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An Accurate Alternative to Hybrid Functionals for Germanium: DFT+α
No full textInternational audienceThe accuracy of bulk-property predictions in density functional theory (DFT) calculations depends on the choice of the exchange-correlation functional. While the Perdew-Burke-Ernzerhof (PBE) functional systematically overestimates lattice parameters and strongly underestimates electronic band gaps, hybrid functionals such as Heyd-Scuseria-Ernzerhof (HSE) offer better overall agreement across a broad range of materials. Using germanium as a critical test case, we challenge the ability of both functionals to capture the semiconductor properties. Although HSE improves PBE's gap error, it fails to reproduce germanium's correct Γ-L indirect and Γ-Γ band gaps simultaneously. Noting that the PBE-underestimated energy separation between the 4p valenceband maximum and 4s conduction-band minimum causes unphysical sp mixing, we propose DFT+α, a semiempirical correction scheme applied selectively to 4s-like orbitals. For germanium, DFT+α restores the proper ordering and orbital character of the band edges and yields accurate lattice constants, bulk modulus, elastic constants, and phonon frequencies at a fraction of hybrid-functional computational cost
Locomotion Mode Transitions: Tackling System- and User-Specific Variability in Lower-Limb Exoskeletons
No full textInternational audienceAccurate detection of locomotion transitions, such as walk to sit, walk to stair ascent, and descent, is crucial to effectively control robotic assistive devices, such as lowerlimb exoskeletons, as each locomotion mode requires specific assistance. Variability in collected sensor data introduced by useror system-specific characteristics makes it challenging to maintain high transition detection accuracy while avoiding latency using non-adaptive classification models. In this study, we identified key factors influencing transition detection performance, including variations in user behavior, and different mechanical designs of the exoskeletons. To boost the transition detection accuracy, we introduced two methods for adapting a finite-state machine classifier to system-and user-specific variability: a Statistics-Based approach and Bayesian Optimization. Our experimental results demonstrate that both methods remarkably improve transition detection accuracy across diverse users, achieving up to an 80% increase in certain scenarios compared to the non-personalized threshold method. These findings emphasize the importance of personalization in adaptive control systems, underscoring the potential for enhanced user experience and effectiveness in assistive devices. By incorporating subjectand system-specific data into the model training process, our approach offers a precise and reliable solution for detecting locomotion transitions, catering to individual user needs, and ultimately improving the performance of assistive devices
Insight into cooling requirements for thermophotovoltaic devices
No full textInternational audiencePerformance of thermophotovoltaic conversion devices depends on the operating temperature of the cell, and thus on how heat generated in the cell is dissipated. The present research examines the cooling requirements that allow the cell to operate at a specified temperature, based on the parameters influencing electrical power generation. A detailed balance approach and a simple thermal model involving an effective heat transfer coefficient are used. Key parameters, such as emitter temperature, view factor, in-band transmission and out-of-band transmission functions, and external radiative efficiency, are systematically varied to evaluate their influence on pairwise efficiency and power density, and on the required effective heat transfer coefficient to ensure that the cell operates at selected temperatures. Although thermophotovoltaic cells are typically presumed to function at close to ambient, our findings indicate that maintaining this operating temperature necessitates a cooling system with a substantially high effective heat transfer coefficient (∼ 10^3 -10^4 Wm -2 K -1 ). The cooling challenge grows when the cell bandgap diminishes, due to the interplay of rising power density and decreasing pairwise efficiency. The cooling requirements increase with the temperature of the emitter and the view factor. Nevertheless, they can be mitigated by reducing both in-band and out-of-band transmission functions. They are underestimated, and the bandgap optimizing pairwise efficiency or power density is inadequately predicted when the cell is assumed to operate in the radiative limit. These insights into cooling requirements imply that they should be considered from the initial stages of thermophotovoltaic device design
