Cognitive Computing Models in Communication Systems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Smart and Sustainable Intelligent Systems
• Sprache: Englisch

Seitenzahl: 292

Veröffentlichungsjahr: 2022

Beschreibung

COGNITIVE COMPUTING MODELS IN COMMUNICATION SYSTEMS A concise book on the latest research focusing on problems and challenges in the areas of data transmission technology, computer algorithms, AI-based devices, computer technology, and their solutions. The book provides a comprehensive overview of state-of-the-art research work on cognitive models in communication systems and computing techniques. It also bridges the gap between various communication systems and solutions by providing the current models and computing techniques, their applications, the strengths and limitations of the existing methods, and the future directions in this area. The contributors showcase their latest research work focusing on the issues, challenges, and solutions in the field of data transmission techniques, computational algorithms, artificial intelligence (AI)-based devices, and computing techniques. Readers will find in this succinctly written and unique book: * Topics covering the applications of advanced cognitive devices, models, architecture, and techniques. * A range of case studies and applications that will provide readers with the tools to apply cutting-edge models and algorithms. * In-depth information about new cognitive computing models and conceptual frameworks and their implementation. Audience The book is designed for researchers and electronics engineers, computer science engineers, industrial engineers, and mechanical engineers (both in academia and industry) working in the fields of machine learning, cognitive computing, mobile communication, and wireless network system.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

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

1.3 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

2.4 Issues and Challenges in Smart Cities: An Overview

2.5 Machine Learning: An Overview

2.6 Unsupervised Learning

2.7 Deep Learning: An Overview

2.8 Deep Learning vs Machine Learning

2.9 Smart Healthcare

2.10 Smart Transport System

2.11 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

4.4 Routing Protocols

4.5 RTF-AHP Model

4.6 Models for Optimal Routing Performance

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

6.4 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 Frequency-Modulated 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

13.3 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

14.3 Background

14.4 Proposed Model

14.5 Results

14.6 Conclusion

14.7 Claims

References

Index

Wiley End User License Agreement

List of Tables

Chapter 1

Table 1.1 Requirements for the design of the two-phase operational amplifier [5–...

Table 1.2 Design requirements of maximum power transfer (MPT) [1–8].

Chapter 2

Table 2.1 Machine learning application evaluation in healthcare assessment.

Table 2.2 Machine learning application evaluation in a smart transportation syst...

Table 2.3 Machine learning application evaluation in the smart grid.

Chapter 3

Table 3.1 Using ML algorithms in design for 3D printing.

Table 3.2 Using ML algorithms security of attack detection.

Table 3.3 Use of ML algorithms in In Situ monitoring.

Table 3.4 Using ML in correlation between process parameters’ and parts’ final c...

Chapter 4

Table 4.1 Metric values from the performance metric equations.

Table 4.2 All possible routes from nodes 1 to 12.

Table 4.3 Simulation parameters.

Chapter 5

Table 5.1 Results from duo of ultrasonic devices.

Chapter 7

Table 7.1 Power layout elements at essential base band analog (BBA) requirements...

Table 7.2 Receiver baseband analog (BBA) execution report [13].

Chapter 11

Table 11.1 Values of the circuit elements of the proposed receiver.

Table 11.2 Performance comparison table.

Chapter 12

Table 12.1 Performance comparison.

List of Illustrations

Chapter 1

Figure 1.1 Schematic diagram of the low dropout control [2].

Figure 1.2 Representation of the planned low-dropout (LDO) control device [3–5].

Figure 1.3 Diagram of fault amplifier [5–8].

Figure 1.4 Intent of the two-stage operational amplifier.

Figure 1.5 Complete blueprint of the maximum power transfer (MPT) stage [9].

Figure 1.6 Dropout voltage.

Figure 1.7 Inactive current.

Figure 1.8 Line regulation.

Figure 1.9 Low dropout (LDO) in the N-channel metal–oxide semiconductor (NMOS) c...

Figure 1.10 Load guideline.

Figure 1.11 Complete load transitory response [10].

Figure 1.12 Power supply rejection ratio (PSRR) of the projected low dropout [10...

Figure 1.13 Gain and stage edge of low dropout [10].

Chapter 2

Figure 2.1 Five aspects of designing the architecture of smart cities [8].

Figure 2.2 Key challenges faced when deploying a smart city plan.

Figure 2.3 Support vector machine architecture.

Figure 2.4 Artificial neural network architecture.

Figure 2.5 Random forest architecture.

Figure 2.6 Naïve Bayes architecture.

Figure 2.7 Architecture of an autoencoder.

Figure 2.8 Architecture of CNN.

Figure 2.9 Architecture of RNN.

Figure 2.10 Healthcare framework.

