• Patent Title: AD HOC MACHINE LEARNING TRAINING THROUGH CONSTRAINTS, PREDICTIVE TRAFFIC LOADING, AND PRIVATE END-TO-END ENCRYPTION
  • Application No.: US18482801
    Application Date: 2023-10-06
  • Publication No.: US20240127059A1
    Publication Date: 2024-04-18
  • Inventor: Keith R. Tinsley
  • Applicant: Tektronix, Inc.
  • Applicant Address: US OR Beaverton
  • Assignee: Tektronix, Inc.
  • Current Assignee: Tektronix, Inc.
  • Current Assignee Address: US OR Beaverton
  • Main IPC: G06N3/08
  • IPC: G06N3/08
AD HOC MACHINE LEARNING TRAINING THROUGH CONSTRAINTS, PREDICTIVE TRAFFIC LOADING, AND PRIVATE END-TO-END ENCRYPTION
Abstract:
A machine learning network has a plurality of test and measurement devices, one or more of the test and measurement devices has one or more communication interfaces configured to allow the device to receive and process physical layer signals, a memory, and one or more processors configured to execute code to cause the one or more processors to receive physical layer data, perform one or more operations on the physical layer data according to a machine learning model to produce changed physical layer data, and transmit the changed physical layer data to at least one other node in the machine learning neural network. The machine learning network may include a learner node.
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