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Found 42 papers, 10 papers with code
FlameFinder: Illuminating Obscured Fire through Smoke with Attentive Deep Metric Learning
However, the dominance of center loss over the other losses leads to the model missing features sensitive to them.
Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals
These models are applied to the Chapman-Shaoxing dataset to classify atrial fibrillation, as well as another common arrhythmia, sinus bradycardia, and normal sinus rhythm heartbeats.
Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks
Unmanned aerial vehicles (UAVs) offer a flexible and cost-effective solution for wildfire monitoring.
Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone Networks
Early wildfire detection in remote and forest areas is crucial for minimizing devastation and preserving ecosystems.
Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning
This emphasizes the importance of using the prediction rApp and distributed actors' information jointly as part of a dynamic xApp.
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife.
Dynamic Online Modulation Recognition using Incremental Learning
This issue renders DL-based modulation recognition models inapplicable in real-world scenarios because the dynamic nature of communication systems necessitate the effective adaptability to new modulation schemes.
Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning
The performance of this method is compared to learning from a demonstration technique called behavioral cloning (BC) using a supervised learning approach.
Attention-based Open RAN Slice Management using Deep Reinforcement Learning
As emerging networks such as Open Radio Access Networks (O-RAN) and 5G continue to grow, the demand for various services with different requirements is increasing.
ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning
In the medical field, current ECG signal analysis approaches rely on supervised deep neural networks trained for specific tasks that require substantial amounts of labeled data.
A Meta-learning based Generalizable Indoor Localization Model using Channel State Information
Indoor localization has gained significant attention in recent years due to its various applications in smart homes, industrial automation, and healthcare, especially since more people rely on their wireless devices for location-based services.
Autoencoder-based Radio Frequency Interference Mitigation For SMAP Passive Radiometer
Passive space-borne radiometers operating in the 1400-1427 MHz protected frequency band face radio frequency interference (RFI) from terrestrial sources.
Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting
To reconcile such, we propose a novel method for high-quality and efficient video resolution upscaling tasks, which leverages the spatial-temporal information to accurately divide video into chunks, thus keeping the number of chunks as well as the model size to minimum.
Synthetic ECG Signal Generation using Probabilistic Diffusion Models
In this work, synthetic ECG signals are generated by the Improved DDPM and by the Wasserstein GAN with Gradient Penalty (WGAN-GP) models and then compared.
Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios
The triplet center loss (TCL) function is applied on all dimensions of the sample's embedding in the embedding space.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN
To solve this problem, a new solution is proposed based on evolutionary-based deep reinforcement learning (EDRL) to accelerate and optimize the slice management learning process in the radio access network's (RAN) intelligent controller (RIC) modules.
Arrhythmia Classification using CGAN-augmented ECG Signals
We employed two models for ECG generation: (i) unconditional GAN; Wasserstein GAN with gradient penalty (WGAN-GP) is trained on each class individually; (ii) conditional GAN; one Auxiliary Classifier WGAN-GP (AC-WGAN-GP) model is trained on all classes and then used to generate synthetic beats in all classes.
Synthetic ECG Signal Generation Using Generative Neural Networks
Electrocardiogram (ECG) datasets tend to be highly imbalanced due to the scarcity of abnormal cases.
UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning
The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users.
Green IoT using UAVs in B5G Networks: A Review of Applications and Strategies
Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4. 0.
Fully-echoed Q-routing with Simulated Annealing Inference for Flying Adhoc Networks
Current networking protocols deem inefficient in accommodating the two key challenges of Unmanned Aerial Vehicle (UAV) networks, namely the network connectivity loss and energy limitations.
A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection
First, we define the constraint graph manually; then, we present a graph learning algorithm that can search for an optimal graph in a greedy scheme.
A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection
This model is based on a new graph learning algorithm to learn the constraint graph given the labeled ECG data.
Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset
FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) offers a dataset of aerial images of fires along with methods for fire detection and segmentation which can help firefighters and researchers to develop optimal fire management strategies.
An Uncertainty Estimation Framework for Risk Assessment in Deep Learning-based Atrial Fibrillation Classification
The uncertainty is estimated by conducting multiple passes of the input through the network to build a distribution; the mean of the standard deviations is reported as the network's uncertainty.
Multi-level Feature Learning on Embedding Layer of Convolutional Autoencoders and Deep Inverse Feature Learning for Image Clustering
This paper introduces Multi-Level feature learning alongside the Embedding layer of Convolutional Autoencoder (CAE-MLE) as a novel approach in deep clustering.
Piece-wise Matching Layer in Representation Learning for ECG Classification
To evaluate the performance of this method in time series analysis, we applied the proposed layer in two publicly available datasets of PhysioNet competitions in 2015 and 2017 where the input data is ECG signal.
Real-time Framework for Trust Monitoring in aNetwork of Unmanned Aerial Vehicles
Unmanned aerial vehicles (UAVs) have been increasingly utilized in various civilian and military applications such as remote sensing, border patrolling, disaster monitoring, and communication coverage extension.
ECG Language Processing (ELP): a New Technique to Analyze ECG Signals
For this reason, the ECG signal is a sequence of heartbeats similar to sentences in natural languages) and each heartbeat is composed of a set of waves (similar to words in a sentence) of different morphologies.
A Graph-constrained Changepoint Detection Approach for ECG Segmentation
Electrocardiogram (ECG) signal is the most commonly used non-invasive tool in the assessment of cardiovascular diseases.
Deep Inverse Feature Learning: A Representation Learning of Error
This paper introduces a novel perspective about error in machine learning and proposes inverse feature learning (IFL) as a representation learning approach that learns a set of high-level features based on the representation of error for classification or clustering purposes.
Inverse Feature Learning: Feature learning based on Representation Learning of Error
This paper proposes inverse feature learning as a novel supervised feature learning technique that learns a set of high-level features for classification based on an error representation approach.
HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks
The cardiologist level performance in detecting this arrhythmia is often achieved by deep learning-based methods, however, they suffer from the lack of interpretability.
An Autonomous Spectrum Management Scheme for Unmanned Aerial Vehicle Networks in Disaster Relief Operations
This paper studies the problem of spectrum shortage in an unmanned aerial vehicle (UAV) network during critical missions such as wildfire monitoring, search and rescue, and disaster monitoring.
Single-modal and Multi-modal False Arrhythmia Alarm Reduction using Attention-based Convolutional and Recurrent Neural Networks
This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multimodal biosignals.
An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs
In this paper, we propose a novel set of high-level features based on unsupervised feature learning technique in order to effectively capture the characteristics of different arrhythmia in electrocardiogram (ECG) signal and differentiate them from irregularity in signals due to different sources of signal disturbances.
A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks
In this paper, we study the problem of spectrum scarcity in a network of unmanned aerial vehicles (UAVs) during mission-critical applications such as disaster monitoring and public safety missions, where the pre-allocated spectrum is not sufficient to offer a high data transmission rate for real-time video-streaming.
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders.
Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmia.
Ranked #1 on
Arrhythmia Detection
on MIT-BIH AR
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach
Reinforcement learning (RL) techniques, while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces.
A Unified Framework for Joint Mobility Prediction and Object Profiling of Drones in UAV Networks
In recent years, using a network of autonomous and cooperative unmanned aerial vehicles (UAVs) without command and communication from the ground station has become more imperative, in particular in search-and-rescue operations, disaster management, and other applications where human intervention is limited.
A Shapley Value Solution to Game Theoretic-based Feature Reduction in False Alarm Detection
False alarm is one of the main concerns in intensive care units and can result in care disruption, sleep deprivation, and insensitivity of care-givers to alarms.
