This paper presents AdaMask, a machine-centric video streaming framework for remote
deep neural network (DNN) inference. The objective is to optimize the accuracy of …

This paper presents a self-cueing real-time frame-work for attention prioritization in AI-
enabled visual perception systems that minimizes a notion of state uncertainty. By attention …

This paper presents a real-time multi-view scheduling framework for DNN-based live video
analytics at the edge to minimize frame processing latency. The work is motivated by …

Recent work suggests that computing resources, such as GPUs in real-time edge-based
perception systems, need not have sufficient capacity to keep up with the input frame rates of …

Recent concepts in defense herald an increasing degree of automation of future military
systems, with an emphasis on accelerating sensing-to-decision loops at the tactical edge …

Existing solutions for scheduling arbitrarily complex distributed applications on networks of
computational nodes are insufficient for scenarios where the network topology is changing …

This paper extends a previous conference publication that proposed a real-time task
scheduling framework for criticality-based machine perception, leveraging image resizing as …

In this work, we present a novel scheduling frame-work enabling anytime perception for
deep neural network (DNN) based 3D object detection pipelines. We focus on …

Fault detection, which requires a lot of time and complexity, is one of the most difficult tasks
for cloud computing. In this research, we investigate the utilization of the anytime algorithm …

Real-time object detection is crucial in autonomous driving. To avoid catastrophic accidents,
an autonomous car should detect objects with multiple cameras and make decisions within …