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Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
(
Institute of Electrical and Electronics Engineers Inc.
, 2016 , Article)
Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such ...
Arrhythmia classification using DWT-coefficient energy ratios
(
Institute of Electrical and Electronics Engineers Inc.
, 2019 , Conference Paper)
Certain features present in electrocardiogram (ECG) signals are used to detect different heart conditions. Hence, by developing a system to extract these features, useful information related to the heart conditions could ...
Robust detection of acoustic partial discharge signals in noisy environments
(
Institute of Electrical and Electronics Engineers Inc.
, 2017 , Conference Paper)
Partial discharge (PD) can be used to predict insulation failures in power transformers. Accurate detection of particular PD types has a significant role in anticipating forthcoming outages. However, the noise encountered ...
Robust Feature Extraction and Classification of Acoustic Partial Discharge Signals Corrupted With Noise
(
Institute of Electrical and Electronics Engineers Inc.
, 2017 , Article)
Partial discharge (PD) can be used as an indicator of impending failure in electrical plant insulation making the accurate classification of particular occurrence patterns useful for anticipating forthcoming outages. In ...
Handcrafted features with convolutional neural networks for detection of tumor cells in histology images
(
IEEE Computer Society
, 2016 , Conference Paper)
Detection of tumor nuclei in cancer histology images requires sophisticated techniques due to the irregular shape, size and chromatin texture of the tumor nuclei. Some very recently proposed methods employ deep convolutional ...
Design and analysis of an adaptive compressive sensing architecture for epileptic seizure detection
(
IEEE Computer Society
, 2013 , Conference Paper)
Epileptic detection techniques rely heavily on the Electroencephalography (EEG) as a representative signal carrying valuable information pertaining to the current brain state. In this work, we investigate the stability of ...
EEG feature extraction and selection techniques for epileptic detection: A comparative study
(
IEEE Computer Society
, 2013 , Conference Paper)
Epileptic detection techniques rely heavily on the Electroencephalography (EEG) as representative signal carrying valuable information pertaining to the current brain state. For these techniques to be efficient and reliable, ...
Performance evaluation for compression-accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monitoring
(
IEEE
, 2013 , Conference Paper)
Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities. EEG signals capture important information pertinent to different ...
Performance Comparison of classification algorithms for EEG-based remote epileptic seizure detection in Wireless Sensor Networks
(
IEEE Computer Society
, 2014 , Conference Paper)
Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems. Classification is the most important technique for wide-ranging ...
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 ...