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Section 1: Understanding the algorithm and designing the GUI

pipe

Deconvolution

 

Deconvolution is an integral based mathematical technique that in this case relies on input and output data derived from experimental results:

 

deconvolution formular Eq. 1 flowing manhole

 

Where the variable y(t) is the concentration, u(τ) is the unit impulse response and f (t-τ) can be seen as the input. This will give the output response based on the input.

 

· This technique works because the function emulates the predictable and natural process of water flow.

 

· However it is of extreme importance that the input data is accurate with as little ‘noise’ as possible in the data set.

 

By taking the input data and a predicted set of data determined by the user a function is determined. This function is placed in the convolution integral:

 

Algorithm theory


 

· The manhole can be seen as a ‘black box’ which applies a function to an input to give an output.

 

· The algorithm uses a distribution of contaminant for the input and output, and can calculate the convolution integral required for this process.

 

· The function of the integral can then be applied to other inputs of the same system to give predicted outputs.

 

deconvolution black box

 

 

Noise

 

· Noise is fluctuations in and the addition of external factors to the stream of target information being received at a detector.

· The predicted result can be plotted anywhere between these higher and lower boundaries, causing it to vary from the actual result.

· To improve accuracy of results the noise level needs to be predicted so that the algorithm can take this into account. A system with little or no noise will produce a higher accuracy prediction.

 

 Noise graph

 

 

 Preliminary Plot Placement

 

· A set of data points are used to model the input distribution, which is used to produce the predicted output.

· To increase speed of iteration as few points are used as possible, therefore an exponential distribution is used.

· This is a limitation to the algorithm as an input of large distribution or occurs later in data set has a lower accuracy estimate.

 

graph1graph2

Plots show the same data set modelled in different areas of plot distribution, resulting in a good and poor estimate

 

Creating a Graphical User Interface

 

For the algorithm to be applied to real world situations by members of industry, or to be used as a teaching tool, it needs to be as simple as possible to operate.

The best way to achieve this is through the creation of a Graphical User Interface or GUI. This allows the user to manipulate parameters and variables within the algorithm without needing to understand the programming language behind it.

 

flow-volution GUI screen printMatLab Logo 

 

The GUI was created in GUIDE, Matlab’s own GUI creator, and is split into three distinct areas:

· The ‘Load Data Set’ area allows the user to specify the exact data for the algorithm to use.

· The ‘Data Checker’ allows the user to plot the loaded data as a graph.

· The ‘Run Programme’ area allows the user to alter parameters within the algorithm.

The GUI will have a built in ‘User Manual’ explaining how the algorithm works.

 

 

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