Zeitschriftenartikel: „Data tracking“

1

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

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2

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

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3

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

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4

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

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5

Li, Chaofeng. "Data Mining-Based Tracking Method for Multisource Target Data of Heterogeneous Networks." Wireless Communications and Mobile Computing 2022 (August 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/1642925.

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In order to solve the problem that the target is easily lost in the process of multisource target data fusion tracking, a multisource target data fusion tracking method based on data mining is proposed. Multisource target data fusion tracking belongs to location level fusion. Firstly, a hybrid heterogeneous network fusion model is established, and then, data features are extracted, and a fusion source big data acquisition algorithm is designed based on compressed sensing to complete data preprocessing to reduce the amount of data acquisition. Based on data mining association multisource fusion target, get the relationship between each measurement and target, and build multisource target data fusion tracking model to ensure the stable state of fusion results. It shows that the proposed method can save the tracking time and improve the tracking accuracy compared with the methods based on NNDA and PDA, which is more conducive to the real-time tracking of multisource targets.

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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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Dureja, Pankaj. "Tracking ETL Data Load Management Using JIRA: A Comprehensive Approach." International Journal of Science and Research (IJSR) 7, no. 4 (2018): 1787–89. http://dx.doi.org/10.21275/sr24615145303.

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8

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

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9

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

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10

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

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11

R.Gayathri*1, S.Kiruthika2 &. P.Keerthana3. "T-TRACKING ALGORITHM FOR DATA TRACKING IN WIRELESS SENSOR NETWORKS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 4 (2018): 517–23. https://doi.org/10.5281/zenodo.1219647.

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In Wireless Sensor Network the sensor nodes are being dispersed spatially, so the target tracking has become a key factor. In the existing system they have used the Face Tracking for tracking the target. They have developed non-overlapping region called Face. In that they have used Brink Detection algorithm for selecting the edges and Optimal Selection algorithm for selecting sensor node in each face. However, if the selected node fails then tracking accuracy will be lost. In this paper we have a new tracking scheme, called t-Tracking is designed to overcome the target tracking problem in WSNs considering multiple objectives: low capturing time, high quality of tracking (QoT). A set of fully distributed tracking algorithms is proposed, which answers the query whether a target remains in a “specific area” (called a “face”). When a target moves from one face to another face all the possible movements will be mentioned. Then query will be sent to all those nodes about their energy and coverage area. Based on the response from those nodes the best nodes will be selected for continuing tracking when the target moves to the next face. The result of this t-Tracking is compared with already existing face tracking..

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Gabor-Siatkowska, Karolina, Izabela Stefaniak, and Artur Janicki. "Eye tracking data cleansing for dialogue agent." Biuletyn Wrocławskiej Wyższej Szkoły Informatyki Stosowanej. Informatyka 10, no. 1 (2024): 1–14. https://doi.org/10.5281/zenodo.11370427.

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13

Guo, Hongyan, and Xintao Li. "Multisource Target Data Fusion Tracking Method for Heterogeneous Network Based on Data Mining." Wireless Communications and Mobile Computing 2022 (June 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/9291319.

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This research is on heterogeneous network fusion method of multisource target data based on data mining. Firstly, it is a distributed storage structure model for building heterogeneous network multisource target data. Then, using the phase space reconstruction method, a grid distribution structure model for data fusion tracking is constructed, and realize visual scheduling and automatic monitoring of multisource target data. Finally, according to the feature extraction results, analyze the statistical characteristics of multisource target data in heterogeneous networks, combined with the fuzzy tomographic analysis method, multilevel fusion, and adaptive mining of multisource target data, extract the associated feature quantities in it, and realize the fusion tracking of data. The simulation results show that, in relatively simple heterogeneous networks, the feature mining error of the proposed method is nearly 2.11% lower than the two traditional methods. In relatively complex heterogeneous networks, the feature mining error of the proposed method is nearly 6.48% lower than the two traditional methods. It can be seen that this method has better adaptability for fusion tracking of heterogeneous network multisource target data, the anti-interference ability is strong, and the tracking accuracy in the data fusion tracking process is also improved.

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Kumar Agarwal, Vivek. "A Novel Methodology for Tracking and Remediating Third - Party Data Breaches." International Journal of Science and Research (IJSR) 13, no. 10 (2024): 1001–2. http://dx.doi.org/10.21275/sr241013000734.

