Relevant bibliographies by topics / Data filtering

Contents

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

Academic literature on the topic 'Data filtering'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Data filtering"

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Павло Б. Олійник. "DATA FILTERING METHODS FOR HYDROGRAPHIC SURVEY DATA." MECHANICS OF GYROSCOPIC SYSTEMS, no. 27 (October 6, 2014): 10–18. http://dx.doi.org/10.20535/0203-377127201437908.

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Current trends in navigation are characterized by the further increase of demands on the precision of hydrographic information, especially of the nautical maps. Thus, precision of both spatial position and depth bathymetric data is important for ensuring safe navigation, and so problem of data filtering and elimination of outliers arises.In the present work, comparison of methods, used for postprocessing of depth data, measured by echosounder, is done.First of all, review of commonly used data filtering and outlier elimination methods is done, and their advantages and disadvantages are analyzed.As improved outlier elimination algorithm and median filtering has their flaws, Kalman filtering is considered as a measure of outlier elimination and real data estimation. It’s shown that Kalman filter can both effectively filter noise and eliminate outliers; however, quality of the filtered data strongly depends on measurement noise covariation and process noise covariation estimates, and respectively. At that, the lower is, the better noise is filtered and the smoother depth profile is; the higher is, the better outliers are eliminated. However, care must be taken, as depth profile is distorted at high values, and noise is almost not filtered at low ones.It’s shown that noise covariation estimate has more influence on data filtering; therefore, one should pay attention to correct estimation. For practical reasons, values of ; =10 are recommended.In the recent works, wavelet filtering is considered as a promising method of data filtering in postprocessing. Therefore, as a next step, comparison of Kalman filtering and wavelet filtering is done using the real-world data. To that end, white noise is added to filtered and smoothed data, and then those data are filtered by methods, mentioned above. Corellation of source and denoised data is chosen as a criterion of filter effectiveness.It’s shown that Kalman filter is somewhat less effective in data postprocessing than wavelet filter. However, as Kalman filter allows one both to filter noises form the measured data and to eliminate outliers, and can be used for “on-the-fly” data filtering, it’s advisable to use Kalman filtering for real-time measurements during surveys, and wavelets for data postprocessing.Future studies may be devoted to improvement of existing and introduction of new data filtering and postrprocessing methods.
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Iske, Armin. "Progressive scattered data filtering." Journal of Computational and Applied Mathematics 158, no. 2 (September 2003): 297–316. http://dx.doi.org/10.1016/s0377-0427(03)00449-7.

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Gdanskiy, N. I., А. М. Кarpov, and P. Y. Коmova. "Using prediction in filtering data for solving model tasks." Contemporary problems of social work 1, no. 2 (June 30, 2015): 81–91. http://dx.doi.org/10.17922/2412-5466-2015-1-2-81-91.

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Kim, DaeYoub. "A Study on Fake Data Filtering Method of CCN." Journal of the Korea Institute of Information Security and Cryptology 24, no. 1 (February 28, 2014): 155–63. http://dx.doi.org/10.13089/jkiisc.2014.24.1.155.

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Xiaohui, Cheng, Feng Li, and Gui Qiong. "Collaborative Filtering Algorithm based on Data Mixing and Filtering." International Journal of Performability Engineering 15, no. 8 (2019): 2267. http://dx.doi.org/10.23940/ijpe.19.08.p27.22672276.

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Kamaludin, Hazalila, Hairulnizam Mahdin, and Jemal H. Abawajy. "Filtering Redundant Data from RFID Data Streams." Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7107914.

