| Contributors |
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xi | |
| Foreword |
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xiii | |
| Introduction |
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xv | |
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PART 1 Wireless sensor networks and cyber-physical systems: New approaches and methods |
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Chapter 1 Routing protocols for wireless sensor networks: A survey |
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3 | (14) |
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3 | (2) |
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5 | (1) |
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1.3 Characteristics and constraints of WSNs |
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6 | (1) |
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1.4 Network topology of WSNs |
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7 | (1) |
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7 | (1) |
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7 | (1) |
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7 | (1) |
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7 | (1) |
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1.5 Routing protocol for WSNs |
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8 | (5) |
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1.5.1 Hierarchical routing protocol |
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8 | (2) |
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1.5.2 Data-centric routing protocol |
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10 | (1) |
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1.5.3 Location-based protocol |
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11 | (2) |
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13 | (4) |
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13 | (4) |
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Chapter 2 Replay attack detection using excitation source and system features |
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17 | (28) |
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17 | (4) |
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21 | (1) |
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2.3 Features used for replay detection |
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22 | (6) |
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2.3.1 LP residual-based implicit source features |
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22 | (4) |
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2.3.2 Explicit source features |
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26 | (1) |
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27 | (1) |
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28 | (12) |
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28 | (3) |
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31 | (2) |
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33 | (1) |
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2.4.4 Experimental results and discussion |
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34 | (6) |
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2.5 Summary and future scope |
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40 | (5) |
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41 | (1) |
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41 | (4) |
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Chapter 3 Hypergraph-based type theory for software development in a Cyber-Physical context |
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45 | (94) |
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3.1 Hub applications and gatekeeper code |
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48 | (15) |
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55 | (4) |
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59 | (1) |
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3.1.3 Core language vs. external tools |
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60 | (3) |
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63 | (14) |
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3.2.1 How "Internet of Things" interoperability affects data modeling priorities |
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69 | (3) |
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3.2.2 Linguistic case study |
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72 | (3) |
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75 | (2) |
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3.3 Directed Hypergraphs and generalized lambda calculus |
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77 | (21) |
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3.3.1 Generalized lambda calculus |
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78 | (3) |
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3.3.2 Directed Hypergraphs and "channel abstractions" |
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81 | (4) |
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3.3.3 Channelized hypergraphs and RDF |
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85 | (8) |
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3.3.4 Procedural input/output protocols via type theory |
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93 | (5) |
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3.4 Modeling procedures via channelized hypergraphs |
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98 | (13) |
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3.4.1 Initializing function-typed values |
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98 | (6) |
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3.4.2 Dependent types and co-constructors |
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104 | (7) |
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3.5 Channels and carriers |
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111 | (15) |
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113 | (5) |
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3.5.2 Channelized-type interpretations of larger-scale source code elements |
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118 | (8) |
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126 | (13) |
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129 | (10) |
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Chapter 4 A new method of power efficient speech transmission over 5G networks using new signaling techniques |
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139 | (24) |
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139 | (1) |
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140 | (1) |
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4.3 Ultra-filter bank multicarrier modulation |
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141 | (2) |
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143 | (5) |
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4.4.1 Bit error rate analysis using Alamouti coding |
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144 | (2) |
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4.4.2 Orthogonal space-time block code |
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146 | (1) |
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4.4.3 Maximal ratio combining |
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147 | (1) |
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4.5 Bit error rate (BER) analysis of BPSK modulated system |
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148 | (1) |
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4.6 Bit error rate (BER) analysis of a QAM modulated system |
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149 | (1) |
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150 | (2) |
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152 | (5) |
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157 | (1) |
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158 | (5) |
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PART 2 Smart cities/smart homes/smart communities |
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Chapter 5 Study of robust language identification techniques for future smart cities |
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163 | (22) |
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163 | (2) |
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165 | (1) |
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166 | (1) |
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167 | (4) |
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5.4.1 The i-vector-based LID system |
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167 | (1) |
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5.4.2 LID system using DNN |
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167 | (2) |
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5.4.3 LID system using DNN with attention |
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169 | (2) |
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5.5 Performance of LID in mismatched environments |
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171 | (1) |
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5.5.1 Language identification in noisy conditions |
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171 | (1) |
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5.5.2 Language identification in a mobile environment |
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171 | (1) |
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5.6 Proposed approaches for improving performance language identification in mismatched conditions |
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172 | (8) |
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5.6.1 Improving the performance of LID systems by vowel region-based front-end system |
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172 | (5) |
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5.6.2 Improving the performance of LID systems by enhancing the speech signals |
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177 | (2) |
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5.6.3 Improving the performance of LID systems by using CL strategies |
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179 | (1) |
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5.7 Conclusion and future scope |
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180 | (5) |
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181 | (1) |
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181 | (4) |
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Chapter 6 Effective natural interaction with our sensorized smart homes |
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185 | (38) |
