Human communication technology : internet-of-robotic-things and ubiquitous computing

Human communication technology internet-of-robotic-things and ubiquitous computing

HUMAN COMMUNICATION TECHNOLOGY A unique book explaining how perception, location, communication, cognition, computation, networking, propulsion, integration of federated Internet of Robotic Things (IoRT) and digital platforms are important components of new-generation IoRT applications through conti...

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Detalles Bibliográficos
Otros Autores: Anandan, R., editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, New Jersey : John Wiley & Sons, Incorporated [2022]
Edición:1st edition
Colección:Artificial Intelligence and Soft Computing for Industrial Transformation
Materias:
Internet of things.
Artificial intelligence.
Computational intelligence.
Telecommunication.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009645693906719
Tabla de Contenidos:
  • Cover
  • Half-Title Page
  • Series Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • 1 Internet of Robotic Things: A New Architecture and Platform
  • 1.1 Introduction
  • 1.1.1 Architecture
  • 1.1.1.1 Achievability of the Proposed Architecture
  • 1.1.1.2 Qualities of IoRT Architecture
  • 1.1.1.3 Reasonable Existing Robots for IoRT Architecture
  • 1.2 Platforms
  • 1.2.1 Cloud Robotics Platforms
  • 1.2.2 IoRT Platform
  • 1.2.3 Design a Platform
  • 1.2.4 The Main Components of the Proposed Approach
  • 1.2.5 IoRT Platform Design
  • 1.2.6 Interconnection Design
  • 1.2.7 Research Methodology
  • 1.2.8 Advancement Process-Systems Thinking
  • 1.2.8.1 Development Process
  • 1.2.9 Trial Setup-to Confirm the Functionalities
  • 1.3 Conclusion
  • 1.4 Future Work
  • References
  • 2 Brain-Computer Interface Using Electroencephalographic Signals for the Internet of Robotic Things
  • 2.1 Introduction
  • 2.2 Electroencephalography Signal Acquisition Methods
  • 2.2.1 Invasive Method
  • 2.2.2 Non-Invasive Method
  • 2.3 Electroencephalography Signal-Based BCI
  • 2.3.1 Prefrontal Cortex in Controlling Concentration Strength
  • 2.3.2 Neurosky Mind-Wave Mobile
  • 2.3.2.1 Electroencephalography Signal Processing Devices
  • 2.3.3 Electromyography Signal Extraction of Features and Its Signal Classifications
  • 2.4 IoRT-Based Hardware for BCI
  • 2.5 Software Setup for IoRT
  • 2.6 Results and Discussions
  • 2.7 Conclusion
  • References
  • 3 Automated Verification and Validation of IoRT Systems
  • 3.1 Introduction
  • 3.1.1 Automating V&amp
  • V-An Important Key to Success
  • 3.2 Program Analysis of IoRT Applications
  • 3.2.1 Need for Program Analysis
  • 3.2.2 Aspects to Consider in Program Analysis of IoRT Systems
  • 3.3 Formal Verification of IoRT Systems
  • 3.3.1 Automated Model Checking
  • 3.3.2 The Model Checking Process
  • 3.3.2.1 PRISM
  • 3.3.2.2 UPPAAL.
  • 3.3.2.3 SPIN Model Checker
  • 3.3.3 Automated Theorem Prover
  • 3.3.3.1 ALT-ERGO
  • 3.3.4 Static Analysis
  • 3.3.4.1 CODESONAR
  • 3.4 Validation of IoRT Systems
  • 3.4.1 IoRT Testing Methods
  • 3.4.2 Design of IoRT Test
  • 3.5 Automated Validation
  • 3.5.1 Use of Service Visualization
  • 3.5.2 Steps for Automated Validation of IoRT Systems
  • 3.5.3 Choice of Appropriate Tool for Automated Validation
  • 3.5.4 IoRT Systems Open Source Automated Validation Tools
  • 3.5.5 Some Significant Open Source Test Automation Frameworks
  • 3.5.6 Finally IoRT Security Testing
  • 3.5.7 Prevalent Approaches for Security Validation
  • 3.5.8 IoRT Security Tools
  • References
  • 4 Light Fidelity (Li-Fi) Technology: The Future Man-Machine-Machine Interaction Medium
  • 4.1 Introduction
  • 4.1.1 Need for Li-Fi
  • 4.2 Literature Survey
  • 4.2.1 An Overview on Man-to-Machine Interaction System
  • 4.2.2 Review on Machine to Machine (M2M) Interaction
  • 4.2.2.1 System Model
  • 4.3 Light Fidelity Technology
  • 4.3.1 Modulation Techniques Supporting Li-Fi
  • 4.3.1.1 Single Carrier Modulation (SCM)
  • 4.3.1.2 Multi Carrier Modulation
  • 4.3.1.3 Li-Fi Specific Modulation
  • 4.3.2 Components of Li-Fi
  • 4.3.2.1 Light Emitting Diode (LED)
  • 4.3.2.2 Photodiode
  • 4.3.2.3 Transmitter Block
  • 4.3.2.4 Receiver Block
  • 4.4 Li-Fi Applications in Real Word Scenario
  • 4.4.1 Indoor Navigation System for Blind People
  • 4.4.2 Vehicle to Vehicle Communication
  • 4.4.3 Li-Fi in Hospital
  • 4.4.4 Li-Fi Applications for Pharmacies and the Pharmaceutical Industry
  • 4.4.5 Li-Fi in Workplace
  • 4.5 Conclusion
  • References
  • 5 Healthcare Management-Predictive Analysis (IoRT)
  • 5.1 Introduction
  • 5.1.1 Naive Bayes Classifier Prediction for SPAM
  • 5.1.2 Internet of Robotic Things (IoRT)
  • 5.2 Related Work
  • 5.3 Fuzzy Time Interval Sequential Pattern (FTISPAM).
