Abstract The proliferation of communication and mobile computing devices and local‐area wireless networks has cultivated a growing interest in location‐aware systems and services. An essential problem in location‐aware computing is the determination of physical locations. RFID technologies are gaining much attention, as they are attractive solutions to indoor localization in many healthcare applications. In this paper, we propose a new indoor localization methodology that aims to deploying RFID technologies in achieving accurate location‐aware undertakings with real‐time computation. The proposed algorithm introduces means to improve the accuracy of the received RF signals. Optimal settings for the parameters in terms of reader and reference tag properties were investigated through simulations and experiments. The experimental results indicate that our indoor localization methodology is promising in applications that require fast installation, low cost and high accuracy. Copyright © 2009 John Wiley & Sons, Ltd.

Physics-based particle systems are an effective tool for shape modeling. Also, there has been much interest in the study of shape modeling using deformable contour approaches. In this paper, we describe a new deformable model with electric flows based upon computer simulations of a number of charged particles embedded in an electrostatic system. Making use of optimized numerical techniques, the electric potential associated with the electric field in the simulated system is rapidly calculated using the finite-size particle (FSP) method. The simulation of deformation evolves based upon the vector sum of two interacting forces: one from the electric fields and the other from the image gradients. Inspired by the concept of the signed distance function associated with the entropy condition in the level set framework, we efficiently handle topological changes at the interface. In addition to automatic splitting and merging, the evolving contours enable simultaneous detection of various objects with varying intensity gradients at both interior and exterior boundaries. This electric flows approach for shape modeling allows one to connect electric properties in electrostatic equilibrium and classical active contours based upon the theory of curve evolution. Our active contours can be applied to model arbitrarily complicated objects including shapes with sharp corners and cusps, and to situations where no a priori knowledge about the object's topology and geometry is made. We demonstrate the capabilities of this new algorithm in recovering a wide variety of structures on simulated and real images in both 2D and 3D.

Compared to object-based registration, feature-based registration is much less complex. However, in order for feature-based registration to work, the two image stacks under consideration must have the same acquisition tilt angle and the same anatomical location - two requirements that are not always fulfilled. In this paper, we propose a technique that reconstructs two sets of medical images acquired with different acquisition angles and anatomical cross sections into one set of images of identical scanning orientation and positions. The space correlation information among the two image stacks is first extracted and is used to correct the tilt angle and anatomical position differences in the image stacks. Satisfactory reconstruction results were presented to prove our points.