Relevant bibliographies by topics / Random finite set

Academic literature on the topic 'Random finite set'

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Published: 10 December 2022
Last updated: 10 March 2023

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Journal articles on the topic "Random finite set"

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Gao, Lin, Giorgio Battistelli, and Luigi Chisci. "Random-Finite-Set-Based Distributed Multirobot SLAM." IEEE Transactions on Robotics 36, no. 6 (December 2020): 1758–77. http://dx.doi.org/10.1109/tro.2020.3001664.

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Vo, Ba-Tuong, Ba-Ngu Vo, and Antonio Cantoni. "Bayesian Filtering With Random Finite Set Observations." IEEE Transactions on Signal Processing 56, no. 4 (April 2008): 1313–26. http://dx.doi.org/10.1109/tsp.2007.908968.

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Mullane, John, Ba-Ngu Vo, Martin D. Adams, and Ba-Tuong Vo. "A Random-Finite-Set Approach to Bayesian SLAM." IEEE Transactions on Robotics 27, no. 2 (April 2011): 268–82. http://dx.doi.org/10.1109/tro.2010.2101370.

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Nasekhian, Ali, and Helmut F. Schweiger. "Random set finite element method application to tunnelling." International Journal of Reliability and Safety 5, no. 3/4 (2011): 299. http://dx.doi.org/10.1504/ijrs.2011.041182.

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MACLEAN, C. F., and NEIL O'CONNELL. "Random Finite Topologies and their Thresholds." Combinatorics, Probability and Computing 10, no. 3 (May 2001): 239–49. http://dx.doi.org/10.1017/s0963548301004680.

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For each integer n, there is a natural family of probability distributions on the set of topologies on a set of n elements, parametrized by an integer variable, m. We will describe how these are constructed and analysed, and find threshold functions (for m in terms of n) for various topological properties; we focus attention on connectivity and the size of the largest component.
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Okazaki, Hiroyuki, and Yasunari Shidama. "Probability on Finite Set and Real-Valued Random Variables." Formalized Mathematics 17, no. 2 (January 1, 2009): 129–36. http://dx.doi.org/10.2478/v10037-009-0014-x.

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Probability on Finite Set and Real-Valued Random Variables In the various branches of science, probability and randomness provide us with useful theoretical frameworks. The Formalized Mathematics has already published some articles concerning the probability: [23], [24], [25], and [30]. In order to apply those articles, we shall give some theorems concerning the probability and the real-valued random variables to prepare for further studies.
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Uchiyama, Kôhei. "One dimensional random walks killed on a finite set." Stochastic Processes and their Applications 127, no. 9 (September 2017): 2864–99. http://dx.doi.org/10.1016/j.spa.2017.01.003.

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Yang, Wei, Yaowen Fu, and Xiang Li. "Multiple-model Bayesian filtering with random finite set observation." Journal of Systems Engineering and Electronics 23, no. 3 (June 2012): 364–71. http://dx.doi.org/10.1109/jsee.2012.00045.

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Osthus, Deryk. "Maximum Antichains in Random Subsets of a Finite Set." Journal of Combinatorial Theory, Series A 90, no. 2 (May 2000): 336–46. http://dx.doi.org/10.1006/jcta.1999.3048.

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GREENHALGH, ANDREW S. "A Model for Random Random-Walks on Finite Groups." Combinatorics, Probability and Computing 6, no. 1 (March 1997): 49–56. http://dx.doi.org/10.1017/s096354839600257x.

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A model for a random random-walk on a finite group is developed where the group elements that generate the random-walk are chosen uniformly and with replacement from the group. When the group is the d-cube Zd2, it is shown that if the generating set is size k then as d → ∞ with k − d → ∞ almost all of the random-walks converge to uniform in k ln (k/(k − d))/4+ρk steps, where ρ is any constant satisfying ρ > −ln (ln 2)/4.
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Dissertations / Theses on the topic "Random finite set"

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Gardiyawasam, Punchihewa Yuthika Samanmali. "Robust Multi-Object Tracking: A Labeled Random Finite Set Approach." Thesis, Curtin University, 2018. http://hdl.handle.net/20.500.11937/75844.