  • 5.3.1 FTI SPAM Using GA Algorithm
  • 5.3.1.1 Chromosome Generation
  • 5.3.1.2 Fitness Function
  • 5.3.1.3 Crossover
  • 5.3.1.4 Mutation
  • 5.3.1.5 Termination
  • 5.3.2 Patterns Matching Using SCI
  • 5.3.3 Pattern Classification Based on SCI Value
  • 5.3.4 Significant Pattern Evaluation
  • 5.4 Detection of Congestive Heart Failure Using Automatic Classifier
  • 5.4.1 Analyzing the Dataset
  • 5.4.2 Data Collection
  • 5.4.2.1 Long-Term HRV Measures
  • 5.4.2.2 Attribute Selection
  • 5.4.3 Automatic Classifier-Belief Network
  • 5.5 Experimental Analysis
  • 5.6 Conclusion
  • References
  • 6 Multimodal Context-Sensitive Human Communication Interaction System Using Artificial Intelligence-Based Human-Centered Computing
  • 6.1 Introduction
  • 6.2 Literature Survey
  • 6.3 Proposed Model
  • 6.3.1 Multimodal Data
  • 6.3.2 Dimensionality Reduction
  • 6.3.3 Principal Component Analysis
  • 6.3.4 Reduce the Number of Dimensions
  • 6.3.5 CNN
  • 6.3.6 CNN Layers
  • 6.3.6.1 Convolution Layers
  • 6.3.6.2 Padding Layer
  • 6.3.6.3 Pooling/Subsampling Layers
  • 6.3.6.4 Nonlinear Layers
  • 6.3.7 ReLU
  • 6.3.7.1 Fully Connected Layers
  • 6.3.7.2 Activation Layer
  • 6.3.8 LSTM
  • 6.3.9 Weighted Combination of Networks
  • 6.4 Experimental Results
  • 6.4.1 Accuracy
  • 6.4.2 Sensibility
  • 6.4.3 Specificity
  • 6.4.4 A Predictive Positive Value (PPV)
  • 6.4.5 Negative Predictive Value (NPV)
  • 6.5 Conclusion
  • 6.6 Future Scope
  • References
  • 7 AI, Planning and Control Algorithms for IoRT Systems
  • 7.1 Introduction
  • 7.2 General Architecture of IoRT
  • 7.2.1 Hardware Layer
  • 7.2.2 Network Layer
  • 7.2.3 Internet Layer
  • 7.2.4 Infrastructure Layer
  • 7.2.5 Application Layer
  • 7.3 Artificial Intelligence in IoRT Systems
  • 7.3.1 Technologies of Robotic Things
  • 7.3.2 Artificial Intelligence in IoRT
  • 7.4 Control Algorithms and Procedures for IoRT Systems.
