Quantum Computing Models for Cybersecurity and Wireless Communications E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Sustainable Computing and Optimization
• Sprache: Englisch

Seitenzahl: 478

Veröffentlichungsjahr: 2025

Beschreibung

The book explores the latest quantum computing research focusing on problems and challenges in the areas of data transmission technology, computer algorithms, artificial intelligence-based devices, computer technology, and their solutions.

Future quantum machines will exponentially boost computing power, creating new opportunities for improving cybersecurity. Both classical and quantum-based cyberattacks can be proactively identified and stopped by quantum-based cybersecurity before they harm. Complex math-based problems that support several encryption standards could be quickly solved by using quantum machine learning.

This comprehensive book examines how quantum machine learning and quantum computing are reshaping cybersecurity, addressing emerging challenges. It includes in-depth illustrations of real-world scenarios and actionable strategies for integrating quantum-based solutions into existing cybersecurity frameworks. A range of topics are examined, including quantum-secure encryption techniques, quantum key distribution, and the impact of quantum computing algorithms. Additionally, it talks about machine learning models and how to use machine learning to solve problems. Through its in-depth analysis and innovative ideas, each chapter provides a compilation of research on cutting-edge quantum computer techniques, like blockchain, quantum machine learning, and cybersecurity.

Audience

This book serves as a ready reference for researchers and professionals working in the area of quantum computing models in communications, machine learning techniques, IoT-enabled technologies, and various application industries such as finance, healthcare, transportation and utilities.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Acknowledgment

1 Performance Evaluation of Avionics System Under Hardware-In-Loop Simulation Framework with Implementation of an AS9100 Quality Management System

