Abstract: The present disclosure relates to an automated method, computer program, and system for estimating the average fish weight in an aquaculture environment. An underwater camera in the aquaculture environment captures video frames from the environment. These frames are inputted into a machine learning detection model that has been trained to detect fish. The machine-learning vision detection model identifies a virtual box-region around detected fish and outputs size and location for the virtual-box regions, as well pixel data related to the virtual-box region and the frame as a whole. This size, location, and pixel data is processed and then inputted into an AI-based length estimation model, which computes a fish length for each virtual-box region inputted into the model. The system then calculates the average fish weight from the fish length using an analytical model.

Abstract: An access point (AP) device for controlling spectrum usage of a hierarchical communication system, in which a spectrum reserved for an Incumbent is usable by at least one user equipment (UE) for transmission when the spectrum is not required by the Incumbent, is disclosed. The AP device includes a processor configured to receive a message from the Incumbent requesting vacating of a spectrum; generate a group of users affected by the message from the Incumbent requesting vacating of the spectrum; and perform a spectrum management operation on the group of users.

Abstract: An access point (AP) device for controlling spectrum usage of a hierarchical communication system, in which a spectrum reserved for an Incumbent is usable by at least one user equipment (UE) for transmission when the spectrum is not required by the Incumbent, is disclosed. The AP device includes a processor configured to receive a message from the Incumbent requesting vacating of a spectrum; generate a group of users affected by the message from the Incumbent requesting vacating of the spectrum; and perform a spectrum management operation on the group of users.

Abstract: The disclosure relates to an access point (AP) device for controlling spectrum usage of a hierarchical communication system, in which a spectrum reserved for an Incumbent is usable by at least one user equipment (UE) for transmission when the spectrum is not required by the Incumbent, the AP device comprising a processor configured to: enforce the at least one UE into a mode protecting use of the spectrum by the Incumbent based on a spectrum request indication from the Incumbent; and enable transmission of the at least one UE using the spectrum reserved for the Incumbent based on a spectrum availability indication from the Incumbent.

Abstract: The present disclosure relates to a system, method, and computer program for predicting migraines using machine learning. More specifically, the system predicts the likelihood that a user will have a migraine within a certain period of time (e.g., a month) based on migraine causes/triggers X1 . . . n identified from a migraine pattern built using deep neural networks. The system collects data from the user with respect to his/her profile, his/her migraine incidents (having experienced a migraine or not) and his/her daily habits. Based on these data, it creates the user's migraine pattern and combines this pattern with the migraine patterns from similar users (when necessary). As a result, it is able to predict the probability of getting a migraine within a period of time.

Abstract: The disclosure relates to an access point (AP) device for controlling spectrum usage of a hierarchical communication system, in which a spectrum reserved for an Incumbent is usable by at least one user equipment (UE) for transmission when the spectrum is not required by the Incumbent, the AP device comprising a processor configured to: enforce the at least one UE into a mode protecting use of the spectrum by the Incumbent based on a spectrum request indication from the Incumbent; and enable transmission of the at least one UE using the spectrum reserved for the Incumbent based on a spectrum availability indication from the Incumbent.

Abstract: The present disclosure relates to an early warning and recommendation system for proactive management of a wireless broadband network. Without human intervention, the system processes highly heterogeneous network and non-network data and applies unsupervised machine learning to the data to predict and understand the situations that lead to different network state conditions. More specifically, unsupervised clustering is applied to the data to understand “situations” that can lead to non-normal network state conditions. A deep neural network model of situations is then created to further understand the underlying data relationships between the elements of a situation and network states. The deep neural network model and Reinforcement Learning is used to provide recommendations as to changes in network configuration parameters to improve the state of a predicted situation associated with non-normal network conditions.

Abstract: The present disclosure relates to a system, method, and computer program for predicting migraines using machine learning. More specifically, the system predicts the likelihood that a user will have a migraine within a certain period of time (e.g., a month) based on migraine causes/triggers X1 . . . n identified from a migraine pattern built using deep neural networks. The system collects data from the user with respect to his/her profile, his/her migraine incidents (having experienced a migraine or not) and his/her daily habits. Based on these data, it creates the user's migraine pattern and combines this pattern with the migraine patterns from similar users (when necessary). As a result, it is able to predict the probability of getting a migraine within a period of time.

Abstract: The present disclosure relates to an early warning and recommendation system for proactive management of a wireless broadband network. Without human intervention, the system processes highly heterogeneous network and non-network data and applies unsupervised machine learning to the data to predict and understand the situations that lead to different network state conditions. More specifically, unsupervised clustering is applied to the data to understand “situations” that can lead to non-normal network state conditions. A deep neural network model of situations is then created to further understand the underlying data relationships between the elements of a situation and network states. The deep neural network model and Reinforcement Learning is used to provide recommendations as to changes in wireless/mobile broadband network configuration parameters that will improve the state of a predicted situation associated with non-normal network conditions.

Abstract: A communication controller is described comprising a determiner configured to determine, for each communication connection of a set of communication connections between a communication device and another communication device, a characteristic of the communication connection, a selector configured to select a plurality of the communication connections based on the characteristics of the communication connections and a controller configured to control a transmission of data between the communication device and the other communication device to use the selected plurality of communication connections.

Abstract: In embodiments, apparatuses, methods, and storage media may be described for presenting, by a user equipment (UE), a parameter of a network policy to a user, and receiving an indication of the user preference related to that parameter of the network policy. In response to receiving the indication, the UE may select one or more radio links with which the UE should communicatively couple based on the user preference and the network policy. Other embodiments may be described and/or claimed.

Abstract: In embodiments, apparatuses, methods, and storage media may be described for presenting, by a user equipment (UE), a parameter of a network policy to a user, and receiving an indication of the user preference related to that parameter of the network policy. In response to receiving the indication, the UE may select one or more radio links with which the UE should communicatively couple based on the user preference and the network policy. Other embodiments may be described and/or claimed.

Abstract: A communication controller is described comprising a determiner configured to determine, for each communication connection of a set of communication connections between a communication device and another communication device, a characteristic of the communication connection, a selector configured to select a plurality of the communication connections based on the characteristics of the communication connections and a controller configured to control a transmission of data between the communication device and the other communication device to use the selected plurality of communication connections.