Transferring Learned Behaviors between Similar and Different Radios.

Transfer learning (TL) techniques have proven useful in a wide variety of applications traditionally dominated by machine learning (ML), such as natural language processing, computer vision, and computer-aided design. Recent extrapolations of TL to the radio frequency (RF) domain are being used to increase the potential applicability of RFML algorithms, seeking to improve the portability of models for spectrum situational awareness and transmission source identification. Unlike most of the computer vision and natural language processing applications of TL, applications within the RF modality must contend with inherent hardware distortions and channel condition variations.

Adolescent's explicit and implicit cigarette cognitions predict experimentation with both cigarettes and e-cigarettes.

Past year, month, and lifetime adolescent e-cigarette use rates remain persistently high, despite falling cigarette use rates. Previous investigations have noted a strong relationship between an individual's positive and negative cognitions related to a behavior, and subsequent initiation of that behavior. This investigation was conducted to determine the impact positive and negative explicit and implicit cigarette-related cognitions may have on the use of cigarettes and e-cigarettes among at-risk, cigarette-naive adolescents.

Low-Latency Wireless Network Extension for Industrial Internet of Things.

The timely delivery of critical messages in real-time environments is an increasing requirement for industrial Internet of Things (IIoT) networks. Similar to wired time-sensitive networking (TSN) techniques, which bifurcate traffic flows based on priority, the proposed wireless method aims to ensure that critical traffic arrives rapidly across multiple hops to enable numerous IIoT use cases. IIoT architectures are migrating toward wirelessly connected edges, creating a desire to extend TSN-like functionality to a wireless format.