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Introduction

Neuromorphic computing is an emerging field in the intersection of neuroscience and computer science, aiming to replicate the functions of the human brain using electronic circuits. This technology offers promising solutions for real-time sensor processing, as it can efficiently process and analyze massive amounts of data with low power consumption. Neuromorphic computing has the potential to revolutionize various applications such as artificial intelligence, robotics, and sensor networks.

Chapter 1: 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 2: Literature Review
2.1 History of Neuromorphic Computing
2.2 Neuromorphic Hardware architectures
2.3 Applications of Neuromorphic Computing
2.4 Neuromorphic Algorithms
2.5 Neuromorphic Sensors
2.6 Real-time Sensor Processing Techniques
2.7 Challenges in Neuromorphic Computing
2.8 Neuromorphic Computing vs. Traditional Computing
2.9 Neuromorphic Computing in Industry
2.10 Future Trends in Neuromorphic Computing

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Experimental Setup
3.5 Neuromorphic Hardware Selection
3.6 Software Development
3.7 Simulation Environment
3.8 Performance Metrics

Chapter 4: Discussion of Findings
4.1 Data Processing Speed
4.2 Energy Efficiency
4.3 Accuracy of Sensor Data Analysis
4.4 Comparison with Traditional Computing Approaches
4.5 Scalability of Neuromorphic Systems
4.6 Robustness to Noise
4.7 Adaptability to Changing Environments
4.8 Real-world Applications of Neuromorphic Computing

Chapter 5: Conclusion and Summary
In conclusion, this thesis explores the potential of Neuromorphic computing for real-time sensor processing. The literature review highlights the current state of the art and future trends in Neuromorphic computing. The research methodology outlines the experimental setup and data analysis techniques used in this study. The discussion of findings presents the results and implications of using Neuromorphic computing for real-time sensor processing. Overall, this thesis contributes to the growing body of knowledge on Neuromorphic computing and its applications in sensor processing.

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