Directional light scattering in Mie-resonant Si particles with ultra-thin Au shells
No full textInternational audienceMetamaterial research has sought to create nanostructures with strong directional optical scattering to control light propagation at the nanoscale. Core–shell architectures comprised of both resonant cores and resonant shells are suggested as candidate particles in which the spectral overlap of the electric and magnetic dipoles is controlled to create strong directional scattering. In this study, Au-decorated Si core–shell (Si@Au) particles are presented, studying the role of the architecture (particulate, discontinuous shells vs continuous) and dimensions of the shell. The core–shell particles are synthesized by first creating Si particles, through the thermal disproportionation of hydrogen silsesquioxane (HSQ), which are then decorated with ≈4 nm diameter Au nanoparticles. The resonant behavior of the core–shell particles is characterized using electron energy-loss spectroscopy mapping and optical single-particle scatter spectroscopy. These observations are supported by T-matrix simulations and Mie-theory calculations of the scattering spectra, which show that, compared to Si, Si@Au particles demonstrate a dampened magnetic dipole resonance for smaller Si core diameters (100–130 nm) and an enhanced magnetic dipole resonance for larger Si core sizes (150–200 nm). The study indicates that the previously reported hybridized modes do not exist in particulate Au shells around a Si core and can only exist in continuous plasmonic shells. Thus, it is shown here how important it is to be as precise as possible regarding the nanomaterial architecture used in simulations. No configuration of Si@Au core–shell particles with a particulate shell could be found that strongly enhanced directional scattering, and a continuous shell may do so only modestly. However, the simulations show that the synthesis of thin, continuous Ag shells might represent an alternative route towards achieving good directional scattering properties.</p
TD-CD-MPPI: Temporal-Difference Constraint-Discounted Model Predictive Path Integral Control
No full textInternational audiencePath Integral methods have demonstrated remarkable capabilities for solving non-linear stochastic optimal control problems through sampling-based optimization. However, their computational complexity grows linearly with the prediction horizon, limiting long-term reasoning, while constraints are merely enforced through handcrafted penalties.In this work, we propose a unified and efficient framework for enabling long-horizon reasoning and constraint enforcement within Model Predictive Path Integral (MPPI) control. First, we introduce a practical method to incorporate a terminal value function, learned offline via temporal-difference learning, to approximate the long-term cost-to-go. This allows for significantly shorter roll-outs while enabling infinite-horizon reasoning, thereby improving computational efficiency and motion performance. Second, we propose a discount modulation strategy that adjusts the return of sampled trajectories based on constraint violations. This provides a more interpretable and effective mechanism for enforcing constraints compared to traditional cost shaping. Our formulation retains the flexibility and sampling efficiency of MPPI while supporting structured integration of long-term objectives and constraint handling. We validate our approach on both simulated and real-world robotic locomotion tasks, demonstrating improved performance, constraint-awareness, and generalization under reduced computational budgets
Exact Outlier Cancellation in Discrete-Time Observers via Stubborn Redesign
No full textInternational audienceThis paper addresses the problem of robust state estimation for nonlinear discrete-time systems subject to sporadic high-magnitude measurement outliers. Given a nominal convergent observer, we propose a "stubborn" redesign by saturating the output injection term with a dynamic threshold. In contrast to previous approaches that rely on scalar filters, we introduce a higher order augmentation based on a finite-memory buffer. Our redesign ensures a deadbeat recovery property, where the influence of an outlier on the saturation threshold vanishes exactly after a finite number of steps determined by the system's observability index. This allows for perfect rejection, at the steady state, of sporadic disturbances enjoying a suitable dwell-time property. In the absence of disturbances, we recover the global convergence properties of the nominal observer. The proposed redesign is illustrated through simulation on a single-link flexible-joint manipulator
Dommages par déplacement atomiques induits par les rayons gamma dans des microvolumes de silicium : taux de génération des défauts et signal télégraphique aléatoire.
