10 papers
Adaptive Approach to Enhance Machine Learning Scheduling Algorithms During Runtime Using Reinforcement Learning in Metascheduling Applications
Samer Alshaer, Ala Khalifeh, Roman Obermaisser
Metascheduling in time-triggered architectures has been crucial in adapting to dynamic and unpredictable environments, ensuring the reliability and efficiency of task execution. Ho…
Reconstruction-Based Adaptive Scheduling Using AI Inferences in Safety-Critical Systems
Samer Alshaer, Ala Khalifeh, Roman Obermaisser
Adaptive scheduling is crucial for ensuring the reliability and safety of time-triggered systems (TTS) in dynamic operational environments. Scheduling frameworks face significant c…
Integration of Computer Vision with Adaptive Control for Autonomous Driving Using ADORE
Abu Shad Ahammed, Md Shahi Amran Hossain, Sayeri Mukherjee +2
Ensuring safety in autonomous driving requires a seamless integration of perception and decision making under uncertain conditions. Although computer vision (CV) models such as YOL…
Enhanced Drift-Aware Computer Vision Architecture for Autonomous Driving
Md Shahi Amran Hossain, Abu Shad Ahammed, Sayeri Mukherjee +1
The use of computer vision in automotive is a trending research in which safety and security are a primary concern. In particular, for autonomous driving, preventing road accidents…
Diagnosing Psychiatric Patients: Can Large Language and Machine Learning Models Perform Effectively in Emergency Cases?
Abu Shad Ahammed, Sayeri Mukherjee, Roman Obermaisser
Mental disorders are clinically significant patterns of behavior that are associated with stress and/or impairment in social, occupational, or family activities. People suffering f…
Real-Time Object Detection and Classification using YOLO for Edge FPGAs
Rashed Al Amin, Roman Obermaisser
Object detection and classification are crucial tasks across various application domains, particularly in the development of safe and reliable Advanced Driver Assistance Systems (A…