Relevant bibliographies by topics / Distributed filtering

Contents

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

Academic literature on the topic 'Distributed filtering'

Author: Grafiati
Published: 19 February 2023

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Journal articles on the topic "Distributed filtering"

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FELIACHI, ALI. "Distributed filtering." International Journal of Systems Science 23, no. 11 (November 1992): 1857–69. http://dx.doi.org/10.1080/00207729208949426.

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Lang Hong. "Multiresolutional distributed filtering." IEEE Transactions on Automatic Control 39, no. 4 (April 1994): 853–56. http://dx.doi.org/10.1109/9.286269.

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Hong, L. "Distributed filtering using set models." IEEE Transactions on Aerospace and Electronic Systems 28, no. 4 (1992): 1144–53. http://dx.doi.org/10.1109/7.165375.

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Coutino, Mario, Elvin Isufi, and Geert Leus. "Advances in Distributed Graph Filtering." IEEE Transactions on Signal Processing 67, no. 9 (May 1, 2019): 2320–33. http://dx.doi.org/10.1109/tsp.2019.2904925.

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Yiu, Simon, and Robert Schober. "Optimized Distributed Space-Time Filtering." IEEE Transactions on Wireless Communications 6, no. 3 (March 2007): 982–92. http://dx.doi.org/10.1109/twc.2007.05279.

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Karydi, Efthalia, and Konstantinos Margaritis. "Parallel and Distributed Collaborative Filtering." ACM Computing Surveys 49, no. 2 (November 11, 2016): 1–41. http://dx.doi.org/10.1145/2951952.

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Boutet, Antoine, Davide Frey, Rachid Guerraoui, Arnaud Jégou, and Anne-Marie Kermarrec. "Privacy-preserving distributed collaborative filtering." Computing 98, no. 8 (March 26, 2015): 827–46. http://dx.doi.org/10.1007/s00607-015-0451-z.

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Matei, Ion, and John S. Baras. "Consensus-based linear distributed filtering." Automatica 48, no. 8 (August 2012): 1776–82. http://dx.doi.org/10.1016/j.automatica.2012.05.042.

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Manuel, Isaac L., and Adrian N. Bishop. "Distributed Monte Carlo Information Fusion and Distributed Particle Filtering." IFAC Proceedings Volumes 47, no. 3 (2014): 8681–88. http://dx.doi.org/10.3182/20140824-6-za-1003.00929.

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Izumi, Shinsaku, Ryosuke Katayama, Xin Xin, and Taiga Yamasaki. "Distributed Spatial Filtering Over Networked Systems." IEEE Control Systems Letters 5, no. 2 (April 2021): 617–22. http://dx.doi.org/10.1109/lcsys.2020.3004728.

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Dissertations / Theses on the topic "Distributed filtering"

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Shahid, Arslan. "Distributed ensemble Kalman filtering." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=123276.

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Distributed estimation in a wireless sensor network has many advantages. It eliminates the need of the centralized knowledge of the measurement model parameters and does not have a single point of failure. Also, any sensor node can be queried to retrieve the state estimate. Furthermore, for high dimensional measurements, local processing of information also results in a significant reduction in the communication overhead. The existing distributed Kalman filtering schemes have good computational and communication efficiency, but do not work well for non-linear and non-Gaussian problems. On the other hand, the distributed particle filtering schemes handle the non-linearities well, but have a much higher computational and communication cost. In this thesis, we propose novel distributed filtering schemes based on the ensemble Kalman filter (EnKF). We consider three forms of the EnKF and express their update equations in an alternative information form. This allows us to use the randomized gossip algorithm to reach consensus on the sufficient statistics and perform local updates. The simulation results show that all three forms of the EnKF have a considerably lower computational cost compared to the particle filters. The results suggest that the proposed distributed schemes outperform the existing state-of-the-art distributed filtering schemes in two scenarios, a) linear measurement model with non-linear state dynamics, and b) high dimensional measurements (model parameters known everywhere in the network) with non-linear measurement model and state dynamics. In both of these scenarios, the proposed schemes achieve an estimation accuracy comparable to the existing state-of-the-art schemes while significantly reducing the communication cost.
L'estimation distribuée dans un réseau de capteurs sans fil possède plusieurs avantages. Elle élimine le besoin d'une connaissance centralisée des paramtres du modèle de mesure et n'apas un point de défaillance unique. Aussi, n'importe quel agent-capteur peut-être consulté pour obtenir une approximation de l'état général. De plus, pour des mesures de hautesdimensions, le calcul local d'informations résulte en une réduction significative des coûts decommunication.Les implémentations courantes du filtre de Kalman sont efficaces sur les plans de lacharge de calcul et de la communication mais ne le sont pas pour les problmes non-linéaires et non-gaussiens. D'un autre cˆoté, les techniques distribuées de filtrage particulaire gèrentavec succès les cas non-linéaires mais sont coûteuses sur les plans de la charge de calcul etde la communication.Dans cette thèse, nous proposons des techniques de filtrage distribué basées sur le filtre de Kalman d'ensemble (FKEn). Nous considérons trois formes du FKEn et exprimonsleurs équations de changement sous une forme alternative. Cela nous permet d'utiliser unalgorithme de gossip aléatoire afin d'atteindre un consensus sur les statistiques suffisantes etcalculer les changements locaux. Les résultats des simulations montrent que les trois formesde FKEn ont une charge de calcul bien moindre que les filtres de particules équivalents. Les rèsultats suggèrent que les techniques de filtrage distribué proposées sont plus efficaces quecelles de pointe pour deux scénarios: a) un modèle de mesure linéaire avec des dynamiques d'états non-linéaires et b) des mesures de hautes dimensions (les paramtres du modèle sontconnus de chaque agent) avec un modle de mesure non-linéaire et des dynamiques d'états non-linéaires. Dans les deux scénarios considérés, les techniques proposées atteignent une précision d'estimation comparable à celle des techniques de pointe tout en réduisant significativement les coûts de communication.
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Shahbaz, Muhammad. "Active Harmonics Filtering of Distributed AC System." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elkraftteknikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-20731.

