A method for multi-source communication for triple-modular redundancy (TMR), a method for branched communication, and a method for virtual buses are disclosed. A method includes a) transmitting by at least two different source nodes in each case at least two identical messages which contain at least flow control data, payload data and check data to at least one predetermined receive node where the messages reach the receive node together at a predetermined time, b) combining by the receive node the messages received by the receive node into a combined message containing flow control data, payload data and check data, or comparing by the receive node of messages received by the receive node, and c) further processing of the combined message by the receive node or further processing of at least one of the messages received by the receive node based on comparison from step b).

The present disclosure relates to a fiber optic network including a plurality of fiber distribution components daisy chained together to form a chain of fiber distribution components, the chain of fiber distribution components having a first set of optical fiber paths that are indexed along a length of the chain and a second set of optical fiber paths that are not indexed along a length of the chain.

The present disclosure relates to a fiber optic network including a plurality of fiber distribution components daisy chained together to form a chain of fiber distribution components, the chain of fiber distribution components having a first set of optical fiber paths that are indexed along a length of the chain and a second set of optical fiber paths that are not indexed along a length of the chain.

Embodiments of the present invention provide a transmission apparatus, a connection device, and a method, where the apparatus includes N Ethernet Medium Access Control MAC ports, where each Ethernet MAC port is corresponding to a first MII interface, K Ethernet physical layer interfaces, where each Ethernet physical layer interface is corresponding to a second MII interface, and a connection device, where both N and K are positive integers; where the connection device is configured to control a time-division interconnect bus in the connection device or a time-division space-division switching matrix in the connection device to implement a connection between a timeslot of the first MII interface and a timeslot of the second MII interface, where the N Ethernet MAC ports and the K Ethernet physical layer interfaces are separately connected to the connection device by using the first MII interfaces and the second MII interfaces.

Embodiments of the present invention provide a transmission apparatus, a connection device, and a method, where the apparatus includes N Ethernet Medium Access Control MAC ports, where each Ethernet MAC port is corresponding to a first MII interface, K Ethernet physical layer interfaces, where each Ethernet physical layer interface is corresponding to a second MII interface, and a connection device, where both N and K are positive integers; where the connection device is configured to control a time-division interconnect bus in the connection device or a time-division space-division switching matrix in the connection device to implement a connection between a timeslot of the first MII interface and a timeslot of the second MII interface, where the N Ethernet MAC ports and the K Ethernet physical layer interfaces are separately connected to the connection device by using the first MII interfaces and the second MII interfaces.

Systems and methods for using machine-learning techniques for labeling incoming calls with categories relating to a risk level. A model is generated using call log data. The call log data is augmented using information from additional data sources to generate features for the model. The model may then be used to categorize additional incoming calls. The model may be used in real-time to categorize incoming calls, or categorization results may be stored for a plurality of calling numbers. Various embodiments provide various technical advantages by virtue of how the components of the system are deployed between an endpoint communication device, a telephony provider system, and possibly other systems.

Systems and methods for using machine-learning techniques for labeling incoming calls with categories relating to a risk level. A model is generated using call log data. The call log data is augmented using information from additional data sources to generate features for the model. The model may then be used to categorize additional incoming calls. The model may be used in real-time to categorize incoming calls, or categorization results may be stored for a plurality of calling numbers. Various embodiments provide various technical advantages by virtue of how the components of the system are deployed between an endpoint communication device, a telephony provider system, and possibly other systems.

The present disclosure relates to a fiber optic network including a plurality of fiber distribution components daisy chained together to form a chain of fiber distribution components, the chain of fiber distribution components having a first set of optical fiber paths that are indexed along a length of the chain and a second set of optical fiber paths that are not indexed along a length of the chain.

The present disclosure relates to a fiber optic network including a plurality of fiber distribution components daisy chained together to form a chain of fiber distribution components, the chain of fiber distribution components having a first set of optical fiber paths that are indexed along a length of the chain and a second set of optical fiber paths that are not indexed along a length of the chain.

A dual band radio frequency signal acquisition and source location system, provided with a steerable phased array antenna operable in a first and a second radio frequency band. A digital signal processor electrically connected to the steerable phased array antenna is configured to control steering of an antenna beam of the steerable phased array antenna and apply frequency time division multiplexing to radio frequency signaling in the first and the second radio frequency bands. In particular, the first frequency band may be 2.4 GHz Bluetooth/Bluetooth Low Energy, and the second frequency band may be 900 MHz passive UHF RFID.