Flight track pattern recognition based on few labeled data with outliers

2021 ◽  
Vol 30 (03) ◽  
Author(s):  
Yuqi Fan ◽  
Guangming Shen ◽  
Xiong Xu ◽  
Juan Xu ◽  
Xiaohui Yuan
Keyword(s):  
Pattern Recognition ◽  
Track Pattern

scholarly journals Quantum annealing algorithms for track pattern recognition

2020 ◽  
Vol 245 ◽  
pp. 10006
Author(s):  
Masahiko Saito ◽  
Paolo Calafiura ◽  
Heather Gray ◽  
Wim Lavrijsen ◽  
Lucy Linder ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Simulated Annealing ◽  
Hadron Collider ◽  
Recognition Algorithm ◽  
Quantum Annealing ◽  
Discovery Potential ◽  
Energy Frontier ◽  
Track Pattern ◽  
Annealing Algorithms ◽  
D Wave

The High-Luminosity Large Hadron Collider (HL-LHC) starts from 2027 to extend the physics discovery potential at the energy frontier. The HL-LHC produces experimental data with a much higher luminosity, requiring a large amount of computing resources mainly due to the complexity of a track pattern recognition algorithm. Quantum annealing might be a solution for an efficient track pattern recognition in the HL-LHC environment. We demonstrated to perform the track pattern recognition by using the D-Wave annealing machine and the Fujitsu Digital Annealer. The tracking efficiency and purity for the D-Wave quantum annealer are comparable with those for a classical simulated annealing at a low pileup condition, while a drop in performance is found at a high pileup condition, corresponding to the HL-LHC pileup environment. The tracking efficiency and purity for the Fujitsu Digital Annealer are nearly the same as the classical simulated annealing.

scholarly journals Correction to: ArborTracking: a tree topology track pattern recognition algorithm

2020 ◽  
Vol 4 (4) ◽  
pp. 513-513
Author(s):  
Mingrui Zhao ◽  
Manqi Ruan ◽  
Shouyang Hu ◽  
Jing Zhou ◽  
Yuliang Yan ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Recognition Algorithm ◽  
Tree Topology ◽  
Pattern Recognition Algorithm ◽  
Track Pattern

The tree-search processor for real time track pattern recognition

1990 ◽  
Vol 287 (3) ◽  
pp. 431-435 ◽  
Author(s):  
P. Battaiotto ◽  
M. Budinich ◽  
M. Dell'orso ◽  
S. Esquivel ◽  
P. Giannetti ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Tree Search ◽  
Track Pattern

ArborTracking: a tree topology track pattern recognition algorithm

2020 ◽  
Vol 4 (3) ◽  
pp. 377-382
Author(s):  
Mingrui Zhao ◽  
Manqi Ruan ◽  
Shouyang Hu ◽  
Jing Zhou ◽  
Yuliang Yan ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Recognition Algorithm ◽  
Tree Topology ◽  
Pattern Recognition Algorithm ◽  
Track Pattern

scholarly journals Quantum Associative Memory in Hep Track Pattern Recognition

2019 ◽  
Vol 214 ◽  
pp. 01012 ◽  
Author(s):  
Illya Shapoval ◽  
Paolo Calafiura
Keyword(s):  
Pattern Recognition ◽  
Associative Memory ◽  
Quantum Algorithms ◽  
High Energy ◽  
Detector Resolution ◽  
The Public ◽  
Computing Paradigm ◽  
Quantum Processor ◽  
Lower Complexity ◽  
Track Pattern

We have entered the Noisy Intermediate-Scale Quantum Era. A plethora of quantum processor prototypes allow evaluation of potential of the Quantum Computing paradigm in applications to pressing computational problems of the future. Growing data input rates and detector resolution foreseen in High-Energy LHC (2030s) experiments expose the often high time and/or space complexity of classical algorithms. Quantum algorithms can potentially become the lower-complexity alternatives in such cases. In this work we discuss the potential of Quantum Associative Memory (QuAM) in the context of LHC data triggering. We examine the practical limits of storage capacity, as well as store and recall errorless efficiency, from the viewpoints of the state-of-the-art IBM quantum processors and LHC real-time charged track pattern recognition requirements. We present a software prototype implementation of the QuAM protocols and analyze the topological limitations for porting the simplest QuAM instances to the public IBM 5Q and 14Q cloud-based superconducting chips.

scholarly journals Track Pattern Recognition for the SHiP Spectrometer Tracker

2017 ◽  
Vol 898 ◽  
pp. 042027
Author(s):  
M Hushchyn ◽  
A Ustyuzhanin ◽  
O Alenkin ◽  
E van Herwijnen
Keyword(s):  
Pattern Recognition ◽  
Track Pattern

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

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