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[null,"1.","Record Nr.",null,"UNISA996485669803316"]
[null,null,"Titolo",null,"Broadband communications, computing, and control for ubiquitous intelligence / / Lin Cai, Brian L. Mark, and Jianping Pan, editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer International Publishing, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-030-98064-2"]
[null,null,"Descrizione fisica",null,"1 online resource (353 pages)"]
[null,null,"Collana",null,"Wireless Networks"]
[null,null,"Disciplina",null,"621.384"]
[null,null,"Soggetti",null,"Wireless communication systems - Automatic control"]
[{"$nulls":4},"Wireless communication systems"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Contents -- Contributors -- 1 Tribute to Professor Jon W. Mark -- Personal Stories -- Greeting Messages from Alumni -- Part I Broadband Communications for Ubiquitous Connectivity -- 2 Network Slicing for 5G Networks and Beyond -- 2.1 Introduction to 5G Communication Networks -- 2.2 Network Slicing -- 2.2.1 Network Slicing in 5G Wireless Networks -- 2.2.1.1 Dynamic Radio Resource Slicing Framework -- 2.2.2 Network Slicing in 5G Core Networks -- 2.2.2.1 Joint Computing and Transmission Resource Slicing -- 2.2.3 AI-Assisted Network Slicing in Beyond 5G Networks -- 2.2.3.1 Beyond 5G Networks -- 2.2.3.2 AI-Assisted Network Slicing -- 2.3 Case Study -- 2.4 Conclusion -- References -- 3 Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learning -- 3.1 Introduction -- 3.2 Related Works -- 3.3 System Model and Problem Formulation -- 3.3.1 Network Environment -- 3.3.2 Spectrum Access -- 3.3.3 Energy-Related Considerations -- 3.3.4 Problem Formulation -- 3.4 Preliminaries -- 3.5 Learning Algorithm Design for Proactive Self-Regulation Strategy -- 3.5.1 State Space -- 3.5.1.1 Case of UAV Quit -- 3.5.1.2 Case of UAV Join-In -- 3.5.2 Action Definition -- 3.5.3 Reward Function Design -- 3.5.4 State Transition Definition -- 3.5.4.1 Case of UAV Quit -- 3.5.4.2 Case of UAV Join-In -- 3.5.5 Training Tune-Ups -- 3.5.5.1 Tune-Ups for Neural Network Training -- 3.5.5.2 Tune-Ups for RL Training -- 3.5.6 Parallel Computing -- 3.6 Proactive Self-Regulation with Dynamic User Distribution -- 3.7 Numerical Results -- 3.7.1 Simulation Setup -- 3.7.2 Simulation Results -- 3.7.2.1 Case Without UAV or User Dynamics "]
[{"$nulls":4},"-- 3.7.2.2 Case of UAV Quit -- 3.7.2.3 Case of UAV Join-In -- 3.7.2.4 Case of UAV and User Dynamics -- 3.8 Conclusions -- References."]
[{"$nulls":4},"4 Utility-Based Dynamic Resource Allocation in IEEE 802.11ax Networks: A Genetic Algorithm Approach -- 4.1 Introduction -- 4.2 Related Works -- 4.3 Background on OFDMA and RU Allocation in IEEE 802.11ax -- 4.4 System Model -- 4.5 Utility-Based Dynamic Resource Allocation Scheme -- 4.5.1 Optimal Resource Allocation Problem Formulation -- 4.5.2 Genetic Algorithm -- 4.6 Simulation Results -- 4.6.1 UDRA vs. Exhaustive Search -- 4.6.2 Network-Wise Throughputs and Fairness Indexes -- 4.7 Conclusion -- References -- 5 Intelligentized Radio Access Network for Joint Optimization of User Association and Power Allocation -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Main Contribution -- 5.4 System Model -- 5.5 Problem Formulation -- 5.6 DQL Framework -- 5.6.1 DQN -- 5.6.2 Design the DQN -- 5.6.2.1 Actions -- 5.6.2.2 Reward -- 5.7 Results and Discussions -- 5.7.1 Training and Testing Results -- 5.7.2 UE Performance -- 5.7.3 Robustness -- 5.7.4 Scalability -- 5.7.5 Closer Look at DQN -- 5.8 Summary -- References -- 6 Routing Algorithms for Heterogeneous Vehicular Networks -- 6.1 Introduction -- 6.2 Background -- 6.2.1 Unicast Routing Algorithms -- 6.2.2 Broadcast Routing Algorithms -- 6.2.3 Geocast Routing Algorithms -- 6.2.4 Related Work in Routing Algorithms -- 6.3 Machine Learning-Based Routing Algorithm for IoV with Mobility Prediction -- 6.3.1 Network Model -- 6.3.2 Statistical Mobility Model -- 6.3.2.1 Inter-Arrival Time Distribution -- 6.3.2.2 Inter-Vehicle Spacing Distribution -- 6.3.3 Channel Model -- 6.3.4 ANN Model -- 6.4 Performance Evaluation -- 6.5 Conclusion -- References -- 7 Teaching from Home: Computer and Communication Network Perspectives -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Network Technologies Involved -- 7.3.1 Host Computers -- 7.3.1.1 Desktop, Laptop, or Tablet? -- 7.3.1.2 Windows, Mac OS, or Linux?."]