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Radio Frequency Identification (RFID) enabled systems are evolving in many applications that need to know the physical location of objects such as supply chain management. Naturally, RFID systems create large volumes of duplicate data. As the duplicate data wastes communication, processing, and storage resources as well as delaying decision-making, filtering duplicate data from RFID data stream is an important and challenging problem. Existing Bloom Filter-based approaches for filtering duplicate RFID data streams are complex and slow as they use multiple hash functions. In this paper, we propose an approach for filtering duplicate data from RFID data streams. The proposed approach is based on modified Bloom Filter and uses only a single hash function. We performed extensive empirical study of the proposed approach and compared it against the Bloom Filter, d-Left Time Bloom Filter, and the Count Bloom Filter approaches. The results show that the proposed approach outperforms the baseline approaches in terms of false positive rate, execution time, and true positive rate.
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Li, Jianchao, and Ken Larner. "Differential‐equation‐based seismic data filtering." GEOPHYSICS 58, no. 12 (December 1993): 1809–19. http://dx.doi.org/10.1190/1.1443396.

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Suppressing noise and enhancing useful seismic signal by filtering is one of the important tasks of seismic data processing. Conventional filtering methods are implemented through either the convolution operation or various mathematical transforms. We describe a methodology for studying and implementing filters, which, unlike conventional filtering methods, is based on solving differential equations in the time and space domain. We call this differential‐equation‐based filtering (DEBF). DEBF does not require that seismic data be stationary, so filtering parameters can vary with every time and space point. Examples with two‐dimensional (2-D) synthetic and field seismic data demonstrate that the DEBF method accomplishes the desired time‐ and space‐varying temporal and move‐out filtering at lower cost than conventional frequency‐wavenumber‐domain filtering. The computational advantage in 3-D would be much greater.
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Burguera, Antoni, Yolanda González, and Gabriel Oliver. "PROBABILISTIC FILTERING OF SONAR DATA." IFAC Proceedings Volumes 40, no. 15 (2007): 49–54. http://dx.doi.org/10.3182/20070903-3-fr-2921.00011.

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Grzesiak, M. "Wavelet filtering of chaotic data." Nonlinear Processes in Geophysics 7, no. 1/2 (June 30, 2000): 111–16. http://dx.doi.org/10.5194/npg-7-111-2000.

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Abstract. Satisfactory method of removing noise from experimental chaotic data is still an open problem. Normally it is necessary to assume certain properties of the noise and dynamics, which one wants to extract, from time series. The wavelet based method of denoising of time series originating from low-dimensional dynamical systems and polluted by the Gaussian white noise is considered. Its efficiency is investigated by comparing the correlation dimension of clean and noisy data generated for some well-known dynamical systems. The wavelet method is contrasted with the singular value decomposition (SVD) and finite impulse response (FIR) filter methods.
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Elliott, P. J. "Digital Filtering of Sirotem Data." Exploration Geophysics 19, no. 1-2 (March 1988): 258–59. http://dx.doi.org/10.1071/eg988258.

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Dissertations / Theses on the topic "Data filtering"

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Faber, Marc. "On-Board Data Processing and Filtering." International Foundation for Telemetering, 2015. http://hdl.handle.net/10150/596433.