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The labeled random finite set based generalized multi-Bernoulli filter is a tractable analytic solution for the multi-object tracking problem. The robustness of this filter is dependent on certain knowledge regarding the multi-object system being available to the filter. This dissertation presents techniques for robust tracking, constructed upon the labeled random finite set framework, where complete information regarding the system is unavailable.
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Wood, Trevor M. "Random finite sets for multitarget tracking with applications." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:09211ed9-7cc1-4401-9ae9-a1ebc2a1f782.

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Multitarget tracking is the process of jointly determining the number of targets present and their states from noisy sets of measurements. The difficulty of the multitarget tracking problem is that the number of targets present can change as targets appear and disappear while the sets of measurements may contain false alarms and measurements of true targets may be missed. The theory of random finite sets was proposed as a systematic, Bayesian approach to solving the multitarget tracking problem. The conceptual solution is given by Bayes filtering for the probability distribution of the set of target states, conditioned on the sets of measurements received, known as the multitarget Bayes filter. A first-moment approximation to this filter, the probability hypothesis density (PHD) filter, provides a more computationally practical, but theoretically sound, solution. The central thesis of this work is that the random finite set framework is theoretically sound, compatible with the Bayesian methodology and amenable to immediate implementation in a wide range of contexts. In advancing this thesis, new links between the PHD filter and existing Bayesian approaches for manoeuvre handling and incorporation of target amplitude information are presented. A new multitarget metric which permits incorporation of target confidence information is derived and new algorithms are developed which facilitate sequential Monte Carlo implementations of the PHD filter. Several applications of the PHD filter are presented, with a focus on applications for tracking in sonar data. Good results are presented for implementations on real active and passive sonar data. The PHD filter is also deployed in order to extract bacterial trajectories from microscopic visual data in order to aid ongoing work in understanding bacterial chemotaxis. A performance comparison between the PHD filter and conventional multitarget tracking methods using simulated data is also presented, showing favourable results for the PHD filter.
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Nuss, Dominik [Verfasser]. "A random finite set approach for dynamic occupancy grid maps / Dominik Nuss." Ulm : Universität Ulm, 2017. http://d-nb.info/1133544290/34.

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Deusch, Hendrik [Verfasser]. "Random finite set-based localization and SLAM for highly automated vehicles / Hendrik Deusch." Ulm : Universität Ulm, 2016. http://d-nb.info/1105559750/34.

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Ong, Jonah Soon Xuan. "Online Audio-Visual Multi-Source Tracking and Separation: A Labeled Random Finite Set Approach." Thesis, Curtin University, 2021. http://hdl.handle.net/20.500.11937/89300.

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The dissertation proposes an online solution for separating an unknown and time-varying number of moving sources using audio and visual data. The random finite set framework is used for the modeling and fusion of audio and visual data. This enables an online tracking algorithm to estimate the source positions and identities for each time point. With this information, a set of beamformers can be designed to separate each desired source and suppress the interfering sources.
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Beard, Michael Anthony. "Estimation and control of multi-object systems with high-fidenlity sensor models: A labelled random finite set approach." Thesis, Curtin University, 2016. http://hdl.handle.net/20.500.11937/627.

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Principled and novel multi-object tracking algorithms are proposed, that have the ability to optimally process realistic sensor data, by accommodating complex observational phenomena such as merged measurements and extended targets. Additionally, a sensor control scheme based on a tractable, information theoretic objective is proposed, the goal of which is to optimise tracking performance in multi-object scenarios. The concept of labelled random finite sets is adopted in the development of these new techniques.
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Hauschildt, Daniel [Verfasser], Uwe [Akademischer Betreuer] Schwiegelshohn, and Daniel [Gutachter] Clark. "Random finite set filters for superpositional sensors : Application to multi-object filtering / Daniel Hauschildt ; Gutachter: Daniel Clark ; Betreuer: Uwe Schwiegelshohn." Dortmund : Universitätsbibliothek Dortmund, 2017. http://d-nb.info/1139892622/34.

