Abstract: A method performed by a node for predicting a behavior of users of a communications network is described. The node manages an artificial neural network. The node merges a first pre-existing predictive model of the behavior in a first group of users with a second model of the behavior in a second group of users. The merging comprises establishing connections between the first model and the second model. Each of the connections has a respective weight. The respective weights of the connections are learned by respective connections of neurons in the artificial neural network based on data from a third group of users. The node also obtains a third model for predicting the behavior in the third group of users, based on the merged models and the data from the third group of users.

Abstract: Bulk acoustic wave resonators of two or more different filters can be on a common die. The two filters can be included in a multiplexer, such as a duplexer, or implemented as standalone filters. With bulk acoustic wave resonators of two or more filters on the same die, the filters can be implemented in less physical space compared to implementing the same filters of different die. Related methods, radio frequency systems, radio frequency modules, and wireless communication devices are also disclosed.

Abstract: Bulk acoustic wave resonators of two or more different filters can be on a common die. The two filters can be included in a multiplexer, such as a duplexer, or implemented as standalone filters. With bulk acoustic wave resonators of two or more filters on the same die, the filters can be implemented in less physical space compared to implementing the same filters of different die. Related methods, radio frequency systems, radio frequency modules, and wireless communication devices are also disclosed.

Abstract: Bulk acoustic wave resonators of two or more different filters can be on a common die. The two filters can be included in a multiplexer, such as a duplexer, or implemented as standalone filters. With bulk acoustic wave resonators of two or more filters on the same die, the filters can be implemented in less physical space compared to implementing the same filters of different die. Related methods, radio frequency systems, radio frequency modules, and wireless communication devices are also disclosed.

Abstract: Bulk acoustic wave resonators of two or more different filters can be on a common die. The two filters can be included in a multiplexer, such as a duplexer, or implemented as standalone filters. With bulk acoustic wave resonators of two or more filters on the same die, the filters can be implemented in less physical space compared to implementing the same filters of different die. Related methods, radio frequency systems, radio frequency modules, and wireless communication devices are also disclosed.

Abstract: Bulk acoustic wave resonators of two or more different filters can be on a common die. The two filters can be included in a multiplexer, such as a duplexer, or implemented as standalone filters. With bulk acoustic wave resonators of two or more filters on the same die, the filters can be implemented in less physical space compared to implementing the same filters of different die. Related methods, radio frequency systems, radio frequency modules, and wireless communication devices are also disclosed.

Abstract: Bulk acoustic wave resonators of two or more different filters can be on a common die. The two filters can be included in a multiplexer, such as a duplexer, or implemented as standalone filters. With bulk acoustic wave resonators of two or more filters on the same die, the filters can be implemented in less physical space compared to implementing the same filters of different die. Related methods, radio frequency systems, radio frequency modules, and wireless communication devices are also disclosed.

Abstract: A system and method for removing personally identifiable information (PII) from medical data and ensuring strict compliance with privacy laws and regulations. The PII removal tool removes, deletes or redacts sensitive personally identifiable information, which may comprise any health care data that may be linked to any individual. The system and method is preferably applied to medical data, images, video, or other electronic media captured from medical procedures, endoscopes, or arthroscopic surgery. Artificial intelligence powered software and models are applied to the medical data for the detection of PII comprising patient names, medical history, faces, account numbers, digital identities, biometric data, or other sensitive personal information which may be linked to an individual. Medical data is re-written without PII and the system generates metadata describing the deleted PII.