Over the last decade, machine learning (ML) and deep learning (DL) algorithms have
significantly evolved and been employed in diverse applications, such as computer vision …

This study presents and evaluates a modified ant colony optimization (ACO) approach for
the precedence and resource-constrained multiprocessor scheduling problems. A modified …

In this paper, we study a set of real-time scheduling problems whose objectives can be
expressed as piecewise linear utility functions. This model has very wide applications in …

As machine learning (ML) has been proven effective in solving various problems,
researchers in the real-time systems (RT) community have recently paid increasing attention …

Ensuring control stability is essential for safety-critical systems. Task redundancy is one
commonly employed technique to enhance control performance in applications afflicted by …

This article presents and evaluates the Slack Method, a new constructive heuristic for the
allocation (mapping) of periodic hard real-time tasks to multiprocessor or distributed …

Computing systems are increasingly being used for embedded applications. In an
embedded system a special purpose computer is used for the control of physical processes …

Computer vision and image understanding processes are not very robust; small changes in
exposure parameters or in internal parameters of algorithms can lead to significantly …

Generally, how to satisfy the deadline constraint is the major issue in solving real-time
scheduling. Recently, neural network using competitive learning rule provides a highly …

In previous work, the authors proposed an approach to the approximate solution of
scheduling problems, neural network based algorithms, applied to the preemptive and non …