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Turntable Paper-Based Device to Detect Escherichia coli

Micromachines (Basel), 2021-02, Vol.12 (2), p.194

2021

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Enhanced PML Based on the Long Short Term Memory Network for the FDTD Method

IEEE access, 2020, Vol.8, p.21028-21035

2020

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Embedding the Behavior Macromodel Into TDIE for Transient Field-Circuit Simulations

IEEE transactions on antennas and propagation, 2016-07, Vol.64 (7), p.3233-3238

2016

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Applying Deep Learning Approach to the Far-Field Subwavelength Imaging Based on Near-Field Resonant Metalens at Microwave Frequencies

IEEE access, 2019, Vol.7, p.63801-63808

2019

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Transient Heterogeneous Electromagnetic Simulation With DGTD and Behavioral Macromodel

IEEE transactions on electromagnetic compatibility, 2017-08, Vol.59 (4), p.1152-1160

2017

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Lateral Two-Dimensional Material Heterojunction Photodetectors with Ultrahigh Speed and Detectivity

ACS applied materials & interfaces, 2019-02, Vol.11 (6), p.6384-6388

2019

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Creating an Optimal Microenvironment within Mesoporous Silica MCM-41 for Capture of Tobacco-Specific Nitrosamines in Solution

ACS applied materials & interfaces, 2017-08, Vol.9 (32), p.26805-26817

2017

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New shape-selectivity discovered on graphene-based materials in catching tobacco specific nitrosamines

Journal of hazardous materials, 2018-09, Vol.358, p.234-242

2018

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New activated carbon sorbent with the zeolite-like selectivity to capture tobacco-specific nitrosamines in solution

Chemical engineering journal (Lausanne, Switzerland : 1996), 2018-05, Vol.339, p.170-179

2018

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New sucker-type precise capturer of tobacco specific nitrosamines derived from the SBA-15 in situ modified with polyaniline

Chemical engineering journal (Lausanne, Switzerland : 1996), 2018-12, Vol.354, p.1174-1184

2018

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Improvement on the manipulation of a single nitrogen-vacancy spin and microwave photon at single-quantum level

Communications in theoretical physics, 2021-06, Vol.73 (6), p.65101

2021

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Novel mesoporous composite with zeolite-like selectivity to capture tobacco specific nitrosamine NNK

Chemical engineering journal (Lausanne, Switzerland : 1996), 2018-01, Vol.332, p.331-339

2018

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Machine-Learning-Based PML for the FDTD Method

IEEE antennas and wireless propagation letters, 2019-01, Vol.18 (1), p.192-196

2019

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Deep learning based source reconstruction method using asymmetric encoder–decoder structure and physics-induced loss

Journal of computational and applied mathematics, 2024-03, Vol.438, p.115503, Article 115503

2024

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Implementing the Fast Full-Wave Electromagnetic Forward Solver Using the Deep Convolutional Encoder-Decoder Architecture

IEEE transactions on antennas and propagation, 2023-01, Vol.71 (1), p.1152-1157

2023

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Enhanced Deep Learning Approach Based on the Conditional Generative Adversarial Network for Electromagnetic Inverse Scattering Problems

IEEE transactions on antennas and propagation, 2024-07, Vol.72 (7), p.6133-6138

2024

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Deep Learning Electromagnetic Inversion Solver Based on a Two-Step Framework for High-Contrast and Heterogeneous Scatterers

IEEE transactions on antennas and propagation, 2024-06, Vol.72 (6), p.5337-5342

2024

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Deep-Learning-Based Source Reconstruction Method Using Deep Convolutional Conditional Generative Adversarial Network

IEEE transactions on microwave theory and techniques, 2024-05, Vol.72 (5), p.2949-2960

2024

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Coupling DGTD and behavioral macromodel for transient heterogeneous electromagnetic simulations

