function?[y_final?f_final?ckIter]?=?mckd（xfilterSizetermIterTMplotMode）
????%MAXIMUM?CORRELATED?KURTOSIS?DECONVOLTUION
????%??code?and?method?by?Geoff?McDonald?（glmcdona@gmail.com）?May?2011
????%??This?code?file?is?an?external?reference?for?a?paper?being?submitted
????%??for?review.
????%
????%?mckd（xfilterSizetermIterplotModeTM）
????%
????%?Description:
????%????This?method?tries?to?deconvolve?a?periodic?series?of?impulses?from
????%????a?1d?vector.?It?does?this?by?designing?a?FIR?filter?to?maximize
????%????a?norm?criterion?called?Correlated?Kurtosis.?This?method?is?has
????%????applications?in?fault?detection?of?rotating?machinery?（such?as
????%????ball?bearing?and?gear?faults）.?該方法可以將一系列1維沖擊周期信號解卷積。
????%
????%?Algorithm?Reference:
????%????（Paper?link?coming?soon.?If?you?are?interested?in?this?please
????%????contact?me?at?glmcdona@gmail.com.?I?will?add?the?link?if/when?the
????%????paper?is?available?online）
????%
????%?Inputs:
????%????x:?
????%???????Signal?to?perform?deconvolution?on.?This?should?be?a?1d?vector.
????%???????MCKD?will?be?performed?on?this?vector?by?designing?a?FIR
????%???????filter.?一維信號
????%?
????%????filterSize:
????%???????This?is?the?length?of?the?finite?impulse?filter?filter?to?
????%???????design.?Using?a?value?of?around?100?is?appropriate?depending?on
????%???????the?data.?Investigate?the?performance?difference?using
????%???????different?values.
????%???????這是要設計的有限脈沖濾波器濾波器的長度.取決于數據，使用大約100左右的值是合適的。使用不同的值來檢查性能的差異。
????%
????%????termIter:?（OPTIONAL）
????%???????This?is?the?termination?number?of?iterations.這是迭代終止數。?If?the?
????%???????the?number?of?iterations?exceeds?this?number?the?MCKD?process
????%???????will?complete.?Specify?[]?to?use?default?value?of?30.?如果迭代次數超過這個數字，MCKD過程將完成。?指定[]使用默認值30。
????%
????%????T:
????%???????This?is?the?period?for?the?deconvolution.?反褶積周期。The?algorithm?will
????%???????try?to?deconvolve?periodic?impulses?separated?by?this?period.
????%???????This?period?should?be?specified?in?number?of?samples?and?can?be
????%???????fractional?（such?as?106.29）.?In?the?case?of?a?fractional?T?the
????%???????method?will?resample?the?data?to?the?nearest?larger?integer?T:
????%????????i.e.?106.29?->?107?and?the?y_final?output?will?still?be?at?this?resampled?factor.
????%????????該算法將嘗試通過此周期用反褶積方法將周期性脈沖分離。?這個周期T應該以樣本數量來指定，可以是小數（如106.29）。?在小數T的情況下，該方法將數據重新采樣到最接近的較大整數T：即106.29→107并且y_final輸出仍將處于這個重采樣因子。
????%????M:
????%???????This?is?the?shift?order?of?the?deconvolution?algorithm.?這是解卷積算法的偏移順序。
????%???????Typically?an?integer?value?between?1?and?5?is?good.?通常，1到5之間的整數值是好的。
????%???????Increasing?the?number?increases?the?number?of?periodic?impulses?it?tries
????%???????to?find?in?a?row.增加數量會增加它嘗試連續查找的周期性脈沖的數量。?
????%???????For?example?M?=?5?would?try?to?extract?at?least?5?impulses?in?a
????%???????row.?例如選擇M=5則會在每行中嘗試提取至少5個連續沖擊。
????%???????When?you?use?a?larger?M?you?need?a?better?estimate?of?T.?當你使用更大的M時，你需要更好的估計T.
????%???????Using