Journal articles: 'Data tracking'

1

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

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

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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 (October 2015): 659–61. http://dx.doi.org/10.1890/0012-9623-96.4.659.

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Gao, Tao. "Data Association Based Tracking Traffic Objects." International Journal of Advanced Pervasive and Ubiquitous Computing 5, no. 2 (April 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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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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Gupta, Naina, and Tanu Jindal. "Target Tracking using Personalized Data Management." International Journal of Computer Applications 62, no. 17 (January 18, 2013): 11–14. http://dx.doi.org/10.5120/10171-4838.

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

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

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Skolnick, Andrew A. "Joint Commission Begins Tracking Outcome Data." JAMA: The Journal of the American Medical Association 278, no. 19 (November 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 (November 19, 1997): 1562. http://dx.doi.org/10.1001/jama.278.19.1562.

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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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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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Niemczynowicz, Janusz. "Storm tracking using rain gauge data." Journal of Hydrology 93, no. 1-2 (August 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 (March 2007): 46–59. http://dx.doi.org/10.1016/j.compag.2007.01.002.

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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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Tilmes, Curt, Yelena Yesha, and Milton Halem. "Tracking provenance of earth science data." Earth Science Informatics 3, no. 1-2 (April 9, 2010): 59–65. http://dx.doi.org/10.1007/s12145-010-0046-3.

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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 (February 6, 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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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 &ndash; including on-board and roadside systems &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 (June 30, 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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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 (January 1, 2015): 24. http://dx.doi.org/10.12962/j24068535.v13i1.a385.

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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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N. N., Patil. "Meta Data Based Data Integrity Tracking Scheme in Cloud Environment." International Journal for Research in Applied Science and Engineering Technology 7, no. 1 (January 31, 2019): 824–28. http://dx.doi.org/10.22214/ijraset.2019.1129.

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Lupton, Deborah. "Data mattering and self-tracking: what can personal data do?" Continuum 34, no. 1 (November 22, 2019): 1–13. http://dx.doi.org/10.1080/10304312.2019.1691149.

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Hou, Rui, Shuo Zhou, Mengtian Cui, Lingyun Zhou, Deze Zeng, Jiangtao Luo, and Maode Ma. "Data Forwarding Scheme for Vehicle Tracking in Named Data Networking." IEEE Transactions on Vehicular Technology 70, no. 7 (July 2021): 6684–95. http://dx.doi.org/10.1109/tvt.2021.3081448.

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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 (November 19, 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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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 (July 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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Woodham, Catherine A., William A. Sandham, and Tariq S. Durrani. "3-D seismic tracking with probabilistic data association." GEOPHYSICS 60, no. 4 (July 1995): 1088–94. http://dx.doi.org/10.1190/1.1443837.

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In this paper, a new approach to the problem of tracking a seismic event through a 3-D data set is presented. The method under consideration was originally developed for tracking targets in a cluttered environment and uses Probabilistic Data Association (PDA) to assess the probability of each return being the correct return. This theory has been modified for use in seismic event tracking, which unlike target tracking, is a static problem, and this new approach has been tested on both real and synthetic 3-D data sets. The tracker successfully picks out the chosen horizon in both the synthetic and real 3-D data sets. The accuracy of the 3-D tracker may be improved by tracking through the data set in two perpendicular directions and correlating the results. Results show that it is also possible to include a diagonal track in the correlation.

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Shao Chunyan, 邵春艳, 丁庆海 Ding Qinghai, 罗海波 Luo Haibo, and 李玉莲 Li Yulian. "Target tracking using high-dimension data clustering." Infrared and Laser Engineering 45, no. 4 (2016): 0428002. http://dx.doi.org/10.3788/irla201645.0428002.

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Yin, Yichun, Lifeng Shang, Xin Jiang, Xiao Chen, and Qun Liu. "Dialog State Tracking with Reinforced Data Augmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9474–81. http://dx.doi.org/10.1609/aaai.v34i05.6491.

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Neural dialog state trackers are generally limited due to the lack of quantity and diversity of annotated training data. In this paper, we address this difficulty by proposing a reinforcement learning (RL) based framework for data augmentation that can generate high-quality data to improve the neural state tracker. Specifically, we introduce a novel contextual bandit generator to learn fine-grained augmentation policies that can generate new effective instances by choosing suitable replacements for specific context. Moreover, by alternately learning between the generator and the state tracker, we can keep refining the generative policies to generate more high-quality training data for neural state tracker. Experimental results on the WoZ and MultiWoZ (restaurant) datasets demonstrate that the proposed framework significantly improves the performance over the state-of-the-art models, especially with limited training data.

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Cheng, Lu, Jundong Li, K. Selcuk Candan, and Huan Liu. "Tracking Disaster Footprints with Social Streaming Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 370–77. http://dx.doi.org/10.1609/aaai.v34i01.5372.

