{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":22,"n_cols":5,"rows_kept":22,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[13,2,13,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996500063503316"]
[null,null,"Titolo",null,"Wireless algorithms, systems, and applications : 17th International Conference, WASA 2022, Dalian, China, October 28-30 2022, proceedings. Part II / / edited by Lei Wang, [and three others]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-19214-1"]
[null,null,"Descrizione fisica",null,"1 online resource (677 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.13472"]
[null,null,"Disciplina",null,"929.605"]
[null,null,"Soggetti",null,"Computer algorithms"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part II -- Algorithms, Systems, and Applications of Internet of Things -- Unsupervised Deep Learning-Based Hybrid Beamforming in Massive MISO Systems -- 1 Introduction -- 2 System Model and Problem Formulation -- 2.1 System Model -- 2.2 Problem Formulation -- 3 Proposed CNN-Based Hybrid Beamforming Optimization -- 3.1 CNN Structure -- 3.2 Training Strategy -- 3.3 Complexity Analysis -- 4 Simulation Results -- 5 Conclusion -- References -- An Adaptive BSCO Algorithm of Solid Color Optimization for 3D Reconstruction System with PIFuHD -- 1 Introduction -- 1.1 A Subsection Sample -- 2 Related Work -- 2.1 Research Status of 3D Reconstruction Technology -- 2.2 Symbol Table -- 3 Method -- 3.1 PIFuHD Algorithm Framework for Solid Color Optimization -- 3.2 BSCO -- 3.3 PIFuHD -- 4 Experiment -- 4.1 Experimental Environment -- 4.2 Experimental Results -- 4.3 Experimental Analysis -- 5 Conclusion and Future Work -- References -- RF-Line: RFID-Based Line Crossing Detection -- 1 Introduction -- 2 Problem Definition -- 3 RF-Line -- 3.1 Basic Idea -- 3.2 Line Crossing Detection with Time Warping -- 3.3 Generalized Cases -- 4 Evaluation -- 4.1 System Setup -- 4.2 Accuracy -- 5 Related Work -- 6 Conclusion -- References -- Joint Beamforming and Deployment Optimization for UAV-Assisted Maritime Monitoring Networks -- 1 Introduction -- 2 System Model and Problem Formulation -- 2.1 System Model -- 2.2 Problem Formulation -- 3 Problem Solution -- 3.1 UAV Deployment and Beam Gain Allocation Sub-problem -- 3.2 Beamforming Optimization Sub-problem -- 4 Simulation Results -- 5 "]
[{"$nulls":4},"Conclusion -- References -- A Monte Carlo Algorithm Based on Stochastic Geometry for Simulating Satellite Systems Interference -- 1 Introduction -- 2 Problem Definition -- 3 Proposed Monte Carlo Algorithm Based on Stochastic Geometry."]
[{"$nulls":4},"3.1 Calculation of C/Iagg -- 3.2 Monte Carlo Method -- 4 Performance Evaluation -- 4.1 Simulation Settings -- 4.2 Simulation Results -- 5 Conclusion -- References -- An Efficient Interference Calculation Model Based on Large Scale Constellations Probabilistic Analysis -- 1 Introduction -- 2 Scenario Model -- 2.1 Interference Scenario -- 2.2 Probability Method -- 3 Interference Calculation Model -- 3.1 Determine the Visual Area -- 3.2 Determine the Position of the NGSO Satellites in the Visual Area -- 3.3 Interference Calculation -- 3.4 Interference Probability Distribution Statistics -- 4 Experiment -- 4.1 Simulation Model -- 4.2 Results and Discussion -- 5 Conclusion -- References -- FedALP: An Adaptive Layer-Based Approach for Improved Personalized Federated Learning -- 1 Introduction -- 2 Methodology -- 2.1 Motivation -- 2.2 Algorithm Design -- 3 Experiments -- 3.1 Datasets and Model Architectures -- 3.2 FedALP Evaluation -- 4 Conclusion -- References -- Recovering the Weights of Convolutional Neural Network via Chosen Pixel Horizontal Power Analysis -- 1 Introduction -- 2 Background -- 2.1 Side-Channel Attack -- 2.2 Convolutional Neural Network and CMSIS-NN -- 3 Chosen Pixel Horizontal Power Analysis -- 3.1 Targeted Model -- 3.2 Correlating Selected Multiplications to Reverse the Weights of CNN -- 4 Experiments -- 5 Conclusion -- References -- MineSOS: Long-Range LoRa-Based Distress Gesture Sensing for Coal Mine Rescue -- 1 Introduction -- 2 System Overview -- 3 Gesture Design -- 3.1 Sub-gestures -- 3.2 Distress Information Expression -- 3.3 Observation -- 4 MineSOS System Design -- 4.1 Optimal Attribute Selection -- 4.2 Emergency Gesture Recognition -- 5 System Evaluation -- 5.1 Implementation -- 5.2 Overall Performance -- 5.3 Evaluation of Designed Methods -- 5.4 Exploring the Limit of Sensing Range -- 6 Conclusion -- References."]
