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2026 EN
Riccardo Moleri · Simone M. Dartizio · Michele Rossoni
+6 more
This work presents a digital-to-time converter (DTC)-based fractional-N digital phase-locked loop (PLL) designed to achieve simultaneously low jitter and low spurs. We introduce a novel DTC chopping technique that effectively mitigates fractional spurs, which we identify as predominantly arising from even-order nonlinearity invariable-slope (VS) DTCs. The proposed technique suppresses such spurs by randomly alternating the DTC position between the reference and divider signal paths. In addition, it proves effective in reducing the DTC flicker noise contribution. To ensure robust operation across process, voltage, and temperature (PVT) spreads, the design incorporates several background adaptive digital algorithms. Fabricated in a 28-nm CMOS process, the synthesizer occupies an active area of 0.22 mm 2 and consumes 16.7 mW. At a 9.275-GHz near-integer channel, the measured worst case in-band fractional spur remains below −63 dBc, and the integrated rms jitter under 80 fs, yielding a jitter-power figure of merit (FoM) of −249.8 dB.
Resource
2026 EN
Daniele Lodi Rizzini · Francesco Tesolin · Michele Rossoni
+6 more
This article presents a 28–38-GHz frequency tripler implemented in 55-nm SiGe BiCMOS technology with a novel on-chip background calibration technique. This technique continuously optimizes the circuit performance by maximizing output power and improving fundamental harmonic rejection. The proposed tripler achieves wideband operation and robust performance across varying operating conditions and input-power levels. The calibrated tripler demonstrates more than 40 dB of fundamental harmonic rejection and a 10.2-GHz (30.3%) 3-dB bandwidth. It occupies an area of 0.16 mm 2 and consumes 54 mW of power. The results show the tripler maintains its performance over a 13-dB input-power variation, confirming the effectiveness of the proposed calibration method.
Resource
2026 EN
Maria Cuevas-Gonzalez · Anna Barra · Riccardo Palama
+8 more
The recent availability of high-resolution, open-access MT-InSAR data, alongside free tools for data interpretation such as ADAtools , has enabled the development of wide-area, value-added geospatial products. This study demonstrates the integration of data delivered by the European Ground Motion Service with open-access ancillary datasets and ADAtools to efficiently identify, map, and classify ground deformation phenomena across Spain between 2015 and 2021. By processing extensive datasets and clustering measurement points into Active Deformation Areas (ADAs), this approach reduces data complexity and simplifies the interpretation by focusing the analysis and classification on significant deformation areas. A central component of this study is the validation of the ADA classification results. Expert user validation addressed discrepancies and ensured the accuracy of the classifications, particularly in complex scenarios. In addition, the ADAclassifier output was compared with an independently generated map using a machine learning approach based on the extreme gradient boosting method. The high degree of consistency observed between the two methods reinforces the reliability of the ADAclassifier . The classification results highlighted subsidence and landslides as prevalent phenomena, aligning with known geohazard distributions. While the ADAclassifier effectively identified subsidence, landslides, and uplifts, it faced challenges in distinguishing construction settlement and sinkholes, indicating a need for further refinement. Future work should focus on refining decision-tree methodologies, integrating time-series data, and enhancing classification accuracy for overlapping deformation types. This methodology offers a scalable approach for systematic geohazard monitoring that can be adapted to other regions, supporting risk assessment and the development of targeted mitigation strategies.
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2026 EN
Federica Cotugno · Nestor Yague-Martinez · Paolo Berardino
+9 more
In recent years, small Synthetic Aperture Radar (SAR) satellite constellations have emerged as a viable solution due to their ease of design and relatively low launch costs. These next generation systems aim to meet the growing needs of the Differential Interferometric SAR (DInSAR) community, including high spatial resolution and temporal acquisition frequency. Nevertheless, despite their benefits, small satellites face drawbacks such as low power budgets and limited imaging capabilities, needing the exploration of new orbital configurations to meet specific mission objectives. Among these, mid-inclination orbits (MIOs) offer the unique advantage of enabling the retrieval of North-South surface displacements, overcoming a key limitation of conventional sun-synchronous orbits (SSOs). In this study, we analyze three SAR datasets acquired by Capella Space over the Campi Flegrei (CF) caldera (Italy), exploiting a 45° MIO. The presented results, validated against GNSS measurements, show a mean standard deviation of 3-4 mm between DInSAR and GNSS LOS-projected time series, while the uncertainty for the North South component is estimated to be less than 5 mm. Furthermore, we retrieve, for the first time, comprehensive North-South deformation products of the CF caldera, including both a high resolution map and displacement time series. These outcomes represent a precursor for the upcoming Italian SAR constellation NIMBUS, part of the IRIDE program, which will be launched in a similar MIO configuration and become operational during 2027.
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2026 EN
Michele Tonan · Matteo Bottin · Daniele Businaro
+2 more
This paper presents the design, modeling, and experimental validation of a macro-micro robot featuring a 2-degrees-of-freedom (DOF) serial underactuated mechanism mounted on a cable-driven pulley. The main contribution consists in demonstrating that, by ensuring a specific mass distribution, the system achieves differential flatness, enabling accurate point-to-point and via-point trajectory planning without feedback from the passive joint. The dynamic model incorporates damping effects and is validated both numerically and experimentally. Results confirm the feasibility and benefits of the proposed system in terms of dexterity, trajectory control, and reduction of workspace occupation, particularly when obstacles are present.