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15

Zhang, J., W. Xiao, B. Coifman, and J. P. Mills. "IMAGE-BASED VEHICLE TRACKING FROM ROADSIDE LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1177–83. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1177-2019.

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<p><strong>Abstract.</strong> Vehicle tracking is of great importance in urban traffic systems, and the adoption of lidar technologies &amp;ndash; including on-board and roadside systems &amp;ndash; has significant potential for such applications. This research therefore proposes and develops an image-based vehicle-tracking framework from roadside lidar data to track the precise location and speed of a vehicle. Prior to tracking, vehicles are detected in point clouds through a three-step procedure. Cluster tracking then provides initial tracking results. The second tracking stage aims to provide more precise results, in which two strategies are developed and tested: frame-by-frame and model-matching strategies. For each strategy, tracking is implemented through two threads by converting the 3D point cloud clusters into 2D images relating to the plan and side views along the tracked vehicle’s trajectory. During this process, image registration is exploited in order to retrieve the transformation parameters between every image pair. Based on these transformations, vehicle speeds are determined directly based on (a) the locations of the chosen tracking point in the first strategy; (b) a vehicle model is built and tracking point locations can be calculated after matching every frame with the model in the second strategy. In contrast with other existing methods, the proposed method provides improved vehicle tracking via points instead of clusters. Moreover, tracking in a decomposed manner provides an opportunity to cross-validate the results from different views. The effectiveness of this method has been evaluated using roadside lidar data obtained by a Robosense 32-line laser scanner.</p>

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Quan, Xunzhong, and Jie Chen. "Multi-Source Data Fusion and Target Tracking of Heterogeneous Network Based on Data Mining." Traitement du Signal 38, no. 3 (2021): 663–71. http://dx.doi.org/10.18280/ts.380313.

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Thanks to the technical development of target tracking, the multi-source data fusion and target tracking has become a hotspot in the research of huge heterogenous networks. Based on millimeter wave heterogeneous network, this paper constructs a multi-source data fusion and target tracking model. The core of the model is the data mining deep Q network (DM-DQN). Through image filling, the length of the input vector (time window) was extended from 25 to 31, with the aid of CNN heterogeneous network technology. This is to keep the length of input vector in line with that of output vector, and retain the time features of eye tracking data to the greatest extent, thereby expanding the recognition range. Experimental results show that the proposed model achieved a modified mean error of only 1.5m with a tracking time of 160s, that is, the tracking effect is ideal. That is why the DM-DQN outperformed other algorithms in total user delay. The algorithm can improve the energy efficiency of the network, while ensuring the quality of service of the user. In the first 50 iterations, DM-DQN worked poorer than structured data mining. After 50 iterations, DM-DQN began to learn the merits of the latter. After 100 iterations, both DM-DQN and structured data mining tended to be stable, and the former had the better performance. Compared with typical structured data mining, the proposed DM-DQN not only converges fast, but also boasts a relatively good performance.

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Fein, Rebecca, and Leila R. Kalankesh. "Data Fuels Detection: How to Prevent Epidemics Using Data." Frontiers in Health Informatics 10, no. 1 (2021): 59. http://dx.doi.org/10.30699/fhi.v10i1.269.

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Data for prevention and tracking of disease should begin prior to the outbreak. The bottleneck for early detecting outbreaks is data. The data are collected from different points of care and aggregated, then analyzed centrally to warn us about what is happening. However, this current pandemic has not utilized data for prevention and tracking in a meaningful way. We believe the prevention problem is the data problem and it should be addressed to prevent the future pandemics in an effective way.

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Singh, Arjun. "The HR Data Landscape: Transforming HR with Data-Driven Insights." Engineering and Applied Sciences Journal 2, no. 1 (2025): 01–02. https://doi.org/10.64030/3067-8005.02.01.06.

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In today’s rapidly evolving corporate world, the role of data in shaping HR practices and decisions cannot be overstated. Human Resources (HR) departments in large corporations are increasingly relying on data-driven approaches to drive their strategies, improve existing processes, and motivate employees to succeed in their roles. Tracking HR data allows organizations to gain valuable insights into employee feedback, performance, and engagement, ultimately leading to a healthier and more productive workplace.