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ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV
One of the requirements resulting from mounting pressure on flight test schedules is the reduction of time needed for data analysis, in pursuit of shorter test cycles. This requirement has ramifications such as the demand for record and processing of not just raw measurement data but also of data converted to engineering units in real time, as well as for an optimized use of the bandwidth available for telemetry downlink and ultimately for shortening the duration of procedures intended to disseminate pre-selected recorded data among different analysis groups on ground. A promising way to successfully address these needs consists in implementing more CPU-intelligence and processing power directly on the on-board flight test equipment. This provides the ability to process complex data in real time. For instance, data acquired at different hardware interfaces (which may be compliant with different standards) can be directly converted to more easy-to-handle engineering units. This leads to a faster extraction and analysis of the actual data contents of the on-board signals and busses. Another central goal is the efficient use of the available bandwidth for telemetry. Real-time data reduction via intelligent filtering is one approach to achieve this challenging objective. The data filtering process should be performed simultaneously on an all-data-capture recording and the user should be able to easily select the interesting data without building PCM formats on board nor to carry out decommutation on ground. This data selection should be as easy as possible for the user, and the on-board FTI devices should generate a seamless and transparent data transmission, making a quick data analysis viable. On-board data processing and filtering has the potential to become the future main path to handle the challenge of FTI data acquisition and analysis in a more comfortable and effective way.
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Zhou, Yilun S. M. Massachusetts Institute of Technology. "Data-driven path filtering in ConceptNet." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122731.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 49-52).
In many applications, it is important to characterize the way in which two concepts are semantically related. Knowledge graphs such as ConceptNet provide a rich source of information for such characterizations by encoding relations between concepts as edges in a graph. When two concepts are not directly connected by an edge, their relationship can still be described in terms of the paths that connect them. Unfortunately, many of these paths are uninformative and noisy, meaning that the success of applications that use such path features crucially relies on their ability to select high-quality paths. In existing applications, this path selection process is based on relatively simple heuristics. In this thesis I instead propose to learn to predict path quality from crowdsourced human assessments. Since a generic task-independent notion of quality is concerned, human participants are asked to rank paths according to their subjective assessment of the paths' naturalness, without being given specific definitions or guidelines. Experiments show that a neural network model trained on these assessments is able to predict human judgments on unseen paths with near optimal performance. Most notably, the resulting path selection method is substantially better than the current heuristic approaches at identifying meaningful paths in various applications.
by Yilun Zhou.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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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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Torgrimsson, Jan. "Adaptive filtering of VLF data from space." Thesis, KTH, Rymd- och plasmafysik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-91544.

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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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Olsson, Jakob, and Viktor Yberg. "Log data filtering in embedded sensor devices." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175367.

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Data filtering is the disposal of unnecessary data in a data set, to save resources such as server capacity and bandwidth. The method is used to reduce the amount of stored data and thereby prevent valuable resources from processing insignificant information.The purpose of this thesis is to find algorithms for data filtering and to find out which algorithm gives the best effect in embedded devices with resource limitations. This means that the algorithm needs to be resource efficient in terms of memory usage and performance, while saving enough data points to avoid modification or loss of information. After an algorithm has been found it will also be implemented to fit the Exqbe system.The study has been done by researching previously done studies in line simplification algorithms and their applications. A comparison between several well-known and studied algorithms has been done to find which suits this thesis problem best.The comparison between the different line simplification algorithms resulted in an implementation of an extended version of the Ramer-Douglas-Peucker algorithm. The algorithm has been optimized and a new filter has been implemented in addition to the algorithm.
Datafiltrering är att ta bort onödig data i en datamängd, för att spara resurser såsom serverkapacitet och bandbredd. Metoden används för att minska mängden lagrad data och därmed förhindra att värdefulla resurser används för att bearbeta obetydlig information. Syftet med denna tes är att hitta algoritmer för datafiltrering och att undersöka vilken algoritm som ger bäst resultat i inbyggda system med resursbegränsningar. Det innebär att algoritmen bör vara resurseffektiv vad gäller minnesanvändning och prestanda, men spara tillräckligt många datapunkter för att inte modifiera eller förlora information. Efter att en algoritm har hittats kommer den även att implementeras för att passa Exqbe-systemet. Studien är genomförd genom att studera tidigare gjorda studier om datafiltreringsalgoritmer och dess applikationer. Jämförelser mellan flera välkända algoritmer har utförts för att hitta vilken som passar denna tes bäst. Jämförelsen mellan de olika filtreringsalgoritmerna resulterade i en implementation av en utökad version av Ramer-Douglas-Peucker-algoritmen. Algoritmen har optimerats och ett nytt filter har implementerats utöver algoritmen.
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Chilo, José. "Filtering and extracting features from infrasound data /." Stockholm, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3978.

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Karasalo, Maja. "Data Filtering and Control Design for Mobile Robots." Doctoral thesis, KTH, Optimeringslära och systemteori, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-11011.

