Relevant bibliographies by topics / Data tracking

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data tracking'

Author: Grafiati
Published: 25 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Data tracking"

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Robinson, Sarah. "Tracking PICC Data." Journal of the Association for Vascular Access 20, no. 4 (December 2015): 244. http://dx.doi.org/10.1016/j.java.2015.10.025.

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Vasisht, Soumya, and Mehran Mesbahi. "Data-Guided Aerial Tracking." Journal of Guidance, Control, and Dynamics 43, no. 8 (August 2020): 1540–49. http://dx.doi.org/10.2514/1.g004601.

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Bar‐Shalom, Yaakov, Thomas E. Fortmann, and Peter G. Cable. "Tracking and Data Association." Journal of the Acoustical Society of America 87, no. 2 (February 1990): 918–19. http://dx.doi.org/10.1121/1.398863.

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Worton, Bruce J. "Modelling radio-tracking data." Environmental and Ecological Statistics 2, no. 1 (March 1995): 15–23. http://dx.doi.org/10.1007/bf00452929.

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DOLNICAR, SARA. "TRACKING DATA-DRIVEN MARKET SEGMENTS." Tourism Analysis 8, no. 2 (January 1, 2003): 227–32. http://dx.doi.org/10.3727/108354203774076788.

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Baba, Asif Iqbal, Hua Lu, Torben Bach Pedersen, and Manfred Jaeger. "Cleansing indoor RFID tracking data." SIGSPATIAL Special 9, no. 1 (July 13, 2017): 11–18. http://dx.doi.org/10.1145/3124104.3124108.

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Lillibridge, Fred. "Retention tracking using institutional data." New Directions for Community Colleges 2008, no. 143 (June 2008): 19–30. http://dx.doi.org/10.1002/cc.332.

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Xu, Wan Li, Zhun Liu, and Jun Hui Liu. "Extended Probabilistic Data Association Algorithm." Applied Mechanics and Materials 380-384 (August 2013): 1600–1604. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1600.

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[Purpos In order to improve the accuracy of target tracking and reduce losing rate of target in the multiple target tracking, a new algorithm called Extended Probabilistic Data Association (EPDA) is presented in this paper. [Metho This paper defines joint association event based on the number of target and puts forward the EPDA for target tracking. [Result Experimental results show that this algorithm has higher accuracy of target tracking than the Probabilistic Data Association algorithm and costs much less time relative to the Joint Probabilistic Data Association algorithm. [Conclusion Consequently, EPDA is an effective algorithm to balance the accuracy and the losing rate in target tracking.
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Taheem, Anubhav. "Optimization of Sun Tracking Data Handling to Improve Efficiency of PV Module." Journal of Advanced Research in Alternative Energy, Environment and Ecology 06, no. 01 (August 23, 2019): 1–15. http://dx.doi.org/10.24321/2455.3093.201901.

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Buckland, S. T., G. C. White, and R. A. Garrott. "Analysis of Wildlife Radio-Tracking Data." Biometrics 47, no. 1 (March 1991): 353. http://dx.doi.org/10.2307/2532535.

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Dissertations / Theses on the topic "Data tracking"

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Liu, Yang. "Statistical methods for big tracking data." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/60916.

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Recent advances in technology have led to large sets of tracking data, which brings new challenges in statistical modeling and prediction. Built on recent developments in Gaussian process modeling for spatio--temporal data and stochastic differential equations (SDEs), we develop a sequence of new models and corresponding inferential methods to meet these challenges. We first propose Bayesian Melding (BM) and downscaling frameworks to combine observations from different sources. To use BM for big tracking data, we exploit the properties of the processes along with approximations to the likelihood to break a high dimensional problem into a series of lower dimensional problems. To implement the downscaling approach, we apply the integrated nested Laplace approximation (INLA) to fit a linear mixed effect model that connects the two sources of observations. We apply these two approaches in a case study involving the tracking of marine mammals. Both of our frameworks have superior predictive performance compared with traditional approaches in both cross--validation and simulation studies. We further develop the BM frameworks with stochastic processes that can reflect the time varying features of the tracks. We first develop a conditional heterogeneous Gaussian Process (CHGP) but certain properties of this process make it extremely difficult to perform model selection. We also propose a linear SDE with splines as its coefficients, which we refer to as a generalized Ornstein-Ulhenbeck (GOU) process. The GOU achieves flexible modeling of the tracks in both mean and covariance with a reasonably parsimonious parameterization. Inference and prediction for this process can be computed via the Kalman filter and smoother. BM with the GOU achieves a smaller prediction error and better credibility intervals in cross validation comparisons to the basic BM and downscaling models. Following the success with the GOU, we further study a special class of SDEs called the potential field (PF) models, which formulates the drift term as the gradient of another function. We apply the PF approach to modeling of tracks of marine mammals as well as basketball players, and demonstrate its potential in learning, visualizing, and interpreting the trends in the paths.
Science, Faculty of
Statistics, Department of
Graduate
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Brown, K. D., and Chris Allen. "DYNAMIC TRACKING PHASED ARRAY DATA LINKS." International Foundation for Telemetering, 2004. http://hdl.handle.net/10150/605303.

