Computational Efficiency Improvements for Artificial Neural Networks
US-2020364545-A1 · Nov 19, 2020 · US
US12111199B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-12111199-B2 |
| Application number | US-201917416194-A |
| Country | US |
| Kind code | B2 |
| Filing date | Nov 21, 2019 |
| Priority date | Dec 21, 2018 |
| Publication date | Oct 8, 2024 |
| Grant date | Oct 8, 2024 |
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The present disclosure relates to a method for compressing an evaluation curve, which is recorded during a radar-based fill level measurement of a filling material located in a container, and to a corresponding fill level measurement device for carrying out the method. Corresponding to the compression method according to the present disclosure, the present disclosure comprises a corresponding method for decompressing the compressed evaluation curve. The compression method is characterized in that the compression occurs using linear prediction, by corresponding estimation coefficients and an error curve being created. This makes use of the finding according to the present disclosure that evaluation curves can be compressed for diagnostic purposes, in particular in the case of FMCW-based fill level measurement, efficiently and without data loss using the model of linear prediction.
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The invention claimed is: 1. A method for compressing an evaluation curve, which is recorded during a radar-based fill level measurement of a filling material located in a container, the method comprising: emitting a radar signal in the direction of the filling material; receiving a receive signal after reflection of the radar signal inside the container; recording an evaluation curve based on at least the receive signal; and generating an approximated evaluation curve ({circumflex over (x)}[n]) and associated estimation coefficients using a linear prediction of the evaluation curve, wherein the approximated evaluation curve ({circumflex over (x)}[n]) is defined as: x ˆ [ n ] = - ∑ i = 1 N K i x [ n - i ] wherein x[n−i] is a previous value of the evaluation curve, and K i are the estimation coefficients. 2. The method according to claim 1 , wherein an error curve is generated by subtracting the evaluation curve from the approximated evaluation curve. 3. The method according to claim 2 , wherein the estimation coefficients are iteratively calculated using the linear prediction such that the iterative calculating is terminated when a data sum of the estimation coefficients and the error curve is minimized. 4. The method according to claim 2 , wherein a compressed error curve is generated using entropy coding based on the error curve and its corresponding statistical properties. 5. The method according to claim 4 , wherein a logistic distribution is used as the distribution model for the entropy coding, such that a standard deviation of the error curve is calculated as a statistical property. 6. The method according to claim 4 , wherein range coding or Golomb coding is used as the data compression method for the entropy coding. 7. A method for decompressing the evaluation curve according to claim 4 , the method comprising: generating the decompressed error curve by using entropy decoding of the compressed error curve and of the corresponding statistical properties; restoring the approximated evaluation curve by using the estimation coefficients based on the model of linear prediction; and restoring the evaluation curve using addition of the decompressed error curve and the approximated evaluation curve. 8. A radar-based fill level measurement device for performing the method according to claim 2 , the fill level measurement device comprising: a signal-generating unit configured to emit the radar signal in the direction of the filling material; a receiving unit configured to receive the corresponding receive signal after reflection of the radar signal inside the container; an evaluation unit configured to: generate the evaluation curve based on at least the receive signal; generate the approximated evaluation curve and its estimation coefficients, using at least one linear prediction of the evaluation curve; generate the error curve by subtracting the evaluation curve from the approximated evaluation curve; and determine the fill level based on the evaluation curve or the estimation coefficients; and an interface which operable to transmit at least the estimation coefficients and the error curve, and/or a memory operable to store at least the estimation coefficients and the error curve. 9. The fill level measurement device according to claim 8 , wherein the evaluation unit is designed to perform the linear prediction based on the lattice filter model. 10. The fill level measurement device according to claim 8 , wherein the signal-generating unit, the receiving unit and the evaluation unit are designed to determine the evaluation curve based on the FMCW principle.
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