Academic literature on the topic 'Information filters'
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Journal articles on the topic "Information filters"
Lu, Feng, Yihuan Huang, Jinquan Huang, and Xiaojie Qiu. "Gas turbine performance monitoring based on extended information fusion filter." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 2 (May 15, 2018): 483–97. http://dx.doi.org/10.1177/0954410018776398.
Full textKumar, B. V. K. Vijaya. "Partial Information Filters." Digital Signal Processing 4, no. 3 (July 1994): 147–53. http://dx.doi.org/10.1006/dspr.1994.1014.
Full textNewton, Nigel J. "Information geometric nonlinear filtering." Infinite Dimensional Analysis, Quantum Probability and Related Topics 18, no. 02 (June 2015): 1550014. http://dx.doi.org/10.1142/s0219025715500149.
Full textHorvath, Alexander, Michael Murböck, Roland Pail, and Martin Horwath. "Decorrelation of GRACE Time Variable Gravity Field Solutions Using Full Covariance Information." Geosciences 8, no. 9 (August 29, 2018): 323. http://dx.doi.org/10.3390/geosciences8090323.
Full textDávalos, Antonio, Meryem Jabloun, Philippe Ravier, and Olivier Buttelli. "The Impact of Linear Filter Preprocessing in the Interpretation of Permutation Entropy." Entropy 23, no. 7 (June 22, 2021): 787. http://dx.doi.org/10.3390/e23070787.
Full textSrinivasa, K. G., N. Pramod, K. R. Venugopal, and L. M. Patnaik. "Effects of Information Filters." International Journal of Information Retrieval Research 2, no. 2 (April 2012): 1–12. http://dx.doi.org/10.4018/ijirr.2012040101.
Full textCherepanov, Igor Vladimirovich. "Information filters of consciousness." Interactive science, no. 3 (13) (March 22, 2017): 149–52. http://dx.doi.org/10.21661/r-118099.
Full textSHUI, PENG-LANG, and XIAO-LONG WANG. "2M-BAND INTERLEAVED DFT MODULATED FILTER BANKS WITH PERFECT RECONSTRUCTION." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 04 (July 2008): 499–520. http://dx.doi.org/10.1142/s021969130800246x.
Full textLefebvre, Carol, Julie Glanville, Sophie Beale, Charles Boachie, Steven Duffy, Cynthia Fraser, Jenny Harbour, Rachael McCool, and Lynne Smith. "Assessing the performance of methodological search filters to improve the efficiency of evidence information retrieval: five literature reviews and a qualitative study." Health Technology Assessment 21, no. 69 (November 2017): 1–148. http://dx.doi.org/10.3310/hta21690.
Full textRohini, R., N. V. Satya Narayana, and Durgesh Nandan. "A Crystal View on the Design of FIR Filter." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4235–38. http://dx.doi.org/10.1166/jctn.2020.9052.
Full textDissertations / Theses on the topic "Information filters"
Kondo, Daishi. "Preventing information leakage in NDN with name and flow filters." Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0233/document.
Full textIn recent years, Named Data Networking (NDN) has emerged as one of the most promising future networking architectures. To be adopted at Internet scale, NDN needs to resolve the inherent issues of the current Internet. Since information leakage from an enterprise is one of the big issues even in the Internet and it is very crucial to assess the risk before replacing the Internet with NDN completely, this thesis investigates whether a new security threat causing the information leakage can happen in NDN. Assuming that (i) a computer is located in the enterprise network that is based on an NDN architecture, (ii) the computer has already been compromised by suspicious media such as a malicious email, and (iii) the company installs a firewall connected to the NDN-based future Internet, this thesis focuses on a situation that the compromised computer (i.e., malware) attempts to send leaked data to the outside attacker. The contributions of this thesis are fivefold. Firstly, this thesis proposes an information leakage attack through a Data and through an Interest in NDN. Secondly, in order to address the information leakage attack, this thesis proposes an NDN firewall which monitors and processes the NDN traffic coming from the consumers with the whitelist and blacklist. Thirdly, this thesis proposes an NDN name filter to classify a name in the Interest as legitimate or not. The name filter can, indeed, reduce the throughput per Interest, but to ameliorate the speed of this attack, malware can send numerous Interests within a short period of time. Moreover, the malware can even exploit an Interest with an explicit payload in the name (like an HTTP POST message in the Internet), which is out of scope in the proposed name filter and can increase the information leakage throughput by adopting a longer payload. To take traffic flow to the NDN firewall from the consumer into account, fourthly, this thesis proposes an NDN flow monitored at an NDN firewall. Fifthly, in order to deal with the drawbacks of the NDN name filter, this thesis proposes an NDN flow filter to classify a flow as legitimate or not. The performance evaluation shows that the flow filter complements the name filter and greatly chokes the information leakage throughput
Eskiyerli, Mirat Hayri. "Square root domain filters." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299973.
