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Now showing items 11-20 of 22
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 ...
Face segmentation in thumbnail images by data-adaptive convolutional segmentation networks
(
IEEE Computer Society
, 2016 , Conference Paper)
In this study we address the problem of face segmentation in thumbnail images. While there have been several approaches for face detection, none performs detection in such low resolution and segmentation with pixel accuracy. ...
Generalized Operational Classifiers for Material Identification
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
Material is one of the intrinsic features of objects, and consequently material recognition plays an important role in image understanding. The same material may have various shapes and appearance, while keeping the same ...
Sepsis Prediction in Intensive Care Unit Using Ensemble of XGboost Models
(
IEEE Computer Society
, 2019 , Conference Paper)
Sepsis is caused by the dysregulated host response to infection and potentially is the main cause of 6 million death annually. It is a highly dynamic syndrome and therefore the early prediction of sepsis plays a key role ...
Convolutional neural networks for real-time and wireless damage detection
(
Springer New York LLC
, 2020 , Conference Paper)
Structural damage detection methods available for structural health monitoring applications are based on data preprocessing, feature extraction, and feature classification. The feature classification task requires considerable ...
Control of plate vibrations with artificial neural networks and piezoelectricity
(
Springer New York LLC
, 2020 , Conference Paper)
This paper presents a method for active vibration control of smart thin cantilever plates. For model formulation needed for controller design and simulations, finite difference technique is used on the cantilever plate ...
Structural Damage Detection in Civil Engineering with Machine Learning: Current State of the Art
(
Springer
, 2022 , Conference Paper)
This paper presents a brief overview of vibration-based structural damage detection studies that are based on machine learning (ML) in civil engineering structures. The review includes both parametric and nonparametric ...
Structural health monitoring with self-organizing maps and artificial neural networks
(
Springer New York LLC
, 2020 , Conference Paper)
The use of self-organizing maps and artificial neural networks for structural health monitoring is presented in this paper. The authors recently developed a nonparametric structural damage detection algorithm for extracting ...
A k-nearest neighbor multilabel ranking algorithm with application to content-based image retrieval
(
Institute of Electrical and Electronics Engineers Inc.
, 2017 , Conference Paper)
Multilabel ranking is an important machine learning task with many applications, such as content-based image retrieval (CBIR). However, when the number of labels is large, traditional algorithms are either infeasible or ...