Resource
2026 EN
Ayat Zaki-Hindi · Paola Soto · German Castellanos
+33 more
AI-native, programmable, and disaggregated 6G networks will be highly dynamic and distributed, demanding tools that can explain, predict, and safely optimize behavior across the edge–cloud continuum. Network Digital Twins (NDTs) promise this capability, yet current efforts in research and industry are fragmented and lack widely accepted formal definitions and architectural guidelines. This paper proposes a structured framework for NDTs in 6G, addressing these gaps by refining the conceptual foundations of NDTs, introducing a functional architecture, inherited from the 6G-TWIN EU consortium, and clarifying key components such as AI-driven workflows, the place of simulation, data management, and orchestration. Concrete examples illustrate how these components enable network automation, optimization, and predictive analytics. The paper proceeds by reviewing related work and standardization efforts, specifying functional and non-functional requirements, presenting the architecture and its various domains, and detailing lifecycle management across cloud to edge. We then report early implementations and evaluation results, and discuss security, privacy, and governance considerations, concluding with directions for validation and uptake. The key objective is to offer a cohesive reference model that guides the community in shaping NDT development, ensuring interoperability, scalability, adaptability, and seamless integration into AI-native 6G networks for improved intelligence and efficiency.
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2026 EN
A. Astolfi · D. Bhattacharjee · G. Manca
+1 more
The steady-state optimal filtering problem for linear systems is revisited with the objective of establishing further insights on the structure of the underlying solution. It is shown that, in addition to the invariance property of a suitably defined hyperplane, the optimal filter is related to a triangularizing change of coordinates for certain Hamiltonian dynamics associated to the filtering problem. The implication of the above observation is twofold. First, the novel interpretation admits a conceptually straightforward counterpart in the nonlinear setting in terms of invariant distributions. The latter then permit the design of local steady-state optimal filters for nonlinear systems that only rely upon the solutions of linear partial differential equations, the solution of which is independent from the specific time history of the measured output. The conditions may be further leveraged to determine a polynomial algebraic equation, which characterizes the solution of the filtering problem and which is expressed in the entries of the optimal filter gain alone, hence circumventing the need for solving the underlying Riccati equation.
Resource
2026 EN
M. Sassano · A. Astolfi
Within the framework of the Linear Quadratic Regulator it is well known that “all roads lead to the Algebraic Riccati Equation” . The deceptive veil of linearity is torn herein by observing that such an algebraic equation generalizes to three different conditions in the nonlinear setting. The first one is obviously the celebrated Hamilton-Jacobi-Bellman partial differential equation arising by relying on Dynamic Programming arguments. However, it is shown that the optimal solution can be equivalently constructed also by characterizing a specific invariant manifold or an invariant distribution of the associated Hamiltonian vector field. While these three strategies reduce to the Algebraic Riccati Equation in the linear case, they instead lead to distinct conditions in the nonlinear setting, with the latter two yielding quasi-linear and linear partial differential equations, respectively, in place of the quadratic Hamilton-Jacobi-Bellman equation.
Resource
2026 EN
Kailing Song · Michele Bonnin · Alon Ascoli
+1 more
We investigate multistability, noise-induced transitions, and stochastic resonance in a second-order nonlinear oscillator incorporating a nonvolatile memristive device. The memristor provides a programmable nonlinear conductance, enabling bistable dynamics with two asymptotically stable equilibrium points separated by a saddle. Under periodic excitation, the system exhibits coexisting limit cycles, period-doubling cascades, boundary crises, and transitions to chaos. Lyapunov exponent analysis reveals repeated crossings of the edge-of-chaos regime, where the largest nonzero exponent approaches zero, marking a balance between stability and sensitivity to perturbations. The effects of additive Gaussian white noise are analyzed by reformulating the dynamics in terms of an effective potential landscape, where noise induces random transitions between coexisting attractors. Transition rates are accurately described in the weak-noise regime by the Eyring–Kramers formula. When periodic forcing and noise act jointly, the system exhibits stochastic resonance, with optimal synchronization occurring when the forcing period matches the mean noise-induced transition time. These results demonstrate that memristor-based nonlinear circuits naturally operate near critical dynamical regimes and provide a compact hardware platform for studying noise-assisted computation and edge-of-chaos dynamics in neuromorphic systems.
Resource
2026 EN
Pietro Gori · Vincenzo Degiacomo · Michele Pierallini
+2 more
In this article, we propose a novel approach for motion planning and control in quadruped robots that simultaneously optimizes the base trajectory and contact sequence along the longitudinal direction. The method relies on a simplified 2-D single rigid body model to compute an optimal trajectory, which is then mapped at the joint level. To compensate for model approximations and environmental uncertainties, we introduce an iterative learning control (ILC) scheme that progressively improves tracking performance across repetitions. Compared to existing approaches, our formulation unifies contact planning and trajectory optimization in a learning-while-doing framework, enhancing robustness to disturbances. We validate the method on the quadruped robot Solo-12, demonstrating adaptive gait generation and stable locomotion across different terrains and perturbations.