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In this thesis, we consider problems connected to navigation and tracking for autonomousrobots under the assumption of constraints on sensors and kinematics. We study formation controlas well as techniques for filtering and smoothing of noise contaminated input. The scientific contributions of the thesis comprise five papers.In Paper A, we propose three cascaded, stabilizing formation controls for multi-agent systems.We consider platforms with non-holonomic kinematic constraints and directional rangesensors. The resulting formation is a leader-follower system, where each follower agent tracksits leader agent at a specified angle and distance. No inter-agent communication is required toexecute the controls. A switching Kalman filter is introduced for active sensing, and robustnessis demonstrated in experiments and simulations with Khepera II robots.In Paper B, an optimization-based adaptive Kalman filteringmethod is proposed. The methodproduces an estimate of the process noise covariance matrix Q by solving an optimization problemover a short window of data. The algorithm recovers the observations h(x) from a system˙ x = f (x), y = h(x)+v without a priori knowledge of system dynamics. The algorithm is evaluatedin simulations and a tracking example is included, for a target with coupled and nonlinearkinematics. In Paper C, we consider the problem of estimating a closed curve in R2 based on noisecontaminated samples. A recursive control theoretic smoothing spline approach is proposed, thatyields an initial estimate of the curve and subsequently computes refinements of the estimateiteratively. Periodic splines are generated by minimizing a cost function subject to constraintsimposed by a linear control system. The optimal control problem is shown to be proper, andsufficient optimality conditions are derived for a special case of the problem using Hamilton-Jacobi-Bellman theory.Paper D continues the study of recursive control theoretic smoothing splines. A discretizationof the problem is derived, yielding an unconstrained quadratic programming problem. Aproof of convexity for the discretized problem is provided, and the recursive algorithm is evaluatedin simulations and experiments using a SICK laser scanner mounted on a PowerBot from ActivMedia Robotics. Finally, in Paper E we explore the issue of optimal smoothing for control theoretic smoothingsplines. The output of the control theoretic smoothing spline problem is essentially a tradeoff between faithfulness to measurement data and smoothness. This tradeoff is regulated by the socalled smoothing parameter. In Paper E, a method is developed for estimating the optimal valueof this smoothing parameter. The procedure is based on general cross validation and requires noa priori information about the underlying curve or level of noise in the measurements.
QC 20100722
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Walter, Patrick L. "FILTERING CONSIDERATIONS WHEN TELEMETERING SHOCK AND VIBRATION DATA." International Foundation for Telemetering, 2001. http://hdl.handle.net/10150/607681.

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International Telemetering Conference Proceedings / October 22-25, 2001 / Riviera Hotel and Convention Center, Las Vegas, Nevada
The accurate measurement of shock and vibration data via flight telemetry is necessary to validate structural models, indicate off-nominal system performance, and/or generate environmental qualification criteria for airborne systems. Digital telemetry systems require anti-aliasing filters designed into them. If not properly selected and located, these filters can distort recorded time histories and modify their spectral content. This paper provides filter design guidance to optimize the quality of recorded flight structural dynamics data. It is based on the anticipated end use of the data. Examples of filtered shock data are included.
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Wunnava, Sashi Prabha. "Kalman Filtering Approach to Optimize OFDM Data Rate." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc84303/.

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This study is based on applying a non-linear mapping method, here the unscented Kalman filter; to estimate and optimize data rate resulting from the arrival rate having a Poisson distribution in an orthogonal frequency division multiplexing (OFDM) transmission system. OFDM is an emerging multi-carrier modulation scheme. With the growing need for quality of service in wireless communications, it is highly necessary to optimize resources in such a way that the overall performance of the system models should rise while keeping in mind the objective to achieve high data rate and efficient spectral methods in the near future. In this study, the results from the OFDM-TDMA transmission system have been used to apply cross-layer optimization between layers so as to treat different resources between layers simultaneously. The main controller manages the transmission of data between layers using the multicarrier modulation techniques. The unscented Kalman filter is used here to perform nonlinear mapping by estimating and optimizing the data rate, which result from the arrival rate having a Poisson distribution.
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Books on the topic "Data filtering"

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Abramowicz, Witold, Paweł Kalczyński, and Krzysztof Węcel. Filtering the Web to Feed Data Warehouses. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0137-6.

