Metallo-dielectric metasurfaces for thermal emission with controlled spectral bandwidth and angular aperture

Blanchard, C.; Wojszvzyk, Léo; Jamois, Cécile; Leclercq, Jean‐Louis; Chevalier, Céline; Ferrier, Lydie; Viktorovitch, Pierre; Moldovan-Doyen, I.; Marquier, François; Letartre, Xavier

doi:10.1364/ome.443111

Cited by 16 publications

(6 citation statements)

References 40 publications

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“…Spectral Bandwidth: The spectral bandwidth measures the spectral content’s width and the audio signal’s frequency spread. While a low value indicates a lower concentration of frequencies, a high value denotes a wide distribution of frequencies 37 .…”

Section: Proposed Approachmentioning

confidence: 99%

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Artificial intelligence framework for heart disease classification from audio signals

Abbas,

Ojo,

Al Hejaili

et al. 2024

Sci Rep

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As cardiovascular disorders are prevalent, there is a growing demand for reliable and precise diagnostic methods within this domain. Audio signal-based heart disease detection is a promising area of research that leverages sound signals generated by the heart to identify and diagnose cardiovascular disorders. Machine learning (ML) and deep learning (DL) techniques are pivotal in classifying and identifying heart disease from audio signals. This study investigates ML and DL techniques to detect heart disease by analyzing noisy sound signals. This study employed two subsets of datasets from the PASCAL CHALLENGE having real heart audios. The research process and visually depict signals using spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs). We employ data augmentation to improve the model’s performance by introducing synthetic noise to the heart sound signals. In addition, a feature ensembler is developed to integrate various audio feature extraction techniques. Several machine learning and deep learning classifiers are utilized for heart disease detection. Among the numerous models studied and previous study findings, the multilayer perceptron model performed best, with an accuracy rate of 95.65%. This study demonstrates the potential of this methodology in accurately detecting heart disease from sound signals. These findings present promising opportunities for enhancing medical diagnosis and patient care.

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Section: Proposed Approachmentioning

confidence: 99%

“…Chroma STFT: The harmonic content of the audio is represented by the chroma Short-Time Fourier Transform (STFT). The study of tonal qualities and musical notes is made possible by the extraction of information about the pitch class of audio frames 37 .…”

Section: Proposed Approachmentioning

confidence: 99%

Artificial intelligence framework for heart disease classification from audio signals

Abbas,

Ojo,

Al Hejaili

et al. 2024

Sci Rep

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“…Because mid-IR sources are either inefficient thermal sources or expensive quantum cascade lasers (QCLs), achieving highly directional and narrow bandwidth thermal emission would be beneficial for many sensing applications . Metasurfaces have been extensively employed to tailor far-field thermal emission, achieving narrowband, ,− polarized, ,, and directional emission. ,− Leveraging more complex types of structures is predicted to enable higher control over the thermal emission profile, allowing the realization of thermal self-focusing and holography and higher emission efficiencies . Engineering of the radiative heat transfer properties of a material can be achieved with many different nanophotonic designs.…”

Section: Photon–phonon Energy Conversionmentioning

confidence: 99%

Optical Metasurfaces for Energy Conversion

et al. 2022

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Nanostructured surfaces with designed optical functionalities, such as metasurfaces, allow efficient harvesting of light at the nanoscale, enhancing light–matter interactions for a wide variety of material combinations. Exploiting light-driven matter excitations in these artificial materials opens up a new dimension in the conversion and management of energy at the nanoscale. In this review, we outline the impact, opportunities, applications, and challenges of optical metasurfaces in converting the energy of incoming photons into frequency-shifted photons, phonons, and energetic charge carriers. A myriad of opportunities await for the utilization of the converted energy. Here we cover the most pertinent aspects from a fundamental nanoscopic viewpoint all the way to applications.

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“…Conventional approaches to the design of narrowband and partially coherent thermal emitters include the use of structured surfaces supporting leaky modes, 7 nanoresonators, [27][28][29] photonic crystals, 30,31 resonant multi-layered structures, [32][33][34][35] metasurfaces, 36,37 transformation optics 38 and angle selective lters, 39 to name a few. These different strategies could be adapted to concentrate the absorption/emission within a subwavelength metallic body.…”

Section: Introductionmentioning

confidence: 99%

Spectrally stable thermal emitters enabled by material-based high-impedance surfaces

2023

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Metallo-dielectric metasurfaces for thermal emission with controlled spectral bandwidth and angular aperture

Cited by 16 publications

References 40 publications

Artificial intelligence framework for heart disease classification from audio signals

Artificial intelligence framework for heart disease classification from audio signals

Optical Metasurfaces for Energy Conversion

Spectrally stable thermal emitters enabled by material-based high-impedance surfaces

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