Abstract: A solution for detecting and alerting to intrusions or obstacles in an area. One or more sensing devices can be used in combination with computer processing capabilities to monitor an area for intrusions or obstacles. An illustrative area is part of a railyard or railroad track, which can be monitored for intrusions by foot, vehicle, or even by inanimate objects such as falling rocks from a track cut. The sensing, computing, and/or alerting systems can be redundant to ensure operation at all times.

Abstract: A solution for inspecting one or more objects of an apparatus. An inspection component obtains an inspection outcome for an object of the apparatus based on image data of the apparatus. The inspection component can use a deep learning engine to analyze the image data and identify object image data corresponding to a region of interest for the object. A set of reference equipment images can be compared to the identified object image data to determine the inspection outcome for the object. The inspection component can further receive data regarding the apparatus, which can be used to determine a general location of the object on the apparatus and therefore a general location of the region of interest for the object in the image data. The inspection component can be configured to provide image data in order to obtain feedback from a human and/or further train the deep learning engine.

Abstract: A method and system of congestion control in a network are provided. A required quality of service (QoS) parameter, such as a maximum allowable latency, for a packet received at a queue in the network, and a route cost metric, such as accumulated and estimated latency, are determined, and the packet is either discarded if the route cost metric exceeds the required QoS parameter, or a discard bias value is set for the packet. Also, if the required QoS parameter exceeds the route cost metric, the method includes determining whether a congestion condition exists in the network, and if the congestion condition exists, biasing the packet for discard based on its latency if the route cost metric for the packet exceeds a threshold. The network may be an IP network, and a network such as a mobile ad hoc network (MANET).