1.1 Introduction

1.2 HILS Process and Quality Management System

1.3 HILS Testing Phase

1.4 AS9100 QMS Integrated with HILS Process

1.5 Conclusion and Suggestions

References

2 YouTube Comment Summarizer and Time-Based Analysis

2.1 Introduction

2.2 Literature Review

2.3 Methodology

2.4 Result

2.5 Performance

2.6 Conclusion

References

3 Enhancing Gait Recognition Using YOLOv8 and Robust Video Matting for Low-Light and Adverse Conditions

3.1 Introduction

3.2 Related Works

3.3 Methodology

3.4 Comparision with Existing Systems

3.5 Future Scope

3.6 Conclusion

Acknowledgment

References

4 An Ensemble-Based Machine Learning Framework for Breast Cancer Prediction

4.1 Introduction

4.2 Related Works

4.3 Proposed Framework

4.4 Experimental Setup

4.5 Results and Discussion

4.6 Existing Works

4.7 Conclusion and Future Work

Dataset

References

5 Proactive Fault Detection in Weather Forecast Control Systems Through Heartbeat Monitoring and Cloud-Based Analytics

5.1 Introduction

5.2 Related Work

5.3 Proposed Proactive Fault Detection Architecture

5.4 Conclusion

References

6 FlowGuard: Efficient Traffic Monitoring System

6.1 Introduction

6.2 Literature Review

6.3 Methodology

6.4 Results and Discussions

6.5 Conclusion

6.6 Future Scope

Acknowledgment

References

References for Pictures of Components Used

7 A Survey on Heart Disease Prediction Using Ensemble Techniques in ML

7.1 Introduction

7.2 Literature Survey

7.3 Datasets

7.4 Ensemble Learning in Heart Disease

7.5 Challenges and Limitations

7.6 Future Directions

7.7 Conclusion

References

8 A Video Surveillance: Crowd Anomaly Detection and Management Alert System

8.1 Introduction

8.2 Related Work

8.3 Dataset Description

8.4 Problem Definition

8.5 Proposed Methodology and System

8.6 Results

8.7 Conclusion and Future Scope

References

9 Revolutionizing Learning with Qubits: A Review of Quantum Machine Learning Advances

9.1 Introduction

9.2 Review of Literature

9.3 Basic Quantum Operations, Qubits, and Quantum Gates

9.4 Quantum Machine Learning Algorithms

9.5 Quantum Hardware for Machine Learning

9.6 Challenges in Building Scalable and Error-Resistant Quantum Hardware

9.7 Challenges and Limitations in Quantum Machine Learning

9.8 Future Directions

9.9 Conclusion

References

10 Multi-Band Self-Grounding Antenna for Wireless Technologies

10.1 Introduction

10.2 Design of Antenna

10.3 Actual Design of Antenna

10.4 Results of Antenna

10.5 Conclusions

References

11 Navigating Network Security: A Study on Contemporary Anomaly Detection Technologies

11.1 Introduction

11.2 Related Work

11.3 Methodology

11.4 Conclusion

References

12 File Fragment Classification: A Comprehensive Survey of Research Advances

12.1 Introduction

12.2 Methodology

12.3 Approaches for File Fragment Classification

12.4 Survey Findings

12.5 Challenges and Future Directions

12.6 Conclusion

References

13 Deepfake Detection and Forensic Precision for Online Harassment

13.1 Introduction

13.2 Literature

13.3 Theoretical Analysis and Software Simulation

References

14 Design of Automatic Seed Sowing Machine

14.1 Introduction

14.2 Literature Survey

14.3 Proposed System

14.4 Conclusions

References

15 In Motion: Exploring Urban Rides Through Data Analytics

15.1 Introduction

15.2 Literature Survey

15.3 Proposed Methodology

15.4 Result Analysis

15.5 Conclusion

References

16 Design of Novel Chatbot Using Generative Artificial Intelligence

16.1 Introduction

16.2 Conclusion and Future Scope

References

17 The Smart Nebulizer Cap for Enhanced Asthma Management

17.1 Introduction

17.2 Literature Survey

17.3 Methodology

17.4 Conclusions

References

18 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing

18.1 Introduction

18.2 Literature Survey

18.3 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing

18.4 Result Analysis

18.5 Conclusion

References

19 Intrusion Detection System Using Machine Learning

19.1 Introduction

19.2 Literature Survey

19.3 Methodology

19.4 Algorithm

19.5 Implementation

19.6 Results and Outputs

19.7 Conclusion and Future Scope

References

20 Prediction of Arrival Delay Time in Freightage Rails

20.1 Introduction

20.2 Literature Survey

20.3 Methodology

20.4 Experimental Results

20.5 Conclusions

References

21 Predicting Flight Delays with Error Calculation Using Machine Learned Classifiers

21.1 Introduction

21.2 Literature Survey

21.3 Proposed Methodology

21.4 Result Analysis

21.5 Conclusion

References

22 Design and Implementation of 8-Bit Ripple Carry Adder and Carry Select Adder at 32-nm CNTFET Technology: A Comparative Study

22.1 Introduction

22.2 Implementation of RCA & CSA

22.3 Simulation Results

22.4 Conclusion

References

23 XGBoost Classifier Based Water Quality Classification Using Machine Learning

23.1 Introduction

23.2 Related Work

23.3 Proposed Methodology

23.4 Results and Discussion

23.5 Conclusion

References

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Test cases of HILS for OBC software.

Chapter 3

Table 3.1 Indicating the comparison of different threshold values for weather ...

Table 3.2 Comparison of the proposed approach with GAN based approach.

Chapter 4

Table 4.1 Attributes of wisconsin dataset.

Table 4.2 Ablation study with SVC algorithm.

Table 4.3 Ablation study with Decision Tree algorithm.

Table 4.4 Ablation study with Random Forest algorithm.

Table 4.5 Ablation study with ANN algorithm.

Table 4.6 Ablation study with KNN algorithm.

Table 4.7 Ablation study of Adaboosting with KNN.

Table 4.8 Accuracies of all algorithms after parameter tuning.

Table 4.9 After Adaboosting.

Table 4.10 Results of existing paper-I.

Table 4.11 Results of existing paper-II.

Chapter 5

Table 5.1 Difference between Proactive and Reactive fault tolerance.

Table 5.2 Related work on fault tolerance in Cloud computing applications.

Table 5.3 Results obtained after various faults injected in the application an...

Chapter 7

Table 7.1 Comprehensive view of machine learning with accuracy for heart disea...

Chapter 9

Table 9.1 Advantages, and limitations of quantum machine learning algorithms.

Chapter 10

Table 10.1 Parameters to identify antenna design.

Chapter 11

Table 11.1 Key features of anomaly detection study.

Chapter 12

Table 12.1 Comparison of file fragment classification approaches in different ...

Chapter 17

Table 17.1 Comparison of methods.

Chapter 18

Table 18.1 Performance metrics evaluation.

Chapter 19

Table 19.1 Attribute correlation.

Chapter 20

Table 20.1 Performance evaluation table.

Chapter 22

Table 22.1 Performance comparisons of the proposed & different implementations...

List of Illustrations

Chapter 1

Figure 1.1 Testing phases of software and the avionics product lifecycle.

Figure 1.2 Centralized and distributed architecture of the avionics system.

Figure 1.3 Planning of HILS activities of the project.

Figure 1.4 Development of HILS setup” HILS QMS process.

Figure 1.5 “OBC software validation” process flow.

Figure 1.6 KPI and Customer satisfaction index for different projects.

Chapter 2

Figure 2.1 Example evaluation - accurate summary generation by ChatGPT 3.5

Figure 2.2 System architecture (To understand the workflow of the project).

Figure 2.3 This page show the comment extraction part in the project will the ...

Figure 2.4 Generated extractive summary by the methods such as word frequency ...

Figure 2.5 The above images show the homepage of the project.

Figure 2.6 Summarization age of the project. User may choose action what he/sh...

Figure 2.7 Flowchart for understanding abstractive summarization using PEGASUS...

Figure 2.8 Generated abstractive summary by integrating Pegasus model into the...

Figure 2.9 Result of sentiment analysis performed by the project in graphical ...

Chapter 3

Figure 3.1 The proposed project flow diagram of the Gait Recognition system.

Figure 3.2 Depicting the silhouette generated using the robust video matting t...

Figure 3.3 Gait pretreatment process.

Figure 3.4 Indicating the generated images on a frame from the video input fra...

Figure 3.5 Depicting the methodology followed at each stage of the process for...

Figure 3.6 Overall flow diagram of the system.

Figure 3.7 GAN based approach [9].

Figure 3.8 Indicating the comparison between accuracy and time taken by each Y...

Figure 3.9 Indicating the comparison between accuracy and different pixel thre...

Figure 3.10 Pixel threshold values for low light condition.

Figure 3.11 Indicating the comparison between accuracy and different pixel thr...

Figure 3.12 Indicating the comparison between accuracy and different pixel thr...

Chapter 4

Figure 4.1 Incidence and mortality rate by race per 1,00,000 women [NIH Survei...

Figure 4.2 Proposed framework for breast cancer detection.

Figure 4.3 Correlation among the attributes of Wisconsin Dataset (X-Axis: Feat...

Figure 4.4 Box plot of attributes (X-Axis: Diagnosis, Y-Axis: Perimeter mean).

Figure 4.5 Principal component analysis for feature selection.

Figure 4.6 Confusion matrix for all algorithms (Random Forest, Decision Tree, ...

Figure 4.7 Graph representing the accuracies.

Figure 4.8 Comparison of accuracies between existing and proposed systems.

Chapter 5

Figure 5.1 Relationship between fault, error, and failure.

