VibraWave: Sensing the Pulse of Polluted Waters

TL;DR

VibraWave introduces a non-invasive radar-based sensing framework that uses acoustic excitation, tensor decomposition, and deep learning to detect and quantify various water pollutants in real-time.

Contribution

The paper presents VibraWave, a novel approach combining mmWave radar, acoustic excitation, tensor analysis, and neural networks for pollutant detection, offering real-time, portable water quality monitoring.

Findings

High accuracy in pollutant classification and quantification

Robust performance across different water pollutant mixtures

Computational efficiency suitable for real-time deployment

Abstract

Conventional methods for water pollutant detection, such as chemical assays and optical spectroscopy, are often invasive, expensive, and unsuitable for real-time, portable monitoring. In this paper, we introduce VibraWave, a novel non-invasive sensing framework that combines mmWave radar with controlled acoustic excitation, tensor decomposition, and deep learning to detect and quantify a wide range of water pollutants. By capturing radar reflections as a three-dimensional tensor encoding phase dynamics, range bin power, and angle-of-arrival (AoA), we apply PARAFAC decomposition with non-negative constraints to extract compact, interpretable pollutant fingerprints. These are used to train a lightweight student neural network via knowledge distillation, enabling joint classification and quantification of heavy metals (Cu, Fe, Mg), oil emulsions, and sediments. Extensive experiments show that VibraWave achieves high accuracy and low RMSE across pure, binary, and tertiary mixtures, while remaining robust and computationally efficient, making it well-suited for scalable, real-time water quality monitoring.