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Gupta, Naina, and Tanu Jindal. "Target Tracking using Personalized Data Management." International Journal of Computer Applications 62, no. 17 (2013): 11–14. http://dx.doi.org/10.5120/10171-4838.

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20

Gao, Tao. "Data Association Based Tracking Traffic Objects." International Journal of Advanced Pervasive and Ubiquitous Computing 5, no. 2 (2013): 31–46. http://dx.doi.org/10.4018/japuc.2013040104.

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For the widely demanding of adaptive multiple moving objects tracking in intelligent transportation field, a new type of traffic video based multi-object tracking method is presented. Background is modeled by difference of Gaussians (DOG) probability kernel and background subtraction is used to detect multiple moving objects. After obtaining the foreground, shadow is eliminated by an edge detection method. A type of particle filtering combined with SIFT method is used for motion tracking. A queue chain method is used to record data association among different objects, which could improve the detection accuracy and reduce the complexity. By actual road tests, the system tracks multi-object with a better performance of real time and mutual occlusion robustness, indicating that it is effective for intelligent transportation system.

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Cluff, H. Dean, Gary C. White, and Robert A. Garrott. "Analysis of Wildlife Radio-Tracking Data." Journal of Wildlife Management 55, no. 2 (1991): 358. http://dx.doi.org/10.2307/3809166.

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22

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

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23

Iezzoni, Lisa I. "Tracking disability disparities: The data dilemma." Journal of Health Services Research & Policy 13, no. 3 (2008): 129–30. http://dx.doi.org/10.1258/jhsrp.2008.008034.

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24

Yamamoto, Takashi, Yutaka Watanuki, Elliott L. Hazen, Bungo Nishizawa, Hiroko Sasaki, and Akinori Takahashi. "Streaked Shearwaters: Tracking and Survey Data." Bulletin of the Ecological Society of America 96, no. 4 (2015): 659–61. http://dx.doi.org/10.1890/0012-9623-96.4.659.

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25

Gibson, J., and M. Buchheit. "Tracking Uncertainty in Derived Height Data." Cartographica: The International Journal for Geographic Information and Geovisualization 33, no. 1 (1996): 3–10. http://dx.doi.org/10.3138/e247-5528-364w-4n67.

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Koteswara Rao, S., K. S. Linga Murthy, and K. Raja Rajeswari. "Data fusion for underwater target tracking." IET Radar, Sonar & Navigation 4, no. 4 (2010): 576. http://dx.doi.org/10.1049/iet-rsn.2008.0109.

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ERICKSON, TY B. "Tracking Data in the Office Environment." Clinical Obstetrics and Gynecology 53, no. 3 (2010): 500–510. http://dx.doi.org/10.1097/grf.0b013e3181ec16a4.

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Wang, Yong, Xian Wei, Xuan Tang, Hao Shen, and Lu Ding. "CNN tracking based on data augmentation." Knowledge-Based Systems 194 (April 2020): 105594. http://dx.doi.org/10.1016/j.knosys.2020.105594.

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Liu, Ying, Konstantinos Tountas, Dimitris A. Pados, Stella N. Batalama, and Michael J. Medley. "L1-Subspace Tracking for Streaming Data." Pattern Recognition 97 (January 2020): 106992. http://dx.doi.org/10.1016/j.patcog.2019.106992.

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Angerbjörn, Anders. "Analysis of wildlife radio-tracking data." Animal Behaviour 44 (August 1992): 390. http://dx.doi.org/10.1016/0003-3472(92)90048-e.

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Niemczynowicz, Janusz. "Storm tracking using rain gauge data." Journal of Hydrology 93, no. 1-2 (1987): 135–52. http://dx.doi.org/10.1016/0022-1694(87)90199-5.

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Schwager, Mac, Dean M. Anderson, Zack Butler, and Daniela Rus. "Robust classification of animal tracking data." Computers and Electronics in Agriculture 56, no. 1 (2007): 46–59. http://dx.doi.org/10.1016/j.compag.2007.01.002.

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Zgonnikov, A., A. Aleni, P. T. Piiroinen, D. O'Hora, and M. di Bernardo. "Decision landscapes: visualizing mouse-tracking data." Royal Society Open Science 4, no. 11 (2017): 170482. http://dx.doi.org/10.1098/rsos.170482.

