detection of brain tissues using magnetic resonance imaging (mri) is an active and challenging research area in computational neuroscience. brain mri artifacts lead to an uncertainty in pixel values. therefore, brain mri segmentation is a complicated concern which is tackled by a novel data fusion approach. the proposed algorithm has two main steps. in the first step the brain mri is divided to...

We propose and analyze trust-based cooperative spectrum sensing data fusion schemes against spectrum sensing data falsification attacks in cognitive radio networks. We first consider the case in which a centralized data fusion center is in place for decision making. Then we extend it to the case in which the data fusion center is absent leading to autonomous and distributed decision making. Our...

Recognition problems in computer vision often benefit from 5 5 a fusion of different algorithms and/or sensors, with score level fusion be6 6 ing among the most widely used fusion approaches. Score level fusion re7 7 quires the different data to be normalized before combining. Choosing an 8 8 appropriate score normalization technique before fusion is a fundamen9 9 tally difficult problem becaus...

A brief introduction to Bayesian probability theory is given with emphasis on data analysis. The analysis of Thomson scattering data is discussed. Capabilities for assessing diagnostic set-ups are outlined. Extension to more complex analyses aiming at an Integrated Data Analysis of fusion diagnostics are presented. The efforts are motivated both by physics and technical requirements of steady-s...