Figure 2.11 Application of IoT/ML in healthcare.

Figure 2.12 Framework of a smart transport system.

Figure 2.13 Challenges in a smart transportation system.

Figure 2.14 The architecture of smart power generation and distribution.

Chapter 3

Figure 3.1 3D printing with machine learning [1].

Figure 3.2 Deploying of ML in 3D printing [2].

Figure 3.3 Use of ML in process structure properties (PSP) relationship with the...

Figure 3.4 3D model of weapon detection with ML [21].

Figure 3.5 Workflow of using AE and SCNN for in situ monitoring [22].

Figure 3.6 CNN based model for in situ monitoring [25].

Figure 3.7 Offline ML model for integrating AM and ML (Gobert et al. 2018) [6].

Chapter 4

Figure 4.1 Analytical hierarchical process (AHP) architecture flow.

Figure 4.2 Structure of the rough set TOPSIS fuzzy-based analytical hierarchy pr...

Figure 4.3 Comparison of the observed and computed CPU execution times with numb...

Figure 4.4 Comparison of the measured and simulated CPU execution time period vs...

Figure 4.5 Throughput vs Name of the paths.

Figure 4.6 Data transmission through path 2-18-22.

Chapter 5

Figure 5.1 Wamp server screen shot.

Figure 5.2 Existing system diagram.

Figure 5.3 Expected IoT-based traffic control system model.

Figure 5.4 Traffic network system animated diagram.

Figure 5.5 Flow chart diagram.

Figure 5.6 Output of single traffic light prototype.

Figure 5.7 Hardware prototype model of IoT traffic control system.

Figure 5.8 Status of traffic signals stored in database.

Figure 5.9 Status of traffic lights (Output 1).

Figure 5.10 Status of traffic lights when density is 0 (Output 2).

Figure 5.11 Status of traffic lights when density is not zero (Output 3).

Figure 5.12 Status of traffic lights when density is not zero (Output 4).

Chapter 6

Figure 6.1 Proposed architecture.

Figure 6.2 An example of generalization tree.

Figure 6.3 Access tree.

Figure 6.4 Encryption and decryption of keys using AHAC.

Chapter 7

Figure 7.1 Base band analog (BBA) circuit for direct conversion receiver (DCR) l...

Figure 7.2 Base band analog (BBA) strip that changes the filters and gain levels...

Figure 7.3 Two-level operational amplifier (op-amp).

Figure 7.4 Current utilization vs. A

v1

.

Figure 7.5 Simulation results for the transistor circuit-level spurious-free dyn...

Figure 7.6 Diagrammatic representation of the resistor–capacitor (RC) filter. (a...

Figure 7.7 Diagrammatic representation of the programmable gain amplifier (PGA)....

Figure 7.8 Microphotograph of the baseband design.

Figure 7.9 Graph between the frequency (in hertz) and gain (in decibels) of the ...

Figure 7.10 Gain programmable working of the receiver baseband analog (BBA).

Chapter 8

Figure 8.1 Process steps in the detection of neuromuscular disorders.

Chapter 9

Figure 9.1 (a) Photograph of the ring modulator. The upper-right 25% of the ring...

Figure 9.2 (a) P–N diode with diverse reverse biases practical to obtaining the ...

Figure 9.3 (a) Ring modulator with a small-signal circuit model and with circuit...

Figure 9.4 Optical eye of 25 Gb/s from the ring modulator with altered laser wav...

Chapter 10

Figure 10.1 Direct conversion wireless sensor transmitter architecture.

Figure 10.2 Proposed voltage-controlled ring oscillator (VCO) delay cell impleme...

Figure 10.3 Transient response of the voltage-controlled ring oscillator (VCO) a...

Figure 10.4 Proposed subtractor based on complementary metal–oxide–semiconductor...

Figure 10.5 Temporary subtractor reaction at node V

OUT2

for two in-segment compe...

Figure 10.6 (a) Die microphotograph of the voltage-controlled ring oscillator (V...

Chapter 11

Figure 11.1 Block diagram of direct conversion receiver (DCR).

Figure 11.2 Baseband signal degraded by FN.

Figure 11.3 FMCW principle of a sawtooth wave.

Figure 11.4 Schematic of proposed Rx.

Figure 11.5. (a) balanced mixer; (b) balanced mixer with current-bleeding techno...

Figure 11.6 Simulated FN of mixer with different LO power.

Figure 11.7 (a) S-parameters of LNA; (b) Noise figure of LNA; (c)P1 dB of LNA; (...

Figure 11.8 (a) Conversion gain and NF of receiver; (b) S-parameters of RF and L...

Figure 11.9 (a) FN results of mixer; (b) FN results of Rx.

Chapter 12

Figure 12.1 Sharing of power utilization using processing and transferring.

