A Step Towards Agent Agnostic Detection of Biological Hazards

This SBIR Phase I project proposes development of deep learning-based algorithms that can identify biological and chemical threats at a structural level from portal-based spectral measurements with high specificity and sensitivity. The proposed solution leverages recent advancements in the areas of chemical fingerprinting, latent representation learning, chemical structure prediction, classification of chemical functional group, and spectral deconvolution to handle mixtures. The Phase I effort will focus on implementing the proposed algorithms and demonstrating their effectiveness, and technical and operational feasibility for instantaneous identification of biological hazards on portable devices. The proposed effort will build upon Novateur teams in the areas of deep learning networks for threat detection, sensor fusion, biological and chemical threat detection, and optimization and development of deep learning algorithms for SWaP constrained mobile platforms.