Quantum Computing Models for Cybersecurity and Wireless Communications
Description
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.
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Persons
Budati Anil Kumar, PhD, is an associate professor at the Faculty of Electronics & Communication Engineering, Koneru Lakshmaiah Education Foundation (Deemed University), Aziz Nagar Campus, Hyderabad, Telangana, India. His research interests include cognitive radio networks, software-defined radio networks, artificial intelligence, etc. He has published 53 research articles in highly reputed publishing journals and conferences.
Singamaneni Kranthi Kumar, PhD, Faculty of Computer Engineering and Technology, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, Telangana, India. He has authored at least 30 SCI journal articles and received the prestigious "Global Teachers Award" in 2020.
Li Xingwang, PhD, is an associate professor at the School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo, China. He is on the editorial board of many IEEE journals and his research interests include wireless communication, intelligent transport systems, artificial intelligence, and the Internet of Things.
Content
Preface xv
Acknowledgment xvii
1 Performance Evaluation of Avionics System Under Hardware-In- Loop Simulation Framework with Implementation of an AS9100 Quality Management System 1
Rajesh Shankar Karvande and Tatineni Madhavi
1.1 Introduction 2
1.2 HILS Process and Quality Management System 4
1.3 HILS Testing Phase 7
1.4 AS9100 QMS Integrated with HILS Process 8
1.5 Conclusion and Suggestions 10
References 10
2 YouTube Comment Summarizer and Time-Based Analysis 13
Preeti Bailke, Rugved Junghare, Prajakta Kumbhare, Pratik Mandalkar, Pratik Mane and Netra Mohekar
2.1 Introduction 13
2.2 Literature Review 16
2.3 Methodology 18
2.3.1 YouTube Comments Data Collection 18
2.3.1.1 YouTube Data API Integration 18
2.3.1.2 get_video_comments Function 19
2.3.1.3 Comment Processing 19
2.3.1.4 Handling Pagination with get_all_video_ comments 20
2.3.1.5 Excel File Creation with save_to_excel 20
2.3.2 Datasets 20
2.3.3 Extractive Summarization 21
2.4 Result 30
2.5 Performance 30
2.6 Conclusion 31
References 31
3 Enhancing Gait Recognition Using YOLOv8 and Robust Video Matting for Low-Light and Adverse Conditions 33
Premanand Ghadekar, Aadesh Chawla, Sakshi Bodhe, Sharvari Bawane and Dhruv Kshirsagar
3.1 Introduction 34
3.2 Related Works 34
3.3 Methodology 36
3.4 Comparision with Existing Systems 41
3.5 Future Scope 48
3.6 Conclusion 48
Acknowledgment 49
References 49
4 An Ensemble-Based Machine Learning Framework for Breast Cancer Prediction 51
Ramya Palaniappan, Maha Lakshmi, Namitha, Nirmala Devi and Naga Phani
4.1 Introduction 52
4.2 Related Works 53
4.3 Proposed Framework 56
4.3.1 ML Models and Ablation Study 56
4.3.2 Building Ensemble Model Using AdaBoost 57
4.4 Experimental Setup 58
4.4.1 Dataset 58
4.4.2 Data Visualization 59
4.4.3 Data Pre-Processing Phase 60
4.4.4 Proposed Methodology 61
4.4.5 Performance Metrics 62
4.5 Results and Discussion 63
4.5.1 Comparison with Baseline Models 63
4.5.2 Comparison with Existing Literature Works 66
4.6 Existing Works 67
4.7 Conclusion and Future Work 69
Dataset 69
References 69
5 Proactive Fault Detection in Weather Forecast Control Systems Through Heartbeat Monitoring and Cloud-Based Analytics 73
Shelly Prakash and Vaibhav Vyas
5.1 Introduction 74
5.1.1 Cloud Computing 75
5.1.1.1 Fault, Error, Failure 75
5.2 Related Work 77
5.3 Proposed Proactive Fault Detection Architecture 81
5.4 Conclusion 95
References 95
6 FlowGuard: Efficient Traffic Monitoring System 99
Varsha Dange, Atharva Bonde, Om Borse, Harshal Chaudhari and Sanskar Chaudhari
6.1 Introduction 99
6.2 Literature Review 100
6.3 Methodology 113
6.3.1 Theory 113
6.3.2 Requirement 114
6.3.2.1 Hardware Requirements 114
