Data fusion

BE-3S81 Past film replacement system for high resolution X>ray weld inspection with ultrasonic data fusion Mr M. Erikaen ROBITAS... 

In general, different similarity measures yield different rankings, except when they are monotonic. Improved results are obtained by using data fusion methods to combine the rankings resulting from different coefficients. 

Tlic first report was Opportunities to Improve Airport Passenger Screening with Mass Spectrometry (The National Academies Press, Washington, D.C., 2004). Topics to be addressed in future reports are millimeter-wave imaging for detection of explosives and data fusion and integration for airport terminals. 

Donald E. Brown is chair of the Department of Systems Engineering of the University of Virginia. His research focuses on data fusion and simulation optimization, with ap-... 

In this section, we first define the process and observation models for target tracking. Then the foundations of the distributed data fusion architecture are presented. 

Instead of sharing the measurements related to the target state among the collaborating sensors, sharing the information form of the observations results in a simple additive fusion framework that can be run on each of the tiny sensing devices. The distributed data fusion equations are... 

The algorithm employed by a sensor for tracking targets in a collaborative manner within the distributed data fusion framework is depicted in Fig. 7. The information state and the information matrix are defined by (5). The predicted information state and the information matrix are computed by (7). The sensor s current belief is updated by its own... 

We run Monte Carlo simulations to examine the performance of the sensor selection algorithm based on the maximization of mutual information for the distributed data fusion architecture. We examine two scenarios first is the sparser one, which consists of 50 sensors which are randomly deployed in the 200 m x 200 m area. The second is a denser scenario in which 100 sensors are deployed in the same area. All data points in the graphs represent the means of ten runs. A target moves in the area according to the process model described in Section 4. We utilize the Neyman-Pearson detector [20, 30] with a = 0.05, L = 100, r) = 2, 2-dB antenna gain, -30-dB sensor transmission power and -6-dB noise power. 

A mutual information based information measure is adopted to select the most informative subset of sensors to actively participate in the distributed data fusion framework. The duty of the sensors is to accurately localize and track the targets. Simulation results show 36% energy saving for a given tracking quality can be achievable by selecting the sensors to cooperate according to the mutual information metric. 

Chemometrican Data management and data fusion Process data analysis Multivariate data analysis Analyzer calibration model development Method equivalence Process models development (e.g., MSPC) Experimental design (e.g., DOE)... 

Y. Liu and S.D. Brown, Wavelet multiscale regression from the perspective of data fusion. Anal. Bioanalytical Chemistry, 380, 445-52 (2004). 

It is recommended to use data fusion techniques such as the Fuzzy approach or other methods like the Neuro-Fuzzy on surface data to locate the most promising sites for drilling. 

Hert J, WiUett P, Wilton DJ, Addin P, Azzaoui K, Jacoby E, Schuffenhauer A. (2005) New Methods for Ligand-Based Virtual Screening Use of Data-Fusion and Machine-Learning Techniques to Enhance the Effectiveness of Similarity Searching. /. Chem. Inf. Model, (in the press). 

Ginn CMR, Willett P, Bradshaw J. (2000) Combination of Molecular Similarity Measures Using Data Fusion. Perspect. Drug Discov. Des. 20 1-16. 

Sahm N, Holliday J, Willett P. (2003) Combination of Fingerprint-Based Similarity Coefficients Using Data Fusion. /. Chem. Inf. Comp. Set. 43 435-442. Schuffenhauer A, Floersheim P, Acklin P, Jacoby E. (2003) Similarity Metrics for Ligands Reflecting the Similarity of the Target Proteins. J. Chem. Inf. Comp. Set. 43 391-405. 

Ginn, C. M. R., Willett, R, and Bradshaw, J. (2000) Combination of molecular similarity measures using data fusion. Perspec. Drug Disc. Design 20, 1-16. 

Crosta, G.F., Fumarola, L., Malerba, 1. and Gribaldo, L. (2007) Scoring CFU-GM colonies in vitro by data fusion a first account. Experimental Hematology, 35,1-12. 

Prakash, A., Fielding. E. J., Gens, R., van Genderen, J. L. Evans, D. L. 2001. Data fusion for investigating land subsidence and coal fire hazards in a coal mining area. International Journal of Remote Sensing, 22, 921-932. 

Keywords FTIR imaging SPR imaging Microstructured polymer Data fusion... 

The simultaneous use of multisource data can provide a more reliable view on the observed object. In order to exploit the information content, sophisticated color-related and numerical data fusion techniques have been developed [13], These techniques aim at the incorporation of complementary information for the initially independent results into a new combined data set in such a way that an information surplus can be retrieved from the combined data set. This information surplus remains inaccessible if individual data sets are evaluated separately. 

Fig. 10 Access to hidden information by data fusion contrast in the SPR image depends on the refractive index C = O str intensity in the FTIR image indicates the PMMA amount in the image voxel. FTIR differentiates very well between pore and bulk regions but not between pores w/o electrolyte. SPR differentiates very well between pores w/o electrolyte but not between filled pore and bulk regions...

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