We are developing a Structure Health Assessment and Warning System (SHAWS) based on building displacement
measurements and wireless communication. SHAWS will measure and predict the stability/instability of a building,
determine whether it is safe for emergency responders to enter during an emergency, and provide individual warnings on
the condition of the structure. SHAWS incorporates remote sensing nodes (RSNs) installed on the exterior frame of a
building. Each RSN includes a temperature sensor, a three-axis accelerometer making static-acceleration measurements,
and a ZigBee wireless system (IEEE 802.15.4). The RSNs will be deployed remotely using an air cannon delivery
system, with each RSN having an innovative adhesive structure for fast (<10 min) and strong installation under
emergency conditions. Once the building has moved past a threshold (~0.25 in./building story), a warning will be issued
to emergency responders. In addition to the RSNs, SHAWS will include a base station located on an emergency
responder's primary vehicle, a PDA for mobile data display to guide responders, and individual warning modules that
can be worn by each responder. The individual warning modules will include visual and audio indicators with a ZigBee
receiver to provide the proper degree of warning to each responder.
Detection, classification, and localization of potential security breaches in extremely high-noise environments are
important for perimeter protection and threat detection both for homeland security and for military force protection.
Physical Optics Corporation has developed a threat detection system to separate acoustic signatures from unknown,
mixed sources embedded in extremely high-noise environments where signal-to-noise ratios (SNRs) are very low.
Associated neural network structures based on independent component analysis are designed to detect/separate new
acoustic sources and to provide reliability information. The structures are tested through computer simulations for each
critical component, including a spontaneous detection algorithm for potential threat detection without a predefined
knowledge base, a fast target separation algorithm, and nonparametric methodology for quantified confidence measure.
The results show that the method discussed can separate hidden acoustic sources of SNR in 5 dB noisy environments
with an accuracy of 80%.
An unmanned undersea vehicle (UUV) needs an obstacle avoidance capability to make autonomous path planning decisions for successful undersea search and survey, maritime reconnaissance, communication/navigation aids, and tracking and trailing in uncharted shallow water. Physical Optics Corporation (POC) has developed a novel autonomous UUV path optimization navigator system for real-time, robust, self-adjusting, intelligent autonomous obstacle avoidance/navigation of UUVs. The POC system is based on our proprietary fast genetic algorithm, which processes signals from on-board obstacle avoidance sonar sensors to continuously optimize the navigation path while avoiding both moving and stationary obstacles in shallow waters. The system performs autonomous obstacle avoidance, accommodating navigation parameter changes. Vehicle dynamics are also incorporated by hydrodynamic compensation.