arXiv subjects

Andrea Giudici

Publications and source records attributed to Andrea Giudici.

17 recordsLinked to original sources

arXiv2026

Battery open-circuit voltage is not purely chemical

Open-circuit-voltage (OCV) curves are commonly treated as intrinsic chemical properties of electrode materials. However, this view is incomplete. In ion-insertion batteries, OCV also depends on mechanical state and microstructure. Using finite-element simulations and asymptotic analysis, we show that particle swelling and external loads promote particle--particle contact that generates compressive stresses, shifting the inserted-ion chemical potential. The OCV correction is nonlinear, follows Hertzian contact scaling, and depends on particle arrangement. Identical materials can therefore exhibit different OCV curves in different electrode microstructures. Furthermore, in full cells, electrodes are mechanically coupled through the common stack stress. Thus, cell OCV is a chemo-mechanical property of the entire battery architecture, not chemistry alone.

cond-mat.mtrl-sci↗

arXiv2026

Incorporating multiscale mechanics in lithium-ion battery models

Lithiation-induced swelling in lithium-ion batteries generates stresses not only within active particles, but also across the surrounding non-active matrix, electrodes, and cell stack. These stresses can modify the chemical potential of lithium and therefore influence transport, reaction kinetics, and terminal voltage. We derive a reduced-order electro-chemo-mechanical model that captures this multiscale coupling while retaining a complexity comparable to standard Doyle--Fuller--Newman models. The electrode is modelled as a periodic array of spherical active particles embedded in a homogenised elastic non-active matrix. Exploiting the small stiffness of the non-active matrix relative to the active material, together with scale separation between particles, electrodes, and the full cell, we obtain an effective mechanical correction to the active-particle chemical potential and overpotential. This correction depends on particle swelling, electrode-scale strain, and macroscopic boundary conditions such as clamping or applied pressure. The resulting formulation can be incorporated directly into DFN, SPMe, and SPM frameworks, providing a computationally efficient route to include battery-scale mechanical effects in electrochemical simulations.

physics.chem-ph↗

arXiv2026

Fully-sensorized smart-eyewear platform for on-device Machine Learning

This paper presents ARGO, a smart eyewear platform designed to bridge ergonomic comfort, high computational throughput, and energy efficiency. Unlike cloud-dependent solutions, ARGO leverages the STM32N6 microcontroller and its integrated Neural Processing Unit (NPU) to enable on-device machine learning, minimizing latency and preserving user privacy through local data processing. The primary contribution lies in the holistic co-design of hardware, firmware, and artificial intelligence, centered on the deployment of an optimized YOLOv11 model for real-time urban obstacle recognition. To ensure compatibility with the target NPU, we introduce Head-wise Parallel Attention (HPA), an architectural refinement that enables efficient accelerator execution while preserving the original computational logic. The model is trained on the Walking On The Road (WOTR) dataset, and the final deployed configuration achieves an mAP50-95 of 24 under strict memory constraints, with a memory footprint of only 2.483 MB. The platform integrates a multimodal sensor suite, RGB cameras, Time-of-Flight sensors, microphones, and ambient sensors, and delivers 10 FPS at a continuous autonomy of ~113 minutes on a 200 mAh battery. These results demonstrate the feasibility of a high-performance, privacy-preserving, and socially acceptable assistive device, and highlight how competitive edge AI solutions increasingly demand tightly integrated, multidisciplinary co-design approaches.

cs.LG↗

arXiv2026

Efficient Sensor Fusion for Gesture Recognition on Resource-Constrained Devices

Gesture recognition is a cornerstone of Human-Computer Interaction (HCI) for smart eyewear, enabling natural and device-free control in augmented reality environments. Traditional vision-based approaches face significant challenges regarding power consumption, computational latency, and user privacy. This paper proposes a lightweight, privacy-preserving gesture recognition system based on the fusion of low-resolution Time-of-Flight (ToF) and Infrared (IR) thermal sensors. We used an 8 times 8 multizone ToF sensor (VL53L8CH) and an 8 times 8 IR array (AMG8833) to capture complementary depth and thermal cues. A compact Convolutional Neural Network (CNN) with a specialized grouped-convolution architecture is designed to fuse these modalities efficiently on a microcontroller (MCU). Experimental results on a custom dataset of 7 static gestures, validated via k-fold cross-validation, demonstrate that the proposed fusion strategy significantly outperforms single-sensor baselines with an accuracy of 92.3% and a macro F1-score of 0.93. Finally, on-device benchmarks on STM32F4 and STM32H7 MCUs confirm the system's suitability for resource-constrained wearables, requiring only 6,343 parameters and achieving millisecond-level inference latency with a total system power of 50 mW.