[{"$nulls":4},"Weighted Data Loss Minimization in UAV Enabled Wireless Sensor Networks -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 System Model -- 3.2 Problem Formulation -- 4 The Proposed Scheme -- 4.1 Potential Hovering Locations Discretization -- 4.2 Data Collection Tour Planning Algorithm -- 4.3 Data Collection Tour Optimization Algorithm -- 4.4 Algorithm Analysis -- 5 Performance Evaluation -- 5.1 Simulation Setup -- 5.2 Performance Evaluation of Different Algorithms -- 5.3 Impacts of Parameters on the Performance of Algorithm -- 6 Conclusion -- References -- Robust Adaptive Cubature Kalman Filter for Attitude Determination in Wearable Inertial Sensor Networks -- 1 Introduction -- 2 Attitude Estimation System -- 2.1 Attitude Update Model -- 2.2 Attitude Estimation Model -- 3 Design of Attitude Data Fusion Algorithm -- 3.1 Data Fusion Scheme Design -- 3.2 Robust Adaptive Cubature Kalman Filter Algorithm -- 4 Experiment Results and Algorithm Validation -- 5 Conclusion -- References -- Research on the Effect of BBR Delay Detection Interval in TCP Transmission Competition on Heterogeneous Wireless Networks -- 1 Introduction -- 2 Related Work -- 3 BBR Delay Detection Interval -- 3.1 Mechanism of Delay Detection Interval -- 3.2 Dynamic Delay Detection Interval -- 4 Simulation Setup and Result Analysis -- 4.1 Scenarios and Parameters -- 4.2 Simulation Results -- 4.3 Result Analysis -- 5 Conclusion -- References -- BatMapper-Plus: Smartphone-Based Multi-level Indoor Floor Plan Construction via Acoustic Ranging and Inertial Sensing -- 1 Introduction -- 2 Overview -- 3 Methods -- 3.1 Unilateral Acoustic Ranging -- 3.2 Map "]
[{"$nulls":4},"Construction and Refinement -- 3.3 Connection Area Detection -- 4 Evaluation -- 4.1 Unilateral Acoustic Ranging -- 4.2 Floor Plan Construction -- 4.3 Connection Area Detection -- 5 Related Work -- 6 Conclusion -- References."]
