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Institution

University of Peradeniya

EducationKandy, Sri Lanka
About: University of Peradeniya is a education organization based out in Kandy, Sri Lanka. It is known for research contribution in the topics: Population & Poison control. The organization has 5970 authors who have published 7388 publications receiving 197002 citations.


Papers
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Journal ArticleDOI
TL;DR: The undiluted tsunami sediment is a brownish silty fine sand, characterized by a particular grain size distribution and an assemblage of planktonic/benthic microfossils consisting of foraminifera, radiolarians and spicules as mentioned in this paper.

51 citations

Posted Content
TL;DR: This paper considers a dual-hop amplify-and-forward (AF) relaying system where the relay is equipped with multiple antennas, while the source and the destination are equipped with a single antenna and proposes three heuristic relay precoding schemes to combat the CCI.
Abstract: This paper considers a dual-hop amplify-and-forward (AF) relaying system where the relay is equipped with multiple antennas, while the source and the destination are equipped with a single antenna. Assuming that the relay is subjected to co-channel interference (CCI) and additive white Gaussian noise (AWGN) while the destination is corrupted by AWGN only, we propose three heuristic relay precoding schemes to combat the CCI, namely, 1) Maximum ratio combining/maximal ratio transmission (MRC/MRT), 2) Zero-forcing/MRT (ZF/MRT), 3) Minimum mean-square error/MRT (MMSE/MRT). We derive new exact outage expressions as well as simple high signal-to-noise ratio (SNR) outage approximations for all three schemes. Our findings suggest that both the MRC/MRT and the MMSE/MRT schemes achieve a full diversity of N, while the ZF/MRT scheme achieves a diversity order of N-M, where N is the number of relay antennas and M is the number of interferers. In addition, we show that the MMSE/MRT scheme always achieves the best outage performance, and the ZF/MRT scheme outperforms the MRC/MRT scheme in the low SNR regime, while becomes inferior to the MRC/MRT scheme in the high SNR regime. Finally, in the large N regime, we show that both the ZF/MRT and MMSE/MRT schemes are capable of completely eliminating the CCI, while perfect interference cancelation is not possible with the MRC/MRT scheme.

51 citations

Book ChapterDOI
22 Feb 2012

51 citations

Journal ArticleDOI
16 Oct 2019-Sensors
TL;DR: This research suggests an ensemble learning approach for developing a machine learning model that can recognize four major human emotions namely: anger; sadness; joy; and pleasure incorporating electrocardiogram (ECG) signals.
Abstract: Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher classification accuracy incorporating a limited number of bio-sensors. In the domain of machine learning, ensemble learning methods have been successfully applied to solve different types of real-world machine learning problems which require improved classification accuracies. Emphasising on that, this research suggests an ensemble learning approach for developing a machine learning model that can recognize four major human emotions namely: anger; sadness; joy; and pleasure incorporating electrocardiogram (ECG) signals. As feature extraction methods, this analysis combines four ECG signal based techniques, namely: heart rate variability; empirical mode decomposition; with-in beat analysis; and frequency spectrum analysis. The first three feature extraction methods are well-known ECG based feature extraction techniques mentioned in the literature, and the fourth technique is a novel method proposed in this study. The machine learning procedure of this investigation evaluates the performance of a set of well-known ensemble learners for emotion classification and further improves the classification results using feature selection as a prior step to ensemble model training. Compared to the best performing single biosensor based model in the literature, the developed ensemble learner has the accuracy gain of 10.77%. Furthermore, the developed model outperforms most of the multiple biosensor based emotion recognition models with a significantly higher classification accuracy gain.

51 citations

Journal ArticleDOI
TL;DR: A new reconfiguration technique for PV panels using Genetic algorithm (GA) and two main reconfigurable steps based on a switching matrix is suggested, which proves the superiority of the proposed technique over other techniques for overcoming partial shading.

51 citations


Authors

Showing all 5992 results

NameH-indexPapersCitations
David Gunnell11468879867
Michael S. Roberts8274027754
Richard F. Gillum7721784184
Lakshman P. Samaranayake7558619972
Adrian C. Newton7445321814
Nick Jenkins7132522477
Michael Eddleston6331016762
Velmurugu Ravindran6328014057
Samath D Dharmaratne62151103916
Nicholas A. Buckley6241914283
Saman Warnakulasuriya6028215766
Keith W. Hipel5854314045
Geoffrey K. Isbister5746812690
Fiona J Charlson539180274
Abbas Shafiee514188679
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202313
202250
2021648
2020630
2019500
2018539