Real Time
Real-time processing focuses on developing systems capable of analyzing and responding to data instantaneously, crucial for applications demanding immediate feedback. Current research emphasizes efficient algorithms and model architectures, such as those based on deep learning, to reduce computational latency in diverse domains including robotics, healthcare, and AI-assisted tutoring. This field's advancements are driving progress in areas like autonomous navigation, personalized healthcare monitoring, and human-computer interaction, enabling more responsive and effective systems.
Papers
Neuromorphic Optical Flow and Real-time Implementation with Event Cameras
Yannick Schnider, Stanislaw Wozniak, Mathias Gehrig, Jules Lecomte, Axel von Arnim, Luca Benini, Davide Scaramuzza, Angeliki Pantazi
Real Time Bearing Fault Diagnosis Based on Convolutional Neural Network and STM32 Microcontroller
Wenhao Liao
Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation
Ram Dershan, Amir M. Soufi Enayati, Zengjie Zhang, Dean Richert, Homayoun Najjaran
Real-time Trajectory-based Social Group Detection
Simindokht Jahangard, Munawar Hayat, Hamid Rezatofighi
ReBotNet: Fast Real-time Video Enhancement
Jeya Maria Jose Valanarasu, Rahul Garg, Andeep Toor, Xin Tong, Weijuan Xi, Andreas Lugmayr, Vishal M. Patel, Anne Menini
Cosys-AirSim: A Real-Time Simulation Framework Expanded for Complex Industrial Applications
Wouter Jansen, Erik Verreycken, Anthony Schenck, Jean-Edouard Blanquart, Connor Verhulst, Nico Huebel, Jan Steckel