Minimum variance estimates of gyroscopic drift parameters.

1968 ◽  
Vol 5 (6) ◽  
pp. 747-749
Author(s):  
CHARLES F. PRICE
Keyword(s):  
Minimum Variance ◽  
Variance Estimates

scholarly journals HEVC Fast Intra-Mode Selection Using World War II Technique

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 985
Author(s):  
Junaid Tariq ◽  
Ammar Armghan ◽  
Fayadh Alenezi ◽  
Amir Ijaz ◽  
Saad Rehman ◽  
...  
Keyword(s):  
World War Ii ◽  
High Efficiency ◽  
Mode Selection ◽  
Estimation Algorithm ◽  
Minimum Variance ◽  
World War ◽  
High Efficiency Video Coding ◽  
Mode Estimation ◽  
Compression Time ◽  
Variance Estimates

High-Efficiency Video Coding (HEVC) applies 35 intra modes to every block of a frame and selects the mode that gives the best prediction. This brute-force nature of HEVC makes it complex and unfit for real-time applications. Therefore, a fast intra-mode estimation algorithm is presented here based on the classic World War II (WW2) technique known as the ‘German Tanks Problem’ (GTP). This not only is the first article to use GTP for early estimation of intra mode, but also expedites the estimation process of GTP. Secondly, the various elements of the intra process are efficiently mapped to the elements of GTP estimation. Finally, the two variations of GPT are modeled and are also minimum-variance estimates. These experimental results indicate that proposed GTP-based fast estimation reduced the compression time of HEVC from 23.88% to 31.44%.

A property of minimum variance estimates

Biometrika ◽  
1978 ◽  
Vol 65 (3) ◽  
pp. 642-643 ◽  
Author(s):  
GUNNAR BLOM
Keyword(s):  
Minimum Variance ◽  
Variance Estimates

SINGLE‐CHANNEL WHITE‐NOISE ESTIMATORS FOR DECONVOLUTION

Geophysics ◽  
1978 ◽  
Vol 43 (1) ◽  
pp. 102-124 ◽  
Author(s):  
Jerry M. Mendel ◽  
John Kormylo
Keyword(s):  
Prediction Error ◽  
Ad Hoc ◽  
Single Channel ◽  
Minimum Variance ◽  
Wiener Filtering ◽  
Time Varying ◽  
Flexible Modeling ◽  
Variance Estimates ◽  
Time Invariant ◽  
Filtering Approach

The Wiener filtering approach to deconvolution is limited by certain modeling assumptions, which may not always be valid. We develop a Kalman filtering approach to deconvolution which permits more flexible modeling assumptions than the Wiener filtering approach. Our approach is applicable to time‐varying or time‐invariant wavelets as well as to nonstationary or stationary noise processes. We develop equations herein for minimum‐variance estimates of the reflection coefficient sequence, as well as error variances associated with these estimates. Our estimators are compared with an ad hoc “prediction error filter,” which has recently been reported on in the geophysics literature. We show that our estimators perform better than the prediction error filter. Simulation results are included, for both time‐invariant and time‐varying situations, which support our theoretical developments.

Sufficient statistics and minimum variance estimates

1949 ◽  
Vol 45 (2) ◽  
pp. 213-218 ◽  
Author(s):  
C. Radhakrishna Rao
Keyword(s):  
Probability Density ◽  
Unbiased Estimate ◽  
Minimum Variance ◽  
Sufficient Statistics ◽  
Image Position ◽  
Variance Estimates

Let the probability density of observations be denoted by φ(x | θ), where x stands for the variables and θ for the parameters. A function t of the observations is called an unbiased estimate of the function ψ(θ) of the parameters ifwhere dx stands for the product of differentials.

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