Abstract: Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.

Abstract: Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.

Abstract: Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.

Abstract: Systems, methods, and computer readable media for communication in a network having a plurality of transceiver units are provided. The method can include transmitting by each transceiver unit (TU), a first broadcast message including a unique identifier of a respective TU and a network timing source identifier indicating the unique identifier of a TU upon which the respective TU relies for network time. The method can include receiving at the plurality of TUs, the first broadcast messages from one or more of the plurality of TUs. The method can include selecting, by each TU, a network timing source from among the plurality of TUs based on the unique identifiers and the network timing source identifiers. The network timing source can serve as the timing source for communications within the network.

Abstract: A system and method for data communication are presented. The method can include generating a set of precoding weights by autonomously selecting a precoding weight for each antenna at the first transceiver. The method can include transmitting a set of selected precoding weights in a request message. The method can include receiving an indication that the second transceiver unit will accept a transmission from the first transceiver unit. In response to the receiving, the method can include precoding data as precoded data using the set of precoding weights and transmitting the precoded data to the second transceiver unit using antennas of the one or more antennas corresponding to the set of precoding weights.