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Expert Operational GANS: Towards Real-Color Underwater Image Restoration Open
The wide range of deformation artifacts that arise from complex light propagation, scattering, and depth-dependent attenuation makes the underwater image restoration to remain a challenging problem. Like other single deep regressor network…
Progressive Transfer Learning for Multi-Pass Fundus Image Restoration Open
Diabetic retinopathy is a leading cause of vision impairment, making its early diagnosis through fundus imaging critical for effective treatment planning. However, the presence of poor quality fundus images caused by factors such as inadeq…
Blind Underwater Image Restoration using Co-Operational Regressor Networks Open
The exploration of underwater environments is essential for applications such as biological research, archaeology, and infrastructure maintenanceHowever, underwater imaging is challenging due to the waters unique properties, including scat…
High-Quality and Full Bandwidth Seismic Signal Synthesis using Operational GANs Open
Vibration sensors are essential in acquiring seismic activity for an accurate earthquake assessment. The state-of-the-art sensors can provide the best signal quality and the highest bandwidth; however, their high cost usually hinders a wid…
Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery Open
Robust and real-time detection of faults on rotating machinery has become an ultimate objective for predictive maintenance in various industries. Vibration-based Deep Learning (DL) methodologies have become the de facto standard for bearin…
Sound-to-Vibration Transformation for Sensorless Motor Health Monitoring Open
Automatic sensor-based detection of motor failures such as bearing faults is crucial for predictive maintenance in various industries. Numerous methodologies have been developed over the years to detect bearing faults. Despite the appearan…
Improved Active Fire Detection using Operational U-Nets Open
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 contro…
Blind Restoration of Real-World Audio by 1D Operational GANs Open
Objective: Despite numerous studies proposed for audio restoration in the literature, most of them focus on an isolated restoration problem such as denoising or dereverberation, ignoring other artifacts. Moreover, assuming a noisy or rever…
Zero-Shot Motor Health Monitoring by Blind Domain Transition Open
Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing …
Investigation of the Role of Convolutional Neural Network Architectures in the Diagnosis of Glaucoma using Color Fundus Photography Open
An appropriately designed and trained CNN was able to distinguish glaucoma with high accuracy even with a small number of fundus photographs.
Blind ECG Restoration by Operational Cycle-GANs Open
By means of the proposed ECG restoration, the ECG diagnosis accuracy and performance can significantly improve.
Blind ECG Restoration by Operational Cycle-GANs Open
Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings acquired by Holter and wearable ECG sensors often suffer from …
Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks Open
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 learning monitoring me…
Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized\n Operational Neural Networks Open
Preventive maintenance of modern electric rotating machinery (RM) is critical\nfor ensuring reliable operation, preventing unpredicted breakdowns and avoiding\ncostly repairs. Recently many studies investigated machine learning monitoring\…
Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks Open
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 ECG are still few. …
Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network Open
Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signal…
Real-Time Glaucoma Detection From Digital Fundus Images Using Self-ONNs Open
Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later stages, makes it c…
Early Bearing Fault Diagnosis of Rotating Machinery by 1D Self-Organized Operational Neural Networks Open
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 learning monitoring me…