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- W2983266295 abstract "In this letter, we propose a machine learning solution for crowd-size classification in an indoor environment. Narrow-band radio frequency signals are used to identify a pattern according to the number of people. Experimental data collected by a low-cost software-defined radio platform are postprocessed by applying a feature mapping along with the random forest technique for classifying the crowd-size scenarios. The proposed solution has significant accuracy in classification performance." @default.
- W2983266295 created "2019-11-22" @default.
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- W2983266295 date "2019-11-01" @default.
- W2983266295 modified "2023-09-26" @default.
- W2983266295 title "RF-Driven Crowd-Size Classification via Machine Learning" @default.
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- W2983266295 doi "https://doi.org/10.1109/lawp.2019.2932076" @default.
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