Welcome to the INFERLab Blog

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Hi!

We have been working for a few weeks to get this site up and running and we are finally ready to open it up and share it with all of you. A great group of developers has been helping us put it together, and we are very comfortable with the platform they have developed for us. We now have all of our projects, news items and publications on the site, and an easy way to continue updating the content as time goes by.

What will this blog be used for?

This blog will serve as an outlet for us to share technical and non-technical content related to the research topics we are working on. From how to determine a malfunctioning damper on an HVAC system from measurement data, to why continuous on-line monitoring of dams and levees is important, all topics are valid. Everyone in the team will be contributing to this blog at some point.dapoxetine me


Matlab tidbits

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Here’s how you’d implement a low pass butterworth filter in matlab. This has come in very handy.

lowcutoff=1000;
sampling_freq=12000;
fNorm=lowcutoff/(sampling_freq/2);
[b,a] = butter(10, fNorm, 'low');
filtered_signal = filtfilt(b, a, signal);
Here’s how you’d add (gaussian white) noise:
noisy_data=awgn(data, SNR)
Matlab can be a real pain when trying to save CSV files with different data types (string + numeric: lets say). But to try things like SVN and Naive Bayes on weka, you need your labels to be nominal (not numeric). Fortunately, this guy wrote this code which works like a charm. All you have to do (after downloading that file) is :
cell2csv ( 'filename.csv', cell_array_that_you_want_to_write, ' , ');

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Some quick things in R

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Part of a revelation I’ve had this past couple of months relates to how different programs are good at doing different things.

For instance, R is very intuitive and fast when it comes to doing classifications and projections. Here is how you would implement a Naive Bayes and K-NN in R.

naive Bayes in R :