Writing this book was hard work, but also a lot of fun. Thanks to everyone who made it
Possible, especially Jinny Verdonck, Thomas Kraft, Ricky Nkrumah, Kirk Bateman, and the
whole Apress crew. A big thanks goes also to the community that continuously extends and
improves J2ME Polish!
The idea behind differential GPS is to remove as much errors as Possible from the range measurements by establishing these errors at a reference site. In its most simple setup, a GPS receiver is located at a well surveyed position and its (pseudo) range measurements are compared with the actual calculated range from this receiver to the SV s. The differences between measured ranges and calculated ranges at the reference receiver are applied as corrections to the ranges measured by other receiver(s) close by.
漢諾塔！！！
Simulate the movement of the Towers of Hanoi puzzle Bonus is Possible for using animation
eg. if n = 2 A→B A→C B→C
if n = 3 A→C A→B C→B A→C B→A B→C A→C
The existence of numerous imaging modalities makes it Possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented.
-The existence of numerous imaging modalities makes it Possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. Mutual Information is the similarity measure used in this case for optimizing the two images. This method requires estimating joint histogram of the two images. The fusion of images is the process of combining two or more images into a single image retaining important features from each. The Discrete Wavelet Transform (DWT) has become an attractive tool for fusing multimodal images. In this work it has been used to segment the features of the input images to produce a region map. Features of each region are calculated and a region based approach is used to fuse the images in the wavelet domain.