cs.LG↗

arXiv2026

Hardware-Aware Neural Feature Extraction for Resource-Constrained Devices

Visual SLAM is a core component of spatial computing systems, yet deploying learned local feature extractors on microcontroller-class hardware remains challenging due to memory, bandwidth, and quantization constraints. While modern neural descriptors provide strong robustness, their practical adoption is often hindered by system-level bottlenecks that are not captured by FLOP-based efficiency metrics. In this work, we introduce Gideon, a hardware-aware neural feature extractor explicitly designed for resource-constrained devices. Our approach combines relational knowledge distillation from a SuperPoint teacher with differentiable neural architecture search (DNAS) under strict memory and operator constraints. Unlike conventional design pipelines, we treat quantization stability and dynamic-range compactness as first-class objectives. We show that architectural choices such as replacing Batch Normalization with affine layers significantly improve INT8 robustness, and that descriptor dimensionality directly governs quantization resilience. Deployed on STM32N6, Gideon achieves 9.003 ms inference time (111 fps) while remaining below a 1.5 MB memory footprint. Remarkably, INT8 quantization induces negligible degradation and occasionally matches full-precision performance. These results demonstrate that robust learned feature extraction can be reconciled with embedded hardware constraints through holistic hardware-algorithm co-design.

cs.LG↗

arXiv2025

End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers

Keyword spotting (KWS) is a key enabling technology for hands-free interaction in embedded and IoT devices, where stringent memory and energy constraints challenge the deployment of AI-enabeld devices. In this work, we systematically evaluate and compare several state-of-the-art lightweight neural network architectures, including DS-CNN, LiCoNet, and TENet, alongside our proposed Typman-KWS (TKWS) architecture built upon MobileNet, specifically designed for efficient KWS on microcontroller units (MCUs). Unlike prior studies focused solely on model inference, our analysis encompasses the entire processing pipeline, from Mel-Frequency Cepstral Coefficient (MFCC) feature extraction to neural inference, and is benchmarked across three STM32 platforms (N6, H7, and U5). Our results show that TKWS with three residual blocks achieves up to 92.4% F1-score with only 14.4k parameters, reducing memory footprint without compromising the accuracy. Moreover, the N6 MCU with integrated neural acceleration achieves the best energy-delay product (EDP), enabling efficient, low-latency operation even with high-resolution features. Our findings highlight the model accuracy alone does not determine real-world effectiveness; rather, optimal keyword spotting deployments require careful consideration of feature extraction parameters and hardware-specific optimization.

cs.SD↗

arXiv2025

Benchmarking Energy and Latency in TinyML: A Novel Method for Resource-Constrained AI

The rise of IoT has increased the need for on-edge machine learning, with TinyML emerging as a promising solution for resource-constrained devices such as MCU. However, evaluating their performance remains challenging due to diverse architectures and application scenarios. Current solutions have many non-negligible limitations. This work introduces an alternative benchmarking methodology that integrates energy and latency measurements while distinguishing three execution phases pre-inference, inference, and post-inference. Additionally, the setup ensures that the device operates without being powered by an external measurement unit, while automated testing can be leveraged to enhance statistical significance. To evaluate our setup, we tested the STM32N6 MCU, which includes a NPU for executing neural networks. Two configurations were considered: high-performance and Low-power. The variation of the EDP was analyzed separately for each phase, providing insights into the impact of hardware configurations on energy efficiency. Each model was tested 1000 times to ensure statistically relevant results. Our findings demonstrate that reducing the core voltage and clock frequency improve the efficiency of pre- and post-processing without significantly affecting network execution performance. This approach can also be used for cross-platform comparisons to determine the most efficient inference platform and to quantify how pre- and post-processing overhead varies across different hardware implementations.

cs.LG↗

arXiv2024

Mechanics of elliptical JKR-type adhesive contact

The classic Johnson Kendall Roberts (JKR) theory describes the short-ranged adhesive contact of elastic bodies, but is only valid for axisymmetric contact. A theory for non-axisymmetric contact, which relies on approximating the contact region as an ellipse, was proposed by Johnson and Greenwood (JG). The theory includes the effects of adhesion via Griffith's criterion applied only at the semi-major and semi-minor axes of the contact ellipse. Although JG's work is in good agreement with numerical and experimental results for quasi-circular contacts, the agreement worsens as the eccentricity of the contact region increases. In this paper, we show that including the effects of adhesion by instead minimizing the sum of elastic and surface energy yields results that are in excellent agreement with previous numerical simulations over the full range of contact eccentricities.

cond-mat.soft↗

arXiv2024

How do imperfections cause asymmetry in elastic snap-through?