Figure 5.2 System architecture for proactive weather monitoring using heartbea...

Figure 5.3 FrontEnd: Types of fault injection (including false positive case).

Figure 5.4 Heartbeat received at equal time intervals (10s).

Figure 5.5 Message log entry in case of sensor fault.

Figure 5.6 Message log entry in case of temperature/weather fault.

Figure 5.7 Message log entry in case of false positive.

Figure 5.8 Reliability and execution time graph comparing AFTRC, VFT & PRP-FRC...

Figure 5.9 Log of continuous heartbeat messages stream received by the sensors...

Figure 5.10 Get API data and Network IN/OUT data in the monitoring time period...

Figure 5.11 CPU credit balance and CPU credit usage in the monitoring time per...

Figure 5.12 CPU Utilization in the monitoring time period.

Figure 5.13 Average read/write latency in the monitoring time period.

Figure 5.14 Message count & throughput in the monitoring time period.

Figure 5.15 Log report of sensor 1, displaying heartbeat status, sensor detail...

Figure 5.16 Code snippet for retrying heartbeat request at 5 sec interval.

Figure 5.17 Code snippet for shifting fault injection value from “No Fault” to...

Figure 5.18 Code snippet for displaying data packet received and sent.

Figure 5.19 Code snippet for setting heartbeat request at normal 10 s interval...

Chapter 6

Figure 6.1 Arduino UNO interfaced with Servo Motor and LEDs using Jumper wires...

Figure 6.2 Smart traffic system uses Arduino, OpenCV, and Python for real-time...

Figure 6.3 Arduino Uno.

Figure 6.4 Servo motor.

Figure 6.5 LED.

Figure 6.6 Jumper wire.

Figure 6.7 Arduino IDE.

Figure 6.8 Pyfirmata.

Figure 6.9 Adaptive traffic signal timing flowchart.

Figure 6.10 This image contains the prototype of our FlowGuard System.

Figure 6.11 This image shows the Vehicle counter and the indication of the tra...

Figure 6.12 This image shows the working of servo motor when the signal turns ...

Figure 6.13 This image shows the working of servo motor when the signal turns ...

Figure 6.14 This image shows the working of servo motor when the signal turns ...

Chapter 7

Figure 7.1 Illustration of the machine learning process.

Chapter 8

Figure 8.1 Workflow of crowd surveillance system.

Figure 8.2 Flowchart of crowd counting model.

Figure 8.3 Flowchart of abnormal detecting model.

Figure 8.4 Crowd surveillance system crowd count.

Figure 8.5 Crowd surveillance system alert mail.

Figure 8.6 Crowd surveillance system abnormal activity.

Chapter 9

Figure 9.1 Key quantum computing principles.

Figure 9.2 Block diagram of quantum hardware for machine learning.

Chapter 10

Figure 10.1 Primary level of antenna.

Figure 10.2 Secondary level of antenna.

Figure 10.3 Actual design of antenna.

Figure 10.4 Results of antenna through S11 parameter.

Figure 10.5 Results of antenna through VSWR parameter.

Figure 10.6 3D Polar plot of antenna.

Chapter 11

Figure 11.1 Major cybersecurity incidents.

Figure 11.2 Machine learning.

Figure 11.3 Ensemble methods.

Figure 11.4 Blockchain integration and result analysis.

Chapter 12

Figure 12.1 Graphical representation of the structure of the paper.

Chapter 14

Figure 14.1 Functional schematic representation of proposed work.

Figure 14.2 Arduino control kit.

Figure 14.3 DC motor.

Figure 14.4 Automatic seed sowing machine.

Chapter 15

Figure 15.1 Proposed architecture.

Figure 15.2 Raw data – sample (CSV file).

Figure 15.3 Data importing.

Figure 15.4 Bar plot representing the total fare amount with tip amount by bor...

Figure 15.5 Scatter plot and color bar representing the total amount received ...

Figure 15.6 Scatter plot representing the data of the column trip_distance.

Figure 15.7 Scatter plot representing the data after the removal of outliers f...

Figure 15.8 Heat map for fare amount by location.

Figure 15.9 Visualization representing fare amount over time.

Figure 15.10 Analyzing the frequency of pick-ups at various locations in each ...

Figure 15.11 Understanding the patterns of trips in each borough.

Figure 15.12 Understanding one of the key performance indicators - trips by lo...

Figure 15.13 Analyzing pick up locations for understanding the patterns of fra...

Figure 15.14 Analyzing drop off locations for understanding the patterns of fr...

Figure 15.15 Understanding the trends of rides (operational analytics).

Figure 15.16 Understanding the trends of fare amount (financial analytics).

Chapter 17

Figure 17.1 A typical nebulizer schematic.

Figure 17.2 Typical jet nebulizer.

Figure 17.3 Ultrasonic nebulizer schematic.

Chapter 18

Figure 18.1 Reconfigurable processing module for binary and grayscale image pr...

Figure 18.2 Memory (MB) comparison graph.

Figure 18.3 Power consumption comparison graph.

Figure 18.4 Delay comparison graph.

Figure 18.5 Speed comparison graph.

Chapter 19

Figure 19.1 Existing system framework model.

Figure 19.2 Proposed architecture.

Figure 19.3 Use case diagram.

Figure 19.4 Sequence diagram.

Figure 19.5 Data pre-processing.

Figure 19.6 Feature extraction.

Figure 19.7 Creating and training ML model with SVM.

Figure 19.8 Creating and training sequential model.

Figure 19.9 Creating and training ML model with K-NN.

Figure 19.10 User Interface using streamlit.

Figure 19.11 SVM classification report.

Figure 19.12 K-NN classification report.

