Use the Gaussian Filter Function

you can use the gaussian filter function to calculate a smoothed value for each point by considering its adjacent data points it is factored by a simple gaussian digital filter, which depends on the distance from the mean, ensuring a natural weighting of surrounding values understanding gaussian function the following information describes this function calculates the mean, standard deviation, and variance of a set of values within a specific window determines a coefficient using the formula 1 / (sqrt(2 pi) deviation) computes the exponential part with exp( 1/2 ((current mean) / deviation)^2) the gaussian function is derived by multiplying the coefficient with the exponential part the process results in a filtered value that smooths or blurs the original data, applying a natural weight based on proximity to the mean user scenario review the following scenario for the gaussian filter function then, you will simulate plc data and calculate the filtered values in a precision manufacturing facility specializing in aerospace components, high resolution cameras are employed to inspect the surface of parts for microscopic flaws these inspections generate vast amounts of image data, where even the smallest imperfection can signal a potential failure in critical aerospace systems we apply the gaussian filter function to images to reduce noise and lighting changes that can hide defects this helps the inspection algorithms to find any problems with a component step 1 add a device follow the steps to docid 3eyafppweuvmblcey17sq and configure the following parameters device type simulator driver name generator enable alias topics select the checkbox step 2 add tags after connecting the device, add the following tag see docid 8se7z3pmrfwl1nmzcwalx to learn more tag 1 input1 name select s random value generator value type select float64 polling interval enter 1 tag name enter input1 min value enter 101 max value enter 199 step 3 create analytics flows you can now create the analytics flows using data from the device and tag you previously created to create an analytics flow with the gaussian filter processor function in litmus edge, navigate to analytics on the analytics canvas, click add processor the create a processor dialog box displays select datahub subscribe in the topic field, click the search icon, select the device you previously created, and then select the alias topic for the input1 tag click save click add processor again and select the gaussian filter processor the edit a processor dialog box appears window size enter a value that represents the range to apply the gaussian digital function for this example, we input a value of 30 number of deviations number of deviations to use for the calculations for this example, we input a value of 1 timeinterval if you know your input is going to publish at the expected interval, it is better to disable this timer by entering 0 in the field click save connect the datahub subscribe processor (tag input1 ) to the gaussian filter processor with a wire and use the events connection on the analytics canvas, click save the configured analytics flows should look like the following step 4 view output of processor click the view icon in the gaussian filter processor to view the output values the gaussian filter output yielded a filtered value of 1 16882172100685 with a coefficient of 0 013888633443174866 and exponential of 0 6947003584251287