6.3.2.2 Software Requirements 116
6.3.3 Workflow 117
6.3.4 Flowchart 118
6.4 Results and Discussions 118
6.5 Conclusion 121
6.6 Future Scope 121
Acknowledgment 122
References 122
References for Pictures of Components Used 124
7 A Survey on Heart Disease Prediction Using Ensemble Techniques in ml 125
Sudhakar Vecha and M.V.P. Chandra Sekhara Rao
7.1 Introduction 125
7.2 Literature Survey 127
7.3 Datasets 128
7.4 Ensemble Learning in Heart Disease 129
7.5 Challenges and Limitations 134
7.6 Future Directions 134
7.7 Conclusion 135
References 135
8 A Video Surveillance: Crowd Anomaly Detection and Management Alert System 139
Anitha Ponraj, Umasree Mariappan, M. J. Sai Kiran, S. Tejeswar Reddy, N. Vinay and P. Bharath
8.1 Introduction 140
8.2 Related Work 140
8.3 Dataset Description 143
8.4 Problem Definition 143
8.5 Proposed Methodology and System 144
8.5.1 Proposed Methodology 144
8.5.2 Proposed System 146
8.6 Results 148
8.7 Conclusion and Future Scope 150
8.7.1 Conclusion 150
8.7.2 Future Scope 151
References 151
9 Revolutionizing Learning with Qubits: A Review of Quantum Machine Learning Advances 153
Shatakshi Bhusari, Aniket Badakh, Kalyani Daine, Nikita Gagare and Prasad Raghunath Mutkule
9.1 Introduction 154
9.1.1 Parallelism 154
9.1.2 Quantum Speedup 155
9.1.3 Quantum Entanglement 155
9.1.4 Quantum Fourier Transform 155
9.1.5 Quantum Machine Learning Algorithms 155
9.1.6 Quantum Data Representation 155
9.1.7 Quantum Sampling 155
9.1.8 Quantum Annealing 156
9.1.9 Hybrid Quantum-Classical Approaches 156
9.2 Review of Literature 156
9.2.1 Overview of Key Quantum Computing Principles 156
9.2.1.1 Qubits (Quantum Bits) 157
9.2.1.2 Quantum Gates 157
9.2.1.3 Quantum Parallelism 157
9.2.1.4 Quantum Measurement 157
9.2.1.5 Quantum Fourier Transform 158
9.2.1.6 Quantum Entanglement-Based Algorithms 158
9.3 Basic Quantum Operations, Qubits, and Quantum Gates 158
9.3.1 Basic Quantum Operations 158
9.3.2 Quantum Bits (Qubits) 158
9.3.3 Quantum Gates 159
9.4 Quantum Machine Learning Algorithms 159
9.4.1 Quantum Support Vector Machines (QSVM) 161
9.4.2 Quantum Neural Networks (QNN) 161
9.4.3 Quantum Clustering Algorithms 161
9.4.4 Quantum Principal Component Analysis (QPCA) 162
9.4.5 Quantum Boltzmann Machines 162
9.4.6 Quantum Support Vector Clustering (QSVC) 162
9.5 Quantum Hardware for Machine Learning 162
9.6 Challenges in Building Scalable and Error-Resistant Quantum Hardware 163
9.6.1 Decoherence and Quantum Error Correction 163
9.6.2 Quantum Gate Fidelity 163
9.6.3 Scalability 164
9.6.4 Qubit Connectivity and Crosstalk 164
9.6.5 Material Science and Qubit Implementation 164
9.6.6 Quantum Interconnects 164
9.6.7 Thermal Management 164
9.6.8 Error Mitigation Strategies 164
9.7 Challenges and Limitations in Quantum Machine Learning 165
9.7.1 Quantum Computational Overheads 165
9.7.2 Hybrid Quantum-Classical System Integration 165
9.7.3 Limited Quantum Expressibility 165
9.7.4 Data Preprocessing Challenges 165
9.7.5 Quantum Algorithm Verification 166
9.7.6 Quantum Resource Requirements 166
9.7.7 Adaptation to Quantum Hardware Constraints 166
9.7.8 Limited Quantum Hardware Availability 166
9.7.9 Algorithmic Complexity 166
9.7.10 Quantum Model Interpretability 166
9.8 Future Directions 167
9.9 Conclusion 167
References 167
10 Multi-Band Self-Grounding Antenna for Wireless Technologies 169
Ch. Siva Rama Krishna, P. Livingston, S. Jaya Chandra, J. Hari Babu and K. Sai Babu
10.1 Introduction 170
10.1.1 Literature Review 170
10.2 Design of Antenna 174
10.2.1 Design and Results at Primary Level of Antenna 175
10.2.2 Design and Results at Secondary Level of Antenna 175
10.3 Actual Design of Antenna 176
10.4 Results of Antenna 176
10.4.1 Mathematical Analysis 178
10.4.2 3D Polar Plot 178
10.5 Conclusions 179
References 180
11 Navigating Network Security: A Study on Contemporary Anomaly Detection Technologies 183
Sai Ramya, Smera C. and Sandeep J.