Figure 19.13 MLP (sequential) classification report.

Figure 19.14 Working demo of user interface.

Figure 19.15 Working demo of user interface.

Figure 19.16 Working demo of user interface.

Chapter 20

Figure 20.1 System architecture with models.

Figure 20.2 Dataset freight rail.

Figure 20.3 Voting Classifier.

Figure 20.4 MAPE graph.

Figure 20.5 R2SCORE graph.

Figure 20.6 MAE graph.

Figure 20.7 RMSE graph.

Figure 20.8 Home page.

Figure 20.9 Sign-up page.

Figure 20.10 Sign in page.

Figure 20.11 Upload input values.

Figure 20.12 Predict result for given input values.

Chapter 21

Figure 21.1 Proposed system architecture.

Figure 21.2 Accuracy comparison graphs.

Figure 21.3 Home page.

Figure 21.4 Click on prediction.

Figure 21.5 Flight delay prediction screen.

Figure 21.6 Final outcome.

Figure 21.7 Visualization page.

Chapter 22

Figure 22.1 (a) SB-CNFET. (b) MOS-CNFET.

Figure 22.2 Proposed circuit for a 1-bit full adder that uses 16 CNTFETs.

Figure 22.3 8-Bit parallel adder implementation using proposed 1-bit full adde...

Figure 22.4 Block diagram of Carry Select adder (4-Bit).

Figure 22.5 Simulated input and output waveforms of 1-bit Full Adder circuit.

Chapter 23

Figure 23.1 Flowchart of the proposed system.

Figure 23.2 List of extracted parameters for estimation of water quality.

Figure 23.3 Accuracy score of XG Boost classifier method and existing models.

Figure 23.4 Predictor page.

Figure 23.5 Upload input parameters.

Figure 23.6 Water classification.

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Acknowledgment

Begin Reading

Index

Also of Interest

WILEY END USER LICENSE AGREEMENT

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Sustainable Computing and Optimization

Series Editors: Dr. Prasenjit Chatterjee ([email protected]), Morteza Yazdani and Dilbagh Panchal

The objective of the series is to bring together global research scholars, experts, and scientists in the research areas of sustainable computing and optimization to share their knowledge and experiences on current research achievements in these fields. Since the series was launched in 2021, it has provided a golden opportunity for the research community to share their novel research results, findings, and innovations to a wide range of topics and applications. The series promotes sustainable computing and optimization methodologies to solve real-life problems mainly from engineering and management systems domains.

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Quantum Computing Models for Cybersecurity and Wireless Communications

Edited by

Budati Anil Kumar

Faculty of Electronics & Communication Engineering, Koneru Lakshmaiah Education Foundation (Deemed University), Aziz Nagar Campus, Hyderabad, Telangana, India

Singamaneni Kranthi Kumar

Faculty of Computer Engineering and Technology, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, Telangana, India

and

Li Xingwang

School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, China

This edition first published 2025 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© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-27139-9

Front cover images courtesy of Wikimedia CommonsCover design by Russell Richardson

Preface

Artificial intelligence, cybersecurity, and practical cryptography are just a few of the areas where quantum machine learning has the most potential. Advanced data encryption techniques are urgently needed as our growing reliance on the internet exposes us to threats brought on by cyber-attacks. Although many current encryption standards may not be breakable by current quantum-based machines, it is vital to keep ahead of the impending threats and prepare for sophisticated cyberattacks using quantum-proof solutions. The difficult issues that classical computers are unable to address, such as the techniques employed in data encryption, can be solved by quantum computers. Modern encryption techniques are based on mathematical formulas that would be impractically difficult for traditional computers to decrypt. Working tirelessly to create quantum-secure encryption techniques is the research goal. One possible method is for securely transferring a quantum key between two endpoints that makes use of the quantum physics qualities known as quantum key distribution (QKD). Before recent developments, this technique could only be used with fiber optic connections, but now quantum key transfer is also possible over the Internet. Although there are still many unsolved problems concerning quantum computing, it is evident that present methods of cybersecurity and encryption are at risk. We need to adapt the way we safeguard our data and take a defense-intensive strategy characterized by many layers of quantum-secure security to lessen the threat. To protect themselves from potential quantum threats, security-conscious organizations are actively looking for quantum-ready encryption solutions, including those provided by Quantum Exchange.

Future quantum machines will exponentially boost computing power, creating new opportunities for improving cybersecurity. Both classical and quantum-based cyberattacks can be proactively identified and stopped by quantum-based cybersecurity before they harm. Complex math-based problems that support several encryption standards could be quickly solved using quantum machine learning. The traditional cryptography standards on classical computers, which rely on difficult mathematical calculations like prime factorization, would take millennia or more. However, these issues might be resolved by quantum machines in a manageable amount of time. Despite the lack of widespread commercial quantum devices, it is wise to plan for quantum-based cybersecurity difficulties and deal with present restrictions. The security and privacy of commercial organizations will greatly increase as a result of this preparation. For instance, today’s adversaries could penetrate private networks. When large-scale quantum machines are made commercially available in the future, this enormous computational capacity could be used to undermine networks and infrastructures by decrypting critical data. Quantum machine learning-based cybersecurity, especially in the context of quantum computing and quantum chaotic characteristics, presents stronger and more exciting chances for protecting important and sensitive information by outpacing these possible attacks. Through book chapters, academics, researchers, and scientists have contributed their ideas, concepts, and use cases on cutting-edge technologies like blockchain, quantum machine learning, cybersecurity, IoT, and SDN. 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.

This book serves as a ready reference for researchers and professionals working in the area of quantum computing models in communications, machine learning techniques, the healthcare industry, and IoT-enabled technologies.

