ENGG1811 : Peak Detection Algorithm  - Incremental Development - Testing and Debugging - Engineering Assignment Help

Assignment Task :

Learning Objectives: 

1. To apply programming concepts of variable declaration, constant declaration,  assignment, selection and iteration (for loop). 

2. To translate an algorithm described in a blend of natural language and elements of  pseudocode to a computer language. 

3. To organise programs into smaller modules by using functions. 

4. To use good program style including comments, meaning variable names and others.

5. To get a practice on software development, which includes incremental development,  testing and debugging. 

“A low standard deviation indicates that the values tend to be close to the mean (also  called the expected value) of the set, while a high standard deviation indicates that the  values are spread out over a wider range.”  

Observation: 1 

In statistics, the z-score of “standard score” says, how many standard deviations is a data  point above or below the mean value of what is being observed. As such, looking for  values within our data with “high” z-scores might be a way to identify peaks in our data.  

Observation: 2 

We can determine the mean by looking at all the data. Or we could determine a moving  mean value for data within a given window, not for all the data we are observing. We can  refer to this “smoothing window” or lag as our “window”. 

Observation: 3 

Finally, when we do observe something above a particular z-score we can decide how  much (if at all) this should influence the moving mean value for the data within this window  or subsequent windows.  

Description of the peak detection algorithm 

The goal is to detect does a particular part of an input signal have peaks and if so where.  In the following example, the peak to be detected is a sequence of 20 numbers (see Fig 1) begins at the 9th element in the input list and ends at the 12th element.  

However, if you look at the input list you can see lots of smaller up and down patterns in  the data which could each be considered peaks. So, what makes 9 - 12 a peak while the  others aren’t. Firstly, the values 41, 38, 22 and 10 are beyond the average. How far  beyond the average? Well at least 3 standard deviations in this case

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