Institute of Electronics, Computer and Telecommunication Engineering

Spectrum Sensing (SS) is expected to play a crucial role in forthcoming 6G Cognitive Radio Networks (CRNs), where unlicensed users will be able to dynamically access the spectrum and perform opportunistic transmissions without generating interference for licensed users. In this work, we investigate multiple-antenna SS techniques by analyzing the performance of several widely used detection schemes-namely, Roy's Largest Root Test (RLRT), the Generalized Likelihood Ratio Test (GLRT), the Eigenvalue Ratio Detector (ERD), and the Energy Detector (ED)-under varying false-alarm probabilities and signal-to-noise ratios (SNRs). We assume there are a fixed number of sensors at the secondary-user receiver, namely, four. To evaluate the behavior of these detectors in realistic conditions, we developed a software-defined radio (SDR) testbed using Universal Software Radio Peripherals (USRPs), enabling both primary-user signal transmission and secondary-user data acquisition. The experimental results, illustrated through Receiver Operating Characteristic (ROC) and performance curves, are compared with simulation outcomes. The analysis is complemented by a detailed state-of-the-art listing of the available analytical characterizations of the false-alarm probabilities for the considered SS schemes. In particular, the GLRT false-alarm probability, previously unavailable in explicit form for a four-antenna equipped receiver, is computed as well. These results validate the superior detection capability of RLRT over the other schemes tested, confirming its effectiveness not only in theoretical analysis but also in practical SDR-based implementations.

Extended Reality (XR) has reshaped how learning can be structured, yet its integration into formal curricula continues to lag behind its technological potential. Established instructional design models such as ADDIE and ASSURE provide stable planning structures, but were not developed to address the spatial, embodied and interactive characteristics of immersive environments. The XR2Learn framework was developed to bridge this gap by combining structured instructional planning with XR-specific pedagogical considerations. This study presents a multi-round Delphi-based expert evaluation of XR2Learn, involving twenty specialists in instructional design and XR-enhanced education. Experts assessed the framework across four dimensions: validity, clarity, usability and suitability. Qualitative feedback was thematically analyzed and subsequently quantified to establish consensus. The findings show strong agreement regarding the framework’s pedagogical grounding, logical structure and alignment with established instructional design practices. At the same time, experts identified limitations related to practical enactment, accessibility and the explicit integration of XR-specific learning constructs such as presence and social interaction. Overall, the results position XR2Learn as a framework at a transitional stage, moving from conceptual formulation toward practical instructional use. The study provides the first systematic expert validation of XR2Learn and outlines targeted directions for its refinement as a robust instructional design framework for XR-based education.

Networked measurements are essential for the design and implementation of cooperative cyber-physical systems in the Industry 4.0/5.0 era. Indeed, the need for accurate models of real-time communication systems and, especially, of their latency is paramount for enabling technologies such as advanced manufacturing solutions, simulation and industrial internet. This paper presents a novel approach to measuring latency in wireless communication networks, focusing on the metrological characterization of individual subsystem contributions. By leveraging Time-Sensitive Networking (TSN) for synchronization, and employing cost-effective hardware solutions, we aim to provide a structured assessment of the measurement system’s capability to isolate and quantify latency contributions of individual communication modules, from the protocol stack to the Wireless Network Interface Controller (WNIC). The proposed approach is practical, flexible, and adaptable to various environments and deployment scenarios, representing a significant advancement in latency measurement techniques for modern network environments.

This paper presents a kernel-based framework for physics-informed nonlinear system identification that extends kernel-based techniques, accounting for unmodeled dynamics, to seamlessly embed partially known physics-based models, improving parameter estimation and overall model accuracy. The two models' components are identified from data simultaneously, thereby minimizing a suitable cost that balances the relative importance of the physical and the black-box parts of the model. Additionally, nonlinear state smoothing is employed to address scenarios involving state-space models with not-fully measurable states. Numerical simulations on an experimental benchmark system demonstrate the effectiveness of the proposed approach, achieving up to 51% reduction in simulation root mean square error compared to physics-only models and 31% performance improvement over state-of-the-art identification techniques.

