AppleCare Technician Training: How to find the registration number
Learn how to find the registration number for AppleCare Technician Training. (For other types of AppleCare agreements, go to How to find your AppleCare registration number.)
I would THANK if you answer my doubts in DETAILS please.
Here's a sample code to create a device:
if(scull_major) {
dev=MKDEV(scull_major, scull_minor);
result=register_chrdev_region(dev, scull_nr_devs, "scull");
} else{
result=alloc_chrdev_region(&dev, scull_minor,... (2 Replies)
Hi guys,
Can you help me please to find an appropriate course of UNIX in the United States.
Also, can you provide me some information about the schools or institutes that offer it in the U.S.
Thanks, (0 Replies)
ANTS(1) User Commands ANTS(1)NAME
ANTS - part of ANTS registration suite
DESCRIPTION
Example usage:
./ANTS ImageDimension -m MI[fixedimage.nii.gz,movingimage.nii.gz,1,32] -o Outputfname.nii.gz -i 30x20x0 -r Gauss[3,1] -t Elast[3]
Compulsory arguments:
ImageDimension: 2 or 3 (for 2 or 3 Dimensional registration)
-m: Type of similarity model used for registration.
For intramodal image registration, use:
CC = cross-correlation MI = mutual information PR = probability mapping MSQ = mean square difference
For intermodal image registration, use:
MI = mutual information PR = probability mapping
-o Outputfname.nii.gz: the name of the resulting image.
-i Max-iterations in format: JxKxL, where:
J = max iterations at coarsest resolution (here, reduce by power of 2^2) K = middle resolution iterations (here,reduce by power of
2) L = fine resolution iterations (here, full resolution). This level takes much more time per iteration!
Adding an extra value before JxKxL (i.e. resulting in IxJxKxL) would add another iteration level.
-r Regularization
-t Type of transformation model used for registration
For elastic image registration, use:
Elast = elastic transformation model (less deformation possible)
For diffeomorphic image registration, use:
Syn[GradStep,TimePoints,IntegrationStep] --geodesic 2 = SyN with time with arbitrary number of time points in time discretization
SyN[GradStep,2,IntegrationStep] = SyN with time optimized specifically for 2 time points in the time discretization SyN[GradStep] =
Greedy SyN, typicall GradStep=0.25 Exp[GradStep,TimePoints] = Exponential GreedyExp = Diffeomorphic Demons style exponential mapping
Please use the `ANTS -h ` call or refer to the ANTS.pdf manual or antsIntroduction.sh script for additional information and typical
values for transformation models
ANTS 1.9 May 2012 ANTS(1)