A symmetrically-buckled arch whose boundaries are clamped at an angle has two stable equilibria: an inverted and a natural state. When the distance between the clamps is increased (i.e. the confinement is decreased) the system snaps from the inverted to the natural state. Depending on the rate at which the confinement is decreased ('unloading'), the symmetry of the system during snap-through may change: slow unloading results in snap-through occurring asymmetrically, while fast unloading results in a symmetric snap-through. It has recently been shown [Wang et al., Phys. Rev. Lett. 132, 267201 (2024)] that the transient asymmetry at slow unloading rates is the result of the amplification of small asymmetric precursor oscillations (shape perturbations) introduced dynamically to the system, even when the system itself is perfectly symmetric. In reality, however, imperfections, such as small asymmetries in the boundary conditions, are present too. Using numerical simulations and a simple toy model, we discuss the relative importance of intrinsic imperfections and initial asymmetric shape perturbations in determining the transient asymmetry observed. We show that, for small initial perturbations, the magnitude of the asymmetry grows in proportion to the size of the intrinsic imperfection but that, when initial shape perturbations are large, intrinsic imperfections are unimportant - the asymmetry of the system is dominated by the transient amplification of the initial asymmetric shape perturbations. We also show that the dominant origin of asymmetry changes the way that asymmetry grows dynamically. Our results may guide engineering and design of snapping beams used to control insect-sized jumping robots.

cond-mat.soft↗

arXiv2024

Gas-induced bulging in pouch-cell batteries: a mechanical model

Over the long timescale of many charge/discharge cycles, gas formation can result in large bulging deformations of a Lithium-ion pouch cell, which is a key failure mechanism in batteries. Guided by recent experimental X-ray tomography data of a bulging cell, we propose a homogenised mechanical model to predict the shape of the deformation and the stress distribution analytically. Our model can be included in battery simulation models to capture the effects of mechanical degradation. Furthermore, with knowledge of the bending stiffness of the cathode electrodes and current collectors, and by fitting our model to experimental data, we can predict the internal pressure and the amount of gas in the battery, thus assisting in monitoring the state of health (SOH) of the cell without breaking the sealed case.

cond-mat.soft↗

arXiv2024

Transient amplification of broken symmetry in elastic snap-through

A snap-through bifurcation occurs when a bistable structure loses one of its stable states and moves rapidly to the remaining state. For example, a buckled arch with symmetrically clamped ends can snap between an inverted and a natural state as the ends are released. A standard linear stability analysis suggests that the arch becomes unstable to asymmetric perturbations. Surprisingly, our experiments show that this is not always the case: symmetric transitions are also observed. Using experiments, numerics, and a toy model, we show that the symmetry of the transition depends on the rate at which the ends are released with sufficiently fast loading leading to symmetric snap-through. Our toy model reveals that this behavior is caused by a region of the system's state space in which any initial asymmetry is amplified. The system may not enter this region when loaded fast (hence remaining symmetric) but will traverse it for some interval of time when loaded slowly, causing a transient amplification of asymmetry. Our toy model suggests that this behavior is not unique to snapping arches, but rather can be observed in dynamical systems where both a saddle-node and a pitchfork bifurcation occur in close proximity.

cond-mat.soft↗

arXiv2024

Mechanical stresses in pouch cells: a reduced order model

In a pouch cell battery, the intercalation of lithium ions into the active particles means the electrodes want to expand. However, since the electrodes are attached to stiff current collectors, this expansion is constrained, leading to a macro-scale deformation and a residually stressed state. This stress state affects the electrochemistry and can also lead to mechanical degradation, causing a reduction in performance. We model the mechanical state of stress in the battery assuming a known compositional expansion of the electrodes, and use asymptotic techniques to generate reduced order models by exploiting the thin aspect ratio, as well as the large stiffness of the current collectors. We obtain analytic expressions for the stress in the bulk of the electrodes and at the interface between electrodes and current collectors, and a reduced-order equation whose solution describes the tension in the current collectors. We compare our results with full 3D finite element simulations with excellent agreement, and use our results with the battery simulation package PyBaMM to predict a realistic stress state in a discharging battery.

physics.app-ph↗

arXiv2022

Curvature-driven instabilities in thin active shells

Spontaneous material shape changes, such as swelling, growth or thermal expansion, can be used to trigger dramatic elastic instabilities in thin shells. These instabilities originate in geometric incompatibility between the preferred extrinsic and intrinsic curvature of the shell, which may be modified by active deformations through the thickness and in plane respectively. Here, we solve the simplest possible model of such instabilities, which assumes the shells are shallow, thin enough to bend but not stretch, and subject to homogeneous preferred curvatures. We consider separately the cases of zero, positive and negative Gaussian curvature. We identify two types of super-critical symmetry breaking instability, in which the shell's principal curvature spontaneously breaks discrete up-down symmetry and continuous planar isotropy respectively. These are then augmented by inversion instabilities, in which the shell jumps sub-critically between up/down broken symmetry states, and rotation instabilities, in which the curvatures rotate by 90 degrees between states of broken isotropy without release of energy. Each instability has a thickness independent threshold value for the preferred extrinsic curvature proportional to the square-root of Gauss curvature. Finally, we show that the threshold for the isotropy-breaking instability is the same for deep spherical caps, in good agreement with recently published data.

