Slight differences in the percentage of titanium among the three pedicle screw systems does not appear to result in artifact differences during MR imaging. Therefore, with regard to imaging profile considerations, the three systems studied should be considered interchangeable.

Slight differences in the percentage of titanium among the three pedicle screw systems does not appear to result in artifact differences during MR imaging. Therefore, with regard to imaging profile considerations, the three systems studied should be considered interchangeable.

Recently Light Emitting Diodes (LEDs) are considered to represent the next generation of lighting and communication technology. LED-ID (Light Emitting Diode-Identification) is one of the key technologies for identification, data transmission and illumination simultaneously. This is the new paradigm in the identification technology environment. LED-ID system typically needs line of sight (LOS) to support narrow FOV transceivers links to achieve high data rate but reduced the coverage, increase the disconnection rate of the link. Also when the receiver moves horizontally or vertically, in that case coverage also varied and LOS relies upon direct link between transmitter and receiver. Link recovery is a new challenge for reliable LED-ID system. Fast link recovery techniques are essential for the making the system robust. In this paper we discuss about the horizontal and vertical coverage variation in terms of received power and fast link recovery technique for the LED-ID system considering both LOS and NLOS reflection.

No abstract

Multi-modal registration is a fundamental step for many medical imaging procedures. In this paper, the sum of conditional variance (SCV) similarity measure is proposed for 3D multi-modal medical image registration. The SCV similarity measure is based on minimizing the sum of conditional variances that are calculated using the joint histogram of the two images to be registered. Standard Gauss-Newton optimization is used to automatically minimize this measure which allows fast computational time and high accuracy. Experimental results show that our proposed approach is robust, computationally efficient and also more accurate when compared with the standard mutual information (MI) based approach and also the recently proposed sum-of-squared-difference on entropy images (eSSD) approach.