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Tobias, Martin. "Probability Hypothesis Densities for Multitarget, Multisensor Tracking with Application to Passive Radar." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10514.

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The probability hypothesis density (PHD), popularized by Ronald Mahler, presents a novel and theoretically-rigorous approach to multitarget, multisensor tracking. Based on random set theory, the PHD is the first moment of a point process of a random track set, and it can be propagated by Bayesian prediction and observation equations to form a multitarget, multisensor tracking filter. The advantage of the PHD filter lies in its ability to estimate automatically the expected number of targets present, to fuse easily different kinds of data observations, and to locate targets without performing any explicit report-to-track association. We apply a particle-filter implementation of the PHD filter to realistic multitarget, multisensor tracking using passive coherent location (PCL) systems that exploit illuminators of opportunity such as FM radio stations. The objective of this dissertation is to enhance the usefulness of the PHD particle filter for multitarget, multisensor tracking, in general, and within the context of PCL, in particular. This involves a number of thrusts, including: 1) devising intelligent proposal densities for particle placement, 2) devising a peak-extraction algorithm for extracting information from the PHD, 3) incorporating realistic probabilities of detection and signal-to-noise ratios (including multipath effects) to model realistic PCL scenarios, 4) using range, Doppler, and direction of arrival (DOA) observations to test the target detection and data fusion capabilities of the PHD filter, and 5) clarifying the concepts behind FISST and the PHD to make them more accessible to the practicing engineer. A goal of this dissertation is to serve as a tutorial for anyone interested in becoming familiar with the probability hypothesis density and associated PHD particle filter. It is hoped that, after reading this thesis, the reader will have gained a clearer understanding of the PHD and the functionality and effectiveness of the PHD particle filter.
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Zhang, Feihu [Verfasser], Alois [Akademischer Betreuer] [Gutachter] Knoll, and Ren C. [Gutachter] Luo. "Data Fusion for Advanced Driver Assistance Systems Based on Random Finite Set Statistic / Feihu Zhang ; Gutachter: Ren C. Luo, Alois Knoll ; Betreuer: Alois Knoll." München : Universitätsbibliothek der TU München, 2016. http://d-nb.info/1121206786/34.

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FANTACCI, CLAUDIO. "Distributed multi-object tracking over sensor networks: a random finite set approach." Doctoral thesis, 2015. http://hdl.handle.net/2158/1003256.

