VLSI devices downscaling is a very significant part of the design to improve the performance of VLSI industry outcomes, which results in high speed and low power of operation of integrated devices. The increasing use of VLSI circuits dealing with highly sensitive information, such as healthcare information, means adequate security measures are required to be taken for the secure storage and transmission. Advanced Circuits and Systems for Healthcare and Security Applications provides broader coverage of the basic aspects of advanced circuits and security and introduces the corresponding principles. By the end of this book, you will be familiarized with the theoretical frameworks, technical methodologies, and empirical research findings in the field to protect your computers and information from adversaries. Advanced circuits and the comprehensive material of this book will keep you interested and involved throughout.
The book is an integrated source which aims at understanding the basic concepts associated with the security of the advanced circuits and the cyber world as a first step towards achieving high-end protection from adversaries and hackers. The content includes theoretical frameworks and recent empirical findings in the field to understand the associated principles, key challenges and recent real-time applications of the advanced circuits and cybersecurity. It illustrates the notions, models, and terminologies that are widely used in the area of circuits and security, identifies the existing security issues in the field, and evaluates the underlying factors that influence the security of the systems. It emphasizes the idea of understanding the motivation of the attackers to establish adequate security measures and to mitigate security attacks in a better way. This book also outlines the exciting areas of future research where the already-existing methodologies can be implemented. Moreover, this book is suitable for students, researchers, and professionals in the who are looking forward to carry out research in the field of advanced circuits and systems for healthcare and security applications; faculty members across universities; and software developers.
Author(s): Balwinder Raj, Brij B. Gupta, Jeetendra Singh
Edition: 1
Publisher: CRC Press
Year: 2022
Language: English
Pages: 254
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Acknowledgments
Preface
Editors
1. Design and Analysis of Charge Plasma-Based SiGe Vertical TFET for Biosensing Applications
1.1. Introduction
1.2. Architecture and Device Simulation Setup
1.3. Results and Discussion
1.3.1. Drain Current Analysis on Dielectric Constant and Charged Biomolecules
1.3.1.1. Drain Current Is Affected By Cavity Length Variation
1.3.1.2. Drain Current Is Affected By Cavity Thickness Variation
1.3.2. Changes in Drain Current Sensitivity (DCS) as a Function of Various Parameters
1.3.2.1. Effect of Cavity Length on DCS
1.3.2.2. Effect of Cavity Thickness on DCS
1.3.3. Conclusion
References
2. Borospherene-Based Molecular Junctions for Sensing Applications
2.1. Introduction
2.2. Applications of B40 as Sensors
2.3. Transport Phenomenon in Boron Fullerene Molecular Junctions
2.4. Methodology
2.5. Results for Transport Phenomenon in Boron Fullerene Molecular Junctions
2.5.1. Current-Voltage Curve
2.5.2. Transmission Spectra
2.5.3. Transmission Pathways
2.5.4. Transmission Eigen States
2.5.5. Differential Conductance
2.6. Conclusion
Acknowledgments
References
3. Nanowire FETs for Healthcare Applications
3.1. Introduction
3.1.1. MOS Transistor
3.1.2. Scaling of MOSFET
3.1.3. Issues at Nanoscale Level
3.2. Advance MOSFET Structure
3.2.1. Double-Gate (DG) MOSFET
3.2.2. Surrounding-Gate MOSFET
3.2.3. FinFET
3.3. Nanowire for Biomedical Applications
3.4. Modeling of Nanoscale Devices