Full textFischer, Peter Michael. "Adaptive optimization techniques for context-aware information filters." Zürich : ETH, 2006. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=16671.
Full textWalter, Matthew R. "Sparse Bayesian information filters for localization and mapping." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/46498.
Full textIncludes bibliographical references (p. 159-170).
This thesis formulates an estimation framework for Simultaneous Localization and Mapping (SLAM) that addresses the problem of scalability in large environments. We describe an estimation-theoretic algorithm that achieves significant gains in computational efficiency while maintaining consistent estimates for the vehicle pose and the map of the environment.We specifically address the feature-based SLAM problem in which the robot represents the environment as a collection of landmarks. The thesis takes a Bayesian approach whereby we maintain a joint posterior over the vehicle pose and feature states, conditioned upon measurement data. We model the distribution as Gaussian and parametrize the posterior in the canonical form, in terms of the information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability benefits of a sparse parametrization by explicitly pruning these weak links in an effort to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information Filter (SEIF), which has laid much of the groundwork concerning the computational benefits of the sparse canonical form. The thesis performs a detailed analysis of the process by which the SEIF approximates the sparsity of the information matrix and reveals key insights into the consequences of different sparsification strategies. We demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent, suffering from exaggerated confidence estimates.
(cont) This overconfidence has detrimental effects on important aspects of the SLAM process and affects the higher level goal of producing accurate maps for subsequent localization and path planning. This thesis proposes an alternative scalable filter that maintains sparsity while preserving the consistency of the distribution. We leverage insights into the natural structure of the feature-based canonical parametrization and derive a method that actively maintains an exactly sparse posterior. Our algorithm exploits the structure of the parametrization to achieve gains in efficiency, with a computational cost that scales linearly with the size of the map. Unlike similar techniques that sacrifice consistency for improved scalability, our algorithm performs inference over a posterior that is conservative relative to the nominal Gaussian distribution. Consequently, we preserve the consistency of the pose and map estimates and avoid the effects of an overconfident posterior. We demonstrate our filter alongside the SEIF and the standard EKEF both in simulation as well as on two real-world datasets. While we maintain the computational advantages of an exactly sparse representation, the results show convincingly that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the original Gaussian distribution as produced by the EKF, but at much less computational expense. The thesis concludes with an extension of our SLAM filter to a complex underwater environment. We describe a systems-level framework for localization and mapping relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped with a forward-looking sonar. The approach utilizes our filter to fuse measurements of vehicle attitude and motion from onboard sensors with data from sonar images of the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a ship hull.
by Matthew R. Walter.
S.M.
Mohieldin, Ahmed Nader. "High performance continuous-time filters for information transfer systems." Texas A&M University, 2003. http://hdl.handle.net/1969/233.
Full textWicks, Tony. "Design and implementation of PCAS filters." Thesis, University of Warwick, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308510.
Full textAlexiou, Ioannis. "Complex filters and higher-order spatial information for image categorization." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/14488.
Full textBeam, Michael A. "Personalized News: How Filters Shape Online News Reading Behavior." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1315716858.
Full textAlam, Dawood. "Design and VLSI implementation of two-dimensional allpass digital filters." Thesis, University of Warwick, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319797.
Full textPfann, Eugen. "Design and analysis of oversampled #sigma# #delta# adaptive LMS filters." Thesis, University of Strathclyde, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273397.
Full textBooks on the topic "Information filters"
Schneider, Karen G. A practical guide to Internet filters. New York: Neal-Schuman Publishers, 1997.
Find full textRistic, Branko. Particle Filters for Random Set Models. New York, NY: Springer New York, 2013.
Find full textCertain paint filters and strainers from Brazil: Determination of the Commission in investigation no. 701-TA-280 (preliminary) under the Tariff Act of 1930, together with the information obtained in the investigation : determination of the Commission in investigation no. 731-TA-337 (preliminary) under the Tariff Act of 1930, together with the information obtained in the investigation. Washington, DC: U.S. International Trade Commission, 1986.
Find full textDong, Hongli. Filtering, control and fault detection with randomly occurring incomplete information. Chichester, West Sussex, United Kingdom: Wiley, 2013.
Find full textPaarmann, Larry D. Design and Analysis of Analog Filters: A Signal Processing Perspective. Boston, MA: Springer US, 2003.
Find full textK, Wang R., ed. Frequency domain filtering strategies for hybrid optical information processing. Taunton, Somerset, England: Research Studies Press, 1996.
Find full textDavid, Banks. The design, implementation, and testing of an imaging system to provide quantitative ion position information at the exit of a quadrupole mass filter. [Toronto, Ont.]: Graduate Dept. of Aerospace Science and Engineering, University of Toronto, 1992.
Find full textservice), SpringerLink (Online, ed. VLSI Analog Filters: Active RC, OTA-C, and SC. Boston: Birkhäuser Boston, 2013.
