Abstract: Starting a spanning tree protocol (STP) process in a network device excludes performing a root bridge election sequence. The network device receives and learns the STP roles and STP states of its ports based on the received bridge protocol data units (BPDUs) during a quiescent period of time subsequent to starting STP processing. Port role and port state learning occur absent the generation and transmission of BPDUs during the quiescent period of time. Subsequent to the end of the quiescent period, the network device commences generating and transmitting BPDUs.

Abstract: This disclosure relates generally to a micro-activity identification associated with a task. Industrial operations involving complex processes are difficult to monitor due to multifaceted number of micro-activities within it. Surveillance of such complex processes is important as in the real environments there is no control over the working style of workers executing the task and the sequence of assembly process. To effectively monitor the task comprising plurality of micro-activity, the artificial intelligence (AI) based model is presented, which effectively monitors the micro-activity within and generates the quality score for the task under surveillance. The quality score is derived by assigning individual scores to the micro-activity performed correctly and by assigning penalty upon wrong performance of the micro-activity.

Abstract: Starting a spanning tree protocol (STP) process in a network device excludes performing a root bridge election sequence. The network device receives and learns the STP roles and STP states of its ports based on the received bridge protocol data units (BPDUs) during a quiescent period of time subsequent to starting STP processing. Port role and port state learning occur absent the generation and transmission of BPDUs during the quiescent period of time. Subsequent to the end of the quiescent period, the network device commences generating and transmitting BPDUs.

Abstract: Systems, methods and computer software are disclosed for providing a multi-Het Net Gateway (HNG) distributed Self Organizing Network (dSON) for a wireless network. The dSON ascertains information from at least one of User Equipment (UE) reports and neighbor detection for a mobile EnodeB attached to an HNG. A configuration is adjusted for the mobile eNodeB based on location and a state of neighbors obtained from the UE report and neighbor detection. The mobile eNodeB configuration includes at least one of Physical Cell Identification (PCI) allocation, Tracking Area Code (TAC) allocation, reference Transmit (Tx) power management, and Automatic Neighbor Relations (ANR) table.

Abstract: Systems, methods and computer software are disclosed for providing a multi-Het Net Gateway (HNG) distributed Self Organizing Network (dSON) for a wireless network. The dSON ascertains information from at least one of User Equipment (UE) reports and neighbor detection for a mobile EnodeB attached to an HNG. A configuration is adjusted for the mobile eNodeB based on location and a state of neighbors obtained from the UE report and neighbor detection. The mobile eNodeB configuration includes at least one of Physical Cell Identification (PCI) allocation, Tracking Area Code (TAC) allocation, reference Transmit (Tx) power management, and Automatic Neighbor Relations (ANR) table.