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Computerized paradigms have enabled gathering rich data on human behaviour, including information on motor execution of a decision, e.g. by tracking mouse cursor trajectories. These trajectories can reveal novel information about ongoing decision processes. As the number and complexity of mouse-tracking studies increase, more sophisticated methods are needed to analyse the decision trajectories. Here, we present a new computational approach to generating decision landscape visualizations based on mouse-tracking data. A decision landscape is an analogue of an energy potential field mathematically derived from the velocity of mouse movement during a decision. Visualized as a three-dimensional surface, it provides a comprehensive overview of decision dynamics. Employing the dynamical systems theory framework, we develop a new method for generating decision landscapes based on arbitrary number of trajectories. This approach not only generates three-dimensional illustration of decision landscapes, but also describes mouse trajectories by a number of interpretable parameters. These parameters characterize dynamics of decisions in more detail compared with conventional measures, and can be compared across experimental conditions, and even across individuals. The decision landscape visualization approach is a novel tool for analysing mouse trajectories during decision execution, which can provide new insights into individual differences in the dynamics of decision making.

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Tilmes, Curt, Yelena Yesha, and Milton Halem. "Tracking provenance of earth science data." Earth Science Informatics 3, no. 1-2 (2010): 59–65. http://dx.doi.org/10.1007/s12145-010-0046-3.

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Bouguelia, Mohamed-Rafik, Alexander Karlsson, Sepideh Pashami, Sławomir Nowaczyk, and Anders Holst. "Mode tracking using multiple data streams." Information Fusion 43 (September 2018): 33–46. http://dx.doi.org/10.1016/j.inffus.2017.11.011.

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Skolnick, Andrew A. "Joint Commission Begins Tracking Outcome Data." JAMA: The Journal of the American Medical Association 278, no. 19 (1997): 1562. http://dx.doi.org/10.1001/jama.1997.03550190026015.

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Skolnick, A. A. "Joint Commission begins tracking outcome data." JAMA: The Journal of the American Medical Association 278, no. 19 (1997): 1562. http://dx.doi.org/10.1001/jama.278.19.1562.

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Gessinger-Befurt, Paul, Andreas Salzburger, and Joana Niermann. "The Open Data Detector Tracking System." Journal of Physics: Conference Series 2438, no. 1 (2023): 012110. http://dx.doi.org/10.1088/1742-6596/2438/1/012110.

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Abstract Charged particle reconstruction in High Energy Physics experiments is a significant part of overall event reconstruction. Depending on the physics environment, for instance in collider experiments with high multiplicities or luminosities, the tracking problem increases in complexity and often poses not only an algorithmic, but also a computational challenge. With the high-luminosity phase of the LHC at CERN approaching, research for new approaches and algorithms for track reconstruction has seen an increased interest. Both new technological approaches like hardware accelerators, as well as machine learning are being developed. However, testing and developing these new approaches against the existing experiments’ software stacks can prove to be challenging, as they typically focus on stable data taking, discouraging disruptive changes. This document presents a virtual tracking detector that is designed to be a simplified, but realistic model of a real-world detector, that can serve as a robust testbed for new developments.

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Santos, Jose, Katrien Verbert, Joris Klerkx, Erik Duval, Sven Charleer, and Stefaan Ternier. "Tracking Data in Open Learning Environments." JUCS - Journal of Universal Computer Science 21, no. (7) (2015): 976–96. https://doi.org/10.3217/jucs-021-07-0976.

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The collection and management of learning traces, metadata about actions that students perform while they learn, is a core topic in the domain of Learning Analytics. In this paper, we present a simple architecture for collecting and managing learning traces. We describe requirements, different components of the architecture, and our experiences with the successful deployment of the architecture in two different case studies: a blended learning university course and an enquiry based learning secondary school course. The architecture relies on trackers, collecting agents that fetch data from external services, for flexibility and configurability. In addition, we discuss how our architecture meets the requirements of different learning environments, critical reflections and remarks on future work.