No full textInternational audienceSingle displacement damage-induced defects created by 60Co gamma-rays are studied in silicon microvolumes using a state-of-the-art CMOS Image Sensor (CIS). The Dark Current Spectroscopy (DCS) technique used in this work enables spatial and temporal tracking of the identified defect generation rates with a single defect sensitivity. The generation rate, energy level, annealing temperature and quantity of expected point defects (including the divacancy V2 and the E-center As-V or P-V) are estimated. An isochronal annealing experiment up to 300°C highlights the creation of new defects at high temperature such as V2OH, VOH and VmOn complexes. This study also demonstrates that 60Co gamma rays can generate bulk defect-induced Dark Current Random Telegraph Signal (DC-RTS) with a very specific temporal and amplitude signature. A divacancy-impurity complex is suspected to be at the origin of this DC-RTS behavior
LNA-Mixer Front-Ends Using Sampling Techniques for 77 GHz Automotive Radar Receivers
No full textInternational audienceThis paper investigates the use of sampling and subsampling techniques for frequency conversion in 77 GHz automotive radar receivers. Three LNA-mixer front-ends implementing these techniques are presented and compared. The first front-end implements direct sampling at 77 GHz using an innovative local oscillator (LO) pulse-shaper. Both other front-ends implement subsampling with a 25.7 GHz LO, significantly relaxing LO generation constraints while achieving better performance than conventional subharmonic mixers.All three front-ends were fabricated using a 28-nm FD-SOI CMOS process. The first design demonstrates a state-of-the-art noise / linearity trade-off while reducing power consumption by a factor of three compared to the best-performing CMOS and BiC-MOS solutions. It achieves an input-referred 1 dB compression point (ICP) of -7.8 dBm, a 8.9 dB noise figure (NF), and a 11.4 dB front-end gain, consuming only 20 mW at 1 V. The subsampling front-ends achieve an NF below 12 dB and ICP above -12 dBm. A complete description of these front-ends is provided, including the LO signal generation circuits and a theoretical analysis of their double-balanced mixer operation, considering the effects of LO transition time on conversion gain.</p
Surrogate-based ensemble data assimilation for reducing uncertainty in large-eddy simulation of microscale pollutant dispersion
No full textInternational audienceBy capturing the physical complexity of the interactions between atmospheric flows and the built environment, Large-Eddy Simulations (LES) could provide detailed information for risk assessment and mitigation in case of environmental emergency. However, to account for LES uncertainties and cover the range of plausible scenarios in order to support decision making, it is necessary to go beyond deterministic simulation capability. This study introduces a novel ensemble-based data assimilation algorithm to correct the LES meteorological forcing and thereby improve LES spatial predictions of pollutant concentration by making use of available measurements. This approach is demonstrated through the MUST field-scale experiment. Results show that the ensemble smoother with multiple data assimilation (ESMDA) algorithm is a good candidate to address parameter interaction effects in the relationship between uncertain meteorological forcing and LES field quantities. This iterative algorithm is computationally feasible when the LES model is replaced with a machine learning-based surrogate model, from which robust ensemble statistics can be extracted. This surrogate-based data assimilation approach can then be used to examine observability in the system. Results show that the estimation outcome is highly sensitive to the design of the observation network, and that this sensitivity may be underestimated in idealized experiments. It is therefore important to use real data assimilation to optimize sensor placement and extract informative data for modeling, thus improving our ability to monitor accidental dispersion events
On L¹ and time-optimal state transitions in piecewise linear models of gene-regulatory networks
No full textInternational audienceIn this paper, we investigate optimal state transfers for a generic class of piecewise-linear models widely used to qualitatively describe gene-regulatory networks. Motivated by the main practical drawbacks of artificially regulating gene expression through chemical inducers, the optimality of the transitions is defined as the convex combination of the total time and the L¹ cost of the control. Solutions are studied through a Hybrid Pontryagin's Maximum Principle approach, which allows to characterize the optimal trajectories and control for the general formulation of the problem. Then, we focus on two practical examples of two-dimensional regulatory networks: the bistable switch, for which the objective is to induce optimal transitions between its two stable steady states, and the damped genetic oscillator, where the goal is to induce sustained oscillatory behaviors. The resulting optimal control strategies can be expressed in state feedback form, involving both bang arcs and inactive control periods, and are shown to slide over certain separatrices of the uncontrolled system that characterize the boundaries of the admissibility set