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Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.
The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).
Electrochemistry synthesis and characterization testing typically occurs manually at a research facility.
Electrical utility substations are wired with intelligent electronic devices (IEDs), such as protective relays, power meters, and communication switches.
This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.