Stochastic homogenization for functionals with anisotropic rescaling and non-coercive Hamilton-Jacobi equations

We study the stochastic homogenization for a Cauchy problem for a first-order Hamilton-Jacobi equation whose operator is not coercive w.r.t. the gradient variable. We look at Hamiltonians like $H(x,\sigma(x)p,\omega)$ where $\sigma(x)$ is a matrix associated to a Carnot group. The rescaling considered is consistent with the underlying Carnot group structure, thus anisotropic. We will prove that under suitable assumptions for the Hamiltonian, the solutions of the $\varepsilon$-problem converge to a deterministic function which can be characterized as the unique (viscosity) solution of a suitable deterministic Hamilton-Jacobi problem

Enabling Spatial Multiplexing in Guided Waves-based Communication: the case of Quadrature Amplitude Modulation realized via Discrete Frequency Steerable Acoustic Transducers

Guided Waves (GWs) communication using conventional transducers, e.g., PZT, encounters quite a few problems, such as complex hardware systems and waves multipath interference. To overcome such drawbacks, Frequency Steerable Acoustic Transducers (FSATs) which benefit from inherent directional capabilities can be fruitfully adopted to implement a spatial multiplexing strategy. The FSATs work on the frequency-dependent spatial filtering effect to generate/receive waves, resulting in a direct relationship between the direction of propagation and the frequency content of the transmitted/received signals. Thanks to this unique frequency-steering capability, FSATs are best suited to implement frequency-driven modulation protocols, such as the ones typically exploited for GWs-based data communication. Among these, the Quadrature Amplitude Modulation (QAM) scheme is advantageous in terms of noise immunity. Thus, the objective of this work is to combine QAM with the built-in spatial multiplexing capabilities of FSATs to realize, in hardware, frequency directivity, like the solutions that are currently being investigated in 5G communications

Tiny Deep Learning Architectures Enabling Sensor-Near Acoustic Data Processing and Defect Localization

The timely diagnosis of defects at their incipient stage of formation is crucial to extending the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damage, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of acoustic emission (AE)-based inspection techniques through the computation of the time of arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor signal-to-noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative deep learning methods are proposed for ToA retrieval in processing AE signals, namely a dilated convolutional neural network (DilCNN) and a capsule neural network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations

A Combination of Chirp Spread Spectrum and Frequency Hopping for Guided Waves-based Digital Data Communication with Frequency Steerable Acoustic Transducers

To facilitate Guided Waves (GWs) communication in terms of hardware simplification and cost reductions, shaped transducers with inherent directional properties can be used. A promising example of such devices is provided by Frequency Steerable Acoustic Transducers (FSATs), where the propagation direction of waves is controlled by the frequency content of the transmitted/acquired signals, thanks to the spatial filtering effect. These peculiar characteristics make the FSAT devices particularly suited for implementation of frequency-based modulation protocols, in which the signal content assigned to each user is uniquely encoded by a corresponding carrier tone. In this work, the special directivity of FSATs is paired with a novel encoding strategy, which is based on a combination of Chirp Spread Spectrum (CSS) and Frequency Hopping (FH) multiplexing, similar to the LoRaWan solution adopted in radio-frequency environments. The devised strategy is aimed at suppressing the inherent destructive interference due to GWs dispersion and multi-path fading

Vibration Monitoring in the Compressed Domain with Energy-Efficient Sensor Networks

