SynthGen: Synthetic Data Generation

The requirement of incredibly vast amounts of data for Machine Learning (ML) models to learn and make accurate predictions poses a significant challenge. Current datasets fall short in representing the US Army's needs; for example, Project Maven faces challenges in scaling the process of data labeling and cleaning across vast datasets and ensuring accuracy despite the potential for human error and biases. Additionally, the complexity of military surveillance data, with its varied and dynamic environments, adds further difficulty to maintaining consistent and high-quality datasets.To fill this gap, PEO IEW&S has encouraged innovation in the field of synthetic data generation (Topic A244-P037: Subtopic 1). The generation of synthetic data is crucial for enhancing datasets, improving ML model training, and addressing data scarcity. Synthetic data helps accelerate the development and deployment of advanced AI/ML systems. By creating diverse, scalable datasets, the Army can train AI models that better simulate real-world scenarios.The proposed research builds on the theoretical foundations of Fourier analysis, Circular Local Directional Patterns (CLDP), and geometric transformations, extending their application to synthetic data generation. By leveraging these advanced techniques, the research aims to develop robust algorithms that can generate high-quality synthetic data, enhancing the training of ML models.