Publications

Collision detection is one of the most challenging tasks for unmanned aerial vehicles (UAVs). This is especially true for small or micro-UAVs due to their limited computational power. In nature, flying insects with compact and simple visual systems demonstrate their remarkable ability to navigate and avoid collision in complex environments. A good example of this is provided by locusts. They can avoid collisions in a dense swarm through the activity of a motion-based visual neuron called the Lobula giant movement detector (LGMD). The defining feature of the LGMD neuron is its preference for looming. As a flying insect’s visual neuron, LGMD is considered to be an ideal basis for building UAV’s collision detecting system. However, existing LGMD models cannot distinguish looming clearly from other visual cues, such as complex background movements caused by UAV agile flights. To address this issue, we proposed a new model implementing distributed spatial-temporal synaptic interactions, which is inspired by recent findings in locusts’ synaptic morphology. We first introduced the locally distributed excitation to enhance the excitation caused by visual motion with preferred velocities. Then, radially extending temporal latency for inhibition is incorporated to compete with the distributed excitation and selectively suppress the nonpreferred visual motions. This spatial-temporal competition between excitation and inhibition in our model is, therefore, tuned to preferred image angular velocity representing looming rather than background movements with these distributed synaptic interactions. Systematic experiments have been conducted to verify the performance of the proposed model for UAV agile flights. The results have demonstrated that this new model enhances the looming selectivity in complex flying scenes considerably and has the potential to be implemented on embedded collision detection systems for small or micro-UAVs.

Understanding the mechanisms controlling the early (fresh) and long-term (hardened) hydration of one-part alkali-activated slags (AAS) is key to extend their use as low CO2 substitutes for ordinary Portland cement (OPC). Their “just add water” use makes them easier and less hazardous to manipulate than the more studied two-part ones. This is due to the absence of liquid alkaline activators, which are environmentally and energy demanding. In this work, numerous experimental techniques have been linked to obtain a comprehensive physico-chemical characterization of a one-part AAS activated with Na2CO3 and Ca(OH)2 powders at several water to solid ratios (w/s). Calorimetry and pH/conductivity measurements describe the functioning of the activators immediately after contact with water. Early reactivity is characterized through in situ X-ray powder diffraction (XRPD) and small amplitude oscillatory shear (SAOS) rheology, which reveal a rapid precipitation of nanometric hydration products (nano-C-A-S-H), which results in a continuous increase in the paste cohesivity until setting. Moreover, SAOS shows that rejuvenating the paste by means of shearing (performed externally to the rheometer in this study) is enough to restore the initial cohesion (i.e., workability) for long time spans until setting occurs. The long-term hydration is characterized by ex situ XRPD on aged AAS pastes, in parallel with mechanical testing on AAS mortar. A correlation can be observed between the amount of nano-C-A-S-H and the increase in compressive strength. Overall, this formulation shows satisfactory fresh and solid properties, demonstrating suitability for low- and normal-strength applications.

Identifying immobilization mechanisms of potentially toxic elements (PTEs) is of paramount importance in the field application of solidification/stabilization. Traditionally, demanding and extensive experiments are required to better access the underlying retention mechanisms, which are usually challenging to quantify and clarify precisely. Herein, we present a geochemical model with parametric fitting techniques to reveal the solidification/stabilization of Pb-rich pyrite ash through conventional (ordinary Portland cement) and alternative (calcium aluminate cement) binders. We found that ettringite and calcium silicate hydrates exhibit strong affinities for Pb at alkaline conditions. When the hydration products are unable to stabilize all the soluble Pb in the system, part of the soluble Pb may be immobilized as Pb(OH)2. At acidic and neutral conditions, hematite from pyrite ash and newly-formed ferrihydrite are the main controlling factors of Pb, coupled with anglesite and cerussite precipitation. Thus, this work provides a much-needed complement to this widely-applied solid waste remediation technique for the development of more sustainable mixture formulations.

Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a new approach for context reasoning to further advance the field of human motion prediction. We therefore propose a neuro-symbolic approach for human motion prediction (NeuroSyM), which weights differently the interactions in the neighbourhood by leveraging an intuitive technique for spatial representation called Qualitative Trajectory Calculus (QTC). The proposed approach is experimentally tested on medium and long term time horizons using two architectures from the state of art, one of which is a baseline for human motion prediction and the other is a baseline for generic multivariate time-series prediction. Six datasets of challenging crowded scenarios, collected from both fixed and mobile cameras, were used for testing. Experimental results show that the NeuroSyM approach outperforms in most cases the baseline architectures in terms of prediction accuracy.

Chemical Quest is an innovative trivia game based on the 102 elements of the periodic table from H to No, developed collaboratively by upper secondary school and university teachers with the aim of increasing the interest of young students (age 14-18) in chemistry. As part of the project, a software version of the game was successfully played in 24 classes. ‘Challenging, sometimes difficult, highly instructive, relaxing, captivating, ..’ are some of the positive comments by students and teachers. In addition, Chemical Quest was conceived to be adaptable since the rules can be modified and the cards can be selected to match the educational objective.

