Difference between revisions of "Aufgaben:Exercise 4.16Z: Multi-dimensional Data Reduction"

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[[File:P_ID678__Sto_Z_4_16.png|right|frame|Correlation matrices <br>$\mathbf{K_y}$&nbsp; and&nbsp; $\mathbf{K_z}$ ]]
+
[[File:P_ID678__Sto_Z_4_16.png|right|frame|Matrices&nbsp; $\mathbf{K_y}$&nbsp; and&nbsp; $\mathbf{K_z}$ ]]
We consider Gaussian zero mean random variables&nbsp; $\mathbf{x}$,&nbsp; $\mathbf{y}$&nbsp; and&nbsp; $\mathbf{z}$&nbsp; with dimensions&nbsp; $N= 1$,&nbsp; $N= 2$&nbsp; and&nbsp; $N= 3$:
+
We consider Gaussian zero mean random variables &nbsp; $\mathbf{x}$,&nbsp; $\mathbf{y}$ &nbsp; and &nbsp; $\mathbf{z}$ &nbsp; with dimensions&nbsp; $N= 1$,&nbsp; $N= 2$&nbsp; and&nbsp; $N= 3$:
* The one-dimensional random variable&nbsp; $\mathbf{x}$&nbsp; is characterized by the variance&nbsp; $\sigma^2 = 1$&nbsp; and the standard deviation &nbsp; $\sigma = 1$&nbsp; respectively. <br>Because of the dimension&nbsp; $N= 1$&nbsp; $\mathbf{x} = x$ holds.
+
* The one-dimensional random variable&nbsp; $\mathbf{x}$&nbsp; is characterized by the variance&nbsp; $\sigma^2 = 1$&nbsp; and the standard deviation &nbsp; $\sigma = 1$&nbsp; respectively. <br>Because of the dimension&nbsp; $N= 1$ &nbsp; &rArr; &nbsp; $\mathbf{x} = x$.
  
 
* The correlation coefficient between the components&nbsp; $y_1$&nbsp; and&nbsp; $y_2$&nbsp; of the 2D random variable&nbsp; $\mathbf{y}$&nbsp; is&nbsp; $\rho = 1/3$&nbsp; $($see matrix&nbsp; $\mathbf{K_y})$. <br>$y_1$ and $y_2$ also have the standard deviation $\sigma = 1$.
 
* The correlation coefficient between the components&nbsp; $y_1$&nbsp; and&nbsp; $y_2$&nbsp; of the 2D random variable&nbsp; $\mathbf{y}$&nbsp; is&nbsp; $\rho = 1/3$&nbsp; $($see matrix&nbsp; $\mathbf{K_y})$. <br>$y_1$ and $y_2$ also have the standard deviation $\sigma = 1$.
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If one quantizes the random variable&nbsp; $\mathbf{x}$&nbsp; in the range between&nbsp; $-4$&nbsp; and&nbsp; $+4$&nbsp; with interval width&nbsp; $\Delta_x = 1/32$, there are altogether &nbsp;$N_1 = 256$&nbsp; different quantization values, for whose transmission thus &nbsp;$n_1 = 8\ \rm {bit}$&nbsp; would be needed.
+
If one quantizes the random variable&nbsp; $\mathbf{x}$&nbsp; in the range between&nbsp; $-4$&nbsp; and&nbsp; $+4$&nbsp; with interval width&nbsp; $\Delta_x = 1/32$,&nbsp; there are altogether &nbsp;$N_1 = 256$&nbsp; different quantization values,&nbsp; for whose transmission thus &nbsp;$n_1 = 8\ \rm {bit}$&nbsp; would be needed.
  
Similarly, the random variable&nbsp; $\mathbf{y}$&nbsp; results in a total of &nbsp;$N_2 = 256^2 = 65536$&nbsp; different quantized value pairs, if the correlation between&nbsp; $y_1$ &nbsp;and&nbsp; $y_2$&nbsp; is not taken into account.  
+
Similarly,&nbsp; the random variable&nbsp; $\mathbf{y}$&nbsp; results in a total of &nbsp;$N_2 = 256^2 = 65536$&nbsp; different quantized value pairs,&nbsp; if the correlation between&nbsp; $y_1$ &nbsp;and&nbsp; $y_2$&nbsp; is not taken into account.  
  
