scholarly journals Single-Pass Covariance Matrix Calculation on a Hybrid FPGA/CPU Platform

2020 ◽  
Vol 245 ◽  
pp. 09006
Author(s):  
Lukas On Arnold ◽  
Muhsen Owaida
Keyword(s):  
Kalman Filter ◽  
Dimensionality Reduction ◽  
Covariance Matrix ◽  
Particle Physics ◽  
Primary Component ◽  
Covariance Matrices ◽  
Wide Range ◽  
Hybrid Fpga ◽  
Speed Up ◽  
Matrix Calculation

Covariance matrices are used for a wide range of applications in particle physics, including Kálmán filter for tracking purposes or Primary Component Analysis for dimensionality reduction. Based on a novel decomposition of the covariance matrix, a design that requires only one pass of data for calculating the covariance matrix is presented. Two computation engines are used depending on parallelizability of the necessary computation steps. The design is implemented onto a hybrid FPGA/CPU system and yields speed-up of up to 5 orders of magnitude compared to previous FPGA implementation.

scholarly journals An Improved SINS Alignment Method Based on Adaptive Cubature Kalman Filter

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5509 ◽  
Author(s):  
Yonggang Zhang ◽  
Geng Xu ◽  
Xin Liu
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Real Life ◽  
Covariance Matrices ◽  
The Body ◽  
Alignment Method ◽  
Initial Alignment ◽  
Noise Covariance Matrix ◽  
Cubature Kalman Filter ◽  
Noise Covariance

Initial alignment is critical and indispensable for the inertial navigation system (INS), which determines the initial attitude matrix between the reference navigation frame and the body frame. The conventional initial alignment methods based on the Kalman-like filter require an accurate noise covariance matrix of state and measurement to guarantee the high estimation accuracy. However, in a real-life practical environment, the uncertain noise covariance matrices are often induced by the motion of the carrier and external disturbance. To solve the problem of initial alignment with uncertain noise covariance matrices and a large initial misalignment angle in practical environment, an improved initial alignment method based on an adaptive cubature Kalman filter (ACKF) is proposed in this paper. By virtue of the idea of the variational Bayesian (VB) method, the system state, one step predicted error covariance matrix, and measurement noise covariance matrix of initial alignment are adaptively estimated together. Simulation and vehicle experiment results demonstrate that the proposed method can improve the accuracy of initial alignment compared with existing methods.

Microfabrication research at the National Submicron Facility

1983 ◽  
Vol 41 ◽  
pp. 86-89
Author(s):  
E.D. Wolf
Keyword(s):  
Integrated Circuits ◽  
Particle Physics ◽  
High Speed ◽  
New Technology ◽  
High Energy ◽  
High Energy Particle ◽  
New Science ◽  
Wide Range ◽  
Intermediate Domain ◽  
Circuit Function

Most microelectronics devices and circuits operate faster, consume less power, execute more functions and cost less per circuit function when the feature-sizes internal to the devices and circuits are made smaller. This is part of the stimulus for the Very High-Speed Integrated Circuits (VHSIC) program. There is also a need for smaller, more sensitive sensors in a wide range of disciplines that includes electrochemistry, neurophysiology and ultra-high pressure solid state research. There is often fundamental new science (and sometimes new technology) to be revealed (and used) when a basic parameter such as size is extended to new dimensions, as is evident at the two extremes of smallness and largeness, high energy particle physics and cosmology, respectively. However, there is also a very important intermediate domain of size that spans from the diameter of a small cluster of atoms up to near one micrometer which may also have just as profound effects on society as “big” physics.

scholarly journals A flexible framework for anomaly Detection via dimensionality reduction

2021 ◽  
Author(s):  
Alireza Vafaei Sadr ◽  
Bruce A. Bassett ◽  
M. Kunz
Keyword(s):  
Anomaly Detection ◽  
Dimensionality Reduction ◽  
Dimensional Space ◽  
High Dimensions ◽  
Detection Algorithms ◽  
Latent Space ◽  
Wide Range ◽  
Flexible Framework ◽  
Online Anomaly Detection ◽  
Python Package

