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Introduction:

Tensor factorization is a powerful tool used in signal processing to extract relevant information from high-dimensional data. By decomposing a tensor into a set of lower-dimensional factors, tensor factorization allows for efficient representation, compression, and analysis of complex signals. This technique has been widely used in various signal processing applications such as image and video processing, speech recognition, and biomedical signal analysis. In this thesis, we will explore the application of tensor factorization in signal processing and investigate its potential benefits and limitations.

Masters Thesis Table of Content:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Objective of Study
1.4 Limitation of Study
1.5 Scope of Study

Chapter 2: Literature Review
2.1 Overview of Tensor Factorization
2.2 Applications of Tensor Factorization in Signal Processing
2.3 Existing Methods and Techniques
2.4 Comparison of Different Tensor Factorization Approaches

Chapter 3: Research Methodology
3.1 Data Collection and Preprocessing
3.2 Tensor Factorization Algorithms
3.3 Evaluation Metrics
3.4 Experimental Setup

Chapter 4: Discussion of Findings
4.1 Analysis of Results
4.2 Interpretation of Factors
4.3 Comparison with Existing Methods
4.4 Implications for Signal Processing

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Research Directions
5.4 Conclusion

Thesis Overview:

Tensor factorization is a powerful technique in signal processing that allows for the efficient representation and analysis of high-dimensional data. In this thesis, we will explore the application of tensor factorization in signal processing and investigate its potential benefits and limitations. We will begin by providing a comprehensive overview of tensor factorization and its applications in signal processing. This will be followed by a review of existing methods and techniques in the literature. We will then present our research methodology, including data collection, preprocessing, and evaluation metrics. Our findings will be discussed in detail, including the analysis of results and interpretation of factors. We will compare our results with existing methods and discuss the implications for signal processing. Finally, we will provide a summary of our findings, contributions to the field, future research directions, and a concluding remark on the use of tensor factorization in signal processing.

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