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Efficiency validation of one dimensional convolutional neural networks for structural damage detection using a SHM benchmark data
(
International Institute of Acoustics and Vibration, IIAV
, 2018 , Conference Paper)
In this paper, a novel one dimensional convolution neural network (1D-CNN) based structural damage assessment technique is validated with a benchmark study published by IASC-ASCE Structural Health Monitoring Task Group in ...
Calibration of time features and frequency features in the time-frequency domain for improved detection and classification of seizure in newborn EEG signals
(
IEEE Computer Society
, 2012 , Conference Paper)
This paper presents new time-frequency features for seizure detection in newborn EEG signals. These features are obtained by calibrating relevant time features and frequency features in the joint time-frequency domain. The ...
Time-frequency features for pattern recognition using high-resolution TFDs: A tutorial review
(
Elsevier Inc.
, 2015 , Article)
This paper presents a tutorial review of recent advances in the field of time-frequency (t, f) signal processing with focus on exploiting (t, f) image feature information using pattern recognition techniques for detection ...
On the selection of time-frequency features for improving the detection and classification of newborn EEG seizure signals and other abnormalities
(2012 , Conference Paper)
This paper presents new time-frequency features for seizure detection in newborn EEG signals. These features are obtained by translating some relevant time features or frequency features to the joint time-frequency domain. ...
Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study
(
Elsevier B.V.
, 2016 , Article)
Time-frequency (TF) based machine learning methodologies can improve the design of classification systems for non-stationary signals. Using selected TF distributions (TFDs), TF feature extraction is performed on multi-channel ...
Principles of time-frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection
(
Elsevier Ltd
, 2015 , Article)
This paper considers the general problem of detecting change in non-stationary signals using features observed in the time-frequency (t,f) domain, obtained using a class of quadratic time-frequency distributions (QTFDs). ...
Designing high-resolution time–frequency and time–scale distributions for the analysis and classification of non-stationary signals: a tutorial review with a comparison of features performance
(
Elsevier Inc.
, 2018 , Article)
This paper deals with the problem of extracting information from non-stationary signals in the form of features that can be used for effective decision-making in both data analysis and machine learning for automatic ...