Latency and bandwidth considerations in parallel robotics image processing
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
Registration of multi-modal medical images: exploiting sensor relationships
Registration of multi-modal medical images: exploiting sensor relationships
A bandwidth-efficient architecture for media processing
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Computational Complexity Management of Motion Estimation in Video Encoders
DCC '02 Proceedings of the Data Compression Conference
An integrated multi-modal sensor network for video surveillance
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
SensEye: a multi-tier camera sensor network
Proceedings of the 13th annual ACM international conference on Multimedia
Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Virtual high-resolution for sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
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Compressing real-time input through bandwidth constrained connections has been studied within robotics, wireless sensor networks, and image processing. When there are bandwidth constraints on real-time input the amount of information to be transferred will always be greater than the amount that can be transferred per unit of time. We propose a system that utilizes a local diffusion process and a reinforcement learning-based memory system to establish a real-time prediction of an entire input space based upon partial observation. The proposed system is optimized for dealing with multi-dimension input spaces, and maintains the ability to react to rare events. Results show the relation of loss to quality and suggest that at higher resolutions gains in quality are possible.