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Introduction:

Recent advances in artificial intelligence and computing technology have paved the way for the development of autonomous drones that can perform tasks without human intervention. One promising approach is to utilize neuromorphic computing, which is inspired by the functioning of the human brain, to create intelligent and adaptive systems. This thesis aims to explore the application of neuromorphic computing in the development of autonomous drones, with a focus on enhancing their ability to navigate and perform complex tasks in dynamic environments.

Chapter 1

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 2: Literature Review

2.1 Introduction to neuromorphic computing
2.2 Applications of neuromorphic computing in autonomous systems
2.3 Drones technology and challenges
2.4 Current state-of-the-art in drone autonomy
2.5 Neuromorphic computing for drone navigation
2.6 Neuromorphic hardware and software platforms
2.7 Neural network models for drone autonomy
2.8 Neuromorphic sensors and perception
2.9 Challenges and limitations of neuromorphic computing
2.10 Future directions in neuromorphic computing for autonomous drones

Chapter 3: System Design and Methodology

3.1 System architecture for autonomous drones
3.2 Neuromorphic computing algorithms and techniques
3.3 Data preprocessing and feature extraction
3.4 Neural network training and optimization
3.5 Sensor fusion and integration
3.6 Real-time decision-making and control
3.7 Testing and validation methodology
3.8 Performance evaluation metrics

Chapter 4: System Implementation

4.1 Hardware selection and configuration
4.2 Software development and integration
4.3 Sensor setup and calibration
4.4 Neural network implementation
4.5 Real-time processing and feedback loop
4.6 System integration and testing
4.7 Performance tuning and optimization

Chapter 5: Conclusion and Summary

In this final chapter, the thesis will summarize the key findings and contributions of the research, discuss the implications for the field of autonomous drones, and outline potential future research directions. Additionally, the thesis will reflect on the challenges encountered during the project and propose recommendations for further studies in the field of neuromorphic computing for autonomous drones.

Thesis Overview:

The rapid advancement in drone technology has opened up new possibilities for a wide range of applications, including surveillance, infrastructure inspection, and search and rescue missions. However, the autonomy of drones is still limited by the complexity of the environments they operate in and the need for real-time decision-making. This thesis aims to explore the potential of neuromorphic computing in enhancing the autonomy of drones, by leveraging biologically inspired algorithms and neural network models.

The thesis will begin with an introduction to the concept of neuromorphic computing and its applications in autonomous systems. It will then discuss the challenges and limitations of current drone technology, and propose the use of neuromorphic computing to address these challenges. The literature review will explore the state-of-the-art in neuromorphic computing for drone autonomy, including hardware and software platforms, neural network models, and sensor integration.

The system design and methodology chapter will provide a detailed overview of the architecture and components of the neuromorphic computing system for autonomous drones, including data preprocessing, neural network training, sensor fusion, and real-time decision-making. The system implementation chapter will describe the hardware and software setup, sensor calibration, neural network implementation, and performance evaluation.

In the conclusion and summary chapter, the thesis will present the key findings and contributions of the research, discuss the implications for the field of autonomous drones, and propose recommendations for future studies. The thesis aims to bridge the gap between artificial intelligence and drone technology, by developing a neuromorphic computing system that can enhance the autonomy and adaptability of drones in dynamic environments.

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