[{"$nulls":4},"7.3.1.3 Other Necessary Peripherals -- 7.3.2 Home Networks -- 7.3.2.1 Ethernet Structured Wiring -- 7.3.2.2 No-New-Wires Home Backbone -- 7.3.2.3 Wireless Home Network -- 7.3.3 Internet Access -- 7.3.3.1 Fiber, Cellular, or Satellite? -- 7.3.3.2 Telephone Service Providers -- 7.3.3.3 Television Service Providers -- 7.4 Improvement for Online Teaching -- 7.4.1 WiFi Interference Avoidance -- 7.4.1.1 A Better (Al)located WiFi AP -- 7.4.1.2 Wired Interconnected WiFi APs -- 7.4.1.3 Wireless Interconnected WiFi APs -- 7.4.2 WAN Reliability Augmentation -- 7.4.2.1 DSL vs. Cable Modem -- 7.4.2.2 Primary vs. Backup -- 7.4.2.3 Load Balancing -- 7.4.3 Recommendations on Teaching from Home -- 7.5 Further Discussion -- 7.6 Conclusions -- References -- Part II Caching, Computing, and Control for Ubiquitous Intelligence -- 8 State Transition Field: A New Framework for Mobile Dynamic Caching -- 8.1 Introduction -- 8.2 State Transition Field -- 8.2.1 Content Request and Replacement -- 8.2.2 Cache State -- 8.2.3 State and Content Caching Probabilities -- 8.2.4 General Cache State Transition Model -- 8.2.5 State Transition Field -- 8.2.6 Discussions on the Steady State and the Convergence -- 8.3 State Transition Field with Time-Varying Content Popularity -- 8.3.1 General Replacement Model -- 8.3.2 Instantaneous STF: The General Case -- 8.3.3 Impact of STF on Instantaneous Cache Hit Probability -- 8.4 Dynamic Probabilistic Caching with Time-Varying Content Popularity -- 8.4.1 The Content Replacement Markov Chain -- 8.4.2 Generating the State Transition Matrix -- 8.4.3 Discussion on Scalability -- 8.5 Numerical Results -- 8.5.1 State Transition Field with Time-Invariant Content Popularity -- 8.5.2 State Transition Field with Time-Varying Content Popularity -- 8.5.3 Dynamic Probabilistic Caching with Time-Varying Content "]
[{"$nulls":4},"Popularity -- 8.6 Summary -- References."]
[{"$nulls":4},"9 Deep Reinforcement Learning for Mobile EdgeComputing Systems -- 9.1 Introduction -- 9.2 Overview of Deep Reinforcement Learning -- 9.2.1 DRL Problem Formulation -- 9.2.2 Determine the Optimal Policy with Deep Learning -- 9.2.3 Existing DRL Algorithms -- 9.3 Case Study: Deep Q-Learning for Task Offloading in MEC -- 9.3.1 System Model -- 9.3.1.1 Task Model -- 9.3.1.2 Task Offloading Decision -- 9.3.1.3 Local Processing Model -- 9.3.1.4 Edge Node Offloading Model -- 9.3.2 Task Offloading Problem -- 9.3.2.1 State -- 9.3.2.2 Action -- 9.3.2.3 Cost -- 9.3.2.4 Problem Formulation -- 9.3.3 Deep Q-Learning-Based Algorithm -- 9.3.3.1 Neural Network -- 9.3.3.2 Algorithm Design -- 9.3.4 Performance Evaluation -- 9.3.4.1 Algorithm Convergence -- 9.3.4.2 Method Comparison -- 9.4 Challenges and Future Directions -- 9.5 Conclusion -- References -- 10 Mobile Computation Offloading with Hard TaskCompletion Times -- 10.1 Introduction -- 10.2 Continuous Offloading -- 10.2.1 System Description and Problem Formulation -- 10.2.1.1 Local Execution -- 10.2.1.2 Remote Execution -- 10.2.2 Markovian Channel and the Time-Dilated Absorbing Markov Model -- 10.2.3 Offline Bound -- 10.2.4 OnOpt (Online Optimal) Algorithm -- 10.3 Multi-part Offloading -- 10.3.1 Problem Formulation -- 10.3.2 Offline Bound -- 10.3.3 The Time-Dilated Absorbing Markov Model -- 10.3.4 Optimal Algorithm for K-Part Offloading -- 10.4 Numerical Results -- 10.4.1 Simulation Set 1 -- 10.4.2 Simulation Set 2 -- 10.5 Summary -- References -- 11 Online Incentive Mechanism Design in Edge Computing -- 11.1 Introduction -- 11.2 Mechanism Design and Auction -- 11.3 Primal-Dual-Based Online Incentive Mechanism -- 11.3.1 Primal-Dual-Based Method for Linear Systems -- 11.3.2 Primal-Dual-Based Method for Nonlinear Systems -- 11.4 Application of Primal-Dual Online Incentive Mechanism Design in Edge Computing."]