  • 7.4.1 Adaptation of IoRT Technologies
  • 7.4.2 Multi-Robotic Technologies
  • 7.5 Application of IoRT in Different Fields
  • References
  • 8 Enhancements in Communication Protocols That Powered IoRT
  • 8.1 Introduction
  • 8.2 IoRT Communication Architecture
  • 8.2.1 Robots and Things
  • 8.2.2 Wireless Link Layer
  • 8.2.3 Networking Layer
  • 8.2.4 Communication Layer
  • 8.2.5 Application Layer
  • 8.3 Bridging Robotics and IoT
  • 8.4 Robot as a Node in IoT
  • 8.4.1 Enhancements in Low Power WPANs
  • 8.4.1.1 Enhancements in IEEE 802.15.4
  • 8.4.1.2 Enhancements in Bluetooth
  • 8.4.1.3 Network Layer Protocols
  • 8.4.2 Enhancements in Low Power WLANs
  • 8.4.2.1 Enhancements in IEEE 802.11
  • 8.4.3 Enhancements in Low Power WWANs
  • 8.4.3.1 LoRaWAN
  • 8.4.3.2 5G
  • 8.5 Robots as Edge Device in IoT
  • 8.5.1 Constrained RESTful Environments (CoRE)
  • 8.5.2 The Constrained Application Protocol (CoAP)
  • 8.5.2.1 Latest in CoAP
  • 8.5.3 The MQTT-SN Protocol
  • 8.5.4 The Data Distribution Service (DDS)
  • 8.5.5 Data Formats
  • 8.6 Challenges and Research Solutions
  • 8.7 Open Platforms for IoRT Applications
  • 8.8 Industrial Drive for Interoperability
  • 8.8.1 The Zigbee Alliance
  • 8.8.2 The Thread Group
  • 8.8.3 The WiFi Alliance
  • 8.8.4 The LoRa Alliance
  • 8.9 Conclusion
  • References
  • 9 Real Time Hazardous Gas Classification and Management System Using Artificial Neural Networks
  • 9.1 Introduction
  • 9.2 Existing Methodology
  • 9.3 Proposed Methodology
  • 9.4 Hardware &amp
  • Software Requirements
  • 9.4.1 Hardware Requirements
  • 9.4.1.1 Gas Sensors Employed in Hazardous Detection
  • 9.4.1.2 NI Wireless Sensor Node 3202
  • 9.4.1.3 NI WSN Gateway (NI 9795)
  • 9.4.1.4 COMPACT RIO (NI-9082)
  • 9.5 Experimental Setup
  • 9.5.1 Data Set Preparation
  • 9.5.2 Artificial Neural Network Model Creation
  • 9.6 Results and Discussion.
  • 9.7 Conclusion and Future Work
  • References
  • 10 Hierarchical Elitism GSO Algorithm For Pattern Recognition
  • 10.1 Introduction
  • 10.2 Related Works
  • 10.3 Methodology
  • 10.3.1 Additive Kuan Speckle Noise Filtering Model
  • 10.3.2 Hierarchical Elitism Gene GSO of MNN in Pattern Recognition
  • 10.4 Experimental Setup
  • 10.5 Discussion
  • 10.5.1 Scenario 1: Computational Time
  • 10.5.2 Scenario 2: Computational Complexity
  • 10.5.3 Scenario 3: Pattern Recognition Accuracy
  • 10.6 Conclusion
  • References
  • 11 Multidimensional Survey of Machine Learning Application in IoT (Internet of Things)
  • 11.1 Machine Learning-An Introduction
  • 11.1.1 Classification of Machine Learning
  • 11.2 Internet of Things
  • 11.3 ML in IoT
  • 11.3.1 Overview
  • 11.4 Literature Review
  • 11.5 Different Machine Learning Algorithm
  • 11.5.1 Bayesian Measurements
  • 11.5.2 K-Nearest Neighbors (k-NN)
  • 11.5.3 Neural Network
  • 11.5.4 Decision Tree (DT)
  • 11.5.5 Principal Component Analysis (PCA) t
  • 11.5.6 K-Mean Calculations
  • 11.5.7 Strength Teaching
  • 11.6 Internet of Things in Different Frameworks
  • 11.6.1 Computing Framework
  • 11.6.1.1 Fog Calculation
  • 11.6.1.2 Estimation Edge
  • 11.6.1.3 Distributed Computing
  • 11.6.1.4 Circulated Figuring
  • 11.7 Smart Cities
  • 11.7.1 Use Case
  • 11.7.1.1 Insightful Vitality
  • 11.7.1.2 Brilliant Portability
  • 11.7.1.3 Urban Arranging
  • 11.7.2 Attributes of the Smart City
  • 11.8 Smart Transportation
  • 11.8.1 Machine Learning and IoT in Smart Transportation
  • 11.8.2 Markov Model
  • 11.8.3 Decision Structures
  • 11.9 Application of Research
  • 11.9.1 In Energy
  • 11.9.2 In Routing
  • 11.9.3 In Living
  • 11.9.4 Application in Industry
  • 11.10 Machine Learning for IoT Security
  • 11.10.1 Used Machine Learning Algorithms
  • 11.10.2 Intrusion Detection
  • 11.10.3 Authentication
  • 11.11 Conclusion
  • References.
  • 12 IoT-Based Bias Analysis in Acoustic Feedback Using Time-Variant Adaptive Algorithm in Hearing Aids.

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