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The tendency of connecting the Power Electronic loads and distributed power plants through Power Electronic converters is increasing day by day. These Power Electronic converters and loads are the sources of harmonics and reactive power which greatly affect the performance of the power system network. In a weak power grid, the voltage unbalance and non-sinusoidal regimes are very common. Under such circumstances not only the controllability of the power grid itself but also the controllability of the electronic connected to power system equipment’s is heavily affected. So, power quality of the modern power grid (smart grid) is an important issue to address. To overcome the problem of power quality, recent efforts have been made on active filtering. The active power filters have gained much more attention because of excellent performance to mitigate the harmonic and reactive power issues. But the performance of the active filters depends upon the control theory that is employed to formulate the control algorithm of the active filter. A shunt active power filter with controller based on Instantaneous active and reactive power (p-q) theory has been purposed to verify its performance and ability to compensate the harmonics and reactive power. The advantage of p-q theory is that it is instantaneous and works in time domain. The shunt active power filter connected to AC distribution system in the presence of different shares of Power Electronic loads is investigated. It has been investigated through simulations that even under unbalanced and distorted conditions of AC distribution supply voltage and unbalanced loading, shunt active filter is able to produce the unity power factor and mitigate the harmonics (THD) specified by power quality standards. Matlab/Simulink is used as a simulation tool for the research.
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Tsai, Harry Fisk. "A multigrid relevance filtering technique for distributed interactive simulation." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/42719.

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Das, Subhro. "Distributed Linear Filtering and Prediction of Time-varying Random Fields." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/765.

