SITUATION
AI/ML-Based Signal Processing
Traditional signal-processing methods can become limited when signals contain complex nonlinear patterns, noise and large feature spaces.
WHAT I DID
From Raw Signal to Insight
Applied AI/ML-based approaches to engineering signal-processing problems, combining domain-specific preprocessing, feature analysis and data-driven modelling to improve analysis and interpretation.
Step 01
Domain-Specific Preprocessing
Signals containing noise and complex nonlinear patterns are conditioned using preprocessing tailored to the engineering domain.
Step 02
Feature Analysis
Large feature spaces are analysed to identify the characteristics most relevant to the signal-processing problem.
Step 03
Data-Driven Modelling
AI/ML models are applied to improve analysis and interpretation where traditional methods become limited.
Step 04
Complementing Conventional Methods
The work demonstrated how AI/ML can complement conventional signal-processing methods and provided a structured foundation for research, teaching and technical-content development in intelligent signal processing.
RESULT
Impact and Findings
The work demonstrated how AI/ML can complement conventional signal-processing methods and provided a structured foundation for research, teaching and technical-content development in intelligent signal processing.