11.1 Introduction 184
11.2 Related Work 186
11.3 Methodology 194
11.4 Conclusion 197
References 197
12 File Fragment Classification: A Comprehensive Survey of Research Advances 201
Teena Mary and Sreeja C.S.
12.1 Introduction 201
12.2 Methodology 203
12.2.1 Selection Criteria 203
12.2.2 Structure of the Paper 204
12.3 Approaches for File Fragment Classification 204
12.3.1 Signature-Based Approaches 204
12.3.2 Content-Based Approaches 206
12.3.3 Deep Learning-Based Approaches 207
12.3.3.1 Convolutional Neural Networks (CNNs) 208
12.3.3.2 Feed Forward Neural Networks (FFNNs) 209
12.3.4 Hierarchical Classification Methods 209
12.4 Survey Findings 210
12.5 Challenges and Future Directions 214
12.6 Conclusion 215
References 216
13 Deepfake Detection and Forensic Precision for Online Harassment 219
K. Gouthami, K. Sunitha, D.U. Durgarani and M. Prathyusha
13.1 Introduction 220
13.2 Literature 221
13.3 Theoretical Analysis and Software Simulation 222
13.3.1 Theoretical Analysis 222
13.3.2 Software Simulation 223
13.3.3 Testing and Optimization 224
References 225
14 Design of Automatic Seed Sowing Machine 227
Chiluka Ramesh, K. Sarada, V. Ajay Shankar and K. Ravi Kumar
14.1 Introduction 228
14.2 Literature Survey 229
14.3 Proposed System 232
14.4 Conclusions 235
References 235
15 In Motion: Exploring Urban Rides Through Data Analytics 237
Rajkumar Sai Varun, Nimmagadda Narayana, Dudam Vipassana and Mohan Dholvan
15.1 Introduction 237
15.2 Literature Survey 238
15.3 Proposed Methodology 240
15.4 Result Analysis 247
15.5 Conclusion 248
References 249
16 Design of Novel Chatbot Using Generative Artificial Intelligence 251
Sk. Khader Zelani, Sk. Gousiya Begum, M. Chandana and N. Lakshmi Tirupatamma
16.1 Introduction 252
16.2 Conclusion and Future Scope 257
References 257
17 The Smart Nebulizer Cap for Enhanced Asthma Management 259
Rossly Netala, Aadi Praharsha and Mohan Dholvan
17.1 Introduction 259
17.2 Literature Survey 261
17.3 Methodology 262
17.4 Conclusions 265
References 265
18 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing 267
Y. Bhaskara Rao, K. Rajitha, D. Vijay Harsha Vardhan, N. Naga Raja Kumari and D. Vijaya Saradhi
18.1 Introduction 268
18.2 Literature Survey 269
18.3 Design of a Digital VLSI Parallel Morphological Reconfigurable Processing Module for Binary and Grayscale Image Processing 271
18.4 Result Analysis 274
18.5 Conclusion 276
References 277
19 Intrusion Detection System Using Machine Learning 279
Ballikura Dhanunjay, Earla Sanjay, Aakaram Karthik Raj and Mohan Dholvan
19.1 Introduction 280
19.2 Literature Survey 280
19.3 Methodology 281
19.4 Algorithm 283
19.5 Implementation 285
19.6 Results and Outputs 289
19.6.1 User Interface 289
19.7 Conclusion and Future Scope 290
References 291
20 Prediction of Arrival Delay Time in Freightage Rails 293
Bobbala Shriya, Gudishetty Shrita, Vanga Pragnya Reddy and Nanda Kumar M.
20.1 Introduction 294
20.2 Literature Survey 295
20.3 Methodology 297
20.4 Experimental Results 302
20.5 Conclusions 308
References 309
21 Predicting Flight Delays with Error Calculation Using Machine Learned Classifiers 311
L. Sai Nageswara Raju, T. Naman Krishn Raj, Raipole Manihas Goud and Mohan Dholvan
21.1 Introduction 311
21.2 Literature Survey 312
21.3 Proposed Methodology 314
21.4 Result Analysis 322
21.5 Conclusion 322
References 323
22 Design and Implementation of 8-Bit Ripple Carry Adder and Carry Select Adder at 32-nm CNTFET Technology: A Comparative Study 325
Venkata Rao Tirumalasetty, K. Babulu and G. Appala Naidu
22.1 Introduction 326
22.2 Implementation of RCA & CSA 328
22.3 Simulation Results 333
22.4 Conclusion 335
References 335
23 XGBoost Classifier Based Water Quality Classification Using Machine Learning 337
Nagidi Nikhitha, Sudini Poojitha, Vooturi Arjun, K. Sateesh Kumar and D. Mohan
23.1 Introduction 338
23.2 Related Work 338
23.3 Proposed Methodology 339
23.4 Results and Discussion 342
23.5 Conclusion 345
References 345
Index 347
1
Performance 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...
