99 research outputs found
Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias
Each year more than 7 million people die from cardiac arrhythmias. Yet no robust solution exists today to detect such heart anomalies right at the moment they occur. The purpose of this study was to design a personalized health monitoring system that can detect early occurrences of arrhythmias from an individual's electrocardiogram (ECG) signal. We first modelled the common causes of arrhythmias in the signal domain as a degradation of normal ECG beats to abnormal beats. Using the degradation models, we performed abnormal beat synthesis which created potential abnormal beats from the average normal beat of the individual. Finally, a Convolutional Neural Network (CNN) was trained using real normal and synthesized abnormal beats. As a personalized classifier, the trained CNN can monitor ECG beats in real time for arrhythmia detection. Over 34 patients' ECG records with a total of 63,341 ECG beats from the MIT-BIH arrhythmia benchmark database, we have shown that the probability of detecting one or more abnormal ECG beats among the first three occurrences is higher than 99.4% with a very low false-alarm rate. 1 2017 The Author(s).Scopu
Classification of Polarimetric SAR Images Using Compact Convolutional Neural Networks
Classification of polarimetric synthetic aperture radar (PolSAR) images is an
active research area with a major role in environmental applications. The
traditional Machine Learning (ML) methods proposed in this domain generally
focus on utilizing highly discriminative features to improve the classification
performance, but this task is complicated by the well-known "curse of
dimensionality" phenomena. Other approaches based on deep Convolutional Neural
Networks (CNNs) have certain limitations and drawbacks, such as high
computational complexity, an unfeasibly large training set with ground-truth
labels, and special hardware requirements. In this work, to address the
limitations of traditional ML and deep CNN based methods, a novel and
systematic classification framework is proposed for the classification of
PolSAR images, based on a compact and adaptive implementation of CNNs using a
sliding-window classification approach. The proposed approach has three
advantages. First, there is no requirement for an extensive feature extraction
process. Second, it is computationally efficient due to utilized compact
configurations. In particular, the proposed compact and adaptive CNN model is
designed to achieve the maximum classification accuracy with minimum training
and computational complexity. This is of considerable importance considering
the high costs involved in labelling in PolSAR classification. Finally, the
proposed approach can perform classification using smaller window sizes than
deep CNNs. Experimental evaluations have been performed over the most
commonly-used four benchmark PolSAR images: AIRSAR L-Band and RADARSAT-2 C-Band
data of San Francisco Bay and Flevoland areas. Accordingly, the best obtained
overall accuracies range between 92.33 - 99.39% for these benchmark study
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Operational Neural Networks
Feed-forward, fully-connected Artificial Neural Networks (ANNs) or the
so-called Multi-Layer Perceptrons (MLPs) are well-known universal
approximators. However, their learning performance varies significantly
depending on the function or the solution space that they attempt to
approximate. This is mainly because of their homogenous configuration based
solely on the linear neuron model. Therefore, while they learn very well those
problems with a monotonous, relatively simple and linearly separable solution
space, they may entirely fail to do so when the solution space is highly
nonlinear and complex. Sharing the same linear neuron model with two additional
constraints (local connections and weight sharing), this is also true for the
conventional Convolutional Neural Networks (CNNs) and, it is, therefore, not
surprising that in many challenging problems only the deep CNNs with a massive
complexity and depth can achieve the required diversity and the learning
performance. In order to address this drawback and also to accomplish a more
generalized model over the convolutional neurons, this study proposes a novel
network model, called Operational Neural Networks (ONNs), which can be
heterogeneous and encapsulate neurons with any set of operators to boost
diversity and to learn highly complex and multi-modal functions or spaces with
minimal network complexity and training data. Finally, a novel training method
is formulated to back-propagate the error through the operational layers of
ONNs. Experimental results over highly challenging problems demonstrate the
superior learning capabilities of ONNs even with few neurons and hidden layers.Comment: 21 page
Collective Network of Binary Classifier Framework for Polarimetric SAR Image Classification: An Evolutionary Approach
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification
Hyperspectral image (HSI) classification is an important task in many
applications, such as environmental monitoring, medical imaging, and land
use/land cover (LULC) classification. Due to the significant amount of spectral
information from recent HSI sensors, analyzing the acquired images is
challenging using traditional Machine Learning (ML) methods. As the number of
frequency bands increases, the required number of training samples increases
exponentially to achieve a reasonable classification accuracy, also known as
the curse of dimensionality. Therefore, separate band selection or
dimensionality reduction techniques are often applied before performing any
classification task over HSI data. In this study, we investigate recently
proposed subspace learning methods for one-class classification (OCC). These
methods map high-dimensional data to a lower-dimensional feature space that is
optimized for one-class classification. In this way, there is no separate
dimensionality reduction or feature selection procedure needed in the proposed
classification framework. Moreover, one-class classifiers have the ability to
learn a data description from the category of a single class only. Considering
the imbalanced labels of the LULC classification problem and rich spectral
information (high number of dimensions), the proposed classification approach
is well-suited for HSI data. Overall, this is a pioneer study focusing on
subspace learning-based one-class classification for HSI data. We analyze the
performance of the proposed subspace learning one-class classifiers in the
proposed pipeline. Our experiments validate that the proposed approach helps
tackle the curse of dimensionality along with the imbalanced nature of HSI
data
Improved Active Fire Detection using Operational U-Nets
As a consequence of global warming and climate change, the risk and extent of
wildfires have been increasing in many areas worldwide. Warmer temperatures and
drier conditions can cause quickly spreading fires and make them harder to
control; therefore, early detection and accurate locating of active fires are
crucial in environmental monitoring. Using satellite imagery to monitor and
detect active fires has been critical for managing forests and public land.
Many traditional statistical-based methods and more recent deep-learning
techniques have been proposed for active fire detection. In this study, we
propose a novel approach called Operational U-Nets for the improved early
detection of active fires. The proposed approach utilizes Self-Organized
Operational Neural Network (Self-ONN) layers in a compact U-Net architecture.
The preliminary experimental results demonstrate that Operational U-Nets not
only achieve superior detection performance but can also significantly reduce
computational complexity
Self-Organized Operational Neural Networks with Generative Neurons
Operational Neural Networks (ONNs) have recently been proposed to address the
well-known limitations and drawbacks of conventional Convolutional Neural
Networks (CNNs) such as network homogeneity with the sole linear neuron model.
ONNs are heterogenous networks with a generalized neuron model that can
encapsulate any set of non-linear operators to boost diversity and to learn
highly complex and multi-modal functions or spaces with minimal network
complexity and training data. However, Greedy Iterative Search (GIS) method,
which is the search method used to find optimal operators in ONNs takes many
training sessions to find a single operator set per layer. This is not only
computationally demanding, but the network heterogeneity is also limited since
the same set of operators will then be used for all neurons in each layer.
Moreover, the performance of ONNs directly depends on the operator set library
used, which introduces a certain risk of performance degradation especially
when the optimal operator set required for a particular task is missing from
the library. In order to address these issues and achieve an ultimate
heterogeneity level to boost the network diversity along with computational
efficiency, in this study we propose Self-organized ONNs (Self-ONNs) with
generative neurons that have the ability to adapt (optimize) the nodal operator
of each connection during the training process. Therefore, Self-ONNs can have
an utmost heterogeneity level required by the learning problem at hand.
Moreover, this ability voids the need of having a fixed operator set library
and the prior operator search within the library in order to find the best
possible set of operators. We further formulate the training method to
back-propagate the error through the operational layers of Self-ONNs.Comment: 14 pages, 14 figures, journal articl
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