3.4.1. Semiclassical Transport: Diffusive
3.4.2. Ballistic Transport
3.4.3. Quantum Transport
3.5. Modeling of Nanowire
3.5.1. Electrostatic of Nanowire
3.5.1.1. Physical Model
3.5.1.2. Without External Force
3.5.1.3. With External Force
3.5.1.4. Effective Mass Approximation
3.5.1.5. Poisson's Equation
3.5.1.6. Quantum Electron Concentration
3.5.2. Electron Mobility Modeling
3.5.2.1. Low-Field Mobility Model
3.5.2.2. Effective Mobility
3.5.3. Carrier Transport Modeling
3.5.4. Surface Potential Momodeling
3.5.5. Charge Modeling
3.5.5.1. Semiconductor Charge of an Undopped Nanowire
3.5.6. Subband Energy Modeling
3.6. Healthcare Applications of Nanowire FETs
3.6.1. Detection of Viruses
3.6.2. Detection of Biomarkers
3.6.3. Detection of DNA and RNA
3.6.4. Discovery of Medication
3.7. Summary
References
4. TFET-Based Sensor Design for Healthcare Applications
4.1. Introduction
4.2. Types of Sensors and Their Applications in the Healthcare Sector
4.3. TFET Device Operation and Working Principle
4.4. Structures and Workings of TFET-Based Biosensors
4.5. Different Architectures of TFET-Based Biosensors
4.5.1. Doped Dielectric Modulated TFET-Based Biosensor
4.5.2. Charge Plasma-Based Junctionless TFET Biosensors
4.6. Structures and Workings of TFET-Based Gas Sensors
4.7. Different Architectures of TFET-Based Gas Sensors
4.7.1. Metallic Gate Doped TFET-Based Gas Sensors
4.7.2. Metallic Gate-Based Charge Plasma Based TFET Gas Sensors
4.7.3. Conducting Polymer (CP)-Based GAA-TFET Gas Sensors
4.8. Structures and Workings of TFET-Based Photosensor
4.9. Different Architectures of TFET-Based Photosensors
4.9.1. Hybrid Photosensors
4.9.2. Hetero-Material-Based Charge Plasma-Based TFET Photosensors
4.10. Summary
References
5. Modeling and Simulation Analysis of TFET-Based Devices for Biosensor Applications
5.1. Introduction to Tunneling Field Effect Transistor (TFET) Devices
5.2. Types of TFET Devices
5.2.1. Dielectric Modulated Double Source Trench Gate Tunnel FET (DM-DSTGTFET)
5.2.1.1. DM-DSTGTFET Device Structure
5.2.1.2. Simulation Method and Model
5.2.1.3. Device Performance
5.2.2. DM-GUD-TFET (Dielectric Modulated Gate Underlap Doping-Less Tunnel Field Effect Transistor)
5.2.2.1. DM-GUD-TFET Device Structure
5.2.2.2. Device Performance
5.2.3. Dielectric Modulated p-Type Tunnel Field Effect Transistor (DM p-TFET)
5.2.3.1. Device Structure and Simulation Setup
5.2.3.2. Device Performance
5.2.4. DMFET (Dielectrically Modulated Field Effect Transistor)
5.2.4.1. Architecture and Simulation of TFET Biosensors
5.2.4.2. Sensitivity Analysis of the TFET Device
5.3. Conclusion
References
6. Security-Based Genetic Algorithms for Health Care
6.1. Introduction
6.2. The Need of Security-Based Genetic Algorithms in Health Care
6.3. Basic Terminologies Related to Security-Based GA
6.3.1. Chromosomes
6.3.2. Populations
6.3.3. Genes
6.3.4. Alleles
6.3.5. Genotypes and Phenotypes
6.4. General Genetic Algorithm for Health Care
6.5. Operators in GA for Health Care
6.5.1. Encoding
6.5.2. Selection
6.5.3. Crossover
6.5.4. Mutation
6.6. Stopping Condition for Genetic Algorithm for Health Care the Various Stopping Condition
Conclusion
Author's details
References
7. Role of High-Performance VLSI in the Advancement of Healthcare Systems
7.1. Smart Healthcare Systems
7.2. High-Performance VLSI Architecture in a Healthcare Perspective
7.3. VLSI Trends in the Innovation of Healthcare Systems
7.4. Current Utilization of VLSI in Healthcare Systems
7.5. Utilization of Advanced VLSI Systems in Diagnosis and Prevention of Diseases
7.6. Conclusion
References
8. Trust-Based Security Model for Adaptive Decision Making in VANETs
8.1. Introduction
8.1.1. VANET Architecture
8.1.2. Protocols Used for Transmission in VANETs
8.1.3. Security in VANETs
8.1.4. Security Solutions
8.2. Gaps in the Existing Solutions
