86 citations · 186 across the 33 of their papers we have counts for
33 papers
Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration
Ilke Adalioglu, Serkan Kiranyaz, Mete Ahishali +5
Echocardiography is the most widely used imaging to monitor cardiac functions, serving as the first line in early detection of myocardial ischemia and infarction. However, echocard…
Accelerating Deep Learning with Fixed Time Budget
Muhammad Asif Khan, Ridha Hamila, Hamid Menouar
The success of modern deep learning is attributed to two key elements: huge amounts of training data and large model sizes. Where a vast amount of data allows the model to learn mo…
The Future of Aerial Communications: A Survey of IRS-Enhanced UAV Communication Technologies
Zina Chkirbene, Ala Gouissem, Ridha Hamila +1
The advent of Intelligent Reflecting Surfaces (IRS) and Unmanned Aerial Vehicles (UAVs) is setting a new benchmark in the field of wireless communications. IRS, with their groundbr…
Multimodal Crowd Counting with Pix2Pix GANs
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Most state-of-the-art crowd counting methods use color (RGB) images to learn the density map of the crowd. However, these methods often struggle to achieve higher accuracy in dense…
Curriculum for Crowd Counting -- Is it Worthy?
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Recent advances in deep learning techniques have achieved remarkable performance in several computer vision problems. A notably intuitive technique called Curriculum Learning (CL)…
A Comprehensive Survey On Client Selections in Federated Learning
Ala Gouissem, Zina Chkirbene, Ridha Hamila
Federated Learning (FL) is a rapidly growing field in machine learning that allows data to be trained across multiple decentralized devices. The selection of clients to participate…
Crowd Counting in Harsh Weather using Image Denoising with Pix2Pix GANs
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Visual crowd counting estimates the density of the crowd using deep learning models such as convolution neural networks (CNNs). The performance of the model heavily relies on the q…
Visual Crowd Analysis: Open Research Problems
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Over the last decade, there has been a remarkable surge in interest in automated crowd monitoring within the computer vision community. Modern deep-learning approaches have made it…
LCDnet: A Lightweight Crowd Density Estimation Model for Real-time Video Surveillance
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Automatic crowd counting using density estimation has gained significant attention in computer vision research. As a result, a large number of crowd counting and density estimation…
Drones-aided Asset Maintenance in Hospitals
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged th…
CLIP: Train Faster with Less Data
Muhammad Asif Khan, Ridha Hamila, Hamid Menouar
Deep learning models require an enormous amount of data for training. However, recently there is a shift in machine learning from model-centric to data-centric approaches. In data-…
Crowd Density Estimation using Imperfect Labels
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Density estimation is one of the most widely used methods for crowd counting in which a deep learning model learns from head-annotated crowd images to estimate crowd density in uns…
DroneNet: Crowd Density Estimation using Self-ONNs for Drones
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Video surveillance using drones is both convenient and efficient due to the ease of deployment and unobstructed movement of drones in many scenarios. An interesting application of…
Revisiting Crowd Counting: State-of-the-art, Trends, and Future Perspectives
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Crowd counting is an effective tool for situational awareness in public places. Automated crowd counting using images and videos is an interesting yet challenging problem that has…
A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges
Muhammad Asif Khan, Emna Baccour, Zina Chkirbene +4
5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive m…
ML-based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks
Muhammad Asif Khan, Ridha Hamila, Adel Gastli +2
Device mobility in dense Wi-Fi networks offers several challenges. Two well-known problems related to device mobility are handover prediction and access point selection. Due to the…
Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraints
Ilyes Mrad, Lutfi Samara, Alaa Awad Abdellatif +3
The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches.…
Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography
Oumaima Hamila, Sheela Ramanna, Christopher J. Henry +4
Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data including images and videos, which cardiologists use to diagnose cardiac abnormalities in…
Early Detection of Myocardial Infarction in Low-Quality Echocardiography
Aysen Degerli, Morteza Zabihi, Serkan Kiranyaz +4
Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32.4 million people suffer each year. Early diagnosis and t…
Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection
Serkan Kiranyaz, Aysen Degerli, Tahir Hamid +7
Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly kno…
Colorectal cancer diagnosis from histology images: A comparative study
Junaid Malik, Serkan Kiranyaz, Suchitra Kunhoth +4
Computer-aided diagnosis (CAD) based on histopathological imaging has progressed rapidly in recent years with the rise of machine learning based methodologies. Traditional approach…
Joint Frame Synchronization and Channel Estimation: Sparse Recovery Approach and USRP Implementation
Özgür Özdemir, Chethan Kumar Anjinappa, Ridha Hamila +2
Correlation-based techniques used for frame synchronization can suffer significant performance degradation over multi-path frequency-selective channels. In this paper, we propose a…
Real-time PCG Anomaly Detection by Adaptive 1D Convolutional Neural Networks
Serkan Kiranyaz, Morteza Zabihi, Ali Bahrami Rad +4
The heart sound signals (Phonocardiogram - PCG) enable the earliest monitoring to detect a potential cardiovascular pathology and have recently become a crucial tool as a diagnosti…
Security-Enhanced SC-FDMA Transmissions Using Temporal Artificial-Noise and Secret-Key Aided Schemes
Mohamed F. Marzban, Ahmed El Shafie, Naofal Al-Dhahir +1
We investigate the physical layer security of uplink single-carrier frequency-division multiple-access (SC-FDMA) systems. Multiple users, Alices, send confidential messages to a co…