Recent studies show that titanium (Ti)-based alloys combine established mechanical strength, corrosion resistance, and biocompatibility with emerging electrical and electrochemical properties relevant to bioelectronics. The main goal of the present manuscript is to give a wide-ranging overview on the use of Ti-alloys in electronics and biomedicine, focusing on a comprehensive analysis and synthesis of the existing literature to identify gaps and future directions. Concurrently, the identification of possible correlations between the effects of the manufacturing process, alloying elements, and other degrees of freedom influencing the material characteristics are put in evidence, aiming to establish a global view on efficient interdisciplinary efforts to realize high-added-value smart devices useful in the field of biomedicine, such as, for example, implantable apparatuses. This review mostly summarizes advances in surface modification approaches—including anodization, conductive coatings, and nanostructuring that improve conductivity while maintaining biological compatibility. Trends in applications demonstrate how these alloys support smart implants, biosensors, and neural interfaces by enabling reliable signal transmission and long-term integration with tissue. Key challenges remain in balancing electrical performance with biological response and in scaling laboratory modifications for clinical use. Perspectives for future work include optimizing alloy composition, refining surface treatments, and developing multifunctional designs that integrate mechanical, biological, and electronic requirements. Together, these directions highlight the potential of titanium alloys to serve as foundational materials for next-generation bioelectronic medical technologies.

BackgroundDigital communication device use is changing rapidly among young people, and current research on this topic is limited or outdated.ObjectiveWe aimed to describe the use of digital communication devices by young people from 4 European countries and investigate their socioeconomic and demographic characteristics.MethodsIn 2023, we administered an online survey to a convenience sample of 4000 young people aged 16 to 25 years in Italy, Poland, Spain, and Switzerland. Participants reported on their regular use of smartphones, tablets, laptops, cordless phones, and smartwatches or activity trackers. Participants answered which activities they regularly engaged in on their devices, the time spent on these devices and activities, and in what position the device was used with respect to their body over the previous 3 months. We also collected information on participant socioeconomic and demographic characteristics, including age, gender, country of birth, employment status, parental educational level, and urbanicity of the place of residence.ResultsReported prevalence of device use was 90.9% (3635/4000) for smartphones, 33.2% (1329/4000) for tablets, 68.7% (2748/4000) for laptops, 11.6% (462/4000) for cordless phones, and 23.3% (931/4000) for smartwatches or activity trackers. Older age groups and women reported higher use across most devices. The activities reported with the highest engagement for smartphones were voice calls (2553/3635, 70.2%); social media (2693/3635, 74.1%); and texting, emailing, and internet use (2530/3635, 69.6%). For tablets and laptops, they were video streaming (849/1329, 63.9% and 1527/2748, 55.6%, respectively); texting, emailing, and internet use (673/1329, 50.6% and 1218/2748, 44.3%, respectively); and social media (659/1329, 49.6% and 1521/2748, 55.3%, respectively). On average, participants used their smartphones 60.9 (SD 83.1) minutes per day for texting, emailing, and internet use; 85.2 (SD 92.7) minutes per day for social media; 46.9 (SD 70.5) minutes per day for video streaming; and 53.7 (SD 80.3) minutes per day for music streaming. Differences across activities and devices were found among socioeconomic and demographic characteristics. For example, the oldest age groups reported lower duration of smartphone use for voice calls, social media, video streaming, and music streaming compared to the youngest age group but reported higher duration of smartphone use for video calls and texting, emailing, and internet use. Moreover, women reported higher duration of use for most activities on smartphones compared to men, except for online gaming, for which men reported higher duration of use.ConclusionsOur findings provide novel information on digital communication device use by young people. We identified differences between socioeconomic and demographic characteristics that warrant further investigation. These results can be used as a point of reference for digital communication devices in public health research, including health communication strategies and epidemiological research.

Computational modelling can be used to study and improve specific experimental tissue engineering protocols and outcomes. Proper oxygenation and nutrient substances supply such as glucose are crucial in 3D in vitro models. In most cases, hydrogel-based scaffolds are employed as culture systems. However, the diffusion of molecules could be difficult in the innermost areas of the scaffolds, and the presence of gradients could affect cell proliferation, especially in static conditions. Hence, the mathematical modelling of oxygen and nutrient transport, as well as their consumption by the expanding cell culture within the scaffold, can be useful for optimizing tissue construct properties and generating more predictive and robust outcomes. In this work, nutrient diffusion has been studied through two different scaffolds seeded with glial and neuronal cells: chitosan microbeads and PLA fibers covered in chitosan produced with two specific fabrication-based techniques. First, homogenization theory has been applied to the two different porous constructs, formulated as heterogeneous domains composed of two distinct phases: the culture medium and the polymeric material. Then, a continuous mathematical model of nutrient transportation-consumption and cell proliferation has been implemented in COMSOL, aiming to comprehend nutrient diffusion to allow suitable environmental conditions for the growth of neural cells on 3D biopolymeric scaffolds.