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International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California
This paper describes a flexible telemetry data link developed by National Nuclear Safety Administration’s Kansas City Plant (NNSA-KCP) and the University of Kansas (KU) in support of NNSA’s Remote Sensing Laboratory (NNSA-RSL) located at the Nevada Test Site. This data link is based on a beam steerable phased array antenna (PAA). The paper describes the PAA and the Airborne Measurement System (AMS) application requiring signal source tracking. It highlights flight test data collected during recent flight testing on the Nevada Test Site for the AMS.
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Alam, Sayeed Safayet. "Analysis of Eye-Tracking Data in Visualization and Data Space." FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3473.

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Eye-tracking devices can tell us where on the screen a person is looking. Researchers frequently analyze eye-tracking data manually, by examining every frame of a visual stimulus used in an eye-tracking experiment so as to match 2D screen-coordinates provided by the eye-tracker to related objects and content within the stimulus. Such task requires significant manual effort and is not feasible for analyzing data collected from many users, long experimental sessions, and heavily interactive and dynamic visual stimuli. In this dissertation, we present a novel analysis method. We would instrument visualizations that have open source code, and leverage real-time information about the layout of the rendered visual content, to automatically relate gaze-samples to visual objects drawn on the screen. Since such visual objects are shown in a visualization stand for data, the method would allow us to necessarily detect data that users focus on or Data of Interest (DOI). This dissertation has two contributions. First, we demonstrated the feasibility of collecting DOI data for real life visualization in a reliable way which is not self-evident. Second, we formalized the process of collecting and interpreting DOI data and test whether the automated DOI detection can lead to research workflows, and insights not possible with traditional, manual approaches.
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Filippidis, Arthur. "Multisensor data fusion." Title page, contents and abstract only, 1993. http://web4.library.adelaide.edu.au/theses/09ENS/09ensf482.pdf.

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Čirkić, Mirsad. "Modular General-Purpose Data Filtering for Tracking." Thesis, Linköpings universitet, Institutionen för systemteknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-14917.

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In nearly allmodern tracking systems, signal processing is an important part with state estimation as the fundamental component. To evaluate and to reassess different tracking systems in an affordable way, simulations that are in accordance with reality are largely used. Simulation software that is composed of many different simulating modules, such as high level architecture (HLA) standardized software, is capable of simulating very realistic data and scenarios. A modular and general-purpose state estimation functionality for filtering provides a profound basis for simulating most modern tracking systems, which in this thesis work is precisely what is created and implemented in an HLA-framework. Some of the most widely used estimators, the iterated Schmidt extended Kalman filter, the scaled unscented Kalman filter, and the particle filter, are chosen to form a toolbox of such functionality. An indeed expandable toolbox that offers both unique and general features of each respective filter is designed and implemented, which can be utilized in not only tracking applications but in any application that is in need of fundamental state estimation. In order to prepare the user to make full use of this toolbox, the filters’ methods are described thoroughly, some of which are modified with adjustments that have been discovered in the process. Furthermore, to utilize these filters easily for the sake of user-friendliness, a linear algebraic shell is created, which has very straight-forward matrix handling and uses BOOST UBLAS as the underlying numerical library. It is used for the implementation of the filters in C++, which provides a very independent and portable code.
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Cirkic, Mirsad. "Modular General-Purpose Data Filtering for Tracking." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-14917.