[{"$nulls":4},"11.4.1 System Model Descriptions -- 11.4.1.1 System Model -- 11.4.1.2 Problem Formulation -- 11.4.2 The Design of OMAP -- 11.4.2.1 Problem Reformulation -- 11.4.2.2 OMAP -- 11.4.3 Performance Analyses -- 11.4.4 Numerical Simulations -- 11.5 Summary -- References -- 12 Collaborative Deep Neural Network Inference via Mobile Edge Computing -- 12.1 Introduction -- 12.2 Background -- 12.2.1 DNN Inference -- 12.2.2 Mobile Edge Computing -- 12.2.3 Machine Learning -- 12.3 Collaborative DNN Inference via Device-Edge Orchestration -- 12.3.1 Collaborative DNN Inference Framework -- 12.3.2 Service Delay and Accuracy Analysis of Collaborative DNN Inference -- 12.3.2.1  Inference Delay Analysis -- 12.3.2.2  Inference Accuracy Analysis -- 12.3.3 Joint Sampling Rate Selection and Resource Allocation Problem -- 12.3.3.1 Constrained Markov Decision Process -- 12.3.4 Deep RL-Based Solution -- 12.3.4.1 Markov Decision Process Transformation (Step 1) -- 12.3.4.2 Optimization Subroutine for Resource Allocation (Step 3) -- 12.3.4.3  Deep RL-Based Algorithm (Step 2) -- 12.4 Performance Evaluation -- 12.4.1 Experiment Setup -- 12.4.2 Convergence Performance -- 12.4.3 Impact of Task Arrival Rate -- 12.4.4 Impact of Optimization Subroutine -- 12.5 Conclusion -- References -- 13 Automated Data-Driven System for Compliance Monitoring -- 13.1 Introduction -- 13.1.1 Radio Spectrum Management -- 13.1.2 Spectrum Monitoring for Compliance -- 13.1.3 Chapter Contributions and Organization -- 13.2 Automated Data-driven System -- 13.3 Data Sources -- 13.3.1 Spectrum Measurements -- 13.3.2 Spectrum Management Records -- 13.4 Signal Identification -- 13.4.1 Mode Analysis -- 13.4.2 Mode-Sensor Matching -- 13.4.3 License-Measurements Association -- 13.5 Violation Identification -- 13.5.1 Detecting Violations -- 13.5.2 "]
[{"$nulls":4},"Characterizing Violations -- 13.5.2.1 Confidence Indicators."]
[{"$nulls":4},"13.5.2.2 Behavioral Indicators."]
[null,"2.","Record Nr.",null,"UNINA9910155612203321"]
[null,null,"Autore",null,"Miller Emmett"]
[null,null,"Titolo",null,"Loving communication : Guided meditation for developing relationships. / / Emmett Miller"]
[null,null,"Pubbl/distr/stampa",null,"Solon, : Better Listen, 2016"]
[null,null,"ISBN",null,"1-61544-832-2"]
[null,null,"Edizione",null,"[Unabridged.]"]
[null,null,"Descrizione fisica",null,"1 online resource (1 audio file) : digital"]
[null,null,"Classificazione",null,"OCC021000"]
[null,null,"Soggetti",null,"Nonfiction"]
[{"$nulls":4},"New Age"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Audiolibro"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Unabridged."]
[null,null,null,"Sommario/riassunto","Whether or not you are able to communicate effectively and create a respectful, honest, and loving relationship depends on how you mentally picture yourself and the other person. The attitude that emerges from this has a powerful effect on how you communicate with him or her. Here is a most enjoyable way, through Dr. Miller's expert guidance, to improve all your relationships, especially your most intimate ones. This deep relaxation and guided imagery experience will help you: 1. Relax away tension, resistance, and anxiety so you can be open. 2. Gain access to the potential of your Higher Power, the wisdom within. 3. Amplify your ability to feel unconditional acceptance and Love. 4. Communicate from a centered, loving place, with an open attitude."]