Find full textWeber, Axel A. Neue klassische Makroökonomie, rationale Erwartungen und kontemporäre Information: Theoretische Analyse, ökonometrische Testprobleme und empirische Evidenz für die Bundesrepublik Deutschland unter Verwendung des Kalman-Filters. Frankfurt am Main: Haag + Herchen, 1988.
Find full textPríncipe, J. C. Kernel adaptive filtering: A comprehensive introduction. Hoboken, N.J: Wiley, 2010.
Find full textBook chapters on the topic "Information filters"
Tirdad, Kamran, Pedram Ghodsnia, J. Ian Munro, and Alejandro López-Ortiz. "COCA Filters: Co-occurrence Aware Bloom Filters." In String Processing and Information Retrieval, 313–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24583-1_31.
Full textSu, Kendall L. "General Information." In Handbook of Tables for Elliptic-Function Filters, 1–21. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-1547-6_1.
Full textJohansen, Dag, Robbert van Renesse, and Fred B. Schneider. "WAIF:Web of Asynchronous Information Filters." In Future Directions in Distributed Computing, 81–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-37795-6_15.
Full textMartínez-Díaz, Saúl, and Saúl Martínez-Chavelas. "Rotation-Invariant Nonlinear Filters Design." In Advanced Information Systems Engineering, 14–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_2.
Full textEvans, David A., Gregory Grefenstette, Yan Qu, James G. Shanahan, and Victor M. Sheftel. "Agentized, Contextualized Filters for Information Management." In Agent-Mediated Knowledge Management, 229–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24612-1_16.
Full textDi, Mingxuan, Guang Yang, Qinchuan Zhang, Kang Fu, and Hongtao Lu. "Fast Visual Object Tracking Using Convolutional Filters." In Neural Information Processing, 652–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46672-9_73.
Full textBonnabel, Silvère, and Rodolphe Sepulchre. "The Geometry of Low-Rank Kalman Filters." In Matrix Information Geometry, 53–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30232-9_3.
Full textTian, Qinyi. "Object Tracking with Multi-sample Correlation Filters." In Neural Information Processing, 473–85. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63830-6_40.
Full textZhang, Yang, Gang Zhang, Guo-sheng Rui, and Hai-bo Zhang. "Improved Direct Chaotic Nonlinear Filters." In Communications in Computer and Information Science, 314–26. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7983-3_28.
Full textWang, Fasheng. "Particle Filters for Visual Tracking." In Communications in Computer and Information Science, 107–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21402-8_17.
Full textConference papers on the topic "Information filters"
Greenlee, Alison, Timothy Murray, Victor Lesniewski, Mark Jeunnette, and Amos G. Winter. "Design and Testing of a Low-Cost and Low-Maintenance Drip Irrigation Filtration System for Micro-Irrigation in Developing Countries." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35351.
Full textEconomou, D., C. Mavroidis, and I. Antoniadis. "Comparison of Robust Residual Vibration Suppression Capabilities of Conventional Digital Filters." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21472.
Full textGarcia-Alvarez, Julio-Cesar, German Castellanos-Dominguez, and Ben-Hur Ortiz. "Image information access using wedgelet filters." In 2008 First International Symposium on Applied Sciences on Biomedical and Communication Technologies (ISABEL). IEEE, 2008. http://dx.doi.org/10.1109/isabel.2008.4712614.
Full textCasbeer, David W., and Randy Beard. "Distributed information filtering using consensus filters." In 2009 American Control Conference. IEEE, 2009. http://dx.doi.org/10.1109/acc.2009.5160531.
Full textCramer, Henriette S. M. "Interaction with user-adaptive information filters." In CHI '07 extended abstracts. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1240866.1240870.
Full textMountain, David M. "Spatial filters for mobile information retrieval." In the 4th ACM workshop. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1316948.1316964.
Full textIlyas, Saad, and Mohammad I. Younis. "Exploiting Nonlinear Behavior of MEMS Resonators for Filter Applications." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67863.
Full text"Session: digital filters." In 1988 IEEE International Symposium on Information Theory. IEEE, 1988. http://dx.doi.org/10.1109/isit.1988.22294.
Full textDewallef, P., C. Romessis, O. Le´onard, and K. Mathioudakis. "Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53541.
Full textEustice, R., M. Walter, and J. Leonard. "Sparse extended information filters: insights into sparsification." In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2005. http://dx.doi.org/10.1109/iros.2005.1545053.
Full textReports on the topic "Information filters"
Bachner K., Verdugo D., and D. Verdugo. Information Filters for Safeguards Applications: A Scoping Study. Office of Scientific and Technical Information (OSTI), May 2013. http://dx.doi.org/10.2172/1089830.
Full textHanna, Rema, Bridget Hoffmann, Paulina Oliva, and Jake Schneider. The Power of Perception: Limitations of Information in Reducing Air Pollution Exposure. Inter-American Development Bank, July 2021. http://dx.doi.org/10.18235/0003392.
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