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Clive Cheong Took

8 accepted papers

2020

Eeg Connectivity - Informed Cooperative Adaptive Line Enhancer for Recognition of Brain State

ICASSP 2020accepted

Bursts of sleep spindles and paroxysmal fast brain activity waveforms have frequency overlap whilst generally, paroxysmal waveforms have shorter duration than spindles. Both resemble bursts of normal alpha activity during short rests while awake with closed eyes. In this paper, it is shown that for…

Cited by 0SourceScholar
2020

Efficient Algorithm to Implement Sliding Singular Spectrum Analysis with Application to Biomedical Signal Denoising

ICASSP 2020accepted

Previous work [1] has shown that Singular Spectrum Analysis (SSA) can be particularly effective at noise removal or signal separation in the case of single channel mixtures. The work presented here shows how the sliding or updating algorithm which performs best at signal separation can be implemente…

Cited by 0SourceScholar
2018

Quaternion Adaptive Line Enhancer based on Singular Spectrum Analysis

ICASSP 2018accepted

Quaternion adaptive line enhancer (QALE) has been proposed recently for the recovery of two- (2-D) or three-dimensional (3-D) periodic signals from their noisy mixtures [1] with the help of quaternion-valued adaptive filtering theory. Similar to the traditionall-D [2] version, QALE, relies mainly on…

Cited by 0SourceScholar
2018

Segment Parameter Labelling in MCMC Mean-Shift Change Detection

ICASSP 2018accepted

This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models. It is common to assume that the parameters are distinct within each segment. As such, many Bayesian change point detection models do not exploit the segment par…

Cited by 0SourceScholar
2017

Data analysis as a web service: A case study using IoT sensor data

ICASSP 2017accepted

The advent of Internet of Things, has resulted in the development of infrastructure for capturing and storing data from domains ranging from smart devices (e.g. smartphones) to smart cities. This data is often available publicly and has enabled a wider range of data consumers to utilise such data se…

Cited by 0SourceScholar