support vector classification machine
％ soft margin
％ uses "kernel.m"
％
％ xtrain: (Ltrain,N) with Ltrain: number of points N: dimension
％ ytrain: (Ltrain,1) containing class labels (-1 or +1)
％ xrun: (Lrun,N) with Lrun: number of points N: dimension
％ atrain: alpha coefficients (from svcm_train on xtrain and ytrain)
％ btrain: offest coefficient (from svcm_train on xtrain and ytrain)
％
％ ypred: predicted y (Lrun,1) containing class labels (-1 or +1)
％ margin: (signed) separation from the separating hyperplane (Lrun,1
Knowledge of the process noise covariance matrix
is essential for the application of Kalman filtering. However,
it is usually a difficult task to obtain an explicit expression of
for large time varying systems. This paper looks at an adaptive
Kalman filter method for dynamic harmonic state estimation and
harmonic injection tracking.
The aim of this application note is to show to scan the 4x4 matrix keypad
multiplexed with a four 7-segment display. The software attached to this application
note scans the pressed key and displays it on the multiplexed 7- segment LEDs.
This application note makes obvious Interrupt capability of the STR710 device
to offer a better key scan .