AbstractAnomaly detection is challenging, especially for large datasets in high dimensions. Here, we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. DRAMA is released as a general python package that implements the general framework with a wide range of built-in options. This approach identifies the primary prototypes in the data with anomalies detected by their large distances from the prototypes, either in the latent space or in the original, high-dimensional space. DRAMA is tested on a wide variety of simulated and real datasets, in up to 3000 dimensions, and is found to be robust and highly competitive with commonly used anomaly detection algorithms, especially in high dimensions. The flexibility of the DRAMA framework allows for significant optimization once some examples of anomalies are available, making it ideal for online anomaly detection, active learning, and highly unbalanced datasets. Besides, DRAMA naturally provides clustering of outliers for subsequent analysis.

scholarly journals Temporal concatenation for Markov decision processes

2021 ◽  
pp. 1-28
Author(s):  
Ruiyang Song ◽  
Kuang Xu
Keyword(s):  
Markov Decision Processes ◽  
Large Scale ◽  
Optimal Solution ◽  
Upper Bounds ◽  
Black Box ◽  
Decision Processes ◽  
Optimal Solutions ◽  
Wide Range ◽  
Markov Decision ◽  
Speed Up

We propose and analyze a temporal concatenation heuristic for solving large-scale finite-horizon Markov decision processes (MDP), which divides the MDP into smaller sub-problems along the time horizon and generates an overall solution by simply concatenating the optimal solutions from these sub-problems. As a “black box” architecture, temporal concatenation works with a wide range of existing MDP algorithms. Our main results characterize the regret of temporal concatenation compared to the optimal solution. We provide upper bounds for general MDP instances, as well as a family of MDP instances in which the upper bounds are shown to be tight. Together, our results demonstrate temporal concatenation's potential of substantial speed-up at the expense of some performance degradation.

scholarly journals Studying the Potential of Graphcore® IPUs for Applications in Particle Physics

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Samuel Maddrell-Mander ◽  
Lakshan Ram Madhan Mohan ◽  
Alexander Marshall ◽  
Daniel O’Hanlon ◽  
Konstantinos Petridis ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Kalman Filter ◽  
Particle Physics ◽  
Processing Unit ◽  
Network Architectures ◽  
Track Reconstruction ◽  
Event Simulation ◽  
Scattering Correction ◽  
New Type

AbstractThis paper presents the first study of Graphcore’s Intelligence Processing Unit (IPU) in the context of particle physics applications. The IPU is a new type of processor optimised for machine learning. Comparisons are made for neural-network-based event simulation, multiple-scattering correction, and flavour tagging, implemented on IPUs, GPUs and CPUs, using a variety of neural network architectures and hyperparameters. Additionally, a Kálmán filter for track reconstruction is implemented on IPUs and GPUs. The results indicate that IPUs hold considerable promise in addressing the rapidly increasing compute needs in particle physics.

Score matching filters

2021 ◽  
Author(s):  
Marie Turčičová ◽  
Jan Mandel ◽  
Kryštof Eben
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Markov Random Fields ◽  
Computational Cost ◽  
Time Step ◽  
Sample Covariance ◽  
Monte Carlo Approximation ◽  
Gauss Markov Random Fields ◽  
Ensemble Filtering ◽  
Non Gaussian