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Social media has become an indispensable tool in the face of natural disasters due to its broad appeal and ability to quickly disseminate information. For instance, Twitter is an important source for disaster responders to search for (1) topics that have been identified as being of particular interest over time, i.e., common topics such as “disaster rescue”; (2) new emerging themes of disaster-related discussions that are fast gathering in social media streams (Saha and Sindhwani 2012), i.e., distinct topics such as “the latest tsunami destruction”. To understand the status quo and allocate limited resources to most urgent areas, emergency managers need to quickly sift through relevant topics generated over time and investigate their commonness and distinctiveness. A major obstacle to the effective usage of social media, however, is its massive amount of noisy and undesired data. Hence, a naive method, such as set intersection/difference to find common/distinct topics, is often not practical. To address this challenge, this paper studies a new topic tracking problem that seeks to effectively identify the common and distinct topics with social streaming data. The problem is important as it presents a promising new way to efficiently search for accurate information during emergency response. This is achieved by an online Nonnegative Matrix Factorization (NMF) scheme that conducts a faster update of latent factors, and a joint NMF technique that seeks the balance between the reconstruction error of topic identification and the losses induced by discovering common and distinct topics. Extensive experimental results on real-world datasets collected during Hurricane Harvey and Florence reveal the effectiveness of our framework.

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최광일 and Youngjin Kim. "Ambiguity Resolution Processes of Reflexives:Eye-tracking Data." Korean Journal of Cognitive and Biological Psychology 19, no. 4 (December 2007): 263–77. http://dx.doi.org/10.22172/cogbio.2007.19.4.001.

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Lee, Seok Lyong, and Du Hyung Cho. "Efficient Data Association for Multiple Vehicles Tracking." Applied Mechanics and Materials 263-266 (December 2012): 2426–31. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2426.

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Data association problem has been an important issue for the multiple vehicles tracking in a vehicle tracking system. In this paper, we present an efficient data association method to track multiple vehicles in a sequence of traffic video frames. We first introduce the compact rectangular region-of-interest (crROI) that tightly encloses a vehicle and has the rotation-invariant property. The subsequent processing is based on the crROI instead of a vehicle image itself to avoid the processing overhead. Next, we extract the features from the crROI such as shape, size, and spatial relationship. Using these features, we define the similarity metric between two vehicles, and present the association method that matches a vehicle in a frame with the corresponding vehicle in its consecutive frame. An experimental result shows that the proposed method identifies and tracks vehicles effectively and efficiently in the curve or crossroad environment where multiple vehicles appear.

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Gao, Yuan. "Particle Tracking Using Dynamic Water-Level Data." Water 12, no. 7 (July 21, 2020): 2063. http://dx.doi.org/10.3390/w12072063.

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The movement of fluid particles about historic subsurface releases is often governed by dynamic subsurface water levels. Motivations for tracking the movement of fluid particles include tracking the fate of subsurface contaminants and resolving the fate of water stored in subsurface aquifers. This study provides a novel method for predicting the movement of subsurface particles relying on dynamic water-level data derived from continuously recording pressure transducers. At least three wells are needed to measure water levels which are used to determine the plain of the water table. Based on Darcy’s law, particle flow pathlines at the study site are obtained using the slope of the water table. The results show that hydrologic conditions, e.g., seasonal transpiration and precipitation, influence local groundwater flow. The changes of water level in short periods caused by the hydrologic variations made the hydraulic gradient diversify considerably, thus altering the direction of groundwater flow. Although a range of groundwater flow direction and gradient with time can be observed by an initial review of water levels in rose charts, the net groundwater flow at all field sites is largely constant in one direction which is driven by the gradients with higher magnitude.

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Jayasingh, B. "Web Surfer Tracking using Big Data Technologies." CVR Journal of Science & Technology 8, no. 1 (June 1, 2015): 65–68. http://dx.doi.org/10.32377/cvrjst0812.

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Ciobanu, Gabriel, and Ross Horne. "A Provenance Tracking Model for Data Updates." Electronic Proceedings in Theoretical Computer Science 91 (August 15, 2012): 31–44. http://dx.doi.org/10.4204/eptcs.91.3.

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Yu, Shengnan, Gary Montague, and Elaine Martin. "Data Fusion for Enhanced Fermentation Process Tracking." IFAC Proceedings Volumes 43, no. 5 (2010): 37–42. http://dx.doi.org/10.3182/20100705-3-be-2011.00007.

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Duan, Hui Fen, Wei Sun, and Hua Wang. "Data Quality Evaluation in Aerospace Tracking Mission." Applied Mechanics and Materials 651-653 (September 2014): 2138–44. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2138.

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Modeled on data quality evaluating methodology, a standardized real-time rule framework is proposed. The framework is accurate and reliable for estimating aerospace experiment tracking information. By converting all the rule evaluating results that consisted of time-delay, error, range and etc. to a uniform questionable data count format, the framework presents an algorithm that takes both tracking source and station into account based on questionable data statistics for quantified information quality evaluating. The quantitative quality evaluation of measurement information problem can be solved via the proposed framework.

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Khan, A. P., Y. B. Patil, P. R. Patil, M. S. Nagarale, and R. V. Patil. "Automated Library Data Tracking System By Smartphone." International Journal of Computer Sciences and Engineering 6, no. 4 (April 30, 2018): 379–82. http://dx.doi.org/10.26438/ijcse/v6i4.379382.

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Journal articles on the topic 'Data tracking'

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