Lesson 5: More Formulae Basic Data Filtration. Today's Lesson Filtering Data with Matlab Root Means Squared Butterworth Filters Basic Statistical Analysis.

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Lesson 5: More Formulae Basic Data Filtration

Today's Lesson Filtering Data with Matlab Root Means Squared Butterworth Filters Basic Statistical Analysis of Data Mean, Mode, Median Standard Deviation Cross Correlation

Root Means Squared When used with a sliding window, smoothes data. RMS = sqrt(sum(all x^2)/n)

Using Root Means Squared

RMS With a Sliding Window

Filtering Data Goal of removing unwanted frequencies from signal data. Butterworth filters produce no ripple, but slowest roll-off. Elliptical filters produce steepest roll-off, but ripples in the pass and stop band. Typically, Butterworth Filters are the Filters of Choice.

Different Filters

Creating a Butterworth Filter

Effect of Order on the Filter

Rectification of Data Abs  absolute value function

Statistical Functions Mean, std, xcorr

Programming Tips: Error Catching A program's Achilles Heel is unexpected data: Extra Data Missing Data Wrong Data Type It is easy to protect your programs from these sorts of errors by adding data checking loops.

Example: Array Length Check %Imagine that for this function, we know that there %should be only two numbers in the input array. function[sum] = addThemUp(summands) %If there aren't 2 numbers in the array, exit nicely. if(length(summands) ~= 2) disp('There weren't exactly 2 numbers in the input.') return; end sum = summands(1) + summands(2);

Example: Data Type Check %Now, let us add something to the previous function to %further ensure that it will work. function[sum] = addThemUp(summands) %If there aren't 2 numbers in the array, exit nicely. if(length(summands) ~= 2) disp('There weren't exactly 2 numbers in the input.') return; end %If one of the 2 "numbers" isn't a number, exit nicely. if(~(isnumeric(summands)) disp('One of the summands was not a number.') return; end sum = summands(1) + summands(2);

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