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The aim of the present dissertation is to address distributed tracking over a network of heterogeneous and geographically dispersed nodes (or agents) with sensing, communication and processing capabilities. Tracking is carried out in the Bayesian framework and its extension to a distributed context is made possible via an information-theoretic approach to data fusion which exploits consensus algorithms and the notion of Kullback–Leibler Average (KLA) of the Probability Density Functions (PDFs) to be fused. The first step toward distributed tracking considers a single moving object. Consensus takes place in each agent for spreading information over the network so that each node can track the object. To achieve such a goal, consensus is carried out on the local single-object posterior distribution, which is the result of local data processing, in the Bayesian setting, exploiting the last available measurement about the object. Such an approach is called Consensus on Posteriors (CP). The first contribution of the present work is an improvement to the CP algorithm, namely Parallel Consensus on Likelihoods and Priors (CLCP). The idea is to carry out, in parallel, a separate consensus for the novel information (likelihoods) and one for the prior information (priors). This parallel procedure is conceived to avoid underweighting the novel information during the fusion steps. The outcomes of the two consensuses are then combined to provide the fused posterior density. Furthermore, the case of a single highly-maneuvering object is addressed. To this end, the object is modeled as a jump Markovian system and the multiple model (MM) filtering approach is adopted for local estimation. Thus, the consensus algorithms needs to be re-designed to cope with this new scenario. The second contribution has been to devise two novel consensus MM filters to be used for tracking a maneuvering object. The novel consensus-based MM filters are based on the First Order Generalized Pseudo-Bayesian (GPB1) and Interacting Multiple Model (IMM) filters. The next step is in the direction of distributed estimation of multiple moving objects. In order to model, in a rigorous and elegant way, a possibly time-varying number of objects present in a given area of interest, the Random Finite Set (RFS) formulation is adopted since it provides the notion of probability density for multi-object states that allows to directly extend existing tools in distributed estimation to multi-object tracking. The multi-object Bayes filter proposed by Mahler is a theoretically grounded solution to recursive Bayesian tracking based on RFSs. However, the multi-object Bayes recursion, unlike the single-object counterpart, is affected by combinatorial complexity and is, therefore, computationally infeasible except for very small-scale problems involving few objects and/or measurements. For this reason, the computationally tractable Probability Hypothesis Density (PHD) and Cardinalized PHD (CPHD) filtering approaches will be used as a first endeavour to distributed multiobject filtering. The third contribution is the generalisation of the single-object KLA to the RFS framework, which is the theoretical fundamental step for developing a novel consensus algorithm based on CPHD filtering, namely the Consensus CPHD (CCPHD). Each tracking agent locally updates multi-object CPHD, i.e. the cardinality distribution and the PHD, exploiting the multi-object dynamics and the available local measurements, exchanges such information with communicating agents and then carries out a fusion step to combine the information from all neighboring agents. The last theoretical step of the present dissertation is toward distributed filtering with the further requirement of unique object identities. To this end the labeled RFS framework is adopted as it provides a tractable approach to the multi-object Bayesian recursion. The δ- GLMB filter is an exact closed-form solution to the multi-object Bayes recursion which jointly yields state and label (or trajectory) estimates in the presence of clutter, misdetections and association uncertainty. Due to the presence of explicit data associations in the δ-GLMB filter, the number of components in the posterior grows without bound in time. The fourth contribution of this thesis is an efficient approximation of the δ-GLMB filter, namely Marginalized δ-GLMB (Mδ-GLMB), which preserves key summary statistics (i.e. both the PHD and cardinality distribution) of the full labeled posterior. This approximation also facilitates efficient multi-sensor tracking with detection-based measurements. Simulation results are presented to verify the proposed approach. Finally, distributed labeled multi-object tracking over sensor networks is taken into account. The last contribution is a further generalization of the KLA to the labeled RFS framework, which enables the development of two novel consensus tracking filters, namely the Consensus Marginalized δ-Generalized Labeled Multi-Bernoulli (CM-δGLMB) and the Consensus Labeled Multi-Bernoulli (CLMB) tracking filters. The proposed algorithms provide a fully distributed, scalable and computationally efficient solution for multi-object tracking. Simulation experiments on challenging single-object or multi-object tracking scenarios confirm the effectiveness of the proposed contributions.
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Books on the topic "Random finite set"

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Schmidt, Kai-Uwe, and Arne Winterhof, eds. Combinatorics And Finite Fields: Difference Sets, Polynomials, Pseudorandomness And Applications. Boston, USA: De Gruyter, 2019.

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Tao, Terence. Expansion in finite simple groups of Lie type. Providence, Rhode Island: American Mathematical Society, 2015.

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Hankin, David, Michael S. Mohr, and Kenneth B. Newman. Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.001.0001.

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We present a rigorous but understandable introduction to the field of sampling theory for ecologists and natural resource scientists. Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make scientifically and statistically sound inferences about the population as a whole. The inferences fall into two broad categories: (a) estimation of simple descriptive population parameters, such as means, totals, or proportions, for variables of interest, and (b) estimation of uncertainty associated with estimated parameter values. Although the targets of estimation are few and simple, estimates of means, totals, or proportions see important and often controversial uses in management of natural resources and in fundamental ecological research, but few ecologists or natural resource scientists have formal training in sampling theory. We emphasize the classical design-based approach to sampling in which variable values associated with units are regarded as fixed and uncertainty of estimation arises via various randomization strategies that may be used to select samples. In addition to covering standard topics such as simple random, systematic, cluster, unequal probability (stressing the generality of Horvitz–Thompson estimation), multi-stage, and multi-phase sampling, we also consider adaptive sampling, spatially balanced sampling, and sampling through time, three areas of special importance for ecologists and natural resource scientists. The text is directed to undergraduate seniors, graduate students, and practicing professionals. Problems emphasize application of the theory and R programming in ecological and natural resource settings.
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Book chapters on the topic "Random finite set"

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Li, Tiancheng, Kai Da, Hongqi Fan, and Benru Yu. "Multisensor Random Finite Set Information Fusion." In Secure and Digitalized Future Mobility, 33–64. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/b22998-3.

