Evaluating the efficiency of surface treatments is a problem of paramount importance for the cork stopper industry. Generically, these treatments create coatings that aim to enhance the impermeability and lubrification of cork stoppers. Yet, current methods of surface analysis are typically time-consuming, destructive, have poor representativity or rely on indirect approaches. In this work, the use of a laser-induced breakdown spectroscopy (LIBS) imaging solution is explored for evaluating the presence of coating along the cylindrical surface and in depth. To test it, several cork stoppers with different shaped areas of untreated surface were analyzed by LIBS, making a rectangular grid of spots with multiple shots per spot, to try to identify the correspondent shape. Results show that this technique can detect the untreated area along with other features, such as leakage and holes, allowing for a high success rate of identification and for its performance at different depths, paving the way for future industry-grade quality control solutions with more complex surface analysis.

Laser-induced breakdown spectroscopy (LIBS) imaging has now a well-established position in the subject of spectral imaging, leveraging multi-element detection capabilities and fast acquisition rates to support applications both at academic and technological levels. In current applications, the standard processing pipeline to explore LIBS imaging data sets revolves around identifying an element that is suspected to exist within the sample and generating maps based on its characteristic emission lines. Such an approach requires some previous expert knowledge both on the technique and on the sample side, which hinders a wider and more transparent accessibility of the LIBS imaging technique by non-specialists. To address this issue, techniques based on visual analysis or peak finding algorithms are applied on the average or maximum spectrum, and may be employed for automatically identifying relevant spectral regions. Yet, maps containing relevant information may often be discarded due to low signal-to-noise ratios or interference with other elements. In this context, this work presents an agnostic processing pipeline based on a spatial information ratio metric that is computed in the Fourier space for each wavelength and that allows for the identification of relevant spectral ranges in LIBS. The results suggest a more robust and streamlined approach to feature extraction in LIBS imaging compared with traditional inspection of the spectra, which can introduce novel opportunities not only for spectral data analysis but also in the field of data compression.

High-bandwidth and multi-point acoustic and vibration sensing is a critical asset for real-time condition monitoring, maintenance, and surveillance applications. In the case of large scales and harsh environments, optical fiber distributed sensing has emerged as a compelling alternative to electronic transducers, featuring lower installation and maintenance costs, along with compact footprints and enhanced robustness. Yet, current distributed fiber-optic sensing solutions are typically costly and face a resolution-bandwidth tradeoff. In this work, we present an alternative fiber-optic vibration sensing strategy that harnesses a multimodal architecture combining speckle and polarization interrogation. The experimental results demonstrate the concept by achieving speckle-based signal source localization with centimeter-range spatial resolution, while obtaining a high-fidelity waveform reconstruction over at least 100 Hz-40 kHz bandwidth via the polarimetric sensing part. Overall, the work establishes a general and promising blueprint to harness multimodality in fiber sensing and break single-modality constraints.

Photonic extreme learning machines use random optical propagation, detection nonlinearity, and a trained linear readout for energy-efficient and scalable optical computing. However, conventional intensity readout with CCD or CMOS cameras constrains the dimensionality of the hidden representation space. Here, we experimentally replace intensity detection with an event-based camera, whose thresholded log-intensity response provides alternative pixel-wise hidden representations: first-event time, binary activation, and event count. In a nonlinear two-spiral classification task, we obtain accuracies of 93±3% with strong intrinsic generalization, and comparable ridge and pseudo-inverse performance indicating sample-limited effective dimensionality. Regression results reveal sensitivity to systematic representation drift, identifying stability requirements for future event-based PELMs. These results establish event-based detection as an alternative nonlinear optical-to-digital measurement that provides informative representations for the considered tasks while clarifying current limitations.

High-bandwidth and multi-point acoustic and vibration sensing is a critical asset for real-time condition monitoring, maintenance, and surveillance applications. In the case of large scales and harsh environments, optical fiber distributed sensing has emerged as a compelling alternative to electronic transducers, featuring lower installation and maintenance costs, along with compact footprints and enhanced robustness. Yet, current distributed fiber-optic sensing solutions are typically costly and face a resolution-bandwidth tradeoff. This work addresses the current challenges of distributed fiber optic sensors by presenting a dual-modality architecture that combines speckle dynamic analysis and polarization interrogation. The implementation operates under a single-active-source assumption across four discrete points distributed on a 4-m length fiber and uses camera-based speckle analysis to localize the active position, while the polarization interrogation reconstructs the corresponding waveform from 100 Hz to 40 kHz with a 200 kHz acquisition rate, with 35 dB SNR. Overall, the work establishes a general and promising blueprint to harness multimodality in fiber sensing and break single-modality constraints.