In a few years, the world will be populated by billions of connected devices that will be
placed in our homes, cities, vehicles, and industries. Devices with limited resources will …

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous
in recent years. This has led to the generation of large quantities of data in real-time, which …

Artificial Intelligence (AI) has been recently applied to a number of sensing scenarios for
realizing the prediction, control and/or recognition tasks. However, its integration to …

Abstract Deep Neural Network (DNN) models for both resource-rich environments and
resource-constrained devices have become abundant in recent years. As of now, the …

The advent of powerful edge devices and AI algorithms has already revolutionized many
terrestrial applications; however, for both technical and historical reasons, the space industry …

The edge computing paradigm for Internet-of-Things brings computing closer to data
sources, such as environmental sensors and cameras, using connected smart devices. Over …

The challenging deployment of Artificial Intelligence (AI) and Computer Vision (CV)
algorithms at the edge pushes the community of embedded computing to examine …

Machine learning has been a major driver for improving semiconductor laser chip
manufacture process. The virtual metrology system was used to enable the manufacturers to …

Embedded systems are presented today with the challenge of a very rapidly evolving
application diversity followed by increased programming and computational complexity …

The worldwide demand for speed in applications challenges the deployment of compute-
intensive algorithms at the power-constrained edge. Novel embedded devices such as the …