cond-mat.soft↗

arXiv2021

Large deformation analysis of spontaneous twist and contraction in nematic elastomer fibres with helical director

A cylindrical rubber fibre subject to twist will also elongate: a manifestation of Poynting's effect in large strain elasticity. Here, we construct an analogous treatment for an active rubber fibre actuated via an axisymmetric pattern of spontaneous distortion. We start by constructing an exact large-deformation solution to the equations of elasticity for such fibre subject to imposed twist and stretch, which reveals spontaneous warping and twisting of the fibre cross-section absent in passive rubbers. We then compute the corresponding non-linear elastic energy, which encompasses the Poynting effect, but is minimized by a finite spontaneous twist and stretch. In the second half of the paper, we apply these results to understand the twist-contraction actuation of nematic elastomer fibres fabricated with director-fields that encode helical patterns of contraction on heating. We first consider patterns making a constant angle with respect to the local cylindrical coordinate system (conical spiral director curves) and verify the predicted spontaneous twist, contraction and cross-section deformation via finite elements. Secondly, we consider realistic director distributions for the experimentally reported fibres fabricated by cross-linking while simultaneously applying stretch and twist. Counter-intuitively, we find that maximum actuation twist is produced by applying a finite optimal twist during fabrication. Finally, we illustrate that spontaneously twisting fibres will coil into spring-like shapes on actuation if the ends are prevented from twisting relative to each other. Such twist-torsion coupling would allow to make a tendril-like "soft-spring" actuator with low force and high linear stroke compared to the intrinsic contraction of the elastomer itself.

cond-mat.soft↗

arXiv2021

Tracking areas with increased likelihood of surface particle aggregation in the Gulf of Finland: A first look at persistent Lagrangian Coherent Structures (LCS)

We explore the possibility to identify areas of intense patch formation from floating items due to systematic convergence of surface velocity fields by means of a visual comparison of Lagrangian Coherent Structures (LCS) and estimates of areas prone to patch formation using the concept of Finite-Time Compressibility (FTC, a generalisation of the notion of time series of divergence). The LCSs are evaluated using the Finite Time Lyapunov Exponent (FTLE) method. The test area is the Gulf of Finland (GoF) in the Baltic Sea. A basin-wide spatial average of backward FTLE is calculated for the GoF for the first time. This measure of the mixing strength displays a clear seasonal pattern. The evaluated backward FTLE features are linked with potential patch formation regions with high FTC levels. It is shown that areas hosting frequent upwelling or downwelling have consistently stronger than average mixing intensity. The combination of both methods, FTC and LCS, has the potential of being a powerful tool to identify the formation of patches of pollution at the sea surface.

physics.ao-ph↗

arXiv2020

Giant deformations and soft-inflation in LCE balloons

We propose that ballooning can be controlled, enriched and amplified by using rubbery networks of aligned molecular rods known as liquid crystal elastomers (LCEs). Firstly, LCEs are promising artificial muscles, showing large spontaneous deformations in response to heat and light. In LCE balloons, spontaneous deformations can trigger classic ballooning, either as phase-separation (at constant volume) or a volume jump (at constant pressure), resulting in greatly magnified actuation strains. Secondly, even at constant temperature, LCEs have unusual mechanics augmented by soft-modes of deformation in which the nematic director rotates within the elastomer. These soft modes enrich the mechanics of LCE balloons, which can also "balloon" between rotated and unrotated states, either during the classic instability, or as a separate pre-cursor, leading to successive instabilities during inflation.

cond-mat.soft↗

arXiv2020

Ballooning, bulging and necking: an exact solution for longitudinal phase separation in elastic systems near a critical point

Prominent examples of longitudinal phase separation in elastic systems include elastic necking, the propagation of a bulge in a cylindrical party balloon and the beading of a gel fiber subject to surface tension. Here we demonstrate that, if the parameters of such a system are tuned near a critical point (where the difference between the two phases vanishes) then the behaviour of all systems is given by the minimization of a simple and universal elastic energy familiar from Ginzburg-Landau theory in an external field. We minimize this energy analytically, which yields not only the well known interfacial tanh solution, but also the complete set of stable and unstable solutions in both finite and infinite length systems, unveiling the elastic system's full shape evolution and hysteresis. Correspondingly, we also find analytic results for the the delay of onset, changes in criticality and ultimate suppression of instability with diminishing system length, demonstrating that our simple near-critical theory captures much of the complexity and choreography of far-from-critical systems. Finally, we find critical points for the three prominent examples of phase separation given above, and demonstrate how each system then follows the universal set of solutions.

cond-mat.soft↗