Mobile Wireless Middleware, Operating Systems and Applications: 10th International Conference on Mobile Wireless Middleware, Operating Systems and ... Innovations in Communication and Computing)

Preface
Conference Organization
Contents
Human Behavior Estimation Using Micro-vibration Sensor Based on Deep Boltzmann Machine
	1 Introduction
	2 Related Method
		2.1 Boltzmann Machine
		2.2 Restricted Boltzmann Machine
		2.3 Deep Boltzmann Machine
	3 Human Behavior Estimation System
	4 Behavior Estimation Algorithm
		4.1 Low-Pass Filter (LPF)
		4.2 Weighted Root Mean Square (WRMS)
		4.3 Autocorrelation Function (ACF)
		4.4 Binarization
		4.5 Feature Extraction by DBM
		4.6 Classification by Neural Network
		4.7 Time Series Majority Decision
	5 Human Behavior Estimation Experiment
		5.1 Training the Proposed Model
		5.2 Experimental Methods
		5.3 Behavior Estimation Results
		5.4 Consideration
	6 Conclusion
	References
Topological Tracking for Mobility Support Robots Based on Multi-scale Batch Learning Growing Neural Gas
	1 Introduction
	2 Multi-scopic Simulations for Topological Twin
		2.1 Multi-scopic Approach
		2.2 Macroscopic Analysis Based on Human-Robot Mesoscopic Simulations
	3 Topological Tracking
		3.1 Multi-scale Batch Learning Growing Neural Gas
		3.2 Topological Analysis by Adjacent Matrix
	4 Simulation Results
	5 Summary
	References
Bibliographic Analysis of the Capacity and Applicability of Li-Fi Networks
	1 Introduction
	2 Related Works
	3 Theoretical Background
		3.1 Optical Communication
		3.2 Photodiode
		3.3 Li-Fi
	4 Methodology
	5 Results
		5.1 Signal Modulation
		5.2 Multi-User Access
		5.3 Network Structure
		5.4 Upload
		5.5 Topology
		5.6 Cells
		5.7 Data Rates
		5.8 Use Cases
		5.9 Li-Fi and 5G
	6 Conclusion
	References
Layered-MAC: An Energy-Protected and Efficient Protocol for Wireless Sensor Networks
	1 Introduction
	2 Related Work
	3 Requirements Analysis
		3.1 Requirements Identification
		3.2 Functional Requirements
		3.3 Algorithm Design
	4 Protocol Simulation
		4.1 Test Plan
		4.2 Experimental Results
		4.3 Simulation Results for the Protocols Under Attack
	5 Conclusion and Future Work
	References
Study on Urban Travel Volume During the Outbreak of COVID-19
	1 Introduction
	2 Data and Methods
		2.1 Study Area and Data
		2.2 Hierarchical Spatiotemporal Model
		2.3 Stability Judgment Based on Space-Time Effect
	3 Conclusion
		3.1 Model Implementation and Convergence
		3.2 Model Performance Test
		3.3 Results
	4 Discussion
	References
A Review of Additive Manufacturing (3D Printing) in Aerospace: Technology, Materials, Applications, and Challenges
	1 Introduction
	2 Main Processes in Aerospace
		2.1 Fused Deposition Modeling (FDM)
		2.2 Selective Laser Melting (SLM)
	3 Materials
		3.1 Metals and Alloys
		3.2 Polymers and Composites
		3.3 Ceramics
	4 Aerospace Applications
		4.1 Unmanned Aerial Vehicle (UAVs)
		4.2 Satellites and Rockets
		4.3 Aero-engine
		4.4 Aerospace Flight
		4.5 Machine Learning
	5 Superiorities
	6 Challenges
	7 Prospects
	8 Conclusions
	References
Instantaneous Availability Analysis of Maintenance Process Based on Semi-Markov Model
	1 Introduction
	2 Preliminaries
	3 Instantaneous Availability Analysis of Maintenance Process
	4 Analysis Case of Instantaneous Availability
	5 Conclusion
	References
A Survey of Techniques for Constructing Mongolian Domain-Specific Knowledge Graph
	1 Introduction
	2 Related Work
		2.1 Construction of Knowledge Graph
		2.2 Challenges in Constructing Mongolian Knowledge Graph
	3 Mongolian Named Entity Recognition
	4 Conclusions and Future Work
	References
Mongolian Word Segmentation Based on BiLSTM-CNN-CRF Model
	1 Introduction
	2 Neural Network Model
		2.1 BiLSTM Model Layer
		2.2 CNN Model Layer
		2.3 CRF Model Layer
	3 BiLSTM-CNN-CRF-Based Mongolian Word Segmentation Model
		3.1 Data Annotation
		3.2 Character Vectorization
		3.3 BiLSTM-CNN-CRF Model Layer
	4 Experiments and Analysis
		4.1 Experimental Data
		4.2 Experimental Parameter Settings and Evaluating Indicator
		4.3 Experimental Results and Analysis
	5 Conclusion
	References
Safety Helmet Wearing Recognition Based on YOLOv5
	1 Introduction
	2 Method
		2.1 Common Target Detection Models
			Faster R-CNN
			SSD
			YOLO
		2.2 YOLOv5
			YOLOv5 Network Structure
			Enhanced YOLOv5 Network Model
			YOLOv5 Loss Function
	3 Experiment
		3.1 Training Environment
		3.2 Dataset
		3.3 Experiment and Results
			Evaluation Index
			Model Setting
			Result
			Comparison of Helmet Test Results
	4 Conclusion
	References
Index