Linux Screen Cmd

https://www.rackaid.com/blog/linux-screen-tutorial-and-how-to/

How to copy file from remote machine to local machine

User command (Similar like the following):

Use pscp command copy data from remote machine to local machine:
pscp -r yyang57@hydra27.eecs.utk.edu:"./Videos/aa.a" D:\


Use scp command copy local data to remote machine:
scp User30.csv  User31.csv  User32.csv  User33.csv  User34.csv  User35.csv  User36.csv  User37.csv  User38.csv  User39.csv User40.csv  User41.csv  User42.csv  User43.csv  User44.csv  User45.csv  User46.csv  User47.csv  User48.csv  User49.csv User 50.csv yyang57@hydra26.eecs.utk.edu:./SVMMeasure/Dataset/

How to use PSCP in cmd

If you want to send files to remote machine, and your machine is Win-OS, then use this command:

pscp "D:\LDA Training and Testing\FriendandFamily\*.csv" yyang57@hydra26.eecs.utk.edu:./SVMMeasure/

to send all files in the folder to the remote machine.

Be sure to get the PSCP before running the cmd

Be sure to run the cmd in Admin model


e.g.
pscp "C:\Users\yin2\Desktop\MultiLevel\StableLevelHydra.m" yyang57@hydra26.eecs.utk.edu:./SVMMeasure/StableH20.m

pscp -r yyang57@hydra26.eecs.utk.edu:"./SVMMeasure/FinalData/*" C:\Users\yin2\Desktop\MultiLevel\HydraData\

C:\Windows\system32>pscp "D:\Hydra\Dataset\*" yyang57@login.newton.utk.edu:./SVM
Measure/Dataset/

Kalman Filter Understanding

I found a good way of thinking intuitively of Kalman Gain K. If you write K this way

 Kk=P−kHTk(HkP−kHTk+Rk)−1=P−kHTkHkP−kHTk+Rk

you will realize that the relative magnitudes of matrices (Rk) and (Pk) control a relation between the filter's use of predicted state estimate (xk⁻) and measurement (ỹk).

 limRk→0P−kHTk HkP−kHTk+Rk =H−1k

 limPk→0P−kHTk HkP−kHTk+Rk =0

Substituting the first limit into the measurement update equation

 x^k=x−k+Kk(y~k−Hkx−k)

suggests that when the magnitude of R is small, meaning that the measurements are accurate, the state estimate depends mostly on the measurements.

When the state is known accurately, then HP⁻HT is small compared to R, and the filter mostly ignores the measurements relying instead on the prediction derived from the previous state (xk⁻)