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Abramowicz, Witold. Filtering the Web to Feed Data Warehouses. London: Springer London, 2002.

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Brenner, Marty. Nonstationary dynamics data analysis with wavelet-SVD filtering. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 2001.

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Stinson, Catherine Elizabeth. Adaptive information filtering with labelled and unlabelled data. Ottawa: National Library of Canada, 2002.

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Consens, Mariano P. Creating and filtering structural data visualizations using hygraph patterns. Toronto: Computer Systems Research Institute, University of Toronto, 1994.

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Maskey, Liam. Digital filtering of sigma-delta modulator data using FPGA's. (s.l: The Author), 2000.

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Oceans, Canada Dept of Fisheries and. Efficient Frequency Domain Filtering Algorithm For Small Data Sets. S.l: s.n, 1987.

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Chui, C. K. Kalman filtering: With real-time applications. 3rd ed. Berlin: Springer, 1999.

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G, Chen, ed. Kalman filtering: With real-time applications. 2nd ed. Berlin: Springer-Verlag, 1991.

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G, Chen, ed. Kalman filtering: With real-time applications. Berlin: Springer-Verlag, 1987.

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Book chapters on the topic "Data filtering"

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Aspin, Adam. "Filtering Data." In Pro Power BI Desktop, 401–27. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1805-1_13.

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Aspin, Adam. "Filtering Data." In Pro Power BI Desktop, 737–78. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5763-0_21.

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Aspin, Adam. "Filtering Data." In Pro Power BI Desktop, 611–41. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3210-1_20.

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Aspin, Adam. "Filtering Data." In Pro Power BI Dashboard Creation, 307–49. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8227-4_13.

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Haslwanter, Thomas. "Data Filtering." In Hands-on Signal Analysis with Python, 71–104. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57903-6_5.

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Kovanic, Pavel. "Data Filtering." In Mathematical Gnostics, 235–42. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9780429441196-20.

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Diniz, Paulo S. R. "Data-Selective Adaptive Filtering." In Adaptive Filtering, 249–304. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-4106-9_6.

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Diniz, Paulo S. R. "Data-Selective Adaptive Filtering." In Adaptive Filtering, 1–57. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-68606-6_6.

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Althbiti, Ashrf, and Xiaogang Ma. "Collaborative Filtering." In Encyclopedia of Big Data, 179–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_274.

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Althbiti, Ashrf, and Xiaogang Ma. "Collaborative Filtering." In Encyclopedia of Big Data, 1–4. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-32001-4_274-1.

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Conference papers on the topic "Data filtering"

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Ditta, Marilena, Fabrizio Milazzo, Valentina Ravì, Giovanni Pilato, and Agnese Augello. "Data-driven Relation Discovery from Unstructured Texts." In Special Session on Information Filtering and Retrieval. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005614205970602.

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Johnson, Alister. "Scaling Collaborative Filtering with PETSc." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622202.

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Yang, Xiaochun, Yaoshu Wang, Bin Wang, and Wei Wang. "Local Filtering." In SIGMOD/PODS'15: International Conference on Management of Data. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2723372.2749445.

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Li, Jun, Wenyu Zang, Jianlong Tan, and Peng Zhang. "Predictive Data Stream Filtering." In 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2011. http://dx.doi.org/10.1109/wi-iat.2011.95.

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Xia, Shuyin, Guoyin Wang, Yunsheng Liur, Qun Liu, and Hong Yu. "Noise self-filtering K-nearest neighbors algorithms." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258130.

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Walker, Edwin P., and Tomas D. Milster. "Superresolution by optical and electronic filtering." In Optical Data Storage '95, edited by Gordon R. Knight, Hiroshi Ooki, and Yuan-Sheng Tyan. SPIE, 1995. http://dx.doi.org/10.1117/12.218711.