2016 IEEE International Symposium on Electromagnetic Compatibility (EMC), 2016, p.212-215

2016

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Two-Step Enhanced Deep Learning Approach for Electromagnetic Inverse Scattering Problems

IEEE antennas and wireless propagation letters, 2019-11, Vol.18 (11), p.2254-2258

2019

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Deep Learning Based Fast Full Wave Electromagnetic Forward Solver Using Physics-Induced Loss

IEEE antennas and wireless propagation letters, 2024-07, p.1-5

2024

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Enhanced Deep Learning Approach Based on the Deep Convolutional Encoder-Decoder Architecture for Electromagnetic Inverse Scattering Problems

IEEE antennas and wireless propagation letters, 2020-07, Vol.19 (7), p.1211-1215

2020

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Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint

IEEE transactions on geoscience and remote sensing, 2021-09, Vol.59 (9), p.7982-7995

2021

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Enhanced Two-Step Deep-Learning Approach for Electromagnetic-Inverse-Scattering Problems: Frequency Extrapolation and Scatterer Reconstruction

IEEE transactions on antennas and propagation, 2023-02, Vol.71 (2), p.1662-1672

2023

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Antenna Array Diagnosis Using a Deep Learning Approach

IEEE transactions on antennas and propagation, 2024-06, Vol.72 (6), p.5396-5401

2024

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Turntable Paper-Based Device to Detect Escherichia coli

Enhanced PML Based on the Long Short Term Memory Network for the FDTD Method

Embedding the Behavior Macromodel Into TDIE for Transient Field-Circuit Simulations

Applying Deep Learning Approach to the Far-Field Subwavelength Imaging Based on Near-Field Resonant Metalens at Microwave Frequencies

Transient Heterogeneous Electromagnetic Simulation With DGTD and Behavioral Macromodel

Lateral Two-Dimensional Material Heterojunction Photodetectors with Ultrahigh Speed and Detectivity

Creating an Optimal Microenvironment within Mesoporous Silica MCM-41 for Capture of Tobacco-Specific Nitrosamines in Solution

New shape-selectivity discovered on graphene-based materials in catching tobacco specific nitrosamines

New activated carbon sorbent with the zeolite-like selectivity to capture tobacco-specific nitrosamines in solution

New sucker-type precise capturer of tobacco specific nitrosamines derived from the SBA-15 in situ modified with polyaniline

Improvement on the manipulation of a single nitrogen-vacancy spin and microwave photon at single-quantum level

Novel mesoporous composite with zeolite-like selectivity to capture tobacco specific nitrosamine NNK

Machine-Learning-Based PML for the FDTD Method

Deep learning based source reconstruction method using asymmetric encoder–decoder structure and physics-induced loss

Implementing the Fast Full-Wave Electromagnetic Forward Solver Using the Deep Convolutional Encoder-Decoder Architecture

Enhanced Deep Learning Approach Based on the Conditional Generative Adversarial Network for Electromagnetic Inverse Scattering Problems

Deep Learning Electromagnetic Inversion Solver Based on a Two-Step Framework for High-Contrast and Heterogeneous Scatterers

Deep-Learning-Based Source Reconstruction Method Using Deep Convolutional Conditional Generative Adversarial Network

Coupling DGTD and behavioral macromodel for transient heterogeneous electromagnetic simulations

Two-Step Enhanced Deep Learning Approach for Electromagnetic Inverse Scattering Problems

Deep Learning Based Fast Full Wave Electromagnetic Forward Solver Using Physics-Induced Loss

Enhanced Deep Learning Approach Based on the Deep Convolutional Encoder-Decoder Architecture for Electromagnetic Inverse Scattering Problems

Joint Inversion of Audio-Magnetotelluric and Seismic Travel Time Data With Deep Learning Constraint

Enhanced Two-Step Deep-Learning Approach for Electromagnetic-Inverse-Scattering Problems: Frequency Extrapolation and Scatterer Reconstruction

Antenna Array Diagnosis Using a Deep Learning Approach

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