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Mullane, John, Ba-Ngu Vo, Martin Adams, and Ba-Tuong Vo. "Mobile Robotics in a Random Finite Set Framework." In Lecture Notes in Computer Science, 519–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21524-7_64.

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Sibuya, Masaaki. "The Most Random Partition of a Finite Set and its Application to Classification." In Studies in Classification, Data Analysis, and Knowledge Organization, 241–46. Tokyo: Springer Japan, 1998. http://dx.doi.org/10.1007/978-4-431-65950-1_25.

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Goldstein, Harrison, John Hughes, Leonidas Lampropoulos, and Benjamin C. Pierce. "Do Judge a Test by its Cover." In Programming Languages and Systems, 264–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_10.

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AbstractProperty-based testing uses randomly generated inputs to validate high-level program specifications. It can be shockingly effective at finding bugs, but it often requires generating a very large number of inputs to do so. In this paper, we apply ideas from combinatorial testing, a powerful and widely studied testing methodology, to modify the distributions of our random generators so as to find bugs with fewer tests. The key concept is combinatorial coverage, which measures the degree to which a given set of tests exercises every possible choice of values for every small combination of input features.In its “classical” form, combinatorial coverage only applies to programs whose inputs have a very particular shape—essentially, a Cartesian product of finite sets. We generalize combinatorial coverage to the richer world of algebraic data types by formalizing a class of sparse test descriptions based on regular tree expressions. This new definition of coverage inspires a novel combinatorial thinning algorithm for improving the coverage of random test generators, requiring many fewer tests to catch bugs. We evaluate this algorithm on two case studies, a typed evaluator for System F terms and a Haskell compiler, showing significant improvements in both.
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Martin, Juliette, Leslie Regad, Anne-Claude Camproux, and Grégory Nuel. "Finite Markov Chain Embedding for the Exact Distribution of Patterns in a Set of Random Sequences." In Advances in Data Analysis, 171–80. Boston: Birkhäuser Boston, 2009. http://dx.doi.org/10.1007/978-0-8176-4799-5_16.

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Moosbrugger, Marcel, Ezio Bartocci, Joost-Pieter Katoen, and Laura Kovács. "Automated Termination Analysis of Polynomial Probabilistic Programs." In Programming Languages and Systems, 491–518. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_18.

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AbstractThe termination behavior of probabilistic programs depends on the outcomes of random assignments. Almost sure termination (AST) is concerned with the question whether a program terminates with probability one on all possible inputs. Positive almost sure termination (PAST) focuses on termination in a finite expected number of steps. This paper presents a fully automated approach to the termination analysis of probabilistic while-programs whose guards and expressions are polynomial expressions. As proving (positive) AST is undecidable in general, existing proof rules typically provide sufficient conditions. These conditions mostly involve constraints on supermartingales. We consider four proof rules from the literature and extend these with generalizations of existing proof rules for (P)AST. We automate the resulting set of proof rules by effectively computing asymptotic bounds on polynomials over the program variables. These bounds are used to decide the sufficient conditions – including the constraints on supermartingales – of a proof rule. Our software tool Amber can thus check AST, PAST, as well as their negations for a large class of polynomial probabilistic programs, while carrying out the termination reasoning fully with polynomial witnesses. Experimental results show the merits of our generalized proof rules and demonstrate that Amber can handle probabilistic programs that are out of reach for other state-of-the-art tools.
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Winkler, Peter. "Random Structures and Zero-One Laws." In Finite and Infinite Combinatorics in Sets and Logic, 399–420. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2080-7_27.

