This paper presents lossless compression schemes for ECG signals based on neural
network predictors and entropy encoders. Decorrelation is achieved by nonlinear prediction …

A method for compressing seismic data to reduce data storage and transmission
requirements applies wavelet transforms to digitized trace sequential data obtained from …

In a geophysical exploration survey, thousands of geophones are deployed where each
geophone must transmit hundreds of recording over a narrow band channel to a fusion …

The intrinsic low‐dimensional structure, which is implicit in the large‐scale data sets of
structural seismic and typhoon responses, is exploited for efficient data compression. Such a …

Introduction of processor based instruments in power systems is resulting in the rapid growth
of the measured data volume. The present practice in most of the utilities is to store only …

We present a lightweight lossless compression algorithm for realtime sensor networks. Our
proposed adaptive linear filtering compression (ALFC) algorithm performs predictive …

For some classes of signals, particularly those dominated by low frequency components,
such as seismic data first and higher order differences between adjacent signal samples are …

Seismic data compression is highly demanded to reduce the cost for transmission and
storage due to an enormous volume of collected data. This paper presents a prediction …

This paper presents a comparison of adaptive linear predictors as applied to the area of
lossless compression of seismic waveform data. Three methods are explored: the normalize …

In recent years, deep Recurrent Neural Networks (RNNs) have been successfully applied to
the prediction and processing of time series data. Since there exists significant temporal …