Exploiting this correlation&ndash; for example, by coordinate transformation from the original system&nbsp; $(y_1, y_2)$&nbsp; to the new system&nbsp; $(\eta_1, \eta_2)$&nbsp; &ndash; results in a smaller number&nbsp; $N_2\hspace{0.01cm}'$&nbsp; of quantized value pairs.
+
Exploiting this correlation&nbsp; &ndash; for example, by coordinate transformation from the original system&nbsp; $(y_1, y_2)$&nbsp; to the new system&nbsp; $(\eta_1, \eta_2)$&nbsp; &ndash;&nbsp; results in a smaller number&nbsp; $N_2\hspace{0.01cm}'$&nbsp; of quantized value pairs.
  
*Here, it is to be considered that each component is to be quantized according to its respective dispersion&nbsp; $(\sigma_1$&nbsp; resp.&nbsp; $\sigma_2)$&nbsp; in the range of&nbsp; $-4$&nbsp; to&nbsp; $+4$&nbsp; and the quantization intervals should be the same in both directions: &nbsp; $\Delta_x = \Delta_y =1/32$.
+
*Here, it is to be considered that each component is to be quantized according to its respective standard deviation&nbsp; $(\sigma_1$&nbsp; resp.&nbsp; $\sigma_2)$&nbsp; in the range of&nbsp; $-4$&nbsp; to&nbsp; $+4$&nbsp; and the quantization intervals should be the same in both directions: &nbsp; $\Delta_x = \Delta_y =1/32$.
  
*We denote the quotient&nbsp; $N_2\hspace{0.01cm}'/N_2$&nbsp; as the data reduction factor with respect to the 2D random variable&nbsp; $\mathbf{y}$.  
+
*We denote the quotient&nbsp; $N_2\hspace{0.01cm}'/N_2$&nbsp; as the data reduction factor with respect to the two-dimensional random variable&nbsp; $\mathbf{y}$.  
*In analogous definition&nbsp; $N_3'/N_3$&nbsp; is the corresponding reduction factor of the 3D random variable&nbsp; $\mathbf{z}$&nbsp; for&nbsp; $\Delta_x = \Delta_y =\Delta_z =1/32.$  
+
*In analogous definition&nbsp; $N_3'/N_3$&nbsp; is the corresponding reduction factor of the three-dimensional random variable&nbsp; $\mathbf{z}$&nbsp; for&nbsp; $\Delta_x = \Delta_y =\Delta_z =1/32.$  
 
*Note that in both cases the smallest possible value of this quotient would be favorable.
 
*Note that in both cases the smallest possible value of this quotient would be favorable.
 
 
 
 
  
  
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Hints:  
 
Hints:  
 
*The exercise belongs to the chapter &nbsp;[[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables|Generalization to N-Dimensional Random Variables]].
 
*The exercise belongs to the chapter &nbsp;[[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables|Generalization to N-Dimensional Random Variables]].
*In particular, reference is made to the page &nbsp;[[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Eigenvalues_and_eigenvectors|Eigenvalues and Eigenvectors]]&nbsp;.
+
*In particular,&nbsp; reference is made to the section &nbsp;[[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Eigenvalues_and_eigenvectors|Eigenvalues and Eigenvectors]]&nbsp;.
*Some basics on the application of vectors and matrices can be found on the pages &nbsp;[[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Basics_of_matrix_operations:_Determinant_of_a_matrix|Determinant of a Matrix]]&nbsp; and &nbsp;[[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Basics_of_matrix_operations:_Inverse_of_a_matrix|Inverse of a Matrix]]&nbsp;.
+
*Some basics on the application of vectors and matrices can be found on the pages &nbsp;
+
**[[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Basics_of_matrix_operations:_Determinant_of_a_matrix|Determinant of a Matrix]],&nbsp;  
*The equation of determination of the eigenvalues of&nbsp; $\mathbf{K_z}$&nbsp; is: &nbsp; $\lambda^3 - 3 \lambda^2 + {24}/{9}\lambda - {20}/{27} = 0.$
+
**[[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Basics_of_matrix_operations:_Inverse_of_a_matrix|Inverse of a Matrix]]&nbsp;.
 +
*The equation for determination the eigenvalues of&nbsp; $\mathbf{K_z}$&nbsp; is: &nbsp; $\lambda^3 - 3 \lambda^2 + {24}/{9}\lambda - {20}/{27} = 0.$
 
