Cover Image

Cognitive computing models in communication systems /

Bibliographic Details
Main Authors: Kumar, Budati Anil (Author), Goyal, S. B. (Author), Islam, Sardar M.N (Author)
Format: eBook
Language:English
Published: Hoboken, NJ : John Wiley & Sons, Inc., 2022.
Series:Concise introductions to AI and data science
Subjects:
Soft computing.
Computer networks.
Machine learning.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Cover
  • Half-Title Page
  • Series Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Acknowledgement
  • 1 Design of a Low-Voltage LDO of CMOS Voltage Regulator for Wireless Communications
  • 1.1 Introduction
  • 1.2 LDO Controller Arrangement and Diagram Drawing
  • 1.2.1 Design of the LDO Regulator
  • 1.2.1.1 Design of the Fault Amplifier
  • 1.2.1.2 Design of the MPT Phase
  • 1.3 Conclusion
  • References
  • 2 Performance Analysis of Machine Learning and Deep Learning Algorithms for Smart Cities: The Present State and Future Directions
  • 2.1 Introduction
  • 2.2 Smart City: The Concept
  • 2.3 Application Layer
  • 2.3.1 Smart Homes and Buildings
  • 2.3.1.1 Smart Surveillance
  • 2.3.2 Smart Transportation and Driving
  • 2.3.3 Smart Healthcare
  • 2.3.4 Smart Parking
  • 2.3.5 Smart Grid
  • 2.3.6 Smart Farming
  • 2.3.7 Sensing Layer
  • 2.3.8 Communication Layer
  • 2.3.9 Data Layer
  • 2.3.10 Security Layer
  • 2.4 Issues and Challenges in Smart Cities: An Overview
  • 2.5 Machine Learning: An Overview
  • 2.5.1 Supervised Learning
  • 2.5.2 Support Vector Machines (SVMs)
  • 2.5.3 Artificial Neural Networks
  • 2.5.4 Random Forest
  • 2.5.5 Naïve Bayes
  • 2.6 Unsupervised Learning
  • 2.7 Deep Learning: An Overview
  • 2.7.1 Autoencoder
  • 2.7.2 Convolution Neural Networks (CNNs)
  • 2.7.3 Recurrent Neural Networks (RNNs)
  • 2.8 Deep Learning vs Machine Learning
  • 2.9 Smart Healthcare
  • 2.9.1 Evolution Toward a Smart Healthcare Framework
  • 2.9.2 Application of ML/DL in Smart Healthcare
  • 2.10 Smart Transport System
  • 2.10.1 Evolution Toward a Smart Transport System
  • 2.10.2 Application of ML/DL in a Smart Transportation System
  • 2.11 Smart Grids
  • 2.11.1 Evolution Toward Smart Grids
  • 2.11.2 Application of ML/DL in Smart Grids
  • 2.12 Challenges and Future Directions
  • 2.13 Conclusion
  • References.
