Result: done

Result: had to do some updating things on git.

Result: mmse equalizer just compares signals. zero force inverts frequency response in order to find the optimal weights (only one 1, rest 0s) that minimize ISI

Result: didn't realize that zero force equalizer is not the minimum least squares equalizer, so im looking that up and trying to understand the optimization variables

Result: went through about 7 minutes of a tutorial (to 36 min) and got stuck at the equalizer

Result: there is a theorem that i found in the notes that helps. moving on for today.

Result: done

Result: understand svd, but i need to meditate now.

Result: scratch that. im going through a worked-out example.

Result: done

Result: i have to do admin stuff for TAing.

Result: still tryna figure out how null spaces will help me. gonna try to sleep now

Result: going to bed early

Result: started- took quick napec

Result: just about done lol

Result: nearly done

Result: really confused because the matrix he provided was singular

Result: k got a system of equations

Result: thinking about problem, but not fully focused. gonna prep food.

Result: done

Result: done

Result: pretty sure i need some files. gonna email my adviser

Result: not fully focused. moving files around. trying to figure out what to change into what.

Result: ayeeee forgot to meditate

Result: done with food and bike prep for tomorrow

Result: done. sent.

Result: still memorizing. also prepped bike a bit. not gonna timmy the next hour of memorizing.

Result: calling people, still memorizing

Result: QKD demo about netwk archtr not QDK; not sure what network streaming is for OPM of networks; phase retrieval relying on algorithms-not hardware; gotta go2class!

Result: I have a rough idea of both. Not sure exactly how MMSE filter relates to Weiner filter. Remembered more about estimators. last 5 min spent reviewing bike route

Result: mainly went through what filters were. also the Wiener Kintchin theorem. Heard about (un)biased estimators, but I still dont know what they mean. coffee now

Result: I meant to say 4.1 and 4.2. I got what i needed out of the book - that the PSD is Fourier Trm of the autocovariance, and that PSD shows variances in freq domain

Result: had to go back to a proof relating covariance to std dev in a specific case

Result: spectral density function - skimmed a good (?) resource

Result: some LA: range space, positive (semi)definite, briefly glancing at other topics

Result: I think I found them, but I will have to edit some of the code

Result: sent email, couldn't find files- need to see how to update something. started planning a bike route for saturday since I couldn't concentrate... nap soon.

Result: gotta go!

Result: switched to a bit of ML bc im nervous about that

Result: read through most of a lecture, and took some notes

Result: example for WSS, and autocorrelation

Result: done

Result: remembered to send an important email to my adviser, which took about 10 min. didn't see ex, but i sorta understand the joint stationarity of impulse response

Result: sent the email. Realized I need to take notes on the matlab program so I will do that last. worked on my understanding of covariance.

Result: ended up perusing through a huge tutorial powerpoint that went through seemingly all the important components in DSP. also learned error floor estimation = hard

Result: tried to run the simulation, but i didn't have the right file, so I emailed my adviser.

Result: sent email to adviser explaining what i will talk about. cross correlation notes. Good job Shoo!

Result: expected value of a function of two RVs, cov(XY), correlation coefficient

Result: marginal DFs and conditional PDF

Result: decided to talk about joint pdf and cdf for multiple RVs next