Patents by Inventor Renato M.M. Medeiros

Renato M.M. Medeiros has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Heteroscedastic data compression using ARIMA-GARCH model estimation
    Patent number: 9858311
    Abstract: Methods and apparatus are provided for compression and decompression of heteroscedastic data, such as seismic data, using Autoregressive Integrated Moving Average (ARIMA)-Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model estimation. Heteroscedastic data is compressed by obtaining the heteroscedastic data; applying the heteroscedastic data to an ARIMA-GARCH model; determining residuals between the obtained heteroscedastic data and the ARIMA-GARCH model; and compressing parameters of the ARIMA-GARCH model and the residuals using entropy encoding, such as an arithmetic encoding, to generate compressed residual data. Parameters of the ARIMA-GARCH model are adapted to fit the obtained heteroscedastic data. The compressed residual data is decompressed by performing an entropy decoding and obtaining the parameters of the ARIMA-GARCH model and the residuals. The ARIMA-GARCH model predicts heteroscedastic data values and then the decompressed residuals are added.
    Type: Grant
    Filed: March 31, 2014
    Date of Patent: January 2, 2018
    Assignee: EMC IP Holding Company LLC
    Inventors: Alex L. Bordignon, Angelo E. M. Ciarlini, Timothy A. Voyt, Silvana Rossetto, Renato M. M. Medeiros
  • Heteroscedastic Data Compression Using Arima-Garch Model Estimation
    Publication number: 20150278284
    Abstract: Methods and apparatus are provided for compression and decompression of heteroscedastic data, such as seismic data, using Autoregressive Integrated Moving Average (ARIMA)-Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model estimation. Heteroscedastic data is compressed by obtaining the heteroscedastic data; applying the heteroscedastic data to an ARIMA-GARCH model; determining residuals between the obtained heteroscedastic data and the ARIMA-GARCH model; and compressing parameters of the ARIMA-GARCH model and the residuals using entropy encoding, such as an arithmetic encoding, to generate compressed residual data. Parameters of the ARIMA-GARCH model are adapted to fit the obtained heteroscedastic data. The compressed residual data is decompressed by performing an entropy decoding and obtaining the parameters of the ARIMA-GARCH model and the residuals. The ARIMA-GARCH model predicts heteroscedastic data values and then the decompressed residuals are added.
    Type: Application
    Filed: March 31, 2014
    Publication date: October 1, 2015
    Applicant: EMC Corporation
    Inventors: Alex L. Bordignon, Angelo E.M. Ciarlini, Timothy A. Voyt, Silvana Rossetto, Renato M.M. Medeiros