Structural Health Monitoring (SHM) is crucial for the development of safe infrastructures. Onboard vibration diagnostics implemented by means of smart embedded sensors is a suitable approach to achieve accurate prediction supported by low-cost systems. Networks of sensors can be installed in isolated infrastructures allowing periodic monitoring even in the absence of stable power sources and connections. To fulfill this goal, the present paper proposes an effective solution based on intelligent extreme edge nodes that can sense and compress vibration data onboard, and extract from it a reduced set of statistical descriptors that serve as input features for a machine learning classifier, hosted by a central aggregating unit. Accordingly, only a small batch of meaningful scalars needs to be outsourced in place of long time series, hence paving the way to a considerable decrement in terms of transmission time and energy expenditure. The proposed approach has been validated using a real-world SHM dataset for the task of damage identification from vibration signals. Results demonstrate that the proposed sensing scheme combining data compression and feature estimation at the sensor level can attain classification scores always above 94%, with a sensor life cycle extension up to 350x and 1510x if compared with compression-only and processing-free implementations, respectively

Directional Multi-Frequency Guided Waves Communications Using Discrete Frequency-Steerable Acoustic Transducers

: A novel directional transducer based on Guided Waves (GW) is introduced in this paper, designed for use in structural health monitoring (SHM) and acoustic data communication applications, i.e., systems in which the elastic medium serves as a transmission channel and information is conveyed through the medium via elastic waves. Such systems can overcome difficulties associated with traditional communication methods like wire-based or radio frequency (RF), which can be complex and have limitations in harsh environments or hard-to-reach places. However, the development of these techniques is hampered by GW dispersive and multi-modal propagation and by multi-path interference. The shortcomings can be effectively addressed by employing Frequency Steerable Acoustic Transducers (FSATs), which leverage their inherent directional capabilities. This can be achieved through the exploitation of a frequency-dependent spatial filtering effect, yielding to a direct correlation between the frequency content of the transmitted or received signals and the direction of propagation. The proposed transducer is designed to actuate or sense the A0 Lamb wave propagating in three orientations using varying frequencies, and has three channels with distinct frequencies for each direction, ranging from 50 kHz to 450 kHz. The transducer performance was verified through Finite Element (FE) simulations, accompanied by experimental testing using a Scanning Laser Doppler Vibrometer (SLDV). The unique frequency-steering capability of FSATs is combined with the On-Off Keying (OOK) modulation scheme to achieve frequency directivity in hardware, similar to ongoing research in 5G communications. The MIMO capabilities of the transducer were finally tested over a thin aluminum plate, showing excellent agreement with the FE simulation results

Cluster-based Vibration Analysis of Structures with GSP

This article describes a divide-and-conquer strategy suited for vibration monitoring applications. Based on a low-cost embedded network of microelectromechanical accelerometers, the proposed architecture strives to reduce both power consumption and computational resources. Moreover, it eases the sensor deployment on large structures by exploiting a novel clustering scheme, which consists of unconventional and nonoverlapped sensing configurations. Signal processing techniques for inter- and intracluster data assembly are introduced to allow for a fullscale assessment of the structural integrity. More specifically, the capability of graph signal processing is adopted for the first time in vibration-based monitoring scenarios to capture the spatial relationship between acceleration data. The experimental validation, conducted on a steel beam perturbed with additive mass, reveals high accuracy in damage detection tasks. Deviations in spectral content and mode shape envelopes are correctly revealed regardless of environmental factors and operational uncertainties. Furthermore, an additional key advantage of the implemented architecture relies on its compliance with blind modal investigations, an approach that favors the implementation of autonomous smart monitoring systems

Low-Thrust Reconfiguration Strategy for Flexible Satellite Constellations

Flexible and responsive space systems are needed to satisfy changing mission requirements and react to unforeseen challenges. Reconfigurable constellations are a promising approach to overcome limitations of the current design philosophy, offering operators the ability to actively adapt the constellation configuration to future needs, promptly modifying their pattern to focus the available resources towards dynamic objectives. This work introduces a low-thrust reconfiguration strategy to optimize these constellations through a multiobjective Genetic Algorithm able to trade-off between observation performance over a desired target area and maneuvering cost to reach the new pattern. The presented approach is validated in a LEO scenario, considering the reconfiguration from a global coverage to a regional one. The obtained results are compared with the ones available in literature to show the suitability of the proposed solution