Interference caused by unpredictable body motion is notorious in RF-based ranging and tracking. For passive tags, the strong self-leakage signal and large unwanted direct reflections encountered in conventional RFID transceivers make precision ranging even more difficult. To achieve fast, high accuracy ranging capability against body motion interference, we investigate a second harmonic backscattering solution in the opportunistic method of sequential-test heuristic multi-frequency continuous wave ranging (ST-HMFCW). We show that direct reflection and self-interference are greatly suppressed, so the received second harmonic phase variation is decoupled from the power-dependent phase distortion caused by large interference. ST-HMFCW further seeks opportunities in phase fluctuations caused by interférer motion with drastic performance improvement as bandwidth increases. Robust millimeter-accuracy ranging is achieved under strong body-motion interference with the broadband nonlinear transmission line (NLTL) backscatter tags. We present the theory, simulation and experimental prototypes to verify the effectiveness of the proposed ST-HMFCW ranging method.
The emergence of novel computational hardware is enabling a new paradigm for rapid machine learning model training. For the Department of Energy’s major research facilities, this developing technology will enable a highly adaptive approach to experimental sciences. In this manuscript we present the per-epoch and end-to-end training times for an example of a streaming diagnostic that is planned for the upcoming high-repetition rate x-ray Free Electron Laser, the Linac Coherent Light Source-II. We explore the parameter space of batch size and data parallel training across multiple Graphics Processing Units and Reconfigurable Dataflow Units. We show the landscape of training times with a goal of full model retraining in under 15 min. Although a full from scratch retraining of a model may not be required in all cases, we nevertheless present an example of the application of emerging computational hardware for adapting machine learning models to changing environments in real-time, during streaming data acquisition, at the rates expected for the data fire hoses of accelerator-based user facilities.
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