*One of the three solutions of this equation is&nbsp; $\lambda_1 = 5/3$.
 
*One of the three solutions of this equation is&nbsp; $\lambda_1 = 5/3$.
  
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{What is the data reduction factor for the 2D random variable&nbsp; $\mathbf{y}$?
+
{What is the data reduction factor for the two-dimensional random variable&nbsp; $\mathbf{y}$?
 
|type="{}"}
 
|type="{}"}
 
$N_2\hspace{0.01cm}'/N_2 \ = $ { 0.943 3% }
 
$N_2\hspace{0.01cm}'/N_2 \ = $ { 0.943 3% }
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{What is the data reduction factor for the 3D random variable&nbsp; $\mathbf{z}$?
+
{What is the data reduction factor for the three-dimensional random variable&nbsp; $\mathbf{z}$?
 
|type="{}"}
 
|type="{}"}
 
$N_3\hspace{0.01cm}'/N_3 \ = $ { 0.861 3% }
 
$N_3\hspace{0.01cm}'/N_3 \ = $ { 0.861 3% }
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===Solution===
 
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; From the condition&nbsp; $\mathbf{K_y} - \lambda \cdot\mathbf{E} = 0$&nbsp; follows:
+
'''(1)'''&nbsp; From the condition &nbsp; $\mathbf{K_y} - \lambda \cdot\mathbf{E} = 0$ &nbsp; follows:
 
:$${\rm det}\left[ \begin{array}{cc}
 
:$${\rm det}\left[ \begin{array}{cc}
1- \lambda & 1/3 \
+
1- \lambda & 1/3 \\
 
1/3 & 1- \lambda
 
1/3 & 1- \lambda
 
\end{array} \right] = (1-\lambda)^2 -{1}/{9} = 0
 
\end{array} \right] = (1-\lambda)^2 -{1}/{9} = 0
\hspace{0.3cm}\rightarrow \hspace{0.3cm}\lambda^2 -2\lambda+ {8}/{9}= 0
+
\hspace{0.3cm}\Rightarrow \hspace{0.3cm}\lambda^2 -2\lambda+ {8}/{9}= 0
 
\hspace{0.3cm}\Rightarrow \hspace{0.3cm}\lambda_{1/2}= 1 \pm
 
\hspace{0.3cm}\Rightarrow \hspace{0.3cm}\lambda_{1/2}= 1 \pm
 
\sqrt{1-{8}/{9}}= 1 \pm {1}/{3}.$$
 
\sqrt{1-{8}/{9}}= 1 \pm {1}/{3}.$$
  
*The eigenvalues of this&nbsp; $2\times2$ matrix are thus&nbsp; $\lambda_1 = 4/3\hspace{0.15cm}\underline{=1.333}$&nbsp; and&nbsp; $\lambda_2 = 2/3\hspace{0.15cm}\underline{=0.667}$.
+
*The eigenvalues of this&nbsp; $2\times2$&nbsp; matrix are thus&nbsp; $\lambda_1 = 4/3\hspace{0.15cm}\underline{=1.333}$&nbsp; and&nbsp; $\lambda_2 = 2/3\hspace{0.15cm}\underline{=0.667}$.
  