[{"$nulls":4},"Prediction of Cancellation Probability of Online Car-Hailing Orders Based on Multi-source Heterogeneous Data Fusion -- 1 Introduction -- 2 Literature Review -- 3 Data Design -- 3.1 Problem Definition -- 3.2 Social Factors -- 3.3 Public Transportation Resources -- 4 Model Design -- 4.1 Attribute Fusion Module -- 4.2 Encoder Layer -- 4.3 Decoder Layer -- 5 Experiments -- 5.1 Dataset Introduction -- 5.2 Quantitative Analysis -- 5.3 Ablation Experiment -- 6 Conclusion -- References -- FedGAN: A Federated Semi-supervised Learning from Non-IID Data -- 1 Introduction -- 2 Overview -- 2.1 Objective of FedGAN -- 2.2 Optimization Process -- 2.3 GAN-Based Training Mechanism -- 2.4 Dynamic Aggregation Mechanism -- 3 Experiment -- 3.1 Ablation Study -- 3.2 Comparison with Related Methods -- 3.3 Robustness -- 3.4 Scalability -- 4 Conclusion -- References -- DEANet: A Real-Time Image Semantic Segmentation Method Based on Dual Efficient Attention Mechanism -- 1 Introduction -- 2 Related Work -- 2.1 Real-Time Semantic Segmentation -- 2.2 Attention Mechanism -- 3 Methodology -- 3.1 Network Structure of DEANet -- 3.2 Attention Module -- 4 Experiments -- 4.1 Dataset -- 4.2 Evaluation Indicators and Experimental Details -- 4.3 Ablation Experiment -- 4.4 Comparison with Other Methods -- 5 Summary -- References -- A Deep Learning Approach Based on Continuous Wavelet Transform Towards Fall Detection -- 1 Introduction -- 2 Related Work -- 2.1 Fall Detection -- 2.2 Image Classification -- 3 Methods -- 3.1 System Overview -- 3.2 Data Preprocessing -- 3.3 Feature Extraction -- 3.4 Classification -- 3.5 Wavelet Scattering Network -- 4 Performance Evaluation -- 4.1 Experiment Setup -- 4.2 Comparison of the Two Methods -- 4.3 Comparison with Existing Fall Detection Efforts -- 5 Conclusion and Future Work -- References."]
[{"$nulls":4},"Data Collection of IoT Devices with Different Priorities Using a Fleet of UAVs -- 1 Introduction -- 2 Preliminaries -- 2.1 Network Model -- 2.2 Problem Definition -- 3 Algorithm for the Simplified Problem -- 4 Algorithm for the Weighted Data Collection Latency Minimization Problem -- 5 Performance Evaluation -- 5.1 Simulation Environment -- 5.2 Algorithm Performance -- 6 Conclusion -- References -- Cross-Model Operator Batching for Neural Network Architecture Search -- 1 Introduction -- 2 Motivation -- 3 Problem Formulation -- 3.1 System Model -- 3.2 Batching -- 3.3 Problem Definition -- 4 Algorithm Design -- 4.1 Clustering Based on Model's Similarity -- 4.2 Design of Batching Strategy -- 5 Evaluation -- 5.1 Experiment Settings -- 5.2 Three Baselines -- 5.3 Experiment Results -- 6 Conclusion -- References -- Reliability-Aware Comprehensive Routing and Scheduling in Time-Sensitive Networking -- 1 Introduction -- 2 Related Work -- 3 System Model -- 3.1 Network Architecture Model -- 3.2 Traffic Model -- 4 Joint Routing and Scheduling Mechanism -- 4.1 Route Constraints -- 4.2 Scheduling Constraints -- 4.3 Optimization Objective -- 4.4 Mechanism Design -- 5 Evaluation -- 5.1 Simulation Setup -- 5.2 Evaluation Results -- 6 Conclusions -- References -- Fundamental Analysis of 3D 6G-Localization Using Reconfigurable Intelligent Surface -- 1 Introduction -- 2 3D System and Channel Model -- 2.1 3D System Model -- 2.2 Channel Model -- 2.3 Received Signal Model -- 3 Cramr-Rao Lower Bound on Position Estimation -- 4 Simulation -- 4.1 Simulation Setting -- 4.2 Different Positioning Approaches -- 4.3 TOA Based Positioning -- 4.4 AOA Based Positioning -- 5 Conclusion -- References -- UltrasonicG: Highly Robust Gesture Recognition "]
[{"$nulls":4},"on Ultrasonic Devices -- 1 Introduction -- 2 Related Work -- 3 System Design -- 3.1 Overview -- 3.2 Data Collection and Processing."]
[{"$nulls":4},"3.3 Feature Extraction and Gesture Classification."]