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The proposed addressing scheme treats MBs and sub-blocks individually and allocates separate address bit ranges for them.
Using these two matrices, a trade-off between speedup and accuracy in various bit ranges can be achieved.
Table 4 shows the bit ranges of the main variables obtained for our EKF-SLAM implementation that were presented in Table 1.
where N s is the number of bit ranges and Xs, 0 is 0 for any s, i.e., any service, respectively.
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Several performance tests are presented, based on the influence of bit ranging encoding in a chromosome.
For service s, we denote the last bit of bit range i and the bit value of the range as X s,i and β s,i, respectively.
As a final result, a table is presented demonstrating the integer and fractional bit range needed for each EKF variable.
Our intention of testing the bit range of 700 900 was just to evaluate the granularity of the designed embedded quantizer.
In the rest of this article, each part configured with the bits of the same bit value is referred to as bit range.
Based on this error, the bit range of the variables was reduced interaction after interaction generating, as a result the graphics shown in Fig. 6.
Values are shifted to a certain bit range, such that a number of values can be packed within a single encryption.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com