Acknowledgment

We, the editors of the Quantum Computing Models for Cybersecurity & Wireless Communications, wish to acknowledge the hard work, commitment and dedication of the authors who have contributed their wonderful chapters to our edited volume in the stipulated time.

Further, we would like to convey our special gratitude to Dr Prasenjit Chatterjee, Dean (Research and Consultancy), 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 referees for providing productive comments to the authors to improve their chapters to meet the required standard. A successful book publication is the integrated result of everyone’s contribution and not just those named as editors or authors.

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

Budati Anil Kumar

Singamaneni Kranthi Kumar

Li Xingwang

1Performance Evaluation of Avionics System Under Hardware-In-Loop Simulation Framework with Implementation of an AS9100 Quality Management System

Rajesh Shankar Karvande1* and Tatineni Madhavi2

1’F’ RCI, DRDO, Hyderabad, TS, India

2EECE, GITAM, Hyderabad, TS, India

Abstract

Performance evaluation of avionics subsystem is mandatory before the deployment of the system. In the aerospace and defense industry it is critical to validate the embedded system software along with the flight subsystem in real time before real launch. The launch of the flight vehicle is single shot operation and involves so many factors. To avoid the catastrophic failures due to errors in algorithms, subsystems integrated working under real time, it is essential and mandatory to validate the software using Hardware-In-Loop Simulation (HILS) platform. This is unique platform that evaluate the performance of mission software i.e. control and guidance software using different criteria and conditions. This is cost effective tool to evaluate the performance for the expensive flight trial and using its rapid prototyping technique designer can validate their software in early stage of development. Development of AS9100 Quality Management System (QMS) in the HILS process is essential and inevitable part of avionics design to improve the process. This paper focus on the embedded system testing, validation, and certification area. The HILS test-bed designed as part of performance evaluation, different configuration of the HILS for centralized and distributed architecture, test plan for all software test cases with different perturbation cases. The lifecycle of the HILS process is explained in details with respect to AS9100 QMS requirements and implementation. Development of HILS test-bed for centralized and distributed architecture configuration is explained in details. The results are discussed and the conclusion and suggestions for future improvement are discussed in last section.

Keywords: 6Dof plant model, hardware-in-loop simulation, inertial navigation system, on board computer, OBC-in-loop, quality management system

1.1 Introduction

Performance Evaluation of avionics system specially used in aerospace vehicle is essential and critical task that ensure the success rate of developmental flight trial. The evaluation of the On-Board Computer (OBC) mission software along with the integrated flight hardware is carried out using the unique Hardware In Loop Simulation Test-bed [1, 2]. There are number of steps involved in testing phase of HILS. Design of the test-bed, development of the simulation software, testing of the OBC software. All the errors or deficiency related with mission software has been validated in HILS with number of test cases. Unit level testing carried out by the developer is not sufficient to test system completely. This unit testing only verifies the system independently working as per design. The integrated level testing and user acceptance testing is performed at HILS as shown in Figure 1.1. This testing highlights the design issues like lags, communication delay, bandwidth etc. for the individual system when it is integrated with other sub-systems.

In the total product life cycle of software development HILS is important phase for the validation and testing of avionics system is shown in Figure 1.1. HILS consists of both Hardware and Software parts: Simulation computer based on the configuration of the avionics system that is helpful to select the I/O cards of the system like MIL-STD 1553 cards, ADC cards, DAC cards and RS-422 cards [7]. The second part is the 6Dof software development part based on the Real Time Operating Systems. The problem is that the HILS process has many branches and there is no process control. It has been experienced the delay and ineffectiveness in the early stages of the HILS. It was highly essential to establish a stepwise process with the effectiveness and timely delivery of the product from HILS. So more focus and effort has been given to develop unified HILS process that will be stepwise process with the effectiveness of the Quality Management System for ensuring the timely completion of the process. The process of HILS is covered under the Aerospace Standard AS9100. The problem is to develop the methodology that defines the scope of the HILS process that is critical part of the project cycle to evaluate the performance of the software and flight hardware in integrated mode. This paper has given the detail explanation about the development of HILS process and the development of AS9100 QMS standard that is adopted for this process that has been bonded together first time to achieve the quality objective for the HILS as well as at the laboratory level to be recognized as global level. First the concept of the performance evaluation is explained with HILS Configuration, then the development of control i.e. Test plan, Test cases, Test results followed by induction of AS9100 quality absorption to HILS activities. The Key Performance Indicator (KPI) that shows the effectiveness of the concept of development of QMS at process level and the performance of the HILS according to that is discussed at the end with conclusion and suggestion at the end.

Figure 1.1 Testing phases of software and the avionics product lifecycle.

There are White Box Testing and Black Box Testing. White Box testing only verify the algorithm by visual inspection or flow chats. Performance evaluation is also called as the Black Box testing methodology that execute the algorithm and evaluate that the development is meeting the goals of design. This uses the input design specifications and parameters and measure output generated after execution of the software in real time. Hardware-In-Loop Simulation Framework is unique setup that is used for the performance evaluation of the Avionics system for both centralized architecture as well as distributed architecture.

Centralized Architecture

In this scheme all the algorithms are built using single processor with On Board Computer is shown in Figure 1.2. All the required interfaces are controlled by the processor. The sub systems are mainly electro mechanical that do not have any processing or computing unit inside the subsystem.

Figure 1.2 Centralized and distributed architecture of the avionics system.

Distributed Architecture

There is processor available in each subsystem and the data processed inside the subsystem itself is shown in Figure 1.2, e.g. in the case of Inertial Navigation System, the raw data gyros and accelerometers samples are processed inside the INS unit and the processed data i.e. positions, velocities, rates, accelerations, quaternions are posted to the OBC at regular interval. Similarly actuator setup has their own processor to process the deflection commands and send back the feedback information about the actuator at regular interval.