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Yang, Feng Wei, Lea Tomášová, Zeno v. Guttenberg, Ke Chen, and Anotida Madzvamuse. "Investigating Optimal Time Step Intervals of Imaging for Data Quality through a Novel Fully-Automated Cell Tracking Approach." Journal of Imaging 6, no. 7 (2020): 66. http://dx.doi.org/10.3390/jimaging6070066.

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Computer-based fully-automated cell tracking is becoming increasingly important in cell biology, since it provides unrivalled capacity and efficiency for the analysis of large datasets. However, automatic cell tracking’s lack of superior pattern recognition and error-handling capability compared to its human manual tracking counterpart inspired decades-long research. Enormous efforts have been made in developing advanced cell tracking packages and software algorithms. Typical research in this field focuses on dealing with existing data and finding a best solution. Here, we investigate a novel approach where the quality of data acquisition could help improve the accuracy of cell tracking algorithms and vice-versa. Generally speaking, when tracking cell movement, the more frequent the images are taken, the more accurate cells are tracked and, yet, issues such as damage to cells due to light intensity, overheating in equipment, as well as the size of the data prevent a constant data streaming. Hence, a trade-off between the frequency at which data images are collected and the accuracy of the cell tracking algorithms needs to be studied. In this paper, we look at the effects of different choices of the time step interval (i.e., the frequency of data acquisition) within the microscope to our existing cell tracking algorithms. We generate several experimental data sets where the true outcomes are known (i.e., the direction of cell migration) by either using an effective chemoattractant or employing no-chemoattractant. We specify a relatively short time step interval (i.e., 30 s) between pictures that are taken at the data generational stage, so that, later on, we may choose some portion of the images to produce datasets with different time step intervals, such as 1 min, 2 min, and so on. We evaluate the accuracy of our cell tracking algorithms to illustrate the effects of these different time step intervals. We establish that there exist certain relationships between the tracking accuracy and the time step interval associated with experimental microscope data acquisition. We perform fully-automatic adaptive cell tracking on multiple datasets, to identify optimal time step intervals for data acquisition, while at the same time demonstrating the performance of the computer cell tracking algorithms.

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Yu, Shu Yan, and Hong Wei Quan. "Class-Dependent Gating Algorithm in Data Association." Advanced Materials Research 546-547 (July 2012): 446–51. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.446.

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Most conventional tracking gate algorithms only use the targets’ kinematic measurement information, which is typically resulted in great uncertainties of measurement-to-track association for multi-target tracking in clutter. The problem of constructing tracking gates using targets' class information is considered. The proposed algorithm integrates targets' identity information into the traditional tracking gating techniques. First, a class-dependent gate corresponding to each class of targets is developed. Second, the algorithm for constructing the class-dependent gate is given. Simulations are carried out to examine the proposed algorithm, where the simulation scenario shows that the measurement-to-track association using the class-dependent gating algorithm is significantly better than traditional method.

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Chilipirea, Cristian, Mitra Baratchi, Ciprian Dobre, and Maarten Steen. "Identifying Stops and Moves in WiFi Tracking Data." Sensors 18, no. 11 (2018): 4039. http://dx.doi.org/10.3390/s18114039.

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There are multiple methods for tracking individuals, but the classical ones such as using GPS or video surveillance systems do not scale or have large costs. The need for large-scale tracking, for thousands or even millions of individuals, over large areas such as cities, requires the use of alternative techniques. WiFi tracking is a scalable solution that has gained attention recently. This method permits unobtrusive tracking of large crowds, at a reduced cost. However, extracting knowledge from the data gathered through WiFi tracking is not simple, due to the low positional accuracy and the dependence on signals generated by the tracked device, which are irregular and sparse. To facilitate further data analysis, we can partition individual trajectories into periods of stops and moves. This abstraction level is fundamental, and it opens the way for answering complex questions about visited locations or even social behavior. Determining stops and movements has been previously addressed for tracking data gathered using GPS. GPS trajectories have higher positional accuracy at a fixed, higher frequency as compared to trajectories obtained through WiFi. However, even with the increase in accuracy, the problem, of separating traces in periods of stops and movements, remains similar to the one we encountered for WiFi tracking. In this paper, we study three algorithms for determining stops and movements for GPS-based datasets and explore their applicability to WiFi-based data. We propose possible improvements to the best-performing algorithm considering the specifics of WiFi tracking data.