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Castillo-Santiago, Miguel Ángel, Edith Mondragón-Vázquez, and Roberto Domínguez-Vera. "Sample Data for Thematic Accuracy Assessment in QGIS." In Land Use Cover Datasets and Validation Tools, 85–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_6.

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AbstractWe present an approach that is widely used in the field of remote sensing for the validation of single LUC maps. Unlike other chapters in this book, where maps are validated by comparison with other maps with better resolution and/or quality, this approach requires a ground sample dataset, i.e. a set of sites where LUC can be observed in the field or interpreted from high-resolution imagery. Map error is assessed using techniques based on statistical sampling. In general terms, in this approach, the accuracy of single LUC maps is assessed by comparing the thematic map against the reference data and measuring the agreement between the two. When assessing thematic accuracy, three stages can be identified: the design of the sample, the design of the response, and the estimation and analysis protocols. Sample design refers to the protocols used to define the characteristics of the sampling sites, including sample size and distribution, which can be random or systematic. Response design involves establishing the characteristics of the reference data, such as the size of the spatial assessment units, the sources from which the reference data will be obtained, and the criteria for assigning labels to spatial units. Finally, the estimation and analysis protocols include the procedures applied to the reference data to calculate accuracy indices, such as user’s and producer’s accuracy, the estimated areas covered by each category and their respective confidence intervals. This chapter has two sections in which we present a couple of exercises relating to sampling and response design; the sample size will be calculated, the distribution of sampling sites will be obtained using a stratified random scheme, and finally, a set of reference data will be obtained by photointerpretation at the sampling sites (spatial units). The accuracy statistics will be calculated later in Sect. 5 in chapter “Metrics Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps” as part of the cross-tabulation exercises. The exercises in this chapter use fine-scale LUC maps obtained for the municipality of Marqués de Comillas in Chiapas, Mexico.
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Gerbner, Dániel, and Balázs Patkós. "Random versions of Sperner’s theorem and the Erdő-Ko-Rado theorem." In Extremal Finite Set Theory, 115–38. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780429440809-4.

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"Random versions of Sperner’s theorem and the Erdo˝s-Ko- Rado theorem." In Extremal Finite Set Theory, 131–54. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9780429440809-11.

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Conference papers on the topic "Random finite set"

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Zhang, Feihu, Can Wang, Chensheng Cheng, and Guang Pan. "Vessel Tracking Under Random Finite Set Framework." In 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO). IEEE, 2018. http://dx.doi.org/10.1109/oceanskobe.2018.8559196.

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Bryant, Daniel S., Ba-Tuong Vo, Ba-Ngu Vo, and Brandon A. Jones. "A labeled random finite set spawning model." In 2017 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, 2017. http://dx.doi.org/10.1109/iccais.2017.8217579.

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Bishop, A. N., and P. Jensfelt. "Global robot localization with random finite set statistics." In 2010 13th International Conference on Information Fusion (FUSION 2010). IEEE, 2010. http://dx.doi.org/10.1109/icif.2010.5711873.

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A., Nasekhian, and Schweige H. F. "Random Set Finite Element Method Application to Tunnelling." In 4th International Workshop on Reliable Engineering Computing (REC 2010). Singapore: Research Publishing Services, 2010. http://dx.doi.org/10.3850/978-981-08-5118-7_074.

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Doerr, Bryce, and Richard Linares. "Control of Large Swarms via Random Finite Set Theory." In 2018 Annual American Control Conference (ACC). IEEE, 2018. http://dx.doi.org/10.23919/acc.2018.8430968.

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Mori, Shozo, Chee-Yee Chong, and Kuo-chu Chang. "A random finite set formalism for multiple hypothesis tracking." In Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX, edited by Lynne L. Grewe, Erik P. Blasch, and Ivan Kadar. SPIE, 2020. http://dx.doi.org/10.1117/12.2566259.