The challenge is to establish testing methodology for both architecture and the develop the uniform methodology in this area. The recent research paper has been studied for the development of the process effectiveness. Paper title “Development of Hardware-In-Loop Simulation Test-bed for testing of Navigation System-INS” by Rajesh K & B Ramesh Kumar explain the testing methodology for INS. It is limited for INS system only. Another paper titled “On joint hardware-in-the-loop simulation of aircraft control system and propulsion system” by Yao Zhao explains about the HILS system of the aircraft system. The development of the process for timely completion of the HILS activities and control for the effectiveness monitoring of the process paper is essential to help the researcher and engineers to have a layout of methodology for future experiments in this area.

1.2 HILS Process and Quality Management System

There are many AS9100 is Quality Management system for Aviation, Space and Defence industry released by International Aerospace Quality Group (IAQG). AS9100 Quality Management System goes hand to hand with each process of the Aerospace Research and Development Laboratory. After the Design and Development phase is finalized then the simulation and testing of the subsystem in integrated mode has been initiated. HILS process is the part of testing of the product and covered under QMS. Four Major processes has been defined and covered under QMS.

HILS Planning and Configuration Management.

Development of the HILS Setup

OBC Software Validation

Hardware In Loop Simulation.

A. HILS Planning and Configuration Management

Planning is crucial as all the schedule of the further testing and real launch depends on the HILS planning as shown in Figure 1.3. In parallel with the development cycle, development of HILS testbed, planning of test cases and HILS testing is established. Test-bed development focuses on the configuration, Timeline required and the HILS test cases for the mission software validation. Development of HILS testbed and development of simulation software mainly depend on the avionics configuration, Interface Control Document (ICD) of each sub-system and interface communication protocol of different sub-systems. This all together is covered under the HILS configuration and planning.

Figure 1.3 Planning of HILS activities of the project.

B. Development of HILS Test-Bed

Generally, the design and configuration of the HILS Setup is based on the avionics system used for the aerospace vehicle. The process block diagram is shown in Figure 1.4. The data acquisition system based on popular communication protocols, MIL-STD 1553, RS-422, ADC, DAC, and Digital Input/Output. These all the I/O systems are integrated with the HILS System for the configuration of Simulation System. Application layer of the simulation computer is plant model algorithms. Two different configurations have been designed and developed for Centralized architecture of avionics system and distributed architecture of the avionics system. The 6Dof equation that is part of simulation system is developed using the mathematical model [4] and under the real time operating system [7]. The other supporting modules like thrust, aero, interpolation computational algorithm has been developed using real time operating system and high-level software language. This integrated plant software is tested using the input data provided by the designer. The output data that is generated after execution of the HILS run that is controlled process. Mainly the 6Dof parameters i.e. three rates and accelerations are compared with the designer data. Here, the process coverage focuses on the coding standard, white box testing as a part of algorithm verification and the output parameters.

Figure 1.4 Development of HILS setup” HILS QMS process.

C. OBC Software Validation

The Second process is the OBC software validation. The Control and Guidance (C&G) algorithm are developed with On-Board Computer [8]. The execution time, lags, transportation delay issues get addressed in HILS by execution of OBC software [5]. The water fall methodology as per the software engineering is followed for the testing of the OBC software is shown in Figure 1.5. The iterative software is tested and the observation has been given to the developer to improve the code. This in the iterative process this testing has been carried out. Only OBC software and hardware is validated in OBC-In-Loop in initial phase of HILS process. Other flight subsystems i.e. INS and actuators are simulated in plat model. Plant model execute the INC and Actuator simulator based on mathematical model of the sensor and the actuator model. The HILS simulation is developed under Real Time Linux operating system [14].

Figure 1.5 “OBC software validation” process flow.

1.3 HILS Testing Phase

Final phase of HILS testing is carried out in step-wise manner. After the validation of OBC-In-Loop, in stepwise manner flight hardware is introduced in HILS for their hardware and software validation [11].

Actuator-In-Loop (AIL)

: In the case real actuators are integrated and the performance of the actuator dynamics is validated

[3]

. The parameters like lags, bandwidth and the dead zone related with Actuator is validated and rectified in AIL [

9

,

10

].

Full-Stimulation-In-Loop (FSIL)

: INS consists of two parts: sensors and Navigation algorithm. In FSIL only navigation algorithm of INS is validated. This is type of static test with bypassing the real sensor and only stimulated data is sent to INS to validate the navigation algorithm

[6]

.

Sensor-In-Loop (SIL)

: INS sensor performance is validated. The INS/IMU experience three directional rotation by the HILS Flight Motion Simulator (FMS). According to the trajectory dynamics, rotations by 6Dof plant model has been sent to FMS with the three directions simultaneously. This Gyros rotate and send the information to OBC for the validation of C&G algorithm

[6]

.

Sensor-Actuator-In-Loop (SAIL)

: This is the final stage of the HILS validation process. Flight hardware Actuators as well as INS are integrated in HILS as per the communication interfaces. Both subsystems are executing their algorithm simultaneously. The integrated performance in real time is validated

[10]

.

Number of test cases has been generated to test the robustness of the software as well as system is tabulated as per Table 1.1. These test cases has been performed in different conditions and in different configurations. Input data like Thrust profile, stability related parameters changed to see the impact on the simulation. These cases are tabulated in Table 1.1. These perturbation cases are defined as Case-2 to Case-5 with variation in input conditions. Nominal case is as per design and called Case-1.

Table 1.1 Test cases of HILS for OBC software.

