The Experts below are selected from a list of 63 Experts worldwide ranked by ideXlab platform
Sylvain Pion - One of the best experts on this subject based on the ideXlab platform.
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interval arithmetic yields efficient dynamic Filters for computational geometry
European Workshop on Computational Geometry, 2001Co-Authors: Herve Bronnimann, Christoph Burnikel, Sylvain PionAbstract:We discuss floating-Point Filters as a means of restricting the precision needed for arithmetic operations while still computing the exact result. We show that interval techniques can be used to speed up the exact evaluation of geometric predicates and describe an efficient implementation of interval arithmetic that is strongly influenced by the rounding modes of the widely used IEEE Standard 754. Using this approach we engineer an efficient floating-Point Filter for the computation of the sign of a determinant that works for arbitrary dimensions. We validate our approach experimentally, comparing it with other static, dynamic and semi-static Filters.
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interval arithmetic yields efficient dynamic Filters for computational geometry
Symposium on Computational Geometry, 1998Co-Authors: Herve Bronnimann, Christoph Burnikel, Sylvain PionAbstract:We discuss interval techniques for speeding up the exact evaluation of geometric predicates and describe an efficient implementation of interval arithmetic that is strongly influenced by the rounding modes of the widely used IEEE 754 standard. Using this approach we engineer an efficient floating Point Filter for the computation of the sign of a determinant that works for arbitrary dimensions. Furthermore we show how to use our interval techniques for exact linear optimization problems of low dimension as they arise in geometric computing. We validate our approach experimentally, comparing it with other static, dynamic and semi-static Filters.
Herve Bronnimann - One of the best experts on this subject based on the ideXlab platform.
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interval arithmetic yields efficient dynamic Filters for computational geometry
European Workshop on Computational Geometry, 2001Co-Authors: Herve Bronnimann, Christoph Burnikel, Sylvain PionAbstract:We discuss floating-Point Filters as a means of restricting the precision needed for arithmetic operations while still computing the exact result. We show that interval techniques can be used to speed up the exact evaluation of geometric predicates and describe an efficient implementation of interval arithmetic that is strongly influenced by the rounding modes of the widely used IEEE Standard 754. Using this approach we engineer an efficient floating-Point Filter for the computation of the sign of a determinant that works for arbitrary dimensions. We validate our approach experimentally, comparing it with other static, dynamic and semi-static Filters.
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interval arithmetic yields efficient dynamic Filters for computational geometry
Symposium on Computational Geometry, 1998Co-Authors: Herve Bronnimann, Christoph Burnikel, Sylvain PionAbstract:We discuss interval techniques for speeding up the exact evaluation of geometric predicates and describe an efficient implementation of interval arithmetic that is strongly influenced by the rounding modes of the widely used IEEE 754 standard. Using this approach we engineer an efficient floating Point Filter for the computation of the sign of a determinant that works for arbitrary dimensions. Furthermore we show how to use our interval techniques for exact linear optimization problems of low dimension as they arise in geometric computing. We validate our approach experimentally, comparing it with other static, dynamic and semi-static Filters.
Ali Yousefi - One of the best experts on this subject based on the ideXlab platform.
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real time Point process Filter for multidimensional decoding problems using mixture models
Journal of Neuroscience Methods, 2021Co-Authors: Mohammad Reza Rezaei, Kensuke Arai, Loren M Frank, Uri T Eden, Ali YousefiAbstract:There is an increasing demand for a computationally efficient and accurate Point process Filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the Point process Filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the Filter solution is computationally expensive in multi-dimensional Filtering problems. Here, we propose an approximate Filter solution for a general Point-process Filter problem when the conditional intensity of a cell's spiking activity is characterized using a Mixture of Gaussians. We propose the Filter solution for a broader class of Point process observation called marked Point-process, which encompasses both clustered - mainly, called sorted - and clusterless - generally called unsorted or raw- spiking activity. We assume that the posterior distribution on each Filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional Point-process Filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each Filtering time-step. We apply this methodology in decoding a rat's position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% highest probability coverage area (HPD) performance metrics. Though the marked-Point Filter solution is better suited for real-time decoding problems, we discuss how the Filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.
