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Albertsson, Kim
Publications (2 of 2) Show all publications
Albertsson, K., Gleyzer, S. & Zapata, O. (2018). Machine Learning in High Energy Physics Community White Paper. Paper presented at 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT2017)21–25 August 2017, Seattle, United States of America. Journal of Physics, Conference Series, 1085, Article ID 022008.
Open this publication in new window or tab >>Machine Learning in High Energy Physics Community White Paper
2018 (English)In: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 1085, article id 022008Article in journal (Refereed) Published
Abstract [en]

Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

Place, publisher, year, edition, pages
Institute of Physics (IOP), 2018
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-71316 (URN)10.1088/1742-6596/1085/2/022008 (DOI)000467872200008 ()
Conference
18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT2017)21–25 August 2017, Seattle, United States of America
Note

Konferensartikel i tidskrift

Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2019-06-18Bibliographically approved
del Campo, S. M., Albertsson, K., Nilsson, J., Eliasson, J. & Sandin, F. (2013). FPGA prototype of machine learning analog-to-feature converter for event-based succinct representation of signals (ed.). In: (Ed.), IEEE International Workshop on Machine Learning for Signal Processing: . Paper presented at IEEE International Workshop on Machine Learning for Signal Processing : 22/09/2013 - 25/09/2013. Piscataway, NJ: IEEE Signal Processing Society, Article ID 6661996.
Open this publication in new window or tab >>FPGA prototype of machine learning analog-to-feature converter for event-based succinct representation of signals
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2013 (English)In: IEEE International Workshop on Machine Learning for Signal Processing, Piscataway, NJ: IEEE Signal Processing Society, 2013, article id 6661996Conference paper, Published paper (Refereed)
Abstract [en]

Sparse signal models with learned dictionaries of morphological features provide efficient codes in a variety of applications. Such models can be useful to reduce sensor data rates and simplify the communication, processing and analysis of information, provided that the algorithm can be realized in an efficient way and that the signal allows for sparse coding. In this paper we outline an FPGA prototype of a general purpose "analog-to-feature converter", which learns an overcomplete dictionary of features from the input signal using matching pursuit and a form of Hebbian learning. The resulting code is sparse, event-based and suitable for analysis with parallel and neuromorphic processors. We present results of two case studies. The first case is a blind source separation problem where features are learned from an artificial signal with known features. We demonstrate that the learned features are qualitatively consistent with the true features. In the second case, features are learned from ball-bearing vibration data. We find that vibration signals from bearings with faults have characteristic features and codes, and that the event-based code enable a reduction of the data rate by at least one order of magnitude.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Signal Processing Society, 2013
Series
Machine Learning for Signal Processing, ISSN 1551-2541
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Industrial Electronics
Identifiers
urn:nbn:se:ltu:diva-31288 (URN)10.1109/MLSP.2013.6661996 (DOI)000345844100102 ()2-s2.0-84893258531 (Scopus ID)56d03ae7-8338-43b1-9be7-7d7862cdba03 (Local ID)56d03ae7-8338-43b1-9be7-7d7862cdba03 (Archive number)56d03ae7-8338-43b1-9be7-7d7862cdba03 (OAI)
Conference
IEEE International Workshop on Machine Learning for Signal Processing : 22/09/2013 - 25/09/2013
Note

Godkänd; 2013; Bibliografisk uppgift: ISSN 1551-2541; 20130805 (fresan)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2019-05-14Bibliographically approved
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