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Introduction

Neuromorphic event-based vision sensors have gained significant attention in recent years due to their ability to mimic the functioning of the human visual system. These sensors operate on a fundamentally different principle compared to traditional frame-based sensors, as they only transmit pixel-level information when a change in brightness occurs in the scene. This event-driven approach offers several advantages, including low latency, low power consumption, and high dynamic range. This thesis aims to explore the application of neuromorphic event-based vision sensors in various fields such as robotics, automotive, surveillance, and augmented reality.

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 History of event-based vision sensors
2.2 Comparison between event-based and frame-based sensors
2.3 Applications of event-based vision sensors
2.4 Challenges in utilizing event-based vision sensors
2.5 Existing research on event-based vision sensors
2.6 Neuromorphic hardware platforms
2.7 Algorithms for event-based data processing
2.8 Event-based sensor fusion techniques
2.9 Event-based vision sensor datasets
2.10 Future trends in event-based vision sensors

Chapter Three: System Design and Methodology
3.1 Selection of neuromorphic event-based vision sensor
3.2 System architecture design
3.3 Event-based data processing algorithms
3.4 Sensor fusion techniques
3.5 Calibration and synchronization methods
3.6 Performance evaluation metrics
3.7 Experimental setup
3.8 Data acquisition and preprocessing
3.9 Testing and validation procedures

Chapter Four: System Implementation
4.1 Hardware integration
4.2 Software development
4.3 Algorithm implementation
4.4 Sensor calibration and testing
4.5 Performance optimization techniques
4.6 Real-world applications
4.7 Performance evaluation results
4.8 Comparison with existing systems

Chapter Five: Conclusion
5.1 Summary of findings
5.2 Contributions to the field
5.3 Future research directions
5.4 Conclusion and recommendations
5.5 Limitations of the study

Thesis Overview on Neuromorphic Event-Based Vision Sensors

Neuromorphic event-based vision sensors have emerged as a promising technology with a wide range of applications in various domains. This thesis aims to investigate the utilization of event-based sensors that operate on spike-based processing principles inspired by the human brain’s visual system. The introduction provides a comprehensive background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms.

The literature review delves into the history of event-based vision sensors, compares them to frame-based sensors, explores applications, challenges, existing research, hardware platforms, algorithms, fusion techniques, and datasets. Future trends in the field are also discussed. The system design and methodology chapter outline the selection of sensors, architecture design, data processing algorithms, fusion techniques, calibration, synchronization, metrics, setup, acquisition, preprocessing, testing, and validation.

The system implementation chapter details hardware integration, software development, algorithm implementation, calibration, testing, optimization, applications, evaluation, and comparison. The conclusion chapter summarizes the findings, discusses contributions, suggests future research directions, concludes, and provides recommendations, including limitations of the study. Overall, this thesis aims to contribute to the advancement of neuromorphic event-based vision sensors and their practical implications in real-world scenarios.

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