Search
Now showing items 11-20 of 34
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 ...
Deep learning and low rank dictionary model for mHealth data classification
(
Institute of Electrical and Electronics Engineers Inc.
, 2018 , Conference Paper)
In the context of mobile Health (mHealth) applications, data are prone to several sources of contamination which would lead to false interpretation and misleading classification results. In this paper, a robust deep learning ...
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 ...
A Deep Learning Model for LoRa Signals Classification Using Cyclostationay Features
(
IEEE Computer Society
, 2021 , Conference Paper)
With the witnessed exponential growth of Internet of Things (IoT) nodes deployment following the emerging applications, multiple variants of technologies have been proposed to handle the IoT requirements. Among the proposed ...
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 ...
Multifrequency Polsar Image Classification Using Dual-Band 1D Convolutional Neural Networks
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
In this work, we propose a novel classification approach based on dual-band one-dimensional Convolutional Neural Networks (1D-CNNs) for classification of multifrequency polarimetric SAR (PolSAR) data. The proposed approach ...
Structural damage detection in real time: Implementation of 1D convolutional neural networks for SHM applications
(
Springer
, 2017 , Conference Paper)
Most of the classical structural damage detection systems involve two processes, feature extraction and feature classification. Usually, the feature extraction process requires large computational effort which prevent the ...
Learned vs. hand-designed features for ECG beat classification: A comprehensive study
(
Springer Verlag
, 2017 , Conference Paper)
In this study, in order to find out the best ECG classification performance we realized comparative evaluations among the state-of-the-art classifiers such as Convolutional Neural Networks (CNNs), multi-layer perceptrons ...
Sleep stage classification using sparse rational decomposition of single channel EEG records
(
Institute of Electrical and Electronics Engineers Inc.
, 2015 , Conference Paper)
A sparse representation of ID signals is proposed based on time-frequency analysis using Generalized Rational Discrete Short Time Fourier Transform (RDSTFT). First, the signal is decomposed into a set of frequency sub-bands ...
Performance Comparison of Learned vs. Engineered Features for Polarimetric SAR Terrain Classification
(
Institute of Electrical and Electronics Engineers Inc.
, 2019 , Conference Paper)
In this work, we propose to use learned features for terrain classification of Polarimetric Synthetic Aperture Radar (PolSAR) images. In the proposed classification framework, the learned features are extracted from sliding ...