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DistPrivacy: Privacy-Aware Distributed Deep Neural Networks in IoT surveillance systems
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
With the emergence of smart cities, Internet of Things (IoT) devices as well as deep learning technologies have witnessed an increasing adoption. To support the requirements of such paradigm in terms of memory and computation, ...
RL-PDNN: Reinforcement Learning for Privacy-Aware Distributed Neural Networks in IoT Systems
(
Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
Due to their high computational and memory demand, deep learning applications are mainly restricted to high-performance units, e.g., cloud and edge servers. Particularly, in Internet of Things (IoT) systems, the data ...
Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks
(
IEEE Computer Society
, 2021 , Article)
Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ...
Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks
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Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
Preventive maintenance of modern electric rotating machinery (RM) is critical for ensuring reliable operation, preventing unpredicted breakdowns and avoiding costly repairs. Recently many studies investigated machine ...
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 ...
Generalized Operational Classifiers for Material Identification
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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 ...
Convolutional neural networks for real-time and wireless damage detection
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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
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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 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 ...
Mhad: Multi-human action dataset
(
Springer
, 2020 , Conference Paper)
This paper presents a framework for a multi-action recognition method. In this framework, we introduce a new approach to detect and recognize the action of several persons within one scene. Also, considering the scarcity ...