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Asoke K. Nandi

Researcher at Brunel University London

Publications -  654
Citations -  20403

Asoke K. Nandi is an academic researcher from Brunel University London. The author has contributed to research in topics: Cluster analysis & Blind signal separation. The author has an hindex of 65, co-authored 625 publications receiving 17099 citations. Previous affiliations of Asoke K. Nandi include Tongji University & University of Strathclyde.

Papers
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Journal ArticleDOI

Applications of machine learning to machine fault diagnosis: A review and roadmap

TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.
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Experimental observation of isolated large transverse energy electrons with associated missing energy at $\sqrt s$ = 540 GeV

G.T.J. Arnison, +134 more
- 24 Feb 1983 - 
TL;DR: In this article, the results of two searches made on data recorded at the CERN SPS Proton-Antiproton Collider were reported, one for isolated large-E T electrons, the other for large E T neutrinos using the technique of missing transverse energy.
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Experimental observation of lepton pairs of invariant mass around 95 GeV/c2 at the Cern SPS collider

David B. Cline, +132 more
- 07 Jul 1983 - 
TL;DR: In this paper, the signature of a two-body decay of a particle of mass ∼ 95 GeV/c2 was observed, which fit well with the hypothesis that they are produced by the process p + p → Z 0 + X (with Z 0 → l + + + l − ), where Z 0 is the Intermediate Vector Boson postulated by the electroweak theories as the mediator of weak neutral currents.
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Algorithms for automatic modulation recognition of communication signals

TL;DR: This paper introduces two algorithms for analog and digital modulations recognition that utilizes the decision-theoretic approach in which a set of decision criteria for identifying different types of modulations is developed and the artificial neural network is used as a new approach.
Journal ArticleDOI

Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms

TL;DR: The performance of both types of classifiers in two-class fault/no-fault recognition examples are examined and the attempts to improve the overall generalisationperformance of both techniques through the use of genetic algorithm based feature selection process are examined.