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In nearly allmodern tracking systems, signal processing is an important part with state estimation as the fundamental component. To evaluate and to reassess different tracking systems in an affordable way, simulations that are in accordance with reality are largely used. Simulation software that is composed of many different simulating modules, such as high level architecture (HLA) standardized software, is capable of simulating very realistic data and scenarios.

A modular and general-purpose state estimation functionality for filtering provides a profound basis for simulating most modern tracking systems, which in this thesis work is precisely what is created and implemented in an HLA-framework. Some of the most widely used estimators, the iterated Schmidt extended Kalman filter, the scaled unscented Kalman filter, and the particle filter, are chosen to form a toolbox of such functionality. An indeed expandable toolbox that offers both unique and general features of each respective filter is designed and implemented, which can be utilized in not only tracking applications but in any application that is in need of fundamental state estimation. In order to prepare the user to make full use of this toolbox, the filters’ methods are described thoroughly, some of which are modified with adjustments that have been discovered in the process.

Furthermore, to utilize these filters easily for the sake of user-friendliness, a linear algebraic shell is created, which has very straight-forward matrix handling and uses BOOST UBLAS as the underlying numerical library. It is used for the implementation of the filters in C++, which provides a very independent and portable code.

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Yeo, Kwang Liang 1977. "Wireless data migration in a tracking application." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/85740.

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Afonso, Ricardo Filipe Dias Seara. "Tracking and data recording system for vehicles." Master's thesis, Universidade de Aveiro, 2013. http://hdl.handle.net/10773/12017.

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Mestrado em Engenharia Mecânica
When talking about road transport the biggest concern nowadays is the increase of the use of the road networks. From this augmentation arise various environmental, economical and social issues. The safety and preservation of the transported goods, especially when those are perishable is another issue gaining relevance. In this context the importance of the traceability of vehicles is increasing to ensure a reduction in fuel consumption and the quality of the transported goods. The aim of this master dissertation is to create an application that run over the Android operative system that will be able to get data from vehicle's ECUs and to send them in real time to a web based database. This application running on an Android mobile device will also provide an interface to enable user interaction. A web-interface will display in real time the data collected by the Android device (global position, data from the ECU, driver and management messages). On this WEB interface data will be displayed clearly, taking advantage of tools like Google Maps API. Being able to track the position of the vehicle and other parameters collected from the vehicle's ECU in real time is the first step towards a final goal of automating the creation of more economical and environmental routes in real time.
Atualmente, uma das maiores preocupações a nível dos transportes prende-se com a crescente utilização das redes rodoviárias. Assim, além dos vários problemas causados aos níveis ambiental, económico e social, é também cada vez maior a preocupação com a segurança e a preservação das mercadorias transportadas, especialmente quando estas são perecíveis. Neste contexto, tem aumentado a importância da rastreabilidade de veículos de forma a reduzir os consumos de combustível, assegurando, no entanto, a qualidade das mercadorias transportadas. O objetivo desta dissertação de mestrado é criar uma aplicação que corre sobre o sistema operativo Android que permitira recolher informação vinda da centralina do veículo, e envia-la em tempo real para uma base de dados na rede. Esta aplicação possibilitara, também, a interação com o seu utilizador. Um interface WEB servira para expor, em tempo real, os diversos dados recolhidos pelo dispositivo Android (posição global, dados provenientes da centralina, mensagens do condutor e do centro de controlo). Nessa interface WEB os dados serão expostos da forma mais clara possível recorrendo a ferramentas como por exemplo o Google Maps API. Ser capaz de saber em tempo real a localização global do veículo assim como adquirir dados da sua centralina, e um primeiro passo para conseguir a criação de um processo automático, capaz de produzir em tempo real rotas mais económicas e mais ecológicas.
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ÇERÇİ, SENA. "Embodying Self-Tracking: A Feminist Exploration of Collective Meaning-Making of Self-Tracking Data." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-22465.

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This Research-through-Design conducted as thesis project within Malmö University Interaction Design Master’s programme is an attempt to bridge the gap between the quantified self and the subjective & collective experiences of the self-tracking for less normative ways of meaning-making of data. In order to accomplish this, it offers a feminist critique of self-tracking and an exploration of new features for self-tracking apps using provotypes to inform the HCI community.
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Agirnas, Emre. "Multi-scan Data Association Algorithm For Multitarget Tracking." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605646/index.pdf.

