Publications

Enhanced weathering (EW) is an emerging carbon dioxide (CO2) removal technology that can contribute to climate change mitigation. This technology relies on accelerating the natural process of mineral weathering in soils by manipulating the abiotic variables that govern this process, in particular mineral grain size and exposure to acids dissolved in water. EW mainly aims at reducing atmospheric CO2 concentrations by enhancing inorganic carbon ...

Characteristics like self-managing, self-adaptation, and self-organization are the main objectives of intelligent network operation. AI and Machine Learning (ML) algorithms will enable future networks to operate entirely autonomously. However, current network architectures are not fully prepared to include and properly handle the promised Network Intelligence (NI). This article looks at scaling, one key Management and Orchestration (MANO) ...

While there is a clear trend towards network automation through the usage of Artificial Intelligence (AI) and Machine Learning (ML) solutions, the major reference network architectures are still not natively including all the mechanisms needed to handle Network Intelligence (NI). This paper introduces a novel architecture proposed within the EU-funded DAEMON project, which includes a Network Intelligence Plane (NIP) that natively integrates NI ...

Motion prediction of the human pose estimates future poses based on the preceding poses. It is a stepping stone toward industrial applications, like human-robot interactions and ergonomy indicators. The goal is to minimize the error in predicted joint positions on the IKEA-ASM dataset which resembles assembly use cases with a high diversity of execution and background of the same action class. In this paper, we use the STS- GCN model to tackle ...

We present a multi-modal AI framework to intuitively instruct and control Automated Guided Vehicles. We define a general multi-modal AI architecture, which has a loose coupling between three different AI modules, including spoken language understanding, visual perception and Reinforcement Learning navigation. We use the same multi-modal architecture for two different use cases implemented in two different platforms: an off-road vehicle, which ...

Situational awareness is getting traction in the field of autonomous inland vessels. Large amounts of data needs to be shared in order to set up this awareness. This ranges from relatively small positional updates, to consistent streams of sensory data. Point clouds, captured by LiDAR sensors, are heavily used by inland vessels as they give a detailed sense of range. This sharing is not a major complexity when vessels are in close proximity with ...

We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike trains. The spike trains are fed into a spiking recurrent neural network, a liquid state machine (LSM). The readout spike ...

This paper presents the first comprehensive tutorial on a promising research field located at the frontier of two well-established domains, neurosciences and wireless communications, motivated by the ongoing efforts to define the Sixth Generation of Mobile Networks (6G). In particular, this tutorial first provides a novel integrative approach that bridges the gap between these two seemingly disparate fields. Then, we present the state-of-the-art ...

Causal structure discovery in complex dynamical systems is an important challenge for many scientific domains. Although data from (interventional) experiments is usually limited, large amounts of observational time series data sets are usually available. Current methods that learn causal structure from time series often assume linear relationships. Hence, they may fail in realistic settings that contain nonlinear relations between the variables. ...