  
  
'''(2)'''&nbsp; Without considering correlations, there are&nbsp; $N_2 = \left({8}/{ \Delta_x}\right)^2= 256^2 = 65536$&nbsp; different pairs of values.  
+
 
*Taking into account the correlations and the fact that the two components created by coordinate rotation&nbsp; $\eta_1$&nbsp; and&nbsp; $\eta_2$&nbsp; are each in the range&nbsp; $-4\sigma_1$&nbsp; to&nbsp; $+4\sigma_1$&nbsp; $($bzw. from&nbsp; $-4\sigma_2$&nbsp; to&nbsp; $+4\sigma_2)$&nbsp;) are to be quantized, one obtains
+
'''(2)'''&nbsp; Without considering correlations,&nbsp; there are&nbsp; $N_2 = \left({8}/{ \Delta_x}\right)^2= 256^2 = 65536$&nbsp; different pairs of values.  
 +
*Taking into account the correlations and the fact that the two components created by coordinate rotation&nbsp; $\eta_1$&nbsp; and&nbsp; $\eta_2$&nbsp; are each in the range&nbsp; $-4\cdot \sigma_1$&nbsp; to&nbsp; $+4\cdot \sigma_1$&nbsp; $($resp. from&nbsp; $-4\cdot \sigma_2$&nbsp; to&nbsp; $+4\cdot \sigma_2)$&nbsp;) are to be quantized,&nbsp; one obtains
 
:$$N_2\hspace{0.01cm}' = \frac{8 \hspace{0.05cm}\sigma_1}{\it \Delta_x}\cdot\frac{8
 
:$$N_2\hspace{0.01cm}' = \frac{8 \hspace{0.05cm}\sigma_1}{\it \Delta_x}\cdot\frac{8
 
\hspace{0.05cm}\sigma_2}{\it \Delta_y}= N_2 \cdot \sigma_1 \cdot
 
\hspace{0.05cm}\sigma_2}{\it \Delta_y}= N_2 \cdot \sigma_1 \cdot
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\frac{24}{9}\lambda - \frac{20}{27}  = 0.$$
 
\frac{24}{9}\lambda - \frac{20}{27}  = 0.$$
  
*This equation has already been given as a solution hint, as well as one of the solutions: &nbsp; $\lambda_1= 5/3$.  
+
*This equation has already been given as a solution hint,&nbsp; as well as one of the solutions: &nbsp; $\lambda_1= 5/3$.  
*This gives the equation of determination for the further eigenvalues&nbsp; $\lambda_2$&nbsp; and&nbsp; $\lambda_3$&nbsp; to.
+
*This gives the equation for the further eigenvalues&nbsp; $\lambda_2$&nbsp; and&nbsp; $\lambda_3$&nbsp; to
 
:$$\frac{\lambda^3 - 3 \lambda^2 + {24}/{9}\lambda -
 
:$$\frac{\lambda^3 - 3 \lambda^2 + {24}/{9}\lambda -
 
{20}/{27}}{\lambda -{5}/{3}} = \lambda^2 -
 
{20}/{27}}{\lambda -{5}/{3}} = \lambda^2 -
 
{4}/{3} \cdot \lambda + {4}/{9} =0.$$
 
{4}/{3} \cdot \lambda + {4}/{9} =0.$$
  
*This equation of determination can be transformed as follows: &nbsp; $(\lambda - {2}/{3})^2 =0.$
+
*This equation can be transformed as follows: &nbsp; $(\lambda - {2}/{3})^2 =0.$
  
 
*The other eigenvalues besides&nbsp; $\lambda_1= 5/3$&nbsp; are thus equal and result in &nbsp; $\lambda_2 = \lambda_3 =2/3\hspace{0.15cm}\underline{=0.667}$.
 
