Abstract: An acoustic monitoring system characterizes, classifies, and geo-locates anthropogenic and natural sounds in near real time. The system includes a compact array of three acoustic vector sensors, which measures acoustic pressure and the three-dimensional particle velocity vector associated with the propagation of an acoustic wave, thereby inherently providing bearing information to an underwater source of sound. Beamforming techniques provide sound source localization, allowing for characterization of the acoustic signature of specific underwater acoustic sources.

Abstract: An acoustic monitoring system characterizes, classifies, and geo-locates anthropogenic and natural sounds in near real time. The system includes a compact array of three acoustic vector sensors, which measures acoustic pressure and the three-dimensional particle velocity vector associated with the propagation of an acoustic wave, thereby inherently providing bearing information to an underwater source of sound. Beamforming techniques provide sound source localization, allowing for characterization of the acoustic signature of specific underwater acoustic sources.

Abstract: The invention is directed towards a method and system for optically-based monitoring of chemicals of potential concern (COPCs), including mercury (Hg), methyl mercury (MeHg), and polychlorinated biphenyls (PCBs). It employs a multi-parameter statistical analysis model, such as partial least square regression (PLS regression), to identify combinations of predictors (e.g., optical, water quality, and physical properties) that have large covariance with the response values. The predictive model is validated against analytic samples from laboratories. The steps in the method are repeated until predictors, or parameters are selected that avoid over-fitting and which yield physically-based results.

Abstract: The invention is directed towards a method and system for optically-based monitoring of chemicals of potential concern (COPCs), including mercury (Hg), methyl mercury (MeHg), and polychlorinated biphenyls (PCBs). It employs a multi-parameter statistical analysis model, such as partial least square regression (PLS regression), to identify combinations of predictors (e.g., optical, water quality, and physical properties) that have large covariance with the response values. The predictive model is validated against analytic samples from laboratories. The steps in the method are repeated until predictors, or parameters are selected that avoid over-fitting and which yield physically-based results.