A T-wave alternans assessment method based on least squares curve fitting technique

Wan, Xiaolin; Li, Yan; Chong, Xia; Wu, Minghu; Liang, Jin; Wang, Na

doi:10.1016/j.measurement.2016.01.046

Cited by 119 publications

(35 citation statements)

References 18 publications

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“…To evaluate the effectiveness of the developed adaptive sampling method, comparisons of the original traffic's data packets and its sampled versions were carried out. Comparisons of mean and standard deviation of the sampled packets to its original populations may not be enough to evaluate the accuracy of sampled version in terms of demonstrating the original population as they can be obscured by outliers [22] [23]. Therefore additional evaluations were used to assess the efficiency of the developed sampling approach.…”

Section: Network Traffic Parametersmentioning

confidence: 99%

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Fuzzy logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

Salama¹,

Saatchi²,

Burke³

2018

Preprint

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Abstract:Organisations and individuals are increasingly relying on large computer networks to access information and to communicate multimedia type data. To assess the effectiveness of these networks, the traffic parameters need to be analysed. Due to quantity of the information carrying packets, examining each packet's transmission parameters individually is not practical, especially in real time. Sampling techniques allow a subset of packets that accurately represents the original traffic to be examined and thus they are important in multimedia networks. In this study an adaptive sampling technique based on regression and fuzzy inference system was developed. The technique dynamically updates the number of packets sampled by responding to the traffic's variations. Its performance was found to be superior to the conventional non-adaptive sampling methods.

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Section: Network Traffic Parametersmentioning

confidence: 99%

“…Therefore additional evaluations were used to assess the efficiency of the developed sampling approach. The bias indicates how far the mean of the sampled data lies from the mean of its original population [23]. Bias is the average of difference of all samples of the same size.…”

Section: Network Traffic Parametersmentioning

confidence: 99%

Fuzzy logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

Salama¹,

Saatchi²,

Burke³

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Many iterative algorithms for system identification are based on the gradient method [31]and the least squares method [32][33][34][35]. The basic idea of iterative methods is to update the parameter estimates using batch data.…”

Section: Introductionmentioning

confidence: 99%

The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Ding

2017

Complexity

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This paper considers the identification problem of multi-input-output-error autoregressive systems. A hierarchical gradient based iterative (H-GI) algorithm and a hierarchical least squares based iterative (H-LSI) algorithm are presented by using the hierarchical identification principle. A gradient based iterative (GI) algorithm and a least squares based iterative (LSI) algorithm are presented for comparison. The simulation results indicate that the H-LSI algorithm can obtain more accurate parameter estimates than the LSI algorithm, and the H-GI algorithm converges faster than the GI algorithm.

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“…Comparisons of mean and standard deviation of the sampled packets to those of its original populations may not be enough to evaluate the accuracy of the sampled version in terms of demonstrating the original population as they can be obscured by outliers [30,31]. Therefore, additional evaluations were used to assess the efficiency of the developed sampling approach.…”

mentioning

confidence: 99%

“…Therefore, additional evaluations were used to assess the efficiency of the developed sampling approach. The bias indicates how far the mean of the sampled data lies from the mean of its original population [31]. Bias is the average of difference of all samples of the same size.…”

mentioning

confidence: 99%

Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

2018

View full text Add to dashboard Cite

Organisations such as hospitals and the public are increasingly relying on large computer networks to access information and to communicate multimedia-type data. To assess the effectiveness of these networks, the traffic parameters need to be analysed. Due to the quantity of the data packets, examining each packet's transmission parameters is not practical, especially in real time. Sampling techniques allow a subset of packets that accurately represents the original traffic to be examined and they are thus important in evaluating the performance of multimedia networks. In this study, an adaptive sampling technique based on regression and a fuzzy inference system was developed. The technique dynamically updates the number of packets sampled by responding to the traffic's variations. Its performance was found to be superior to the conventional nonadaptive sampling methods.

show abstract

A T-wave alternans assessment method based on least squares curve fitting technique

Cited by 119 publications

References 18 publications

Fuzzy logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

Fuzzy logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Fuzzy Logic and Regression Approaches for Adaptive Sampling of Multimedia Traffic in Wireless Computer Networks

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