Abstract: The present disclosure relates generally to medical devices and, more particularly, to a gas mixing system for a medical ventilator. A gas mixer is provided to adjust the oxygen concentration of environmental air for a blower-based ventilator, by adjusting the mix of air upstream of the ventilator and providing the mixed air to the ventilator's environmental air inlet.

Abstract: The present disclosure relates generally to medical devices and, more particularly, to a gas mixing system for a medical ventilator. A gas mixer is provided to adjust the oxygen concentration of environmental air for a blower-based ventilator, by adjusting the mix of air upstream of the ventilator and providing the mixed air to the ventilator's environmental air inlet.

Abstract: The present disclosure relates generally to medical devices and, more particularly, to a gas mixing system for a medical ventilator. A gas mixer is provided to adjust the oxygen concentration of environmental air for a blower-based ventilator, by adjusting the mix of air upstream of the ventilator and providing the mixed air to the ventilator's environmental air inlet.

Abstract: The disclosure is directed to systems and methods for collision avoidance of aerial vehicles. More particularly, the disclosure is directed to systems and methods for avoiding collisions between manned aerial vehicles and unmanned aerial vehicles. The unmanned aerial vehicle includes a low power RF beacon which transmits signals over a predefined frequency monitored by manned aerial vehicles.

Abstract: The disclosure is directed to systems and methods for collision avoidance of aerial vehicles. More particularly, the disclosure is directed to systems and methods for avoiding collisions between manned aerial vehicles and unmanned aerial vehicles. The unmanned aerial vehicle includes a low power RF beacon which transmits signals over a predefined frequency monitored by manned aerial vehicles.

Abstract: A target recognition system and method wherein only target amplitude data, i.e., coherent A-scan data, is interrogated at each of a plurality of range resolution cells along the radar line of sight path for target recognition. Target aspect angle is ignored within the angular segmentation of the feature library without degrading classification performance. Observed signature characteristics are collected at various aspect angles and through unknown roll, pitch and yaw motions of each anticipated target and provided to a neural network as training sets. The neural network forms feature vectors for each target class which are useful for valid classification comparisons in all sea states, especially in calm and littoral waters. These feature vectors are useful for valid classification comparisons over at least 30 degrees of target aspect angle.

Abstract: A target recognition system and method wherein only target amplitude data, i.e., coherent A-scan data, is interrogated for target recognition. Target aspect angle is ignored within the angular segmentation of the feature library without degrading classification performance. Observed signature characteristics are collected at various aspect angles and through unknown and arbitrary roll, pitch and yaw motions of each anticipated target and provided to a neural network as training sets. The neural network forms feature vectors for each target class which are useful for valid classification comparisons in all sea states, especially in calm and littoral waters. These feature vectors are useful for valid classification comparisons over at least 30 degrees of target aspect angle.

Abstract: A target recognition system and method wherein only target amplitude data, i.e., coherent A-scan data, is interrogated for target recognition. Target aspect angle is ignored within the angular segmentation of the feature library without degrading classification performance. Observed signature characteristics are collected at various aspect angles and through unknown and arbitrary roll, pitch and yaw motions of each anticipated target and provided to a neural network as training sets. The neural network forms feature vectors for each target class which are useful for valid classification comparisons in all sea states, especially in calm and littoral waters. These feature vectors are useful for valid classification comparisons over at least 30 degrees of target aspect angle.