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Benson, Abigail, Ward Appeltans, Lenore Bajona, et al. "Outcomes of the International Oceanographic Data and Information Exchange Ocean Biogeographic Information System OBIS-Event-Data Workshop on Animal Tagging and Tracking." Biodiversity Information Science and Standards 2 (July 3, 2018): e25728. https://doi.org/10.3897/biss.2.25728.

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The Ocean Biogeographic Information System (OBIS) began in 2000 as the repository for data from the Census of Marine Life. Since that time, OBIS has expanded its goals beyond simply hosting data to supporting more aspects of marine conservation (Pooter et al. 2017). In order to accomplish those goals, the OBIS secretariat in partnership with its European node (EurOBIS) hosted at the Flanders Marine Institute (VLIZ, Belgium), and the Intergovernmental Oceanographic Commission (IOC) Committee on International Oceanographic Data and Information Exchange (IODE, 23rd session, March 2015, Brugge) established a 2-year pilot project to address a particularly problematic issue that environmental data collected as part of marine biological research were being disassociated from the biological data. OBIS-Event-Data is the solution that was developed from that pilot project, which devised a method for keeping environmental data together with the biological data (Pooter et al. 2017). OBIS is seeking early adopters of the new data standard OBIS-Event-Data from among the marine biodiversity monitoring communities, to further validate the data standard, and develop data products and scientific applications to support the enhancement of Biological and Ecosystem Essential Ocean Variables (EOVs) in the framework of the Global Ocean Observing System (GOOS) and the Marine Biodiversity Observation Network of the Group on Earth Observations (GEO BON MBON). After the successful 2-year IODE pilot project OBIS-ENV-DATA, the IOC established a new 2-year IODE pilot project OBIS-Event-Data for Scientific Applications (2017-2019). The OBIS-Event-Data data standard, building on Darwin Core, provides a technical solution for combined biological and environmental data, and incorporates details about sampling methods and effort, including event hierarchy. It also implements standardization of parameters involved in biological, environmental, and sampling details using an international standard controlled vocabulary (British Oceanographic Data Centre Natural Environment Research Council). A workshop organized by IODE/OBIS in April brought together major animal tagging and tracking networks such as the Ocean Tracking Network (OTN), the Animal Telemetry Network (ATN), the Integrated Marine Observing System (IMOS), the European Tracking Network (ETN) and the Acoustic Tracking Array Platform (ATAP) to test the OBIS-Event-Data standard through the development of some data products and science applications. Additionally, this workshop contributes to the further maturation of the GOOS EOV on fish as well as the EOV on birds, mammals and turtles. We will present the outcomes as well as any lessons learned from this workshop on problems, solutions, and applications of using Darwin Core/OBIS-Event-Data for bio-logging data.

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., Munikrishnan. "Data Leak Localization and Prevention using LDN." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 519–22. http://dx.doi.org/10.22214/ijraset.2023.54993.

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Abstract: While geofencing and geolocation tracking offers valuable tools for data sharing and access, it is crucial to acknowledge their potential risks and concerns. Some individuals may feel uneasy about the constant tracking and monitoring of their location, which can give rise to privacy issues. Moreover, geofencing and geolocation tracking are susceptible to hacking and cyber attacks, potentially leading to the theft or compromise of sensitive data. To address these concerns, businesses, and organizations must implement robust security measures and protocols when employing geofencing and geolocation tracking. These measures may involve encrypting sensitive data, monitoring and auditing access logs to identify potential threats or unauthorized access, and implementing user authentication and access controls to restrict data and information access. In conclusion, while geofencing and geolocation tracking can be powerful tools for data sharing and access, they should be utilized cautiously and accompanied by strong security measures to mitigate potential risks and threats.

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Yang, Lian. "Target Tracking Based on Radon Transform Data Appearance Modeling." Asian Journal of Mathematics and Computer Research 30, no. 3 (2023): 10–18. http://dx.doi.org/10.56557/ajomcor/2023/v30i38309.

