Signal Analysis & AI Detection Platform

Advanced Web Platform for Multi-Domain Signal Processing & Drone Acoustic Detection

Description:

A comprehensive web-based ecosystem designed for the visualization and intelligent analysis of complex signals, including Medical (ECG/EEG), Acoustic, and Radiofrequency (RF) data. The platform features a specialized AI-driven Drone Detection System that identifies and classifies drone signatures from environmental noise using acoustic footprints.

Key Features & Contributions:

Medical Intelligence: Implemented AI models for real-time abnormality detection in multi-channel medical data (ECG/EEG).

Acoustic Surveillance: Developed an AI classification engine to detect and localize Drone Sounds amid high-background noise.

Advanced DSP Simulations: Created interactive tools for simulating the Doppler Effect, under-sampling/aliasing, and anti-alias recovery specifically for human voice signals.

Dynamic Visualization: Engineered four sophisticated viewing modes: Continuous, XOR, Polar, and Recurrence Plots for deep signal inspection.

Real-time Control: Built interactive sampling frequency sliders to demonstrate Nyquist-Shannon theorem constraints in real-time.

Technical Stack:

Backend & Signal Logic: Python, NumPy, SciPy.

AI/ML Frameworks: TensorFlow / PyTorch (for Signal Classification & Parameter Estimation).

Frontend & Visualization: Dash, Plotly (for high-frequency data rendering).

Signal Processing: Fast Fourier Transform (FFT), Spectrogram analysis, and Wavelet transforms.