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SPATIAL DATABASE MODELING FOR INDOOR NAVIGATION SYSTEMS

, The Report on the work of the Department of Cartography , Wydział Geodezji i Kartografii, Politechnika Warszawska. Gotlib, D. & Marciniak, J. (2012). Cartographical aspects in the design of indoor navigation systems. Annual of Navigation no 19/2012, Polish Navigation Forum Isikdag, U., Zlatanova, S. & Underwood, J. (2013). A BIM-Oriented Model for supporting indoor navigation requirements, Computers, Environment and Urban Systems, Volume 4, September 2013, pp. 112-123. Lee, J. & Zlatanova, S. (2008). A 3D data

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Cartographical Aspects in the Design of Indoor Navigation Systems

References [1] Giorgetti G., Farley R., Chikkappa K., Ellis J., Kaleas T., Cortina: Collaborative Indoor Positioning Using Low-Power Sensor Networks, International Conference on Indoor Positioning and Indoor Navigation, Guimarăes, Portugal, 2011. [2] Gotlib D., Methodology of cartographic presentation in mobile positioning and navigation systems (in Polish), Prace Naukowe Politechniki Warszawskiej, Geodezja, 2011, No. 48. [3] Gotlib D., Development of cartographic presentation methods in mobile systems

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Development of a Web-Based Indoor Navigation System Using an Accelerometer and Gyroscope: A Case Study at The Faculty of Natural Sciences of Comenius University

Technology (ICACT), 15th International Conference, IEEE, PyeongChang, South Korea, pp. 1146-1150. Ilkovičová, L. - Kajánek, P. - Kopáčik, A. (2016) Pedestrian Indoor Positioning and Tracking using Smartphone Sensors, Step Detection and Map Matching Algorithm. Geodetski list, Vol. 70 (93), No. 1, pp. 1-24. Jain, M. - Rahul, R. C. P. - Tolety, S. B. (2013) A study on indoor navigation techniques using smartphones. Advances in Computing, Communications and Informatics (ICACCI): IEEE Conference Publications, IEEE, Jaipur, India, pp. 1113

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Indoor Navigation Using Particle Filter and Sensor Fusion

., Lihua X., Secure and robust Wi-Fi fingerprinting indoor localization. International Conference on Indoor Positioning and Indoor Navigation, 2011, pp. 1-7. [7] Retscher G., Fu Q., Continuos Indoor Navigation with RFID and INS, Position Location and Navigation Symposium (PLANS), 2010, pp. 102-112. [8] Song Y., Yu H., A RSS Based Indoor Tracking Algorithm via Particle Filter and Probability Distribution. 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008, pp. 1-4.

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Cloud Based Patient Monitoring Platform Using Android Smartphone Sensors

Abstract

This paper presents a proof of the concept cloud based patient monitoring and self-care platform, powered by measurements provided from various smartphone sensors. The Cloud platform provides the infrastructure and computational capacity for calculation of the navigation and motion tracking system, fall detection monitoring, as well as emergency notifications. The navigation system uses the pedometer and fusion of the accelerometer, gyroscope and magnetometer sensors. It aims to estimate precisely the patient’s movement and location. While both navigation and tracking systems can independently determine the incremental movement and indoor localization of the patients, they are fused in order to provide more accurate estimations. The fall detection monitoring is enabled by processing the raw data collected from the smartphone’s accelerometer and gyroscope. Furthermore, the cloud system provides various statistics for the physical activity of the patients, based on measurements from the pedometer. Consequently, this paper proposes a proof of the concept cloud based platform that is scalable and highly responsive, used for real-time monitoring and tracking a large number of patients. It also provides indoor navigation and other self-care features.

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An Example and Analysis for Ambiguity Resolution in the Indoor ZigBee Positioning System

Abstract

This paper presents ambiguity resolution in the range-based ZigBee positioning system. The system is using the phase shift measurements to determine the distances between user and anchors. In this paper, the ambiguity is defined as the number of full reps of a certain distance added to the measurement result. The way of resolving ambiguities in the positioning system is described and an experiment results are presented. Featured algorithm is successful in finding ambiguities and correct location of the user.

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Online data clustering algorithms in an RTLS system

References [1] S. Albers,H. Bals, Dynamic TCP acknowledgement: Penalizing long delays, SIAM J. Discrete Math. 19, 4 (2005) 938-951. →7 [2] M. Brugger, T. Christ, F. Kemeth, S. Nagy, M. Schaefer, M. M. Pietrzyk, The FMCW technology-based indoor localization system, Proc. Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), Helsinki, Finnland, 2010, pp. 1-6. →6 [3] M. Brugger, F. Kemeth, Locating rate adaptation by evaluating movement specific parameters, Proc. 2010 NASA/ESA Conference on

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Individual Autonomous Navigation System

Navigation Based on a Waist-Worn Inertial Sensor”, Sensors (Basel), 12, pp. 10536-10549. [9] Grejner-Brzezinska D. A., Toth C. K., Moafipoor S., Hyoun Kwon J., 2017, “Design and calibration of a neural network-based adaptive knowledge system for multi-sensor personal navigation”, from: http://www.isprs.org/proceedings/XXXVI/5-C55/papers/dorota_brzezinska.pdf . [10] Nilsson J. O., Skog I., Handel P., 2012, “A note on the limitations of ZUPTs and the implications on sensor error modeling”, 2012 International Conference on Indoor Positioning and Indoor Navigation

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A new model for the linear 1-dimensional online clustering problem

References [1] S. Albers, H. Bals, Dynamic TCP acknowledgment: Penalizing long delays, SIAM J. Discrete Math. 19, 4 (2005) 938-951. ⇒165 [2] A. Borodin, R. El-Yaniv, Online Computation and Competitive Analysis, Cambridge University Press, 1998. ⇒164 [3] M. Brugger, T. Christ, F. Kemeth, S. Nagy, M. Schaefer, M. M.Pietrzyk, The FMCW technology-based indoor localization system, in: Ubiquitous Positioning Indoor Navigation and Location Based Services, 2010, pp. 1-6. ⇒164 [4] M. Brugger, F. Kemeth

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Low-Cost Navigation and Guidance Systems for Unmanned Aerial Vehicles — Part 1: Vision-Based and Integrated Sensors

References [1] Blanc G., Mezouar Y., Martinet P., Indoor navigation of a wheeled mobile robot along visual routes, Proceeding of International Conference of Robotics & Automation, 2005, pp. 3354-3359. [2] CAA Safety Regulation Group Paper 2003/09, GPS Integrity and Potential Impact on Aviation Safety, 2003. [3] Chen Z., Birchfield S. T., Qualitative Vision-Based path following, IEEE Trans. on Robotics, June 2009, Vol. 25, issue 3, pp. 749-754. [4] Courbon J., Mezouar Y., Guenard N

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