  • 3 Application of Machine Learning Algorithms and Models in 3D Printing
  • 3.1 Introduction
  • 3.2 Literature Review
  • 3.3 Methods and Materials
  • 3.4 Results and Discussion
  • 3.5 Conclusion
  • References
  • 4 A Novel Model for Optimal Reliable Routing Path Prediction in MANET
  • 4.1 Introduction
  • 4.2 Analytical Hierarchical Process Technique
  • 4.3 Mathematical Models and Protocols
  • 4.3.1 Rough Sets
  • 4.3.1.1 Pawlak Rough Set Theory Definitions
  • 4.3.2 Fuzzy TOPSIS
  • 4.4 Routing Protocols
  • 4.4.1 Classification of Routing Paths
  • 4.5 RTF-AHP Model
  • 4.5.1 Rough TOPSIS Fuzzy Set Analytical Hierarchical Process Algorithm
  • 4.6 Models for Optimal Routing Performance
  • 4.6.1 Genetic Algorithm Technique
  • 4.6.2 Ant Colony Optimization Technique
  • 4.6.3 RTF-AHP Model Architecture Flow
  • 4.7 Results and Discussion
  • 4.8 Conclusion
  • References
  • 5 IoT-Based Smart Traffic Light Control
  • 5.1 Introduction
  • 5.2 Scope of the Proposed Work
  • 5.3 Proposed System Implementation
  • 5.4 Testing and Results
  • 5.5 Test Results
  • 5.6 Conclusions
  • References
  • 6 Differential Query Execution on Privacy Preserving Data Distributed Over Hybrid Cloud
  • 6.1 Introduction
  • 6.2 Related Work
  • 6.3 Proposed Solution
  • 6.3.1 Data Transformation
  • 6.3.2 Data Distribution
  • 6.3.3 Query Execution
  • 6.4 Novelty in the Proposed Solution
  • 6.5 Results
  • 6.6 Conclusion
  • References
  • 7 Design of CMOS Base Band Analog
  • 7.1 Introduction
  • 7.2 Proposed Technique of the BBA Chain for Reducing Energy Consumption
  • 7.3 Channel Preference Filter
  • 7.4 Programmable Amplifier Gain
  • 7.5 Executed Outcomes
  • 7.6 Conclusion
  • References
  • 8 Review on Detection of Neuromuscular Disorders Using Electromyography
  • 8.1 Introduction
  • 8.2 Materials
  • 8.3 Methods
  • 8.4 Conclusion
  • References.
  • 9 Design of Complementary Metal- Oxide Semiconductor Ring Modulator by Built-In Thermal Tuning
  • 9.1 Introduction
  • 9.2 Device Structure
  • 9.3 DC Performance
  • 9.4 Small-Signal Radiofrequency Assessments
  • 9.5 Data Modulation Operation (High Speed)
  • 9.6 Conclusions and Acknowledgments
  • References
  • 10 Low-Power CMOS VCO Used in RF Transmitter
  • 10.1 Introduction
  • 10.2 Transmitter Architecture
  • 10.3 Voltage-Controlled Ring Oscillator Design
  • 10.4 CMOS Combiner
  • 10.5 Conclusion
  • References
  • 11 A Novel Low-Power FrequencyModulated Continuous Wave Radar Based on Low-Noise Mixer
  • 11.1 Introduction
  • 11.2 FMCW Principle
  • 11.3 Results
  • 11.4 Conclusion
  • References
  • 12 A Highly Integrated CMOS RF Tx Used for IEEE 802.15.4
  • 12.1 Introduction
  • 12.2 Related Work
  • 12.3 Simulation Results and Discussion
  • 12.4 Conclusion
  • References
  • 13 A Novel Feedforward Offset Cancellation Limiting Amplifier in Radio Frequencies
  • 13.1 Introduction
  • 13.2 Hardware Design
  • 13.2.1 Limiting Amplifier
  • 13.2.2 Offset Extractor
  • 13.2.3 Architecture and Gain
  • 13.2.4 Quadrature Detector
  • 13.2.5 Sensitivity
  • 13.3 Experimental Results
  • 13.4 Conclusion
  • References
  • 14 A Secured Node Authentication and Access Control Model for IoT Smart Home Using Double-Hashed Unique Labeled Key-Based Validation
  • 14.1 Introduction
  • 14.2 Challenges in IoT Security and Privacy
  • 14.2.1 Heterogeneous Communication and Devices
  • 14.2.2 Physical Equipment Integration
  • 14.2.3 Resource Handling Limitations
  • 14.2.4 Wide Scale
  • 14.2.5 Database
  • 14.3 Background
  • 14.4 Proposed Model
  • 14.4.1 Communication Flow
  • 14.4.1.1 IoT Node and Registration Authority
  • 14.4.1.2 User and Local Authorization Authority
  • 14.5 Results
  • 14.6 Conclusion
  • 14.7 Claims
  • References
  • Index
  • EULA.