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Data association problem for multitarget tracking is determination of the relationship between targets and the incoming measurements from sensors of the target tracking system. Performance of a multitarget tracking system is strongly related to the chosen method for data association and target tracking algorithm. Incorrect data association effects state estimation of targets. In this thesis, we propose a new multi-scan data association algorithm for multitarget tracking systems. This algorithm was implemented by using MATLAB programming tool. Performances of the new algorithm and JPDA method for multiple targets tracking are compared. During simulations linear models are used and the uncertainties in the sensor and motion models are modeled by Gaussian density. Simulation results are presented. Results show that the new algorithm'
s performance is better than that of JPDA method. Moreover, a survey over target tracking literature is presented including basics of multitarget tracking systems and existing data association methods.
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Books on the topic "Data tracking"

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Bar-Shalom, Yaakov. Tracking and data association. Boston: Academic Press, 1988.

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Bar-Shalom, Yaakov. Tracking and data association. Boston: Academic Press, 1988.

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Koch, Wolfgang. Tracking and Sensor Data Fusion. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-39271-9.

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C, White Gary. Analysis of wildlife radio-tracking data. San Diego: Academic Press, 1990.

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Nassimizadeh, Hamid. Data association and multiple target tracking. Birmingham: University of Birmingham, 1992.

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Adam, Marion A. Mixing simulations based on particle tracking data. Manchester: UMIST, 1996.

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Urbano, Ferdinando, and Francesca Cagnacci, eds. Spatial Database for GPS Wildlife Tracking Data. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03743-1.

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Wildlife radio tagging: Equipment, field techniques, and data analysis. London: Academic Press, 1987.

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Dorfman, Mark H. Tracking toxic chemicals: The value of materials accounting data. New York: INFORM, 1997.

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1971-, Liang Yan, Yang Feng 1977-, and Cheng Yongmei 1960-, eds. Xian dai mu biao gen zong yu xin xi rong he. Beijing Shi: Guo fang gong ye chu ban she, 2009.

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Book chapters on the topic "Data tracking"

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Spiller, Keith, Kirstie Ball, Arosha Bandara, Maureen Meadows, Ciaran McCormick, Bashar Nuseibeh, and Blaine A. Price. "Data Privacy: Users’ Thoughts on Quantified Self Personal Data." In Self-Tracking, 111–24. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65379-2_8.

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Tistarelli, Massimo. "On Tracking Edges." In Data Fusion Applications, 183–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84990-9_17.

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Shekhar, Shashi, and Hui Xiong. "Vehicle Tracking Data." In Encyclopedia of GIS, 1221. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1444.

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Ajana, Btihaj. "Communal Self-Tracking: Data Philanthropy, Solidarity and Privacy." In Self-Tracking, 125–41. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65379-2_9.

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Schulz, Christoph, Michael Burch, Fabian Beck, and Daniel Weiskopf. "Visual Data Cleansing of Low-Level Eye-Tracking Data." In Eye Tracking and Visualization, 199–216. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47024-5_12.

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Ratledge, David. "Satellite Tracking." In Software and Data for Practical Astronomers, 129–41. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0555-8_10.

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Munshi, Usha Mujoo. ": Tracking the Ecosystem." In Studies in Big Data, 1–31. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7515-5_1.

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Boire, Richard. "Tracking and Measuring." In Data Mining for Managers, 133–40. New York: Palgrave Macmillan US, 2014. http://dx.doi.org/10.1057/9781137406194_16.

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Boire, Richard. "Implementation and Tracking." In Data Mining for Managers, 141–42. New York: Palgrave Macmillan US, 2014. http://dx.doi.org/10.1057/9781137406194_17.

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Vrolijk, Benjamin, Freek Reinders, and Frits H. Post. "Feature Tracking with Skeleton Graphs." In Data Visualization, 37–52. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1177-9_3.

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Conference papers on the topic "Data tracking"

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Bather, J. "Tracking and data fusion." In IEE International Seminar Target Tracking: Algorithms and Applications. IEE, 2001. http://dx.doi.org/10.1049/ic:20010234.