Figure 12.2 Schematic of the T

x

radio frequency amplifier.

Figure 12.3 Power amplifier.

Figure 12.4 S-parameters: (a) I/P return loss (S

11

) and (b) re verse isolation (...

Chapter 14

Figure 14.1 IoT applications.

Figure 14.2 IoT network linked devices.

Figure 14.3 Smart home controlling models.

Figure 14.4 Proposed model framework.

Figure 14.5 User authentication time levels.

Figure 14.6 Unauthorized user detection time levels.

Figure 14.7 Unique labeled key generation time levels.

Figure 14.8 Smart home security levels during instruction transmission.

Figure 14.9 Access control restriction level for unauthorized users.

Guide

Cover

Table of Contents

Title Page

Copyright

Preface

Acknowledgement

Begin Reading

Index

End User License Agreement

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Series Editor: Dr. Prasenjit Chatterjee, MCKV Institute of Engineering, West Bengal, India,

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Scope: Reflecting the interdisciplinary and thematic nature of the series, Concise Introductions to (AI) and Data Science presents cutting-edge research and practical applications in the area of Artificial Intelligence and data science. The series aims to share new approaches and innovative perspectives in AI and data analysis from diverse engineering domains to find pragmatic and futuristic solutions for society at large. It publishes peer-reviewed and authoritative scholarly works on theoretical foundations, algorithms, models, applications and case studies on specific issues. The monographs and edited volumes will be no more than 75,000 words.

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Publishers at Scrivener

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Cognitive Computing Models in Communication Systems

Budati Anil Kumar

ECE Department, Gokaraju Rangaraju Institute of Engineering & Technology, Hyderabad, India

S. B. Goyal

Faculty of Information Technology, City University, Malaysia

and

Sardar M.N. Islam

The American University of Ras Al Khaimah (AURAK), United Arab Emirates

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-86507-0

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Preface

Domain-specific system architectures such as software and hardware are attracting attention for use in overcoming the stagnation of size scaling of memory and domain functions in wireless communication systems. The need for improvements in performance to lower latency, and for faster simulation and power efficiency requires dedicated software and hardware focused on accelerating key applications. This type of system is widespread, and artificial intelligence, hardware description languages, machine learning, neural networks, advanced computer algorithms, and deep learning are especially becoming mainstream in all areas. The rapid growth of applications and system software is also reflected in hardware system architectures, signal processing speeds, wired/wireless communication systems, computational algorithms, and data storage/transmission systems.

Ensuring the security and efficiency of communication system design and implementation is a top priority. Recent research has been aimed at a higher degree of autonomy of such systems in architecture/system design, implementation, and optimization, especially in areas such as advanced system architecture, digital signal processing, communication systems, and the internet. This poses new challenges for implementation and validation. Therefore, much research is being conducted in the area of embedded security and autonomous software systems of things and various aspects of communication systems and computing technologies. To this end, this book provides a comprehensive overview of current research on cognitive models in communication systems and computing. Furthermore, it aims to fill in the gap between various communication systems and solutions by providing current models and computing technologies, their applications, the strengths and limitations of existing methods, and future directions in this area.

The main purpose of this book is to publish the latest research papers focusing on problems and challenges in the areas of data transmission technology, computer algorithms, artificial intelligence (AI)-based devices, computer technology, and their solutions to motivate researchers. Therefore, it will serve as an instant ready reference for researchers and professionals working in the area of Cognitive Models.

The Editors

July 2022

Acknowledgement

We, the editors of Cognitive Models in Communication Systems and Computing Methods, wish to acknowledge the hard work, commitment and dedication of the authors who have contributed their wonderful chapters to this book within the stipulated time frame.

Furthermore, we would like to convey our special gratitude to Dr. Prasenjit Chatterjee, Dean (Research and Consultancy) of MCKV Institute of Engineering, West Bengal, India, for his consistent support and guidance at each stage of the book’s development.

We wish to bestow our best regards to all the reviewers for providing constructive comments to the authors to improve their chapters to meet the publisher’s standard, quality and coherence. A successful book publication is the integrated result of more people than the people granted credit as editor and author and we acknowledge these unsung heroes.

Finally, we, the editors, acknowledge everyone who helped us directly and indirectly.

Budati Anil Kumar

S. B. Goyal

Sardar M.N. Islam

August 2022

2Performance Analysis of Machine Learning and Deep Learning Algorithms for Smart Cities: The Present State and Future Directions

Pradeep Bedi1, S. B. Goyal2*, Sardar MN Islam3, Jia Liu4 and Anil Kumar Budati5

1Galgotias University, Greater Noida, Uttar Pradesh, India

2Faculty of Information Technology, City University, Petaling Jaya, Malaysia

3