Operator precedence languages (OPLs) enjoy the local parsability property, which means that a code fragment enclosed within a pair of markers playing the role of parentheses can be parsed with no knowledge of its external context. This property has been exploited to build parallel parsers for languages formalized as OPLs. It has been observed, however, that when the syntax trees of sentences have a linear substructure, parsing must necessarily proceed sequentially, making it ineffective to split such a subtree into chunks to be processed in parallel. This inconvenience derives from the hypothesis that the equality precedence relation cannot be cyclic, which has been so far assumed by most literature on OPLs. This hypothesis was motivated by the need to keep the mathematical notation as simple as possible, although it caused a discrepancy between the expressive power of operator precedence grammars and other formalisms defining OPLs such as operator precedence automata, monadic second order logic and operator precedence expressions, which do not assume acyclicity. We present an enriched version of operator precedence grammars, called cyclic , that allows for a simplified version of regular expressions in the right hand sides of grammar rules. For this class of operator precedence grammars the acyclicity hypothesis of the equality precedence relation is no more needed to guarantee the algebraic properties of the generated languages. The expressive power of the cyclic grammars is now fully equivalent to that of other formalisms defining OPLs. As a result, cyclic operator precedence grammars produce unranked syntax trees and sentences with flat unbounded substructures that can be naturally partitioned into chunks suitable for parallel parsing.

We present the design and numerical analysis of an asymmetric bow-tie vertical-cavity surface-emitting laser (ABT-VCSEL) that enables PAM-2 direct modulation up to 100 GHz. The device concept relies on photon-photon resonance (PPR) between laterally coupled modes, triggered by a structural asymmetry introduced via a hollowed dielectric mirror. This approach avoids the need for asymmetric pumping and probing while ensuring strong modal coupling under uniform carrier injection. A dynamical in-house suite, combining field-carrier interaction, electromagnetic, and thermal problems, is developed and discussed to evaluate device performance under realistic conditions. The simulation results predict a 3-dB bandwidth up to 70 GHz with low modulation swing, and open eye diagrams at 100 GHz PAM-2 modulation and 70 GHz PAM-4 modulation. The proposed ABT-VCSEL concept shows robustness against geometrical variations and ambient temperature when proper cavity detuning is applied, making it a promising candidate for next-generation ultra-fast short-reach interconnects.

Abstract The recent adoption of artificial intelligence (AI) in robotics has driven the development of algorithms that enable autonomous systems to adapt to complex social environments. In particular, safe and efficient social navigation is a key challenge, requiring AI not only to avoid collisions and deadlocks but also to interact intuitively and predictably with its surroundings. To date, methods based on probabilistic models and the generation of conformal safety regions have shown promising results in defining safety regions with a controlled margin of error, primarily relying on classification approaches and explicit rules to describe collision-free navigation conditions. This work extends the existing perspective by investigating how topological features can contribute to the creation of explainable safety regions in social navigation scenarios, enabling the classification and characterization of different simulation behaviors. Rather than relying on behaviors parameters to generate safety regions, we leverage topological features through topological data analysis. We first utilize global rule-based classification to provide interpretable characterizations of different simulation behaviors, distinguishing between safe (free of collisions) and unsafe scenarios based on topological properties. Next, we define safety regions, $$S_\varepsilon $$ S ε , representing zones in the topological feature space where collisions are avoided with a maximum classification error of $$\varepsilon $$ ε . These regions are constructed using adjustable SVM classifiers and order statistics, ensuring a robust and scalable decision boundary. To enhance interpretability, we extract local rules from these safety regions, ensuring that the decision-making process remains transparent and comprehensible. Initially, we generate safety regions that separate simulations with and without collisions, achieving higher accuracy than methods that do not incorporate topological features. This approach also provides a deeper and more intuitive understanding of robot interactions within a navigable space. We then extend our methodology to design safety regions that ensure efficient simulations (i.e., free of deadlocks). Finally, we integrate both aspects to obtain comprehensive safety regions that guarantee both collision-free and deadlock-free simulations, defining an overall compliant simulation space.