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Fahed, Lina, and Ayman Alfalou. "Emergent pattern detection algorithm for big data streams." In Pattern Recognition and Tracking XXXI, edited by Mohammad S. Alam. SPIE, 2020. http://dx.doi.org/10.1117/12.2558536.

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Williams, Elmer F. "IR sensor data fusion for target detection, identification, and tracking." In Acquisition, Tracking, and POinting IV. SPIE, 1990. http://dx.doi.org/10.1117/12.2322205.

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Lu, Thomas T., Kevin Payumo, Landan Seguin, Alexander Huyen, Edward Chow, and Gil Torres. "Augmented reality data generation for training deep learning neural network." In Pattern Recognition and Tracking XXIX, edited by Mohammad S. Alam. SPIE, 2018. http://dx.doi.org/10.1117/12.2305202.

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Zhao, Liang, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. "Dynamic theme tracking in Twitter." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363800.

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Bassie, Abby L., Jeff Barnette, Dale Carter, and Robert Moorhead. "Image-based flight data recording for United States Air Force aircraft." In Pattern Recognition and Tracking XXIX, edited by Mohammad S. Alam. SPIE, 2018. http://dx.doi.org/10.1117/12.2299851.

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Moritz, Lennart, Andreas Specker, and Arne Schumann. "A study of person re-identification design characteristics for aerial data." In Pattern Recognition and Tracking XXXII, edited by Mohammad S. Alam. SPIE, 2021. http://dx.doi.org/10.1117/12.2587946.

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Tanner, G. "Tracking with state dependent measurement errors." In IEE Colloquium on Target Tracking and Data Fusion. IEE, 1996. http://dx.doi.org/10.1049/ic:19961356.

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Gordon, N., and D. Salmond. "Aspects of target tracking: problems and techniques." In IEE Colloquium on Target Tracking and Data Fusion. IEE, 1998. http://dx.doi.org/10.1049/ic:19980419.

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Efe, M., D. P. Atherton, and J. A. Bather. "Adaptive Kalman filters for manoeuvring target tracking." In IEE Colloquium on Target Tracking and Data Fusion. IEE, 1998. http://dx.doi.org/10.1049/ic:19980422.

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Reports on the topic "Data tracking"

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Jones, Brandon A., Gabriel C. LoDolce, Ben K. Bradley, and George H. Born. Data Association Algorithms for Tracking Satellites. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada582504.

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Pao, Lucy Y., Matthew Travers, and Todd Murphey. Data Association Algorithms for Tracking Satellites. Fort Belvoir, VA: Defense Technical Information Center, February 2008. http://dx.doi.org/10.21236/ada585775.

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Streit, Roy L., and Walter R. Lane. Tracking on Intensity-Modulated Data Streams. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada377255.

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Gibson, J. R., and M. Buchheit. Tracking Uncertainty in Derived Height Data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1996. http://dx.doi.org/10.4095/219780.

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Horpedahl, L., and M. Brooks. DOE R and D data tracking base. Office of Scientific and Technical Information (OSTI), December 2000. http://dx.doi.org/10.2172/768859.

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Fravell, Michael D. Joint Patient Tracking Application/Veterans Tracking Application: A Joint Platform for Interdepartmental Data Exchange. Fort Belvoir, VA: Defense Technical Information Center, June 2007. http://dx.doi.org/10.21236/ada493573.

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Parzen G. Tracking studies in RHIC using measured magnet data. Office of Scientific and Technical Information (OSTI), October 2000. http://dx.doi.org/10.2172/1061623.

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Bloomfield, R. A., and G. R. Dobson. Image-Data Transmission Demonstration over the Tracking and Data Relay Satellite System. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada352534.

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Kerr, C., N. McTyer, G. Holman, and C. De Grange. Performance Analysis: Issues Tracking System Data through June 2013. Office of Scientific and Technical Information (OSTI), April 2014. http://dx.doi.org/10.2172/1132021.

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Kerr, C., G. Holman, and N. McTyer. Performance Analysis: Issues Tracking System Data through September 2011. Office of Scientific and Technical Information (OSTI), July 2012. http://dx.doi.org/10.2172/1059079.

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