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real time Point process Filter for multidimensional decoding problems using mixture models
bioRxiv, 2018Co-Authors: Ali Yousefi, Mohammad Reza Rezaei, Kensuke Arai, Loren M Frank, Uri T EdenAbstract:Abstract There is an increasing demand for a computationally efficient and accurate Point process Filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the Point process Filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the Filter solution is computationally expensive in multi-dimensional Filtering problems. Here, we propose an approximate Filter solution for a general Point-process Filter problem when the conditional intensity of a cell’s spiking activity is characterized using a Mixture of Gaussians. We propose the Filter solution for a broader class of Point process observation called marked Point-process, which encompasses both clustered – mainly, called sorted – and clusterless – generally called unsorted or raw– spiking activity. We assume that the posterior distribution on each Filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional Point-process Filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each Filtering time-step. We apply this methodology in decoding a rat’s position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% HPD coverage performance metrics. Though the marked-Point Filter solution is better suited for real-time decoding problems, we discuss how the Filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.
Xiaodong Wang - One of the best experts on this subject based on the ideXlab platform.
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decentralized sigma Point information Filters for target tracking in collaborative sensor networks
IEEE Transactions on Signal Processing, 2005Co-Authors: Tom Vercauteren, Xiaodong WangAbstract:Tracking a target in a cluttered environment is a representative application of sensor networks and a benchmark for collaborative signal processing algorithms. This paper presents a strictly decentralized approach to Bayesian Filtering that is well fit for in-network signal processing. By combining the sigma-Point Filter methodology and the information Filter framework, a class of algorithms denoted as sigma-Point information Filters is developed. These techniques exhibit the robustness and accuracy of the sigma-Point Filters for nonlinear dynamic inference while being as easily decentralized as the information Filters. Furthermore, the computational cost of this approach is equivalent to a local Kalman Filter running in each active node while the communication burden can be made linearly growing in the number of sensors involved. The proposed algorithms are then adapted to the specific problem of target tracking with data association ambiguity. Making use of a local probabilistic data association, we formulate a decentralized tracking scheme that significantly outperforms the existing schemes with similar computational and communication complexity.
Christoph Burnikel - One of the best experts on this subject based on the ideXlab platform.
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interval arithmetic yields efficient dynamic Filters for computational geometry
European Workshop on Computational Geometry, 2001Co-Authors: Herve Bronnimann, Christoph Burnikel, Sylvain PionAbstract:We discuss floating-Point Filters as a means of restricting the precision needed for arithmetic operations while still computing the exact result. We show that interval techniques can be used to speed up the exact evaluation of geometric predicates and describe an efficient implementation of interval arithmetic that is strongly influenced by the rounding modes of the widely used IEEE Standard 754. Using this approach we engineer an efficient floating-Point Filter for the computation of the sign of a determinant that works for arbitrary dimensions. We validate our approach experimentally, comparing it with other static, dynamic and semi-static Filters.
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interval arithmetic yields efficient dynamic Filters for computational geometry
Symposium on Computational Geometry, 1998Co-Authors: Herve Bronnimann, Christoph Burnikel, Sylvain PionAbstract:We discuss interval techniques for speeding up the exact evaluation of geometric predicates and describe an efficient implementation of interval arithmetic that is strongly influenced by the rounding modes of the widely used IEEE 754 standard. Using this approach we engineer an efficient floating Point Filter for the computation of the sign of a determinant that works for arbitrary dimensions. Furthermore we show how to use our interval techniques for exact linear optimization problems of low dimension as they arise in geometric computing. We validate our approach experimentally, comparing it with other static, dynamic and semi-static Filters.