8.3. Trust-Based Security Models
8.4. Literature on Trust Models in VANETs
8.5. Gaps in Existing Trust Model-Based Security Solutions in VANETs
8.6. Proposed Trust-Based Adaptive Decision-Making Security Model
8.7. Conclusion
References
9. Security Attacks and Challenges of VANETs
9.1. Introduction
9.2. The Communication Topology of VANET
9.2.1. Vehicle to Vehicle Communication (V2V)
9.2.2. Vehicle to Infrastructure
9.2.3. Hybrid Architecture
9.3. VANET Characteristics
9.3.1. Mobility Modelling
9.3.2. Frequently Disconnected Network
9.3.3. Network Size
9.3.4. Wireless Communication
9.3.5. Time Critical
9.3.6. Sufficient Energy
9.3.7. Better Physical Protection
9.3.8. Access of Infrastructure
9.4. Requirement of Security Services in VANET
9.4.1. Attacks on Integrity
9.4.2. Attacks on Availability
9.4.3. Attacks on Authentication/Identification
9.4.4. Non-Repudiation
9.4.5. Confidentiality
9.5. Attacks in VANETS
9.5.1. Attacks on Authentication
9.5.1.1. GPS Spoofing Attack
9.5.1.2. Free Riding Attack
9.5.1.3. Tunneling Attack
9.5.1.4. Replication Attack
9.5.1.5. Message Tampering Attack
9.5.1.6. Sybil Attack
9.5.1.7. Impersonation Attack
9.5.1.8. Wormhole Attack
9.5.2. Attacks on Availability
9.5.2.1. Jamming Attack
9.5.2.2. Black Hole Attack
9.5.2.3. Greedy Behavior Attack
9.5.2.4. Denial of Service Attack
9.5.2.5. Grey Hole Attack
9.5.2.6. Broadcast Tampering
9.5.2.7. Spamming
9.5.2.8. Malware Attack
9.5.3. Attacks on Confidentiality
9.5.3.1. Eavesdropping
9.5.3.2. Man in the Middle Attack
9.5.3.3. Traffic Analysis Attack
9.5.3.4. Social Attack
9.5.4. Attacks on Data Integrity
9.5.4.1. Illusion Attack
9.5.4.2. Masquerading Attack
9.5.4.3. Replay Attack
9.5.5. Attacks on Non-Repudiation
9.5.5.1. Repudiation Attack
9.6. Challenges and Future Perspectives
9.6.1. Network Management
9.6.2. Congestion and Collision Control
9.6.3. Environmental Impact
9.6.4. MAC Design
9.6.5. Security
9.6.6. Data Consistency Liability
9.6.7. Low Tolerance for Error
9.6.8. Key Distribution
9.6.9. Incentives
9.6.10. Highly Heterogeneous Vehicular Networks
9.6.11. Data Management and Storage
9.6.12. Localization Systems
9.6.13. Disruptive Tolerant Communications
9.6.14. Tracking a Target
9.7. Conclusion
Abbreviations
References
10. Energy-Efficient Approximate Multipliers for ML-Based Disease Detection Systems
10.1. Introduction to Machine Learning
10.2. Role of Machine Learning in Handling Biomedical Data
10.3. State-of-the-Art Machine Learning-Based Prediction Circuits
10.4. Efficient Approximate Multiplier for Neural Network-Based Disease Prediction Circuits
10.4.1. The Booth Multiplier
10.4.2. The Approximate Multiplier
10.4.3. Characterization of Errors
10.4.4. The Proposed Approximate Multiplier
10.5. Results
10.6. Applications on a Neural Network Case Study
10.7. Conclusion
References
11. Cross-Domain Analysis of Social Data and the Effect of Valence Shifters
11.1. Introduction
11.1.1. Machine Learning
11.1.2. In a Lexical-Based Approach We Have Two Basic Approaches
11.2. Literature Survey
11.2.1. Sentiment Analysis Packages and Techniques
11.2.2. Data Extraction for Sentimental Analysis
11.2.3. Sentiment Analysis Survey, Classifiers, and N Grams
11.2.4. Sentiment Analysis Using Big Data Tools
11.2.5. Sentimental Analysis Using Websites
11.2.6. Sentiment Analysis of Social Sites
11.3. Prerequisite Knowledge/Technique Used
11.3.1. Social Media
11.3.2. Sentimental Analysis
11.3.3. Social Sentiment Analysis
11.3.4. Naïve-Bayesian
11.3.5. N Grams
11.3.6. Data Set
11.3.7. Valance Shifters
11.3.8. Confusion Matrix
11.4. Methodology
11.4.1. Data Extraction
11.4.2. Removing Duplicity
11.4.3. Creating Corpus
11.4.4. Text Cleaning
11.4.5. Stopwords Removal
11.4.6. N Gram Generation
11.4.7. Sentiment Analysis
11.5. Result and Discussion
11.6. Conclusion and Future Scope
References
Index
