PET-CGDNN model
The proposed Parameter Estimation and Transformation-based CNN-GRU Deep Neural Network (PET-CGDNN) model consists of a parameter estimator, a parameter transformer, and a hybrid neural network, as depicted in Fig. 1.
Category: 4. Physics
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Tunable reciprocal and nonreciprocal contributions to 1D Coulomb drag
Device operation and wires characterization
A schematic of the vertically-coupled quantum wires devices used in this work is shown in Fig. 1b, with a typical optical image provided (see Supplementary Fig. 1). As the two wires are defined in two…
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Supervised learning of the Jaynes–Cummings Hamiltonian
Wang, X., Zhao, Y. & Pourpanah, F. Recent advances in deep learning. Int. J. Mach. Learn. Cybernet. 11, 747–750 (2020).
Young, T., Hazarika, D., Poria, S. & Cambria, E. Recent trends in deep…
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Quantum-like nonlinear interferometry with frequency-engineered classical light
Super-resolution can be explained via the definition of maximally entangled state, that can be written as25:
$$\begin{aligned} |\psi \rangle = \frac{|N,0\rangle _{ab} + e^{iN\phi }|0,N\rangle _{ab}}{\sqrt{2}}\,. \end{aligned}$$
…
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Observational evidence of a new type of cyclonic eddy of the Gulf Stream
On the formation mechanism of hook-type eddies
These results necessitate a discussion on the mechanism of formation of hook-type cyclonic eddies. As mentioned earlier, it is possible that the hook-type eddies are realizations of the growth of…
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Generation of nonclassical optomagnonic states under the influences of Stokes scattering process and thermal noise
In this section, we want to investigate the nonclassical feature of the particles. Especially, we are interested in finding under what conditions the strong antibunching and blockade effect can be realized in the dynamical behavior of the photons…
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An on-chip phased array for non-classical light
Theory
Consider a quantized electromagnetic field \(\hat{E}\) transmitted over free space to a phased array receiver. The field can be decomposed into positive and negative frequency components, \(\hat{E}={\hat{E}}^{+}+{\hat{E}}^{-}\), where
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Material analysis through X-ray attenuation decomposition in spectral computed tomography
Calculating (\(\mu _{PE},\mu _{CS}\))
In 1976 Alvarez and Macovski14 described that for energies relevant for medical imaging, the x-ray attenuation \(\mu\) for any material can be approximated as
$$\begin{aligned} \mu (Z,E) = a_{PE}(Z) \times…
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Elastic constants identification for laminated composites using laser doppler vibrometry and an inversion method based on legendre orthogonal polynomial expansion
Inversion framework for determining elastic constants
To determine the elastic constants of laminated composites, this study presents an inversion framework. First, a time–space wavefield of omnidirectional guided waves is acquired using laser…
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Optical fiber sensor based on Lossy-mode resonance for tamoxifen detection and sensing applications
FTIR spectrum analysis
Fourier-transform infrared (FTIR) spectroscopy was employed to analyze the chemical composition and functional groups present in cerium dioxide and non-imprinted polymer nanoparticles32,33. As illustrated in Fig. 2a, the…
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