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Deusch, Hendrik, Jurgen Wiest, Stephan Reuter, Magdalena Szczot, Marcus Konrad, and Klaus Dietmayer. "A random finite set approach to multiple lane detection." In 2012 15th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/itsc.2012.6338772.

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Uney, M., D. E. Clark, and S. J. Julier. "Distributed sensor registration based on random finite set representations." In Sensor Signal Processing for Defence (SSPD 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/ic.2012.0095.

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Correa, Javier, Martin Adams, and Claudio Perez. "A Dirac Delta mixture-based Random Finite Set filter." In 2015 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, 2015. http://dx.doi.org/10.1109/iccais.2015.7338668.

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Feihu Zhang, Hauke Stahle, Guang Chen, Christian Buckl, and Alois Knoll. "Multiple vehicle cooperative localization under random finite set framework." In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013). IEEE, 2013. http://dx.doi.org/10.1109/iros.2013.6696533.

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Reports on the topic "Random finite set"

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Medrano, Juan, Adam Friedmann, Moshe (Morris) Soller, Ehud Lipkin, and Abraham Korol. High resolution linkage disequilibrium mapping of QTL affecting milk production traits in Israel Holstein dairy cattle. United States Department of Agriculture, March 2008. http://dx.doi.org/10.32747/2008.7696509.bard.

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Original objectives: To create BAC contigs covering two QTL containing chromosomal regions (QTLR) and obtain BAC end sequence information as a platform for SNP identification. Use the SNPs to search for marker-QTL linkage disequilibrium (LD) in the test populations (US and Israel Holstein cattle). Identify candidate genes, test for association with dairy cattle production and functional traits, and confirm any associations in a secondary test population. Revisions in the course of the project: The selective recombinant genotyping (SRG) methodology which we implemented to provide moderate resolution QTL mapping turned out to be less effective than expected, due to problems introduced by incomplete marker informativity. This required a no-cost one-year extension of the project. Aside from this, the project was implemented essentially as envisaged, but only with respect to a single QTLR and single population association-test. Background to the topic. Dairy cattle breeders are looking to marker-assisted selection (MAS) as a means of identifying genetically superior sires and dams. MAS based on population-wide LD can be many times more effective than MAS based on within-family linkage mapping. In this proposal we developed a protocol leading from family based QTL mapping to population-wide LD between markers and the QTL Major conclusions, solutions, achievements. The critical importance of marker informativity for application of the SRG design in outcrossing random mating populations was identified, and an alternative Fractioned Pool Design (FPD) based on selective DNA pooling was developed. We demonstrated the feasibility of constructing a BAC contig across a targeted chromosomal region flanking the marker RM188 on bovine chromosome BTA4, which was shown in previous work to contain a QTL affecting milk production traits. BAC end sequences were obtained and successfully screened for SNPs. LD studies of these SNPs in the Israel population, and of an independent set of SNPs taken across the entire proximal region of BTA4 in the USA population, showed a much lower degree of LD than previously reported in the literature. Only at distances in the sub-cM level did an appreciable fraction of SNP marker-pairs show levels of LD useful for MAS. In contrast, studies in the Israel population using microsatellite markers, presented an equivalent degree of LD at a 1-5 separation distance. SNP LD appeared to reflect historical population size of Bostaurus (Ne=5000- 10,000), while microsatellite LD appeared to be in proportion to more recent effective population size of the Holstein breed (Ne=50-100). An appreciable fraction of the observed LD was due to Family admixture structure of the Holstein population. The SNPs MEOX2/IF2G (found within the gene SETMAR at 23,000 bp from RM188) and SNP23 were significantly associated with PTA protein, Cheese dollars and Net Merit Protein in the Davis bull resource population, and were also associated with protein and casein percentages in the Davis cow resource population. Implications. These studies document a major difference in degree of LD presented by SNPs as compared to microsatellites, and raise questions as to the source of this difference and its implications for QTL mapping and MAS. The study lends significant support to the targeted approach to fine map a previously identified QTL. Using high density genotyping with SNP discovered in flanking genes to the QTL, we have identified important markers associated with milk protein percentage that can be tested in markers assisted selection programs.
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