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185 | (2) |
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187 | (9) |
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6.2.1 Home sensors and actuators |
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187 | (1) |
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6.2.2 High level handling of sensor networks |
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187 | (1) |
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188 | (1) |
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6.2.4 Ubiquitous computing, interaction, and multidevice contexts |
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188 | (1) |
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6.2.5 Human building interaction |
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189 | (1) |
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6.2.6 Interaction with smart homes |
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190 | (2) |
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192 | (3) |
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6.2.8 Design challenges in IoT and data contexts |
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195 | (1) |
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6.3 An integrated vision for human-home interaction |
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196 | (1) |
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6.4 Integrated ubiquitous distributed solution for a smart home |
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197 | (6) |
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6.4.1 Smart home's information structuring |
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198 | (1) |
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199 | (1) |
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200 | (1) |
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6.4.4 Multimodal interaction support |
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200 | (1) |
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6.4.5 Smart home test bench |
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201 | (2) |
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6.5 Ubiquitous human-home interaction |
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203 | (5) |
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6.5.1 Multimodal interaction |
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203 | (1) |
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6.5.2 Conversational capabilities |
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204 | (4) |
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6.6 Illustrative scenarios |
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208 | (8) |
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6.6.1 Chatting with the smart home |
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208 | (4) |
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6.6.2 Sending an email to the home |
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212 | (1) |
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6.6.3 Assisting the very young and the elderly |
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213 | (1) |
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6.6.4 Multimodal interaction across rooms |
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214 | (2) |
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216 | (7) |
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217 | (1) |
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217 | (6) |
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Chapter 7 A study on the emotional state of a speaker in voice bio-metrics |
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223 | (16) |
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223 | (2) |
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225 | (1) |
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7.3 Baseline emotional SR system |
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225 | (2) |
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7.4 Analysis of SR system performance in emotional conditions |
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227 | (3) |
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7.4.1 Fundamental frequency |
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228 | (1) |
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7.4.2 Strength of excitation |
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228 | (1) |
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7.4.3 Energy of excitation |
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228 | (1) |
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228 | (2) |
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7.5 Proposed strategies for SR in emotional conditions |
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230 | (3) |
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7.5.1 SR system with emotional UBM |
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230 | (1) |
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7.5.2 SR system with emotional data |
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231 | (1) |
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7.5.3 Selection of speaker model based on emotion recognition |
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232 | (1) |
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7.6 Summary and conclusions |
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233 | (6) |
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234 | (1) |
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234 | (2) |
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236 | (3) |
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PART 3 Ecological monitoring |
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Chapter 8 Ubiquitous computing and biodiversity monitoring |
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239 | (22) |
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8.1 Marine biodiversity monitoring |
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240 | (2) |
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8.2 Terrestrial biodiversity monitoring |
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242 | (10) |
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8.2.1 The ARBIMON acoustics project |
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244 | (3) |
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247 | (5) |
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252 | (2) |
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8.4 Health and disease transmission |
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254 | (1) |
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8.5 Monitoring of urban ecosystems |
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255 | (2) |
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8.6 Discussion and future trends |
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257 | (4) |
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257 | (1) |
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258 | (3) |
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Chapter 9 The use of WSN (wireless sensor network) in the surveillance of endangered bird species |
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261 | (46) |
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261 | (3) |
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264 | (2) |
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266 | (4) |
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9.3.1 The white-headed duck |
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266 | (2) |
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9.3.2 The ferruginous duck |
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268 | (2) |
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9.4 Measurement and data collection |
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270 | (1) |
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9.5 Habitat monitoring and wildlife information gathering |
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271 | (2) |
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9.5.1 Conventional observation method |
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271 | (1) |
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271 | (1) |
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272 | (1) |
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272 | (1) |
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9.6 Wireless sensor networks |
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273 | (3) |
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273 | (1) |
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9.6.2 Wireless sensor node structure |
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274 | (2) |
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9.6.3 WSNs for wildlife monitoring |
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276 | (1) |
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276 | (4) |
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9.7.1 Environmental noise |
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277 | (2) |
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9.7.2 Limited computational capacity of wireless sensor nodes |
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279 | (1) |
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9.8 Bird species recognition systems |
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280 | (13) |
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9.8.1 Acoustic bird recognition system: Proposition 1 |
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280 | (6) |
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9.8.2 Acoustic bird recognition system: Proposition 2 |
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286 | (5) |
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9.8.3 Alternative acoustic features used in birdsong/speech recognition systems |
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291 | (2) |
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9.9 Experimental evaluations |
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293 | (6) |
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9.9.1 Data description (database) and experimental setup |
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293 | (1) |
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9.9.2 Results and discussion |
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294 | (5) |
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299 | (8) |
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300 | (6) |
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306 | (1) |
| Index |
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307 | |