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Modular autonomous vehicles (MAVs) represent a transformative paradigm in the rapidly advancing field of autonomous vehicle technology. The integration of modularity offers numerous advantages, poised to reshape urban mobility systems and…

Microfluidic multipoles (MFMs) have been realized experimentally and hold promise for "open-space" biological and chemical surface processing. Whereas convective flow can readily be predicted using hydraulic-electrical analogies, the design…

A one-dimensional multi-phase flow model for thermomagnetically pumped ferrofluid with heat transfer is proposed. The thermodynamic model is a combination of a simplified particle model and thermodynamic equations of state for the base…

In recent years, the demand for customized, on-demand production has grown in the manufacturing sector. Additive Manufacturing (AM) has emerged as a promising technology to enhance customization capabilities, enabling greater flexibility,…

We present a review of high-performance automatic modulation recognition (AMR) models proposed in the literature to classify various Radio Frequency (RF) modulation schemes. We replicated these models and compared their performance in terms…

Developing machine learning (ML) models requires a deep understanding of real-world problems, which are inherently multi-objective. In this paper, we present VirnyFlow, the first design space for responsible model development, designed to…

The demand for sophisticated tools and approaches in heat management and control has triggered fast development of emerging fields including conductive thermal metamaterials, nanophononics, far-field and near-field radiative thermal…

Recent breakthroughs in large language models (LLMs) have not only advanced natural language processing but also inspired their application in domains with structurally similar problems--most notably, autonomous driving motion generation.…

Assembly systems constitute one of the most important fields in today industry. In this paper we propose an open distributed architecture for the engineering of evolvable flexible hybrid assembly systems. The proposed architecture is based…

Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecting ML…

We review recent advances in the design, synthesis, and modeling of active fluids. Active fluids have been at the center of many technological innovations and theoretical advances over the past two decades. Research on this new class of…

This paper presents a SysML-based approach to enhance functional and software development process within an industrial context. The recent changes in technology such as electromobility and increased automation in heavy construction…

One foundation of the model driven engineering (MDE) is to separate the modelling application description from its technological implementation (i.e. platform). Some of them are dedicated to the system execution. Hence, one promise solution…

Complex mechatronic systems are typically composed of interconnected modules, often developed by independent teams. This development process challenges the verification of system specifications before all modules are integrated. To address…

RRAM technology has experienced explosive growth in the last decade, with multiple device structures being developed for a wide range of applications. However, transitioning the technology from the lab into the marketplace requires the…

One-step generative modeling seeks to generate high-quality data samples in a single function evaluation, significantly improving efficiency over traditional diffusion or flow-based models. In this work, we introduce Modular MeanFlow (MMF),…

As a consequence to the hype of Grid computing, such systems have seldom been designed using formal techniques. The complexity and rapidly growing demand around Grid technologies has favour the use of classical development techniques,…

Transfer learning has recently become the dominant paradigm of machine learning. Pre-trained models fine-tuned for downstream tasks achieve better performance with fewer labelled examples. Nonetheless, it remains unclear how to develop…

The interpretation of ego motion and scene change is a fundamental task for mobile robots. Optical flow information can be employed to estimate motion in the surroundings. Recently, unsupervised optical flow estimation has become a research…

Cells control fluid flows with a spatial and temporal precision that far exceeds the capabilities of current microfluidic technologies. Cells achieve this superior spatio-temporal control by harnessing dynamic networks of cytoskeleton and…