*The other eigenvalues besides&nbsp; $\lambda_1= 5/3$&nbsp; are thus equal and result in &nbsp; $\lambda_2 = \lambda_3 =2/3\hspace{0.15cm}\underline{=0.667}$.
 +
  
  

Latest revision as of 15:58, 29 March 2022

Matrices  $\mathbf{K_y}$  and  $\mathbf{K_z}$

We consider Gaussian zero mean random variables   $\mathbf{x}$,  $\mathbf{y}$   and   $\mathbf{z}$   with dimensions  $N= 1$,  $N= 2$  and  $N= 3$:

  • The one-dimensional random variable  $\mathbf{x}$  is characterized by the variance  $\sigma^2 = 1$  and the standard deviation   $\sigma = 1$  respectively.
    Because of the dimension  $N= 1$   ⇒   $\mathbf{x} = x$.
  • The correlation coefficient between the components  $y_1$  and  $y_2$  of the 2D random variable  $\mathbf{y}$  is  $\rho = 1/3$  $($see matrix  $\mathbf{K_y})$.
    $y_1$ and $y_2$ also have the standard deviation $\sigma = 1$.
  • The statistics of the three-dimensional random variable  $\mathbf{z}$  is completely determined by the correlation matrix  $\mathbf{K_z}$ .


If one quantizes the random variable  $\mathbf{x}$  in the range between  $-4$  and  $+4$  with interval width  $\Delta_x = 1/32$,  there are altogether  $N_1 = 256$  different quantization values,  for whose transmission thus  $n_1 = 8\ \rm {bit}$  would be needed.

Similarly,  the random variable  $\mathbf{y}$  results in a total of  $N_2 = 256^2 = 65536$  different quantized value pairs,  if the correlation between  $y_1$  and  $y_2$  is not taken into account.

Exploiting this correlation  – for example, by coordinate transformation from the original system  $(y_1, y_2)$  to the new system  $(\eta_1, \eta_2)$  –  results in a smaller number  $N_2\hspace{0.01cm}'$  of quantized value pairs.

  • Here, it is to be considered that each component is to be quantized according to its respective standard deviation  $(\sigma_1$  resp.  $\sigma_2)$  in the range of  $-4$  to  $+4$  and the quantization intervals should be the same in both directions:   $\Delta_x = \Delta_y =1/32$.
  • We denote the quotient  $N_2\hspace{0.01cm}'/N_2$  as the data reduction factor with respect to the two-dimensional random variable  $\mathbf{y}$.
  • In analogous definition  $N_3'/N_3$  is the corresponding reduction factor of the three-dimensional random variable  $\mathbf{z}$  for  $\Delta_x = \Delta_y =\Delta_z =1/32.$
  • Note that in both cases the smallest possible value of this quotient would be favorable.




Hints:


Questions

1

Calculate the eigenvalues of the correlation matrix  $\mathbf{K_y}$. Let  $\lambda_1 \ge \lambda_2$ hold.

$\lambda_1 \ = \ $

$\ (\lambda_1 \ge \lambda_2)$
$\lambda_2 \ = \ $

$\ (\lambda_2 \le \lambda_1)$

2

What is the data reduction factor for the two-dimensional random variable  $\mathbf{y}$?

$N_2\hspace{0.01cm}'/N_2 \ = $

3

It holds  $\lambda_1 = 5/3$.  Calculate the eigenvalues  $\lambda_2$  and  $\lambda_3 \le \lambda_2$  of  $\mathbf{K_z}$.

$\lambda_2 \ = \ $

$\ (\lambda_2 \ge \lambda_3)$
$\lambda_3 \ = \ $

$\ (\lambda_3 \le \lambda_2)$

4

What is the data reduction factor for the three-dimensional random variable  $\mathbf{z}$?

$N_3\hspace{0.01cm}'/N_3 \ = $


Solution

(1)  From the condition   $\mathbf{K_y} - \lambda \cdot\mathbf{E} = 0$   follows:

$${\rm det}\left[ \begin{array}{cc} 1- \lambda & 1/3 \\ 1/3 & 1- \lambda \end{array} \right] = (1-\lambda)^2 -{1}/{9} = 0 \hspace{0.3cm}\Rightarrow \hspace{0.3cm}\lambda^2 -2\lambda+ {8}/{9}= 0 \hspace{0.3cm}\Rightarrow \hspace{0.3cm}\lambda_{1/2}= 1 \pm \sqrt{1-{8}/{9}}= 1 \pm {1}/{3}.$$
  • The eigenvalues of this  $2\times2$  matrix are thus  $\lambda_1 = 4/3\hspace{0.15cm}\underline{=1.333}$  and  $\lambda_2 = 2/3\hspace{0.15cm}\underline{=0.667}$.



