The Karhunen-Loève transform (KLT) stands as a well-established discrete transform, demonstrating optimal characteristics in data decorrelation and dimensionality reduction. Its ability to condense energy compres...
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Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain. The training of convent...
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Light scattering is one of the most established wave phenomena in optics, lying at the heart of light-matter interactions and of crucial importance for nanophotonic applications. Passivity, causality, and energy conse...
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Light scattering is one of the most established wave phenomena in optics, lying at the heart of light-matter interactions and of crucial importance for nanophotonic applications. Passivity, causality, and energy conservation imply strict bounds on the degree of control over scattering from small particles, with implications on the performance of many optical devices. Here, we demonstrate that these bounds can be surpassed by considering excitations at complex frequencies, yielding extreme scattering responses as tailored nanoparticles reach a quasi-steady-state regime. These mechanisms can be used to engineer light scattering of nanostructures beyond conventional limits for noninvasive sensing, imaging, and nanoscale light manipulation.
We observe long-range dipole-dipole interactions in a plasmonic lattice. Fluorescence lifetime measurements show density-dependent non-exponential decay dynamics over 800nm mean nearest-neighbour separation distances ...
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In this work, we analyzed numerically a multiscale nanosystem based on sMIM on TBG. Spontaneous formation of a water-meniscus by the approximation between the tip-sample concentrates the microwave fields, reaching res...
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ISBN:
(数字)9781957171050
ISBN:
(纸本)9781665466660
In this work, we analyzed numerically a multiscale nanosystem based on sMIM on TBG. Spontaneous formation of a water-meniscus by the approximation between the tip-sample concentrates the microwave fields, reaching resolutions of up to 1nm.
Light-matter interaction in quantum materials presents opportunities for discovery. We observe a low-intensity light-induced phase transition in 1T-TaS 2 , a quasi-2D material supporting charge-density-waves (CDW). We...
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ISBN:
(纸本)9781943580910
Light-matter interaction in quantum materials presents opportunities for discovery. We observe a low-intensity light-induced phase transition in 1T-TaS 2 , a quasi-2D material supporting charge-density-waves (CDW). We find that the CDW domains stack differently upon illumination.
Device degradation due to hot carrier injection (HCI) in multi-fin 20 nm and 10 nm N- and P-type FinFET devices are thoroughly analyzed. To further understand the HCI reliability of the four FinFET devices, the device...
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Reconfigurable intelligent surfaces (RISs) have been proposed as a key enabler to improve the coverage of the signals and mitigate the frequent blockages in millimeter wave (mmWave) multiple-input multiple-output (MIM...
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Synthetic aperture sonar (SAS) image resolution is constrained by waveform bandwidth and array geometry. Specifically, the waveform bandwidth determines a point spread function (PSF) that blurs the locations of point ...
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ISBN:
(纸本)9781665427883
Synthetic aperture sonar (SAS) image resolution is constrained by waveform bandwidth and array geometry. Specifically, the waveform bandwidth determines a point spread function (PSF) that blurs the locations of point scatterers in the scene. In theory, deconvolving the reconstructed SAS image with the scene PSF restores the original distribution of scatterers and yields sharper reconstructions. However, deconvolution is an ill-posed operation that is highly sensitive to noise. In this work, we leverage implicit neural representations (INRs), shown to be strong priors for the natural image space, to deconvolve SAS images. Importantly, our method does not require training data, as we perform our deconvolution through an analysis-by-synthesis optimization in a self-supervised fashion. We validate our method on simulated SAS data created with a point scattering model and real data captured with an in-air circular SAS. This work is an important first step towards applying neural networks for SAS image deconvolution.
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