Browsing by Subject "Computational efficiency"
Now showing items 1-6 of 6
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Fast and memory-efficient algorithms for computing quadratic time–frequency distributions
( Elsevier Inc , 2013 , Article)Algorithms for computing time–frequency distributions (TFDs) limit computation time by reducing numerical operations. But these fast algorithms do not reduce the memory load. This article presents four TFD algorithms to ... -
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 ... -
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 ... -
Patient-Specific Seizure Detection Using Nonlinear Dynamics and Nullclines
( Institute of Electrical and Electronics Engineers Inc. , 2020 , Article)Nonlinear dynamics has recently been extensively used to study epilepsy due to the complex nature of the neuronal systems. This study presents a novel method that characterizes the dynamic behavior of pediatric seizure ... -
Self-organized Operational Neural Networks with Generative Neurons
( Elsevier Ltd , 2021 , Article)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 ... -
Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering
( Institute of Electrical and Electronics Engineers Inc. , 2016 , Article)In training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or ...