Embedded system testing has been performed under HILS platform to test the hardware and software. After the extensive testing the flight subsystem and final software integrated with the aerospace vehicle. Simulation runs generates the output data and that has to be validated with the designer parameters/results. This is required to confirm the proper execution and software of the control and guidance algorithm. The standard deviation from the design results should not be more than the tolerance limit with all the factors like delays, bandwidth, and bias taken into account

After the results found to be satisfactory, the software is integrated with flight vehicle and proceeded for the real test.

1.4 AS9100 QMS Integrated with HILS Process

HILS testing is final clearance after the software development phase. This is iterative process that means the software errors or improvements has been modified and in the next release version software treated as final one. For every software version, all the test cases are performed in HILS and the data is captured for the analysis.

During the total span of HILS testing, if there is no controlled procedure or layout with plan and configuration then there is huge impact on the further schedule. Hence development of stepwise HILS methodology with the development of AS9100 QMS procedures cover all the aspect of the HILS process. This enables the on time delivery of the tested mission software for deployment that is real launch for user acceptance.

At the AS9100 certified Research and Development Laboratory level, Apex manual is the reference for the QMS standard. At every process level, Function Manual is the main reference document that explains all the aspect of process. All the process, responsibilities for the process, input required for the process/activity, output of the process, KPI of every activity is covered in the systematic form in function manual. The calibration of the equipment used for measurement is required and done at regular interval. The major critical machinery and equipment installed in HILS have to be maintained with all the records and logbooks [12]. QMS with HILS process integrated with each step/activity in such a way that the effect of the implementation of QMS only resulted in better and timely output. Different records are maintained for the functionality and traceability of HILS process.

Version Control and change control Management: Software version control is mandatory. Software undergoes many changes based on the requirement of the configuration. Many times, software change has also been done due to HILS observations. The software version with checksum and release date is maintained in HILS along with the change note. In the case of distributed architecture, version control is maintained for each and every subsystem and the HILS runs are carried out with final software version.

Configuration Management: Different configuration is used for HILS like OIL, AIL, FSIL, SIL, and SAIL etc. All the details are controlled under configuration Management [7, 6].

HILS planning, Reports, and Logbook: HILS planning document is available at the initial level of the project to brief about the configuration, planning, and test cases.

Key Performance Indicator and customer Feedback: KPI is defined milestone of each HILS process achieved during the total cycle of HILS testing of the project. The KPI in the below graph showing the data analysis of projects running in HILS based on the customer i.e. project and the KPI achieved during the process. It is shown in the Figure 1.6 that for every project almost HILS process performance is more than 85 % that shows the QMS importance and implementation level.

Figure 1.6 KPI and Customer satisfaction index for different projects.

1.5 Conclusion and Suggestions

Embedded System testing, validation, and certification process has been carried out systematically in HILS and explained in detail in this paper. Performance evaluation in the real time is carried out using HILS in real time that is unique facility for aerospace applications. It is cost effective and rapid prototype test setup that is used to address any design issues before real launch and improve success rate of real launch. The significance of this paper is to explain about the Embedded system’s testing, validation and QMS process developed during HILS as scope of the HILS is more border with software and hardware are involved in HILS process. The systematic approach after QMS has improved HILS process significantly and the HILS runs are done in short span of project time cycle. AS 9100 QMS development and implementation for aerospace industry goes hand to hand during project cycle for effective completion of the project. This paper focused on HILS process and the development and implementation of the AS9100 QMS for HILS. The paper covered all the HILS processes, development of HILS Test-bed to validation and testing of mission software and implementation of QMS to each of these sub process. The adoption of QMS in the HILS process improved the performance of the HILS process in recent years and the data shown in this paper shows that the objective of HILS process has been met by development of AS9100. In future the QMS standard specifically developed for HILS will be developed specifically focus on HILS for the significant improvement and global recognition of HILS Laboratory.

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Note

*

Corresponding author

:

[email protected]

2YouTube Comment Summarizer and Time-Based Analysis

Preeti Bailke, Rugved Junghare*, Prajakta Kumbhare, Pratik Mandalkar, Pratik Mane and Netra Mohekar

VIT Pune, Maharashtra, India

Abstract

With the explosive growth of YouTube as a platform for sharing videos and fostering online communities, the comments section has become a vital arena for discourse and interaction. The YouTube Comment Analyzer is a powerful tool designed to delve into this vast repository of user-generated comments, offering invaluable insights and analytics. This innovative tool employs cutting-edge Natural Language Processing (NLP) techniques dissect and understand wealth of information contained within YouTube comments. Its primary functionalities include sentiment analysis, comment extraction, real-time monitoring, and summary generation.

Keywords: Extractive summarization, comment extraction, sentiment analysis

2.1 Introduction

The prevalence of short attention spans and the corresponding demand for concise, summarized content underscore a fundamental shift in how individuals consume information in the digital age. This phenomenon is particularly pronounced in online environments, where users are inundated with an abundance of information vying for their attention.

In the digital era, individuals are exposed to an unprecedented volume of information daily. This abundance has led to a heightened sense of information overload, wherein users confront a deluge of content that exceeds their cognitive processing capacities. The prevalence of multitasking, often facilitated by the use of multiple devices, contributes to shortened attention spans. Users engage with content amidst a myriad of distractions, necessitating information to be concise and easily digestible. YouTube has become primary source of the information consumption. The rapid-scrolling nature of these platforms encourages brevity in communication, with users expecting to grasp the essence of content within a matter of seconds.

The rise of visual content, such as videos and images, has further accentuated the demand for concise information. Users are drawn to visually stimulating and quickly consumable content, favoring formats that convey messages without requiring prolonged attention. The ubiquity of smartphones and the ability to access content on-the-go contribute to shorter attention spans. Users engage with content in fragmented time intervals, prompting the need for information to be presented succinctly.

