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Found 7 papers, 2 papers with code
Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems.
VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-Applications
On top of that, we present VegaEdge - a sophisticated AI confluence designed for real-time security and surveillance applications in modern highway settings through edge-centric IoT-embedded platforms equipped with our anomaly detection approach.
VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer
This study seeks to explore both the advantages and the limitations inherent in combining transformer architecture with graphs for VTP.
A POV-based Highway Vehicle Trajectory Dataset and Prediction Architecture
We introduce the \emph{Carolinas Highway Dataset (CHD\footnote{\emph{CHD} available at: \url{https://github. com/TeCSAR-UNCC/Carolinas\_Dataset}})}, a vehicle trajectory, detection, and tracking dataset.
Understanding the Challenges and Opportunities of Pose-based Anomaly Detection
In this work, we analyze and quantify the characteristics of two well-known video anomaly datasets to better understand the difficulties of pose-based anomaly detection.
Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems
These real-world CPS applications need a robust, lightweight path prediction that can provide a universal network architecture for multiple subjects (e. g., pedestrians and vehicles) from different perspectives.
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Trajectory Prediction
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DeepTrack: Lightweight Deep Learning for Vehicle Path Prediction in Highways
Vehicle trajectory prediction is essential for enabling safety-critical intelligent transportation systems (ITS) applications used in management and operations.
