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For serrated edges the [http://wiki.myriadtalk.com/index.php?title=Spyderco_Delica_4_Specs pocket sharpener] has a fold out, tapered diamond file. The only other characteristic for this simple, compact, pocket sharpener is an integrated lanyard hole which allows the person to connect it to gear or a belt loop.<br><br>If you would like a nice larger folding knife that's designed for a bit more rugged use try my evaluate of the Gerber Bear Grylls Scout knife or the Gerber Bear Grylls Folding Sheath knife and the Schrade First Response folder is one other great knife to consider. For a smaller mounted blade knife, verify our Gerber Bear Grylls Compact Mounted blade evaluate and the other other knife reviews beneath the Video tab of our YouTube Channel or search this website. On your convenience I’ve included hyperlinks to the Gerber Bear Grylls Compact Scout knife and all the other knives and instruments that I’ve mentioned, all through this text.<br><br>At a mere 3.zero ounces, this knife simply melts into your pocket and is hardly seen. It is a great every single day carry or gentle responsibility knife, although a little bit small and lightweight to achieve a survival position. General, it seems adequate to be thought [http://abcnews.go.com/blogs/politics/2013/03/tsa-to-allow-pocket-knives-on-planes/ multi tool] of a gentleman’s knife and it performs properly enough that I can strongly suggest one. The Case Cutlery XX Again Pocket knife is a long and slender folding knife that’s meant to be carried inside your pants pocket. You can too slip it into a sheath or bag. Simply because the knife was designed for snug pocket carry doesn’t mean that’s your only possibility. | | The '''autocorrelation matrix''' is used in various digital signal processing algorithms. It consists of elements of the discrete [[Autocorrelation#Signal processing|autocorrelation]] function, <math>R_{xx}(j)</math> arranged in the following manner: |
| | |
| | :<math>\mathbf{R}_x = E[\mathbf{xx}^H] = \begin{bmatrix} |
| | R_{xx}(0) & R^*_{xx}(1) & R^*_{xx}(2) & \cdots & R^*_{xx}(N-1) \\ |
| | R_{xx}(1) & R_{xx}(0) & R^*_{xx}(1) & \cdots & R^*_{xx}(N-2) \\ |
| | R_{xx}(2) & R_{xx}(1) & R_{xx}(0) & \cdots & R^*_{xx}(N-3) \\ |
| | \vdots & \vdots & \vdots & \ddots & \vdots \\ |
| | R_{xx}(N-1) & R_{xx}(N-2) & R_{xx}(N-3) & \cdots & R_{xx}(0) \\ |
| | \end{bmatrix} |
| | </math> |
| | This is clearly a [[Hermitian matrix]] and a [[Toeplitz matrix]]. If <math>\mathbf{x}</math> is [[Stationary_process#Weak_or_wide-sense_stationarity|wide-sense stationary]] then its autocorrelation matrix will be [[Positive-definite_matrix#Negative-definite.2C_semidefinite_and_indefinite_matrices|nonnegative definite]]. |
| | |
| | The ''autocovariance matrix'' is related to the autocorrelation matrix as follows: |
| | |
| | :<math>\begin{align} |
| | \mathbf{C}_x &= E[(\mathbf{x} - \mathbf{m}_x)(\mathbf{x} - \mathbf{m}_x)^H]\\ |
| | &= \mathbf{R}_x - \mathbf{m}_x\mathbf{m}_x^H\\ |
| | |
| | \end{align} |
| | </math> |
| | |
| | Where <math>\mathbf{m}_x</math> is a vector giving the mean of signal <math>\mathbf{x}</math> at each index of time. |
| | |
| | == References == |
| | * Hayes, Monson H., ''Statistical Digital Signal Processing and Modeling'', John Wiley & Sons, Inc., 1996. ISBN 0-471-59431-8. |
| | |
| | [[Category:Matrices]] |
| | [[Category:Signal processing]] |
![{\displaystyle \mathbf {R} _{x}=E[\mathbf {xx} ^{H}]={\begin{bmatrix}R_{xx}(0)&R_{xx}^{*}(1)&R_{xx}^{*}(2)&\cdots &R_{xx}^{*}(N-1)\\R_{xx}(1)&R_{xx}(0)&R_{xx}^{*}(1)&\cdots &R_{xx}^{*}(N-2)\\R_{xx}(2)&R_{xx}(1)&R_{xx}(0)&\cdots &R_{xx}^{*}(N-3)\\\vdots &\vdots &\vdots &\ddots &\vdots \\R_{xx}(N-1)&R_{xx}(N-2)&R_{xx}(N-3)&\cdots &R_{xx}(0)\\\end{bmatrix}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/0503991dfbc2be341fe5dee0ebc2fdbe8a8ea0ed)
![{\begin{aligned}{\mathbf {C}}_{x}&=E[({\mathbf {x}}-{\mathbf {m}}_{x})({\mathbf {x}}-{\mathbf {m}}_{x})^{H}]\\&={\mathbf {R}}_{x}-{\mathbf {m}}_{x}{\mathbf {m}}_{x}^{H}\\\end{aligned}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/947c2985f84ad2b9947f76d52f9beab59b87bc5e)