FOMO culture, driven by the fear of missing out on the latest information or trends, compels users to seek information efficiently. This fear amplifies the desire for content that can be quickly scanned and comprehended to stay abreast of rapidly changing narratives. In the attention economy, where attention is considered a valuable currency, content creators and platforms compete for users’ limited attention. To capture and retain this attention, content must be condensed, impactful, and immediately relevant.

Short attention spans also arise from the constant barrage of notifications, advertisements, and competing stimuli. Users, faced with an array of distractions, gravitate toward content that offers a quick and meaningful engagement. Some studies suggest that prolonged exposure to digital environments may contribute to neuroplastic changes, impacting attention spans. The brain adapts to rapid stimuli, potentially making individuals more adept at processing condensed information.

Both educational and professional environments increasingly value the ability to convey complex information concisely. This emphasis on brevity has permeated various facets of online communication, including comment sections on platforms like YouTube.

Understanding and navigating this landscape of short attention spans is pivotal for content creators, platforms, and tool developers. It requires a strategic approach that not only acknowledges the evolving nature of digital consumption but actively embraces it through innovations like efficient summarization tools to meet the demands of today’s fast-paced information ecosystem.

This research paper introduces an innovative tool designed to address this challenge: the “YouTube Comment Analyser.” Unlike existing tools, this analyzer not only examines sentiment but also allows for time-based analysis, categorizing comments into the latest, recent, and old, providing valuable context to the sentiments expressed. Additionally, it offers a summarization feature to distill the most important insights from a plethora of comments.

In this paper, we will delve into the methodology behind the YouTube Comment Analyser, exploring how it extracts, processes, and analyzes YouTube comments.

In an age where data-driven decisions are paramount, the YouTube Comment Analyser represents a significant step towards harnessing the power of user-generated content for actionable insights. As we progress through this paper, we will uncover the inner workings of this tool, its impact on content creators and marketers, and the ethical considerations that ensure its responsible use in the digital landscape.

Key Features of the Project

This study embarks on a trailblazing journey, where a series of meticulously crafted key features unfold, reshaping the landscape of conventional methodologies. The key features of the project are as follows:

Sentiment Analysis: The tool employs NLP techniques to perform sentiment-analysis, on YouTube comments. It assesses the emotional tone of comments, allowing users to gauge the sentiment of viewers towards a particular video or topic.

Comment Extraction: The tool is capable of efficiently extracting comments from YouTube videos. It can gather a large volume of comments, making it a valuable resource for content creators, researchers, and marketers looking to understand audience engagement.

Real-Time Monitoring: This tool offers real-time monitoring capabilities, providing users with up-to-the-minute insights into comment sentiment. It enables users to track changes in sentiment over time and react promptly to emerging trends or issues.

Summary Generation: The tool employs extractive summarization techniques to condense the vast amount of comments into concise summaries. This feature allows users to quickly grasp the key points and sentiments expressed in the comments without having to read each one individually.

Advanced NLP Techniques: Highlight the utilization of advanced NLP techniques such as tokenization, named entity recognition, and part-of-speech tagging to enhance the accuracy and depth of comment analysis.

User-Friendly Interface: Discuss the user-friendly interface of the tool, making it accessible to a wide range of users, including non-technical individuals, content creators, and marketers.

Figure 2.1 Example evaluation - accurate summary generation by ChatGPT 3.5

The Figure 2.1 demonstrates the successful summarization of a given text by ChatGPT 3.5, with the generated summary achieving 100% accuracy in capturing the essential content and nuances of the original passage.

2.2 Literature Review

The literature review encompasses a comprehensive exploration of sentiment analysis, machine learning, and natural language processing as applied to YouTube comments. Expanding beyond sentiment analysis, the papers also address text summarization and keyword extraction. For instance, text summarization using Natural Language Processing (NLP) techniques, providing insights into approaches for condensing textual information [1]. In the realm of sentiment analysis, one paper stands out by exploring sentiment analysis through the lens of machine learning algorithms. Utilizing the NLTK dataset and employing text mining techniques, their study contributes to the construction of a machine learning classifier for sentiment prediction in comments, showcasing advancements in precision compared to previous works. This reflects the ongoing evolution and refinement of sentiment analysis methodologies [2]. The review also includes studies that concentrate on specific algorithms, such as exploration of sentiment analysis using the Naive Bayes classifier, highlighting the importance of data labeling and tokenization [3]. One study introduces recent advances in the representation and recognition of Arabic text, such as the ArCAR system, demonstrate the ability of deep learning techniques to accurately classify and understand Arabic content based on habits [4]. Research using simulation software such as Ansoft HFSS demonstrates the potential of this model to provide new ways to improve machine learning algorithms in YouTube comments and reviews [5]. The inclusion of studies such as on Malayalam text summarization illustrates a diverse range of summarization techniques, including recurrent neural network-based models and graph reduction approaches [6]. Another paper adds to this diversity by exploring textual keyword extraction and summarization, showcasing the multifaceted nature of text analysis techniques [7]. Moving to medical applications, a study focuses on breast cancer detection using artificial neural networks (ANN) and Naive Bayes algorithms. This research sheds light on the potential of machine learning for medical purposes, emphasizing the classification of biomarker data to aid in cancer diagnosis [8]. This research addresses the differences between sentiment analysis and language recognition by focusing on mixed data. It uses machine learning and deep learning like BERT and RoBERTa and is designed to detect emotions and negative expressions in negative content of different languages, primarily Tamil and English. This study highlights the importance of advances in NLP for managing online conversations, especially on social media platforms [9]