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Windowing techniques have always offered confusion to me. The hardest one was to remember what is what and how they looked in frequency domain (the time domain view was something thankfully I recollect from the names). I hit upon this yet again while trying to map the theoretical spectrum to spectrum computed from discrete samples (for an OFDM modulated signal) and then to analog spectrum measured by an instrument (By the way, I figured out that, the closest discrete technique which maps to the spectrum computation in spectrum analyzer is the Danielle method, which for some reason is not there in Matlab!) . For ages, I was using the pwelch (now spectrum.pwelch) function (pwelch method to estimate the spectrum) to compute/plot the spectrum in Matlab. Usually, some windowing as well is used to adjust the mean square versus resolution.  What window function is more suitable is something that I’ve never mastered. These days, doing a Wikipedia search to find a temporary fix and then move on is the adopted, yet not entirely satisfying strategy. The frequency domain characteristic of the popular window functions are now in here for reference, thanks to Marcel Müller. Since I may need it once in a while, I’ll keep a copy here for my own selfish quick reference. If you ever need to grab a copy, please be aware that, the ownership of this is with them.

A friend of mine, asked me whether I know anything about IPNLMS. Honestly, I didnt even hear about this. He said it is something like progressive NLMS. We did a bit of googling and then found that it is somewhat a new adaptive scheme.

Well, IPNLMS stands for Improved Proportionate Normalized Least Mean Square scheme, which is a modified version of the well known NLMS technique used in adaptive filter theory. OK, the idea of IPNLMS as I understood is, as follows. Each of the coefficient (tap) is independently updated, where the adaptation step is proportional to the estimated filter coefficient. How is this helping?

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