The electric power system is undergoing a significant transformation driven by advances in
digital technologies. This article provides a comprehensive and detailed analysis of recent …

The paper discusses algorithmic priority inversion in mission-critical machine inference
pipelines used in modern neural-network-based cyber-physical applications, and develops …

With the explosive growth of the Internet of Things (IoT), fog computing emerged as a new
paradigm, in an attempt to minimize network latency. Fog computing extends the cloud to the …

On-board computing capacity remains a key bottleneck in modern machine inference
pipelines that run on embedded hardware, such as aboard autonomous drones or cars. To …

This paper explores criticality-based real-time scheduling of neural-network-based machine
inference pipelines in cyber-physical systems (CPS) to mitigate the effect of algorithmic …

Across a range of safety-critical domains, an evolution is underway to endow embedded
systems with" thinking" capabilities by using artificial-intelligence (AI) techniques. This …

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 …

Recurrent neural networks (RNNs) are well-suited to the sequential inference tasks often
found in embedded sensing systems. While RNNs have displayed high accuracy on many …

As real-time object detection systems, such as autonomous cars, need to process input
images acquired from multiple cameras, they face significant challenges in delivering …

This paper introduces and evaluates a general construct for trading off accuracy and overall
execution duration in classification-based machine perception problems—namely, the …