arXiv subjects

Alessandro Cattaneo

Publications and source records attributed to Alessandro Cattaneo.

5 recordsLinked to original sources

arXiv2026

Achieving speedup in Dark Matter search experiments with a transmon-based NISQ algorithm

Coherent detection of ultralight bosonic dark matter can be achieved by monitoring slow Rabi oscillations in superconducting qubits. We introduce an ancilla-assisted, gate-based protocol that enhances sensitivity to the hidden photon kinetic mixing parameter $\epsilon$ using a single two-qubit gate, bypassing the need to maintain long-lived multi-qubit entangled states and remaining compatible with the limitations of modern quantum hardware. We characterized the increase in sensitivity accounting for decoherence, thermal occupation, errors in readout and reset, indicating up to a ten-fold reduction in the required integration time to reach the same exclusion limit on $\epsilon$ achievable via Rabi-sampling experiments. Under plausible hardware assumptions and three years of data taking, the projected $95\%$ C.L. exclusion limit on the hidden photon mixing parameter reaches $\epsilon\approx 1\times 10^{-14}$ across $2.5$-$6.0$ GHz ($10$-$25$ \textmu eV).

quant-ph↗

arXiv2025

Robust Wrapped Gaussian Process Inference for Noisy Angular Data

Angular data are commonly encountered in settings with a directional or orientational component. Regressing an angular response on real-valued features requires intrinsically capturing the circular or spherical manifold the data lie on, or using an appropriate extrinsic transformation. A popular example of the latter is the technique of distributional wrapping, in which functions are "wrapped" around the unit circle via a modulo-$2{\pi}$ transformation. This approach enables flexible, non-linear models like Gaussian processes (GPs) to properly account for circular structure. While straightforward in concept, the need to infer the latent unwrapped distribution along with its wrapping behavior makes inference difficult in noisy response settings, as misspecification of one can severely hinder estimation of the other. However, applications such as radiowave analysis (Shangguan et al., 2015) and biomedical engineering (Kurz and Hanebeck, 2015) encounter radial data where wrapping occurs in only one direction. We therefore propose a novel wrapped GP (WGP) model formulation that recognizes monotonic wrapping behavior for more accurate inference in these situations. This is achieved by estimating the locations where wrapping occurs and partitioning the input space accordingly. We also specify a more robust Student's t response likelihood, and take advantage of an elliptical slice sampling (ESS) algorithm for rejection-free sampling from the latent GP space. We showcase our model's preferable performance on simulated examples compared to existing WGP methodologies. We then apply our method to the problem of localizing radiofrequency identification (RFID) tags, in which we model the relationship between frequency and phase angle to infer how far away an RFID tag is from an antenna.

stat.AP↗

arXiv2024

Demonstrating the Suitability of Neuromorphic, Event-Based, Dynamic Vision Sensors for In Process Monitoring of Metallic Additive Manufacturing and Welding

We demonstrate the suitability of high dynamic range, high-speed, neuromorphic event-based, dynamic vision sensors for metallic additive manufacturing and welding for in-process monitoring applications. In-process monitoring to enable quality control of mission critical components produced using metallic additive manufacturing is of high interest. However, the extreme light environment and high speed dynamics of metallic melt pools have made this a difficult environment in which to make measurements. Event-based sensing is an alternative measurement paradigm where data is only transmitted/recorded when a measured quantity exceeds a threshold resolution. The result is that event-based sensors consume less power and less memory/bandwidth, and they operate across a wide range of timescales and dynamic ranges. Event-driven driven imagers stand out from conventional imager technology in that they have a very high dynamic range of approximately 120 dB. Conventional 8 bit imagers only have a dynamic range of about 48 dB. This high dynamic range makes them a good candidate for monitoring manufacturing processes that feature high intensity light sources/generation such as metallic additive manufacturing and welding. In addition event based imagers are able to capture data at timescales on the order of 100 {\mu}s, which makes them attractive to capturing fast dynamics in a metallic melt pool. In this work we demonstrate that event-driven imagers have been shown to be able to observe tungsten inert gas (TIG) and laser welding melt pools. The results of this effort suggest that with additional engineering effort, neuromorphic event imagers should be capable of 3D geometry measurements of the melt pool, and anomaly detection/classification/prediction.

eess.IV↗

arXiv2023

Tuneable and biodegradable poly(ester amide)s for disposable facemasks

The widespread use of disposable facemasks during the COVID-19 pandemic has led to environmental widespread concern due to microplastic pollution. Biodegradable disposable facemasks are a first step to reducing the environmental impact of pandemics. In this paper we present high-performance facemask components based on novel poly(ester amide) (PEA) grades synthesized from bio-sourced materials and processed into non-woven facemask components. PEA based polymers present an excellent compromise between mechanical performance and biodegradability. Importantly, the properties of the PEA can easily be tuned by changing the ratio of the ester and amides, or variation of diol and diacid part. We synthesized seven polymers which we optimized for biodegradability and processability. Among them, two grades combined electrospinning process compatibility with full degradation within 35 days, using a normalized biodegradation test. The ultra-thin filters thus developed were evaluated for performance on a custom-made characterization bench. The filters achieved a microparticle capture efficiency and breathability comparable to commercial filters. Another PEA grade was optimized to reach optimal visco-thermal properties that made it compatible with solvent-free melt-spinning process as demonstrated with continuous fibres production. Overall, our environmentally friendly solution paves the way for the fabrication of high-performance fibres with excellent biodegradability for the next generation facemasks.

physics.med-ph↗

arXiv2019

Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural Networks

Through deep learning and computer vision techniques, driving manoeuvres can be predicted accurately a few seconds in advance. Even though adapting a learned model to new drivers and different vehicles is key for robust driver-assistance systems, this problem has received little attention so far. This work proposes to tackle this challenge through domain adaptation, a technique closely related to transfer learning. A proof of concept for the application of a Domain-Adversarial Recurrent Neural Network (DA-RNN) to multi-modal time series driving data is presented, in which domain-invariant features are learned by maximizing the loss of an auxiliary domain classifier. Our implementation is evaluated using a leave-one-driver-out approach on individual drivers from the Brain4Cars dataset, as well as using a new dataset acquired through driving simulations, yielding an average increase in performance of 30% and 114% respectively compared to no adaptation. We also show the importance of fine-tuning sections of the network to optimise the extraction of domain-independent features. The results demonstrate the applicability of the approach to driver-assistance systems as well as training and simulation environments.

cs.LG↗