Current state-of-the-art stereo algorithms use a 2D CNN to extract features and then form a
cost volume, which is fed into the following cost aggregation and regularization module …

Two-phase flow regime identification is an essential transdisciplinary topic that spans digital
signal processing, artificial intelligence, chemical engineering, and energy. Multiphase flow …

Embodied self-aware computing systems are embedded in a physical environment with a
rich set of sensors and actuators to interact both with their environment and with their own …

In this article, we make the case for the new class of Self-aware Cyber-physical Systems. By
bringing together the two established fields of cyber-physical systems and self-aware …

Current research in the computer vision field mainly focuses on improving Deep Learning
(DL) correctness and inference time performance. However, there is still little work on the …

Wearable systems require resource-constrained embedded devices for the elaboration of
the sensed data. These devices have to host energy-efficient artificial intelligence (AI) …

Many real-world edge applications including object detection, robotics, and smart health are
enabled by deploying deep neural networks (DNNs) on energy-constrained mobile …

Deep neural networks have seen tremendous success for different modalities of data
including images, videos, and speech. This success has led to their deployment in mobile …

Given the emergence of the Internet of Things (IoT) Cyber-Physical Systems (CPSs) and
their omnipresence, reducing their power consumption is among the major design priorities …

Abstract Machine learning algorithms (MLAs) usually process large and complex datasets
containing a substantial number of features to extract meaningful information about the …