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Li, Xiaohan, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang, and Philip S. Yu. "Dynamic Graph Collaborative Filtering." In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00041.

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Esche, Marko, Michael Tok, and Thomas Sikora. "Theoretical Considerations Concerning Pixelwise Temporal Filtering." In 2014 Data Compression Conference (DCC). IEEE, 2014. http://dx.doi.org/10.1109/dcc.2014.20.

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Moorman, Jacob D., Qinyi Chen, Thomas K. Tu, Zachary M. Boyd, and Andrea L. Bertozzi. "Filtering Methods for Subgraph Matching on Multiplex Networks." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622566.

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Ziffer, Giacomo, Alessio Bernardo, Emanuele Della Valle, and Albert Bifet. "Kalman Filtering for Learning with Evolving Data Streams." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671365.

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Reports on the topic "Data filtering"

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Wilson, Michael J. Combining and Filtering Telemetry Data. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada421434.

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McNabb, J. Kaon Filtering For CLAS Data. Office of Scientific and Technical Information (OSTI), January 2001. http://dx.doi.org/10.2172/774088.

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Li, Jianchao, and K. Larner. Differential equation-based seismic data filtering. Office of Scientific and Technical Information (OSTI), May 1992. http://dx.doi.org/10.2172/10159092.

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Li, Jianchao, and K. Larner. Differential equation-based seismic data filtering. Office of Scientific and Technical Information (OSTI), May 1992. http://dx.doi.org/10.2172/7235596.

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Pados, Dimitiris A. Adaptive Digital Signature Design and Short-Data-Record Adaptive Filtering. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada481007.

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Mathew, Jijo K., Christopher M. Day, Howell Li, and Darcy M. Bullock. Curating Automatic Vehicle Location Data to Compare the Performance of Outlier Filtering Methods. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317435.

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Abstract:
Agencies use a variety of technologies and data providers to obtain travel time information. The best quality data can be obtained from second-by-second tracking of vehicles, but that data presents many challenges in terms of privacy, storage requirements and analysis. More frequently agencies collect or purchase segment travel time based upon some type of matching of vehicles between two spatially distributed points. Typical methods for that data collection involve license plate re-identification, Bluetooth, Wi-Fi, or some type of rolling DSRC identifier. One of the challenges in each of these sampling techniques is to employ filtering techniques to remove outliers associated with trip chaining, but not remove important features in the data associated with incidents or traffic congestion. This paper describes a curated data set that was developed from high-fidelity GPS trajectory data. The curated data contained 31,621 vehicle observations spanning 42 days; 2550 observations had travel times greater than 3 minutes more than normal. From this baseline data set, outliers were determined using GPS waypoints to determine if the vehicle left the route. Two performance measures were identified for evaluating three outlier-filtering algorithms by the proportion of true samples rejected and proportion of outliers correctly identified. The effectiveness of the three methods over 10-minute sampling windows was also evaluated. The curated data set has been archived in a digital repository and is available online for others to test outlier-filtering algorithms.
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Young, Teresa. Using Digital Filtering Techniques as an Aid in Wind Turbine Data Analysis. Office of Scientific and Technical Information (OSTI), November 1994. http://dx.doi.org/10.2172/10113493.

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Church, I., A. Greer, L. Quas, and M. Williamson. Multibeam water column filtering methods to improve data management and bio-acoustic interpretation. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305839.

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Boffo, C., and P. Bauer. FIONDA (Filtering Images of Niobium Disks Application): Filter application for Eddy Current Scanner data analysis. Office of Scientific and Technical Information (OSTI), May 2005. http://dx.doi.org/10.2172/15020167.

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Ezekiel, Shaoul, and Selim Shahriar. Applications of Porous Glass Based Thick Holograms for Optical Data Storage and Narrow-Band Wave Length Filtering. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada406505.

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