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We study distributed estimation of dynamic random fields observed by a sparsely connected network of agents/sensors. The sensors are inexpensive, low power, and they communicate locally and perform computation tasks. In the era of large-scale systems and big data, distributed estimators, yielding robust and reliable field estimates, are capable of significantly reducing the large computation and communication load required by centralized estimators, by running local parallel inference algorithms. The distributed estimators have applications in estimation, for example, of temperature, rainfall or wind-speed over a large geographical area; dynamic states of a power grid; location of a group of cooperating vehicles; or beliefs in social networks. The thesis develops distributed estimators where each sensor reconstructs the estimate of the entire field. Since the local estimators have direct access to only local innovations, local observations or a local state, the agents need a consensus-type step to construct locally an estimate of their global versions. This is akin to what we refer to as distributed dynamic averaging. Dynamic averaged quantities, which we call pseudo-quantities, are then used by the distributed local estimators to yield at each sensor an estimate of the whole field. Using terminology from the literature, we refer to the distributed estimators presented in this thesis as Consensus+Innovations-type Kalman filters. We propose three distinct types of distributed estimators according to the quantity that is dynamically averaged: (1) Pseudo-Innovations Kalman Filter (PIKF), (2) Distributed Information Kalman Filter (DIKF), and (3) Consensus+Innovations Kalman Filter (CIKF). The thesis proves that under minimal assumptions the distributed estimators, PIKF, DIKF and CIKF converge to unbiased and bounded mean-squared error (MSE) distributed estimates of the field. These distributed algorithms exhibit a Network Tracking Capacity (NTC) behavior – the MSE is bounded if the degree of instability of the field dynamics is below a threshold. We derive the threshold for each of the filters. The thesis establishes trade-offs between these three distributed estimators. The NTC of the PIKF depends on the network connectivity only, while the NTC of the DIKF and of the CIKF depend also on the observation models. On the other hand, when all the three estimators converge, numerical simulations show that the DIKF improves 2dB over the PIKF. Since the DIKF uses scalar gains, it is simpler to implement than the CIKF. Of the three estimators, the CIKF provides the best MSE performance using optimized gain matrices, yielding an improvement of 3dB over the DIKF. Keywords: Kalman filter, distributed state estimation, multi-agent networks, sensor networks, distributed algorithms, consensus, innovation, asymptotic convergence, mean-squared error, dynamic averaging, Riccati equation, Lyapunov iterations, distributed signal processing, random dynamical systems.
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FILHO, HELIOS MALEBRANCHE OLBRISCH FRERES. "OPTIMAL SENSORS LOCATION FOR FILTERING AND IDENTIFICATION OF DISTRIBUTED SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1987. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8107@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Este trabalho trata do problema não-linear de estimação simultânea de parâmetros e estado, em sistemas distribuídos, e ainda do problema de localização de sensores associado. A classe de modelos em que estamos interessados é caracterizada por operadores lineares, não- limitados, densamente definidos e dissipativos. Nossa abordagem aplica técnicas de filtragem linear a uma seqüência de linearizações em torno de trajetórias convenientemente escolhidas. A localização ótima de sensores é feita de modo a minimizar uma medida do erro da estimatição simultânea de parâmetros e estados. A contribuição original desta tese compreende o desenvolvimento de : (1) um algoritmo que realiza simultaneamente a identificação e a filtragem de uma classe de sistemas distribuídos operando em ambiente estocástico, e (2) um esquema eficiente de localização ótima de sensores para o problema acima mencionado. Alguns exemplos simulados são apresentados com o objetivo de ilustrar os resultados aqui desenvolvidos
This thesis deals with the nonlinear problem of simultaneous parameter and state estimation for distributed systems, including the associated optimal sensor location. The class of models under consideration is caracterrized by linear unbounded operators which are densely defined and dissipative. Our approach applies linear filtering techniquess to a sequence of linearizations at suitable trajectories. The optimal sensors location is carried out by minimizing a meassure of the state and parameter estimation error. The contribution of this thesis comprises: (1) an algorithm for simultaneous identification and filtering for a classs of distributed systems operting in a stochastic environment and (2) an efficient optimal sensors location scheme for the above mentioned problem. Some simulated exemples are presented to illustrate the proposed approach.
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Caruana, Godwin. "MapReduce based RDF assisted distributed SVM for high throughput spam filtering." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7572.

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Electronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses. Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart. Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure. The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filtering
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Li, Yifu. "Data Filtering and Modeling for Smart Manufacturing Network." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99713.

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A smart manufacturing network connects machines via sensing, communication, and actuation networks. The data generated from the networks are used in data-driven modeling and decision-making to improve quality, productivity, and flexibility while reducing the cost. This dissertation focuses on improving the data-driven modeling of the quality-process relationship in smart manufacturing networks. The quality-process variable relationships are important to understand for guiding the quality improvement by optimizing the process variables. However, several challenges emerge. First, the big data sets generated from the manufacturing network may be information-poor for modeling, which may lead to high data transmission and computational loads and redundant data storage. Second, the data generated from connected machines often contain inexplicit similarities due to similar product designs and manufacturing processes. Modeling such inexplicit similarities remains challenging. Third, it is unclear how to select representative data sets for modeling in a manufacturing network setting, considering inexplicit similarities. In this dissertation, a data filtering method is proposed to select a relatively small and informative data subset. Multi-task learning is combined with latent variable decomposition to model multiple connected manufacturing processes that are similar-but-non-identical. A data filtering and modeling framework is also proposed to filter the manufacturing data for manufacturing network modeling adaptively. The proposed methodologies have been validated through simulation and the applications to real manufacturing case studies.
Doctor of Philosophy
The advancement of the Internet-of-Things (IoT) integrates manufacturing processes and equipment into a network. Practitioners analyze and apply the data generated from the network to model the manufacturing network to improve product quality. The data quality directly affects the modeling performance and decision effectiveness. However, the data quality is not well controlled in a manufacturing network setting. In this dissertation, we propose a data quality assurance method, referred to as data filtering. The proposed method selects a data subset from raw data collected from the manufacturing network. The proposed method reduces the complexity in modeling while supporting decision effectiveness. To model the data from multiple similar-but-non-identical manufacturing processes, we propose a latent variable decomposition-based multi-task learning model to study the relationships between the process variables and product quality variable. Lastly, to adaptively determine the appropriate data subset for modeling each process in the manufacturing network, we further proposed an integrated data filtering and modeling framework. The proposed integrated framework improved the modeling performance of data generated by babycare manufacturing and semiconductor manufacturing.
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Hore, Prodip. "Distributed clustering for scaling classic algorithms." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000395.

