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Radar Data Fusion in the Stradar System

Science and Technology’, 2006, Vol. 14, No. 3, pp. 182–189. [10] National Marine Electronics Association, NMEA 0183: Standard for interfacing marine electronic devices, version 3.01, 2002. [11] Stateczny A., AIS and Radar Data Fusion for Maritime Navigation , ‘Scientific Journals of the Maritime University of Szczecin’, 2004, No. 2, pp. 329–336. [12] Terma A/S, SCANTER Track Management Protocol, Document No. 303949 SI, Denmark, 2011, [online], http://www.umgdy.gov.pl/wp-content/uploads/2018/05/WI_SCANTER_TRACK.pdf [access 21.04.2019]. [13

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Data fusion in a navigational decision support system on a sea-going vessel

References 1. Bar-Shalom Y., Willett P., Tian X.: Tracking and data fusion. YBS Publishing, Storrs 2011 2. Borkowski P.: Algorithm of multi-sensor navigational data fusion - testing of estimation quality. Polish Journal of Environmental Studies, Vol. 17, No. 3B, 2008 (43-47) 3. Borkowski P., Pietrzykowski Z., Magaj J. Mąka M.: Fusion of data from GPS receivers based on a multi-sensor Kalman filter. Transport Problems, Vol. 3, No. 4, 2008 (5-11) 4. Borkowski P., Stateczny A.: An

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Road Traffic Measurement and Related Data Fusion Methodology for Traffic Estimation

References 1. Anand, A., Ramadurai, G. and Vanajakshi, L. (2013). Data Fusion Based Traffic Density Estimation and Prediction, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, accepted author version, DOI: 10.1080/15472450.2013.806844 2. Bachmann, C. (2011). Multi-Sensor Data Fusion for Traffic Speed and Travel Time Estimation , MSc Thesis, Toronto 3. Böker, G., Lunze, J. (2002). Stability and performance of switching Kalman filters, International Journal of Control , 75(16/17): 1269-1281 4. Claudel, C. G

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Architecture of Maritime Awareness System Supplied with External Information

-oriented sensor data fusion for wide maritime surveillance, Waterside Security Conference (WSS), 2010, pp. 1-6. [7] Giraud M. A., Alhadef B., Guarnieri F., Napoli A., Bottala-Gambetta M., et al., SARGOS: Securing Offshore Infrastructures Through a Global Alert and Graded Response System, Maritime System and Technology, 2011, Marseille, France. [8] Hevner A., March S. T., Park J., Ram S., Design science in information systems research, ‘MIS Quartely’, 2004, Vol. 28, No. 1, pp. 75-105. [9] International Maritime Organization

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Target Identification Using Sensors of Different Nature

.pdf [10] F. Castanedo, “A Review of Data Fusion Techniques,” The Scientific World Journal , vol. 2013, pp. 1–19, 2013. https://doi.org/10.1155/2013/704504 [11] J. Thomanek, M. Ritter, H. Lietz, and G. Wanielik, “Comparing Visual Data Fusion Techniques Using FIR and Visible Light Sensors to Improve Pedestrian Detection,” 2011 International Conference on Digital Image Computing: Techniques and Applications , pp. 119–125, Dec. 2011. https://doi.org/10.1109/dicta.2011.27 [12] M. Kristou, A. Ohya, and S. Yuta, “Target Person Identification and Following Based

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Fusion of clinical data: A case study to predict the type of treatment of bone fractures

, Hershey, PA, pp. 805–829. Castanedo, F. (2013). A review of data fusion techniques, The Scientific World Journal 2013 : 704504, DOI: 10.1155/2013/704504. Cha, Y.-H., Ha, Y.-C., Yoo, J.-I., Min, Y.-S., Lee, Y.-K. and Koo, K.-H. (2017). Effect of causes of surgical delay on early and late mortality in patients with proximal hip fracture, Archives of Orthopaedic and Trauma Surgery 137 (5): 625–630. de Bruijne, M. (2016). Machine learning approaches in medical image analysis: From detection to diagnosis, Medical Image Analysis 33 : 94–97, DOI: 10

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Fusion Estimation of Point Sets from Multiple Stations of Spherical Coordinate Instruments Utilizing Uncertainty Estimation Based on Monte Carlo

References Estler, W. T., Edmundson, K. L., Peggs, G. N., Parker, D. H. (2002). Large-scale metrology - an update. CIRP Annals-Manufacturing Technology , 51 (2), 587-608. Luo, R. C., Yih, C.-C., Su, K. L. (2002). Multisensor fusion and integration: Approaches, applications, and future research directions. IEEE Sensors Journal , 2 (2), 107-119. More, K., Ingman, D. (2008). Quality approach for multi-parametric data fusion. NDT&E International , 41 (3), 155

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Using Geobia and Data Fusion Approach for Land use and Land Cover Mapping

-Resolution Imagery for Urban Land Use Classification. In: IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Area, 35-39. Rome, University of Rome LA Sapienza: IEEE . DOI : 10.1109/ DFUA.2001.985721. Nussbaum S., Niemeyer I., Canty M.J., 2006. SEaTH - A New Tool for Automated Feature Extraction in the Context of Object-Oriented Image Analysis. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVI-4/C4. Saadat H., Adamowski J., Bonnell R., Sharifi F., Namdar M., Ale-Ebrahim S., 2011

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On Proxy Variables and Categorical Data Fusion

Abstract

The problem of inference about the joint distribution of two categorical variables based on knowledge or observations of their marginal distributions, to be referred to as categorical data fusion in this paper, is relevant in statistical matching, ecological inference, market research, and several other related fields. This article organizes the use of proxy variables, to be distinguished from other auxiliary variables, both in terms of their effects on the uncertainty of fusion and the techniques of fusion. A measure of the gains of efficiency is provided, which incorporates both the identification uncertainty associated with data fusion and the sampling uncertainty that arises when the theoretical bounds of the uncertainty space are unknown and need to be estimated. Several existing techniques for generating fusion distributions (or datasets) are described and some new ones proposed. Analysis of real-life data demonstrates empirically that proxy variables can make data fusion more precise and the constructed fusion distribution more plausible.

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Statistical Matching as a Supplement to Record Linkage: A Valuable Method to Tackle Nonconsent Bias?

2014, Bari, Italy: 37–40. Fosdick, B.K., M. DeYoreo and J.P. Reiter. 2016. “Categorical Data Fusion using Auxiliary Information.” The Annals of Applied Statistics 10(4): 1907–1929. Doi: http://dx.doi.org/10.1214/16-AOAS925 . Fulton, J.A. 2012. Respondent Consent to Use Administrative Data, Ph. D. thesis, University of Maryland. GDPR. 2016. “Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data

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