This paper explores the potential of using the SAM (Segment-Anything Model) segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. The open-source nature of SAM allows for easy access and implementation. In our experiments, we aim to improve the segmentation performance by providing SAM with checkpoints extracted from the masks produced by mainstream segmentators, and then merging the segmentation masks provided by these two networks. We examine the “oracle” method (as upper bound baseline performance), where segmentation masks are inferred only by SAM with checkpoints extracted from the ground truth. One of the main contributions of this work is the combination (fusion) of the logit segmentation masks produced by the SAM model with the ones provided by specialized segmentation models such as DeepLabv3+ and PVTv2. This combination allows for a consistent improvement in segmentation performance in most of the tested datasets. We exhaustively tested our approach on seven heterogeneous public datasets, obtaining state-of-the-art results in two of them (CAMO and Butterfly) with respect to the current best-performing method with a combination of an ensemble of mainstream segmentator transformers and the SAM segmentator. The results of our study provide valuable insights into the potential of incorporating the SAM segmentator into existing segmentation techniques. We release with this paper the open-source implementation of our method.

The accurate determination of the local temperature is one of the most important challenges in the field of nanotechnology and nanomedicine. For this purpose, different techniques and materials have been extensively studied in order to identify both the best-performing materials and the techniques with greatest sensitivity. In this study, the Raman technique was exploited for the determination of the local temperature as a non-contact technique and titania nanoparticles (NPs) were tested as nanothermometer Raman active material. Biocompatible titania NPs were synthesized following a combination of sol-gel and solvothermal green synthesis approaches, with the aim of obtaining pure anatase samples. In particular, the optimization of three different synthesis protocols allowed materials to be obtained with well-defined crystallite dimensions and good control over the final morphology and dispersibility. TiO2 powders were characterized by X-ray diffraction (XRD) analyses and room-temperature Raman measurements, to confirm that the synthesized samples were single-phase anatase titania, and using SEM measurements, which clearly showed the nanometric dimension of the NPs. Stokes and anti-Stokes Raman measurements were collected, with the excitation laser at 514.5 nm (CW Ar/Kr ion laser), in the temperature range of 293–323 K, a range of interest for biological applications. The power of the laser was carefully chosen in order to avoid possible heating due to the laser irradiation. The data support the possibility of evaluating the local temperature and show that TiO2 NPs possess high sensitivity and low uncertainty in the range of a few degrees as a Raman nanothermometer material.

The Audio Recording Preservation (ARP) technology represents a significant development in the field of audio preservation and is an essential component of the Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Context-based Audio Enhancement (CAE) standard. This standard has been adopted by the IEEE Standard Association as IEEE 3302-2022 and it specifies a range of AI-based technologies for various audio applications, including communication, entertainment, post-production, teleconferencing, and preservation. This article aims to highlight the specific contribution of CAE-ARP technology to audio preservation applications. The CAE-ARP technology has several innovative features that make it a valuable tool in the digitization and preservation of open-reel audio tapes. It leverages automated AI for extracting relevant information from digitized audio files and for creating preservation and access copies. By using the ARP standard, archives can effectively manage all the information stored onto tapes, along with related metadata, to automatically prepare the content for storage and/or immediate use. This technology represents a significant advancement in the field of audio preservation and provides an effective solution for managing small and large collections of open-reel audio tapes.

Background The effect of noninvasive respiratory support (NRS), including high-flow nasal oxygen, bilevel positive airway pressure and continuous positive airway pressure (noninvasive ventilation (NIV)), for preventing and treating post-extubation respiratory failure is still unclear. Our objective was to assess the effects of NRS on post-extubation respiratory failure, defined as re-intubation secondary to post-extubation respiratory failure (primary outcome). Secondary outcomes included the incidence of ventilator-associated pneumonia (VAP), discomfort, intensive care unit (ICU) and hospital mortality, ICU and hospital length of stay (LOS), and time to re-intubation. Subgroup analyses considered “prophylactic” versus “therapeutic” NRS application and subpopulations (high-risk, low-risk, post-surgical and hypoxaemic patients). Methods We undertook a systematic review and network meta-analysis (Research Registry: reviewregistry1435). PubMed, Embase, CENTRAL, Scopus and Web of Science were searched (from inception until 22 June 2022). Randomised controlled trials (RCTs) investigating the use of NRS after extubation in ICU adult patients were included. Results 32 RCTs entered the quantitative analysis (5063 patients). Compared with conventional oxygen therapy, NRS overall reduced re-intubations and VAP (moderate certainty). NIV decreased hospital mortality (moderate certainty), and hospital and ICU LOS (low and very low certainty, respectively), and increased discomfort (moderate certainty). Prophylactic NRS did not prevent extubation failure in low-risk or hypoxaemic patients. Conclusion Prophylactic NRS may reduce the rate of post-extubation respiratory failure in ICU patients.