Search Results for author:
Found 20 papers, 6 papers with code
GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration
The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning.
Multi-Scale U-Shape MLP for Hyperspectral Image Classification
Hyperspectral images have significant applications in various domains, since they register numerous semantic and spatial information in the spectral band with spatial variability of spectral signatures.
Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning
With this dataset, we developed HRV models that combine signal processing and ML to directly infer HRV.
NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks
Our work could provide a useful tool to defend against certain adversarial attacks on deep neural networks.
Exploring Adversarial Attacks on Neural Networks: An Explainable Approach
Finally, we demonstrate that the layers $Block4\_conv1$ and $Block5\_cov1$ of the VGG-16 model are more susceptible to adversarial attacks.
TMHOI: Translational Model for Human-Object Interaction Detection
Our method effectively captures the sentiment representation of HOIs by integrating both spatial and semantic knowledge.
Computational Efficiency
Human-Object Interaction Detection
+1
Communication and Control in Collaborative UAVs: Recent Advances and Future Trends
The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains.
Zero-bias Deep Neural Network for Quickest RF Signal Surveillance
In this paper, we provide a deep learning framework for RF signal surveillance.
Learning to Detect: A Data-driven Approach for Network Intrusion Detection
With massive data being generated daily and the ever-increasing interconnectivity of the world's Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national security.
Federated Variational Learning for Anomaly Detection in Multivariate Time Series
Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical Systems (CPS).
Class-Incremental Learning for Wireless Device Identification in IoT
The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services.
Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT
Abnormality detection is essential to the performance of safety-critical and latency-constrained systems.
Spatio-Temporal Data Mining for Aviation Delay Prediction
A key role of collaborative decision making for air traffic scheduling and airspace resource management is the accurate prediction of flight delay.
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey
In this paper, we provide a comprehensive survey on machine learning technologies for the identification of IoT devices along with the detection of compromised or falsified ones from the viewpoint of passive surveillance agents or network operators.
geoGAT: Graph Model Based on Attention Mechanism for Geographic Text Classification
We use the method of graph convolutional neural network with attention mechanism to achieve this function.
Distant Domain Transfer Learning for Medical Imaging
In this paper, we propose a distant domain transfer learning (DDTL) method for medical image classification.
Counter-Unmanned Aircraft System(s) (C-UAS): State of the Art, Challenges and Future Trends
A Counter-UAS (C-UAS) system is defined as a system or device capable of lawfully and safely disabling, disrupting, or seizing control of an unmanned aircraft or unmanned aircraft system.
Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices
In this paper, we propose an enhanced deep learning framework for IoT device identification using physical layer signals.
SAN: Scale-Aware Network for Semantic Segmentation of High-Resolution Aerial Images
As a result, the network with the proposed module named scale-aware network (SANet) has a stronger ability to distinguish the ground objects with inconsistent scale.
ESFNet: Efficient Network for Building Extraction from High-Resolution Aerial Images
In recent years, deep neural networks have made great achievement in improving accuracy of building extraction from remote sensing imagery.
