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Introduction

Advancements in artificial intelligence (AI) have revolutionized various industries, including healthcare, transportation, and manufacturing. One of the key components driving the success of AI is the use of sensors to collect and process data. Neuromorphic sensors, inspired by the human brain, have gained significant attention in recent years due to their ability to mimic the functionality of biological neural networks. These sensors have shown great potential in edge AI applications, where data processing is done locally on the device, reducing latency and improving efficiency.

Chapter One: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter Two: Literature Review
2.1 Introduction to Neuromorphic Sensors
2.2 Evolution of Neuromorphic Sensors
2.3 Neuromorphic Hardware for Edge AI
2.4 Applications of Neuromorphic Sensors
2.5 Challenges and Limitations of Neuromorphic Sensors
2.6 Emerging Trends in Neuromorphic Sensors
2.7 Comparison with Traditional Sensors
2.8 Neuromorphic Sensor Networks
2.9 Neuromorphic Computing
2.10 Neuromorphic Algorithms

Chapter Three: System Design and Methodology
3.1 Introduction to System Design
3.2 Neuromorphic Sensor Selection
3.3 Data Acquisition and Preprocessing
3.4 Neuromorphic Computing Architecture
3.5 Algorithm Implementation
3.6 Performance Evaluation Metrics
3.7 Testing and Validation
3.8 Optimization Techniques

Chapter Four: System Implementation
4.1 Hardware Platform Selection
4.2 Sensor Integration
4.3 Software Development
4.4 System Integration
4.5 Testing and Validation
4.6 Performance Analysis
4.7 Optimization Strategies
4.8 Data Visualization

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Research Directions
5.4 Concluding Remarks

Thesis Overview on Neuromorphic Sensors for Edge AI

Artificial intelligence (AI) has become an integral part of our daily lives, driving innovation and efficiency in various industries. The use of sensors to collect data is crucial for AI systems to analyze and make informed decisions. Neuromorphic sensors, inspired by the human brain, are a promising technology that offers significant advantages in edge AI applications.

The purpose of this thesis is to explore the potential of neuromorphic sensors for edge AI and develop a comprehensive understanding of their capabilities and limitations. The thesis will begin with an introduction to the background of the study, highlighting the problem statement, objectives, limitations, scope, significance, and structure of the research.

A detailed literature review will provide insights into the evolution of neuromorphic sensors, their applications, challenges, and emerging trends. A focus on comparison with traditional sensors, sensor networks, computing, and algorithms will help in understanding the unique features of neuromorphic sensors.

The thesis will then delve into the system design and methodology, including sensor selection, data acquisition, computing architecture, algorithm implementation, testing, and optimization techniques. The system implementation chapter will cover hardware platform selection, sensor integration, software development, testing, performance analysis, and data visualization.

In the conclusion and summary chapter, the findings of the research will be summarized, highlighting the contributions to the field and suggesting future research directions. Through this thesis, a comprehensive understanding of neuromorphic sensors for edge AI will be achieved, paving the way for innovative applications in AI-driven systems.

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