DEQUANTIZING LOW-RESOLUTION IOT SIGNALS TO PRODUCE HIGH-ACCURACY PROGNOSTIC INDICATORS

摘要

The disclosed embodiments relate to a system that removes quantization effects from a set of time-series signals to produce highly accurate approximations of a set of original unquantized signals. During operation, for each time-series signal in the set of time-series signals, the system determines a number of quantization levels (N_QL) in the time-series signal. Next, the system performs a fast Fourier transform (FFT) on the time-series signal to produce a set of Fourier modes for the time-series signal. The system then determines an optimal number of Fourier modes (N_mode) to reconstruct the time-series signal based on the determined N_QL for the time-series signal. Next, the system selects N_mode largest-amplitude Fourier modes from the set of Fourier modes for the time-series signal. The system then performs an inverse FFT operation using the N_mode largest-amplitude Fourier modes to produce a dequantized time-series signal to be used in place of the time-series signal.