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This article mainly focuses on an important challenge in target tracking in complex environments the real-time performance of algorithm operation. A new target appearance model based on Radon transform data is studied, and it is introduced into the correlation filtering framework for filtering template training. A fast tracking algorithm and target scale update scheme based on correlation filtering are proposed. The experimental results show that the tracking algorithm proposed in this paper has better robustness and real-time performance compared to current mainstream tracking algorithms, providing a new technical approach for research related to object detection and tracking. The tracking algorithm proposed in this article can also be seen as a framework, where the projected object can not only be the grayscale of the original pixel, but also include multi-channel color values, HOG, and other attributes.

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Chundru, Swathi. "AI-Driven Data Provenance: Tracking and Verifying Data Lineage." FMDB Transactions on Sustainable Computing Systems 2, no. 3 (2024): 107–18. https://doi.org/10.69888/ftscs.2024.000258.

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The paper looks into AI-driven data provenance systems for their feasibility in tracing and verification of lineage for healthcare and financial transaction domains. We will use sample data points from Electronic Health Records and transaction data to understand the trade-offs between real-time processing speed and tracking accuracy in the former domain and between detection accuracy and false positives in the latter domain. MATLAB and Python were utilized to analyze the data and model the system. MATLAB was used to create the simulation environment for signal processing tasks, whereas Python, along with libraries such as NumPy and Pandas, facilitated data manipulation, statistical analysis, and generation of visual results. The study comprises impedance and multi-line graphs, which describe the relationships between processing speed, accuracy, and false positives in the systems being investigated. The tables show that processing speed improves healthcare accuracy and finance system detection accuracy and false positives. This means that while AI-driven data provenance systems might improve operational efficiency, they must be adapted to a specific industry to achieve the best balance between performance, accuracy, and reliability. Further development of AI technology using MATLAB and Python should focus on tracking and tracing effective and scalable solution approaches across crucial sectors to validate data lineage.

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Sujatha, D. "Quadcopter for Remote Data Collection." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2516–27. https://doi.org/10.22214/ijraset.2025.70794.

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Reliable location tracking in remote and infrastructure-deficient areas remains a significant challenge, especially where cellular or Wi-Fi networks are sparse or unavailable. Traditional tracking systems often rely on short-range communication or high-cost GSM modules, making them unsuitable for rural applications. This project investigates the potential of combining GPS geolocation with LoRa-based communication to create a low-cost, energy-efficient tracking solution that performs effectively in long-range, low-power scenarios. An evaluation of existing tracking technologies revealed critical limitations in affordability, coverage, and power consumption, particularly for portable and field-based use cases. These findings led to the design and development of a lightweight tracker device that leverages low-power wide-area networking (LPWAN) for data transmission. The system is built around the Arduino UNO microcontroller, integrated with a NEO-6M GPS module for real-time coordinate acquisition and an SX1278 LoRa transceiver for wireless communication. A custom-designed PCB was implemented to streamline the hardware and reduce the footprint of the device. The tracker periodically captures GPS data and transmits it via LoRa to a remote receiver, enabling live monitoring without dependency on cellular infrastructure. Field deployment tests demonstrated successful real-time tracking with reliable transmission over distances up to 2 kilometers, maintaining low packet loss and minimal power draw throughout operation.This LoRa-GPS tracker provides a scalable and adaptable alternative for various use cases, including livestock monitoring, luggage tracking, and search-and-rescue support in disconnected terrains. Unlike conventional GSM-based systems, the proposed solution offers superior range, reduced operational costs, and a modular design suitable for expansion. Future enhancements include replacing the Arduino UNO with more power-efficient microcontrollers, implementing encryption for secure data transfer, and extending communication range through mesh networking.

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Cahyono, Gigih P., and Handayani Tjandrasa. "MULTITARGET TRACKING MENGGUNAKAN MULTIPLE HYPOTHESIS TRACKING DENGAN CLUSTERING TIME WINDOW DATA RADAR." JUTI: Jurnal Ilmiah Teknologi Informasi 13, no. 1 (2015): 24. http://dx.doi.org/10.12962/j24068535.v13i1.a385.

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LI, Yuheng, Jun ZHENG, and Kechu YI. "On a Tracking and Data Relay Satellite (TDRS) Tracking a Lunar satellite." Chinese Journal of Space Science 27, no. 3 (2007): 227. http://dx.doi.org/10.11728/cjss2007.03.227.

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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 (2019): 1–15. http://dx.doi.org/10.24321/2455.3093.201901.

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