(2)  Without considering correlations,  there are  $N_2 = \left({8}/{ \Delta_x}\right)^2= 256^2 = 65536$  different pairs of values.

  • Taking into account the correlations and the fact that the two components created by coordinate rotation  $\eta_1$  and  $\eta_2$  are each in the range  $-4\cdot \sigma_1$  to  $+4\cdot \sigma_1$  $($resp. from  $-4\cdot \sigma_2$  to  $+4\cdot \sigma_2)$ ) are to be quantized,  one obtains
$$N_2\hspace{0.01cm}' = \frac{8 \hspace{0.05cm}\sigma_1}{\it \Delta_x}\cdot\frac{8 \hspace{0.05cm}\sigma_2}{\it \Delta_y}= N_2 \cdot \sigma_1 \cdot \sigma_2 .$$
  • The quotient is thus with  $\sigma_1^2 = \lambda_1$  and  $\sigma_2^2 = \lambda_2$:
$${N_2\hspace{0.01cm}'}/{N_2} = \sigma_1 \cdot \sigma_2 = \sqrt{{4}/{3}} \cdot \sqrt{{2}/{3}} = \frac{2 \cdot \sqrt{2}}{3} \hspace{0.15cm}\underline{ \approx 0.943}.$$


(3)  The equation of determination of the eigenvalues of  $\mathbf{K_z}$  is:

$${\rm det} \left[ \begin{array}{ccc} 1-\lambda & 1/3 & 1/3\\ 1/3 & 1-\lambda & 1/3\\ 1/3 & 1/3 & 1-\lambda \end{array}\right] = 0 \hspace{0.3cm} \Rightarrow \hspace{0.3cm}(1- \lambda) \left[(1- \lambda)^2 - \frac{1}{9} \right]- \frac{1}{3} \left[\frac{1}{3}(1- \lambda) - \frac{1}{9} \right] + \frac{1}{3} \left[\frac{1}{9} - \frac{1}{3}(1- \lambda) \right] = 0$$
$$\Rightarrow \hspace{0.3cm}(1- \lambda) (\lambda^2 -2\lambda+ \frac{8}{9})- \frac{1}{9} (\frac{2}{3}- \lambda )+ \frac{1}{9} ( \lambda - \frac{2}{3})= 0$$
$$\Rightarrow \hspace{0.3cm}\lambda^2 - 2\lambda + \frac{8}{9} - \lambda^3 + 2 \lambda^2 - \frac{8}{9}\lambda - \frac{4}{27} + \frac{2}{9}\lambda = 0 \hspace{0.3cm} \Rightarrow \hspace{0.3cm}\lambda^3 - 3 \lambda^2 + \frac{24}{9}\lambda - \frac{20}{27} = 0.$$
  • This equation has already been given as a solution hint,  as well as one of the solutions:   $\lambda_1= 5/3$.
  • This gives the equation for the further eigenvalues  $\lambda_2$  and  $\lambda_3$  to
$$\frac{\lambda^3 - 3 \lambda^2 + {24}/{9}\lambda - {20}/{27}}{\lambda -{5}/{3}} = \lambda^2 - {4}/{3} \cdot \lambda + {4}/{9} =0.$$
  • This equation can be transformed as follows:   $(\lambda - {2}/{3})^2 =0.$
  • The other eigenvalues besides  $\lambda_1= 5/3$  are thus equal and result in   $\lambda_2 = \lambda_3 =2/3\hspace{0.15cm}\underline{=0.667}$.



(4)  Analogous to the procedure in the subtask  (2)  results here:

$${N_3\hspace{0.01cm}'}/{N_3} = \sqrt{\lambda_1 \cdot \lambda_2\cdot \lambda_3} = \sqrt{\frac{5}{3} \cdot \frac{2}{3}\cdot \frac{2}{3}} = \sqrt{\frac{20}{27}} \hspace{0.15cm}\underline{ \approx 0.861}.$$