<p>A widely popular group of data assimilation methods in meteorological and geophysical sciences is formed by filters based on Monte-Carlo approximation of the traditional Kalman filter, e.g. <span>E</span><span>nsemble Kalman filter </span><span>(EnKF)</span><span>, </span><span>E</span><span>nsemble </span><span>s</span><span>quare-root filter and others. Due to the computational cost, ensemble </span><span>size </span><span>is </span><span>usually </span><span>small </span><span>compar</span><span>ed</span><span> to the dimension of the </span><span>s</span><span>tate </span><span>vector. </span><span>Traditional </span> <span>EnKF implicitly uses the sample covariance which is</span><span> a poor estimate of the </span><span>background covariance matrix - singular and </span><span>contaminated by </span><span>spurious correlations. </span></p><p><span>W</span><span>e focus on modelling the </span><span>background </span><span>covariance matrix by means of </span><span>a linear model for its inverse. This is </span><span>particularly </span><span>useful</span> <span>in</span><span> Gauss-Markov random fields (GMRF), </span><span>where</span> <span>the inverse covariance matrix has </span><span>a banded </span><span>structure</span><span>. </span><span>The parameters of the model are estimated by the</span><span> score matching </span><span>method which </span><span>provides</span><span> estimators in a closed form</span><span>, cheap to compute</span><span>. The resulting estimate</span><span> is a key component of the </span><span>proposed </span><span>ensemble filtering algorithms. </span><span>Under the assumption that the state vector is a GMRF in every time-step, t</span><span>he Score matching filter with Gaussian resamplin</span><span>g (SMF-GR) </span><span>gives</span><span> in every time-step a consistent (in the large ensemble limit) estimator of mean and covariance matrix </span><span>of the forecast and analysis distribution</span><span>. Further, we propose a filtering method called Score matching ensemble filter (SMEF), based on regularization of the EnK</span><span>F</span><span>. Th</span><span>is</span><span> filter performs well even for non-Gaussian systems with non-linear dynamic</span><span>s</span><span>. </span><span>The performance of both filters is illustrated on a simple linear convection model and Lorenz-96.</span></p>

Usage of 3D Anthropometric Data in CAD/CAM Individual Measurement List

2015 ◽  
Vol 1117 ◽  
pp. 283-286
Author(s):  
Inga Dāboliņa ◽  
Ausma Viļumsone ◽  
Jānis Dāboliņš ◽  
Dana Beļakova
Keyword(s):  
The Body ◽  
Human Anatomy ◽  
Anthropometric Data ◽  
Individual Measurement ◽  
Data Usage ◽  
Scanning Device ◽  
Wide Range ◽  
Cad Cam ◽  
Speed Up

Computer aided designing software not only the possibility to speed up the process of putting a new model into production and improve the quality of the products, but also reduces material costs and labour intensity, ensuring an elastic change of the assortment. The designing of clothes includes a row of processes and one of the most time and labour consuming is constructing. A construction displays the layout (pattern) of the surface of the body (garment). As it depends on correct anthropometric data, it is very important to get ones right. The use of 3D surface scanning technologies to produce digitized representations of the human anatomy has the potential to help change the way a wide range of products are designed and produced. Every scanning device is equipped with optic (light) appliances to ensure non-contact measuring. Measurements acquired by 3D scanning device should be checked out for compliance with CAD systems for automatized pattern making procedure. The paper introduces the experiment with scanned data usage in CAD pattern making. The project aims to implement scanned data values in the CAD/CAM individual measurement list for acquiring individualized pattern blocks.

A Rapid Computation Method for Kalman Filtering in Vehicular SINS/GPS Integrated System

2012 ◽  
Vol 182-183 ◽  
pp. 541-545 ◽  
Author(s):  
Qi Ju Zhu ◽  
Gong Min Yan ◽  
Peng Xiang Yang ◽  
Yong Yuan Qin
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Kalman Filtering ◽  
Integrated System ◽  
Computation Method ◽  
System Matrix ◽  
Computational Burden ◽  
Reduced Dimensions ◽  
Classic Method

A new rapid computation method for Kalman filtering is proposed. In this method, the prediction of state covariance matrix is expanded directly rather than computing by a looping program. Sequential filtering for measurement update is also applied. Furthermore, the subsidiary elements in system matrix are set to zero and a reduced-dimensions sub-optimal Kalman filter is presented. The proposed method greatly decreases computational burden and it is only 6.59% of the classic method. In the end, a vehicular test is carried out to prove the feasibility of the filtering.

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