This repository contains a comparative study of various optimization algorithms for task scheduling in fog computing environments, focusing on energy efficiency, response time, deadline satisfaction, ...

This research advances hybrid soft-rigid robot simulations, achieving up to 1000 times faster computations through analytical derivatives in the GVS framework.

This project presents a comparative study and implementation of control system techniques for regulating the speed of a DC motor. The primary focus is the ...

Many engineering challenges come down to the same headache—too many knobs to turn and too few chances to test them. Whether tuning a power grid or designing a safer vehicle, each evaluation can be ...

That’s the job of generative engine optimization (GEO) — and in 2026, it’s no longer optional. This guide shows you how to build, execute, and measure a GEO strategy that actually works. What is GEO — ...

Computer science is the study and development of the protocols required for automated processing and manipulation of data. This includes, for example, creating algorithms for efficiently searching ...

Abstract: Optimization algorithms are widely employed to tackle complex problems, but designing them manually is often labor-intensive and requires significant expertise. Global placement is a ...

Xiang Li is currently an Associate Professor in the Department of Computer Science and Engineering at Santa Clara University. She received her Ph.D. degree from the University of Florida. Her research ...

We are now looking for three summer trainees to support the studies in following topics at Energy Conversion and System Group, Aalto University. Summer trainee positions are open exclusively for Aalto ...

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...

Abstract: Metaheuristic algorithms have demonstrated strong effectiveness in solving complex real-world optimization problems. This paper presents two discrete metaheuristic approaches for the ...