (1) |
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14.5.4 Artificial Intelligence in Smart Systems |
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303 | (1) |
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14.6 Security Requirement for Smart Systems |
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303 | (2) |
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14.6.1 Thwarting of Data Leakage and Falsifications |
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304 | (1) |
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14.6.2 Identification and Prevention of Device Tampering |
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304 | (1) |
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14.6.3 Light Weight Encryption Algorithm for Authentication |
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304 | (1) |
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14.6.4 Access Restrictions to Users |
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305 | (1) |
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14.6.5 Incident Response for Entire Systems |
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305 | (1) |
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14.7 Instruction to Build Strong Privacy Policy |
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305 | (1) |
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305 | (1) |
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306 | (1) |
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14.7.3 Key Reasons Why There Is a Need for Privacy Policy |
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306 | (1) |
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14.8 Role of Internet in Smart Systems |
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306 | (4) |
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307 | (1) |
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307 | (1) |
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308 | (1) |
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14.8.4 Health & Lifestyle |
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309 | (1) |
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14.9 Frameworks, Algorithms, and Protocols for Security Enhancements |
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310 | (2) |
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14.9.1 Framework for the Internet of Things by Cryptography |
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311 | (1) |
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14.9.2 Protocols for Security Enhancements |
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312 | (1) |
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14.10 Design Principles of Privacy Enhancing Methodologies |
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312 | (1) |
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313 | (4) |
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314 | (3) |
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15 Artificial Intelligence and Blockchain Technologies for Smart City |
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317 | (14) |
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318 | (4) |
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15.2 Standard for Designing Smart City and Society |
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322 | (1) |
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322 | (1) |
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15.2.2 Intelligent Health Care |
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322 | (1) |
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15.2.3 Flexible and Interoperable |
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322 | (1) |
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15.2.4 Safeguard Infrastructure |
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322 | (1) |
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15.2.5 Robust Environment |
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323 | (1) |
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15.2.6 Distribution and Sources of Energy |
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323 | (1) |
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15.2.7 Intelligent Infrastructure |
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323 | (1) |
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15.2.8 Choice-Based Backing System |
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323 | (1) |
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15.2.9 Monitoring of Behavior |
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323 | (1) |
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15.3 Blockchain and Artificial Intelligence |
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323 | (1) |
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15.4 Contributions and Literature Study |
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324 | (4) |
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328 | (3) |
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329 | (2) |
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16 Android Application for School Bus Tracking System |
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331 | (10) |
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331 | (1) |
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16.2 Application Methods for Access |
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332 | (3) |
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16.2.1 Driver Portal Screen |
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333 | (1) |
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16.2.2 Parent Portal Screen |
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334 | (1) |
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16.2.3 Teachers Portal Screen |
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334 | (1) |
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16.3 GPS Data Processing Methodology |
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335 | (1) |
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336 | (1) |
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16.5 System Implementation |
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336 | (1) |
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16.6 Result and Discussion |
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336 | (2) |
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16.6.1 Reasons to Utilize Android Application for School Bus Tracking System |
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337 | (1) |
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16.6.1.1 Perfect Child Security |
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337 | (1) |
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16.6.1.2 Elaborate Operational Efficiency |
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337 | (1) |
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16.6.1.3 Valid Timely Maintenance |
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338 | (1) |
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16.6.1.4 Automating Attendance Management |
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338 | (1) |
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16.6.1.5 Better Staff Management |
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338 | (1) |
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16.6.1.6 Addressing Environmental Concerns |
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338 | (1) |
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338 | (3) |
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339 | (2) |
About the Editors |
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341 | (2) |
Index |
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343 | |