RF, Communications and Intelligent Systems

RF, Communications and Intelligent Systems

The RF, Communications and Intelligent Systems (RFCIS) group provides robust wireless communications and networking technologies and strategies, signal signature learning systems, and RF-based measurement systems in support of ORNL's energy, environment, and national security programs.

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Publications

Modeling inter-signal arrival times for accurate detection of CAN bus signal injection attacks

Modern vehicles rely on hundreds of on-board electronic control units (ECUs) communicating over in-vehicle networks. As external interfaces to the car control networks (such as the on-board...

Drebrin-mediated microtubule–actomyosin coupling steers cerebellar granule neuron nucleokinesis and migration pathway selection

Neuronal migration from a germinal zone to a final laminar position is essential for the morphogenesis of neuronal circuits. While it is hypothesized that microtubule–actomyosin crosstalk is required...

Improved Synthetic Aperture Focusing Technique Results of Thick Concrete Specimens through Frequency Banding

A multitude of concrete-based structures are typically part of a light water reactor (LWR) plant to provide the foundation, support, shielding, and containment functions. This use has made its long-...

Research

Research in the RFCIS group is focused on developing new technologies, methods, and systems for RF-related applications. Areas of focus include robust and low-power communications, analysis of RF interference and spectrum usage, signature discovery and detection, and intelligent signal processing using emerging machine learning methods. Current RFCIS research is supporting the security and efficiency of the nation’s energy grid through EM interference analysis, wireless sensor deployment, and low-power wireless communications for solar-powered building sensors. Our group supports ongoing research at ORNL’s Vehicle Security Center by conducting robust measurements of vehicle communications and RF emanations using software-defined radios. We are developing new methods for detection of RF communications and signatures for national security applications through advances in time-frequency signal processing techniques, machine learning-based signature discovery, spectral shape and harmonic analysis, and protocol identification.