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Jägenstedt, Gabriel. "Analysis and Simulation of Threats in an Open, Decentralized, Distributed Spam Filtering System." Thesis, Linköpings universitet, Databas och informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81012.

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The existance of spam email has gone from a fairly small amounts of afew hundred in the late 1970’s to several billions per day in 2010. Thiscontinually growing problem is of great concern to both businesses andusers alike.One attempt to combat this problem comes with a spam filtering toolcalled TRAP. The primary design goal of TRAP is to enable tracking ofthe reputation of mail senders in a decentralized and distributed fashion.In order for the tool to be useful, it is important that it does not haveany security issues that will let a spammer bypass the protocol or gain areputation that it should not have.As a piece of this puzzle, this thesis makes an analysis of TRAP’s protocoland design in order to find threats and vulnerabilies capable of bypassingthe protocol safeguards. Based on these threats we also evaluate possiblemitigations both by analysis and simulation. We have found that althoughthe protocol was not designed with regards to certain attacks on the systemitself most of the attacks can be fairly easily stopped.The analysis shows that by adding cryptographic defenses to the protocola lot of the threats would be mitigated. In those cases where cryptographywould not suffice it is generally down to sane design choices in the implementationas well as not always trusting that a node is being truthful andfollowing protocol.
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Rautenberg, Carlos Nicolas. "A Distributed Parameter Approach to Optimal Filtering and Estimation with Mobile Sensor Networks." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/27103.

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In this thesis we develop a rigorous mathematical framework for analyzing and approximating optimal sensor placement problems for distributed parameter systems and apply these results to PDE problems defined by the convection-diffusion equations. The mathematical problem is formulated as a distributed parameter optimal control problem with integral Riccati equations as constraints. In order to prove existence of the optimal sensor network and to construct a framework in which to develop rigorous numerical integration of the Riccati equations, we develop a theory based on Bochner integrable solutions of the Riccati equations. In particular, we focus on $\I_p$-valued continuous solutions of the Bochner integral Riccati equation. We give new results concerning the smoothing effect achieved by multiplying a general strongly continuous mapping by operators in $\I_p$. These smoothing results are essential to the proofs of the existence of Bochner integrable solutions of the Riccati integral equations. We also establish that multiplication of continuous $\I_p$-valued functions improves convergence properties of strongly continuous approximating mappings and specifically approximating $C_0$-semigroups. We develop a Galerkin type numerical scheme for approximating the solutions of the integral Riccati equation and prove convergence of the approximating solutions in the $\I_p$-norm. Numerical examples are given to illustrate the theory.
Ph. D.
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Books on the topic "Distributed filtering"

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Han, Fei, Zidong Wang, and Hongli Dong. Distributed Filtering, Control and Synchronization. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97075-8.

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Lai, Cristian, Giovanni Semeraro, and Eloisa Vargiu, eds. New Challenges in Distributed Information Filtering and Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31546-6.

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Lai, Cristian, Alessandro Giuliani, and Giovanni Semeraro, eds. Distributed Systems and Applications of Information Filtering and Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40621-8.

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Lai, Cristian. New Challenges in Distributed Information Filtering and Retrieval: DART 2011: Revised and Invited Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Hubbard, James E. Spatial filtering for the control of smart structures: An Introduction. Heidelberg: Springer, 2010.

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Mahmoud, Magdi S. Distributed Control and Filtering for Industrial Systems. Institution of Engineering & Technology, 2012.

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Mahmoud. Distributed Control and Filtering for Industrial Systems. Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/pbce088e.

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Distributed Control And Filtering For Industrial Systems. Institution of Engineering and Technology, 2012.

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Dong, Hongli, Fei Han, and Zidong Wang. Distributed Filtering, Control and Synchronization: Local Performance Analysis Methods. Springer International Publishing AG, 2022.

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Dynamic Relevance Filtering in Asynchronous Transfer Mode-Based Distributed Interactive Simulation Exercises. Storming Media, 1996.

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Book chapters on the topic "Distributed filtering"

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Chui, Charles K., and Guanrong Chen. "Distributed Estimation on Sensor Networks." In Kalman Filtering, 185–95. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47612-4_12.

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Tryfonopoulos, Christos, Stratos Idreos, Manolis Koubarakis, and Paraskevi Raftopoulou. "Distributed Large-Scale Information Filtering." In Transactions on Large-Scale Data- and Knowledge-Centered Systems XIII, 91–122. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45942-3_4.

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Boutet, Antoine, Davide Frey, Rachid Guerraoui, Arnaud Jégou, and Anne-Marie Kermarrec. "Privacy-Preserving Distributed Collaborative Filtering." In Networked Systems, 169–84. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09581-3_12.

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Narang, Ankur, Abhinav Srivastava, and Naga Praveen Kumar Katta. "Distributed Scalable Collaborative Filtering Algorithm." In Euro-Par 2011 Parallel Processing, 353–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23400-2_33.

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Zhang, Dan, Qing-Guo Wang, and Li Yu. "Distributed Filtering with Communication Reduction." In Filtering and Control of Wireless Networked Systems, 111–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53123-6_7.

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Zhang, Dan, Qing-Guo Wang, and Li Yu. "Distributed Filtering with Stochastic Sampling." In Filtering and Control of Wireless Networked Systems, 129–41. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53123-6_8.

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Liu, Qinyuan, Zidong Wang, and Xiao He. "Event-Based Recursive Distributed Filtering." In Stochastic Control and Filtering over Constrained Communication Networks, 117–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00157-5_7.

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Liu, Qinyuan, Zidong Wang, and Xiao He. "Consensus-Based Recursive Distributed Filtering." In Stochastic Control and Filtering over Constrained Communication Networks, 159–72. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00157-5_9.

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Jiao, Zhuang, YangQuan Chen, and Igor Podlubny. "Distributed-Order Filtering and Distributed-Order Optimal Damping." In Distributed-Order Dynamic Systems, 39–58. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2852-6_4.

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Agounad, Said, Younes Khandouch, and Abdelkader Elhanaoui. "Digital Filtering for Circumferential Wave Separation." In Distributed Sensing and Intelligent Systems, 667–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-64258-7_57.

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Conference papers on the topic "Distributed filtering"

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Ryu, Kunhee, and Juhoon Back. "Distributed Kalman-filtering: Distributed optimization viewpoint." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029645.

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Shahid, Arslan, Deniz Ustebay, and Mark Coates. "Distributed ensemble Kalman filtering." In 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2014. http://dx.doi.org/10.1109/sam.2014.6882379.

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Almeida Neto, Fernando G., Vitor H. Nascimento, and Amanda de Paula. "Distributed multichannel adaptive filtering." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362569.

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Zamani, Mohammad, and Valery Ugrinovskii. "Minimum-energy distributed filtering." In 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7039911.

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Xie, Siyu, and Lei Guo. "Compressive distributed adaptive filtering." In 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7554168.

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Wang, Jun, Marcel J. T. Reinders, Reginald L. Lagendijk, and Johan Pouwelse. "Self-organizing distributed collaborative filtering." In the 28th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1076034.1076177.

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Rabbat, Michael, Mark Coates, and Stephane Blouin. "Graph Laplacian distributed particle filtering." In 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. http://dx.doi.org/10.1109/eusipco.2016.7760497.

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Li, Qiongxiu, Mario Coutino, Geert Leus, and Mads Grasboll Christensen. "Privacy-Preserving Distributed Graph Filtering." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287429.

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Yiu, Simon, and Robert Schober. "On distributed space-time filtering." In GLOBECOM '05. IEEE Global Telecommunications Conference. IEEE, 2005. http://dx.doi.org/10.1109/glocom.2005.1578390.

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Matei, Ion, and John S. Baras. "Consensus-based distributed linear filtering." In 2010 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010. http://dx.doi.org/10.1109/cdc.2010.5718072.

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Reports on the topic "Distributed filtering"

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Chin, Toshio M., William C. Karl, and Alan S. Willsky. A Distributed and Iterative Method for Square Root Filtering in Space-Time Estimation. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada459794.

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Nishizuka, K., M. Boucadair, T. Reddy.K, and T. Nagata. Controlling Filtering Rules Using Distributed Denial-of-Service Open Threat Signaling (DOTS) Signal Channel. RFC Editor, September 2021. http://dx.doi.org/10.17487/rfc9133.

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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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