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   Articles with "efficient deep" as a keyword



An Effective Strategy to Construct Highly Efficient Deep‐Blue Organic Light‐Emitting Field‐Effect Transistors

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recommendations! Published in 2017 at "Advanced Materials Interfaces"

DOI: 10.1002/admi.201700453

Abstract: The fabrication of highly efficient deep-blue organic light-emitting field-effect transistors (OLEFETs) remains a challenge due to the large energy bandgap of deep-blue emitters. In this work, an effective strategy is developed by combining an ambipolar… read more here.

Keywords: efficient deep; blue; blue organic; deep blue ... See more keywords

Phenazasiline/Spiroacridine Donor Combined with Methyl-Substituted Linkers for Efficient Deep Blue Thermally Activated Delayed Fluorescence Emitters.

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recommendations! Published in 2019 at "ACS applied materials & interfaces"

DOI: 10.1021/acsami.8b20009

Abstract: Phenazasiline, a sp3 hybridized silicon-bridged diphenylamine, is a promising donor moiety for deep-blue TADF emitters because of its deep highest occupied molecular orbital, high triplet level of 3.1 eV, and orthogonal connection with acceptor moieties.… read more here.

Keywords: deep blue; efficient deep; tadf emitters; methyl substituted ... See more keywords

Highly Efficient Deep-Red Organic Light-Emitting Devices Based on Asymmetric Iridium(III) Complexes with the Thianthrene 5,5,10,10-Tetraoxide Moiety.

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recommendations! Published in 2019 at "ACS applied materials & interfaces"

DOI: 10.1021/acsami.9b06749

Abstract: Highly efficient deep-red organic light-emitting devices (OLEDs) are indispensable for developing high performance red-green-blue (RGB) displays and white OLEDs (WOLEDs). However, the shortage of deep-red emitters with high photoluminescence quantum yields (PLQYs) and balanced charge… read more here.

Keywords: iii; thianthrene tetraoxide; efficient deep; iii complexes ... See more keywords

Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization

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recommendations! Published in 2019 at "Scientific Reports"

DOI: 10.1038/s41598-019-42557-4

Abstract: Automated diagnosis of tuberculosis (TB) from chest X-Rays (CXR) has been tackled with either hand-crafted algorithms or machine learning approaches such as support vector machines (SVMs) and convolutional neural networks (CNNs). Most deep neural network… read more here.

Keywords: network; efficient deep; visualization; tuberculosis ... See more keywords

An efficient deep learning-based morphology aware hierarchical mixture of features for tuberculosis screening using segmentation of chest X-ray images

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recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-19179-0

Abstract: Tuberculosis (TB) is a chronic lung disorder caused by bacterial infection and is a major cause of death. Lung cancer also has a significant impact, and existing solutions concentrate on initial screening, which mainly results… read more here.

Keywords: deep learning; methodology; lung; segmentation ... See more keywords

Iridium(iii) phosphors with rigid fused-heterocyclic chelating architectures for efficient deep-red/near-infrared emissions in polymer light-emitting diodes

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recommendations! Published in 2019 at "Journal of Materials Chemistry C"

DOI: 10.1039/c9tc03645h

Abstract: Highly efficient deep-red/near-infrared emissions with maximum EQEs of 7.04% and 4.14%, respectively, are realized for Ir(iii) complexes by designing rigid fused-heterocyclic ligands. read more here.

Keywords: efficient deep; near infrared; red near; deep red ... See more keywords

An efficient deep learning framework for mixed-type wafer map defect pattern recognition

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recommendations! Published in 2024 at "AIP Advances"

DOI: 10.1063/5.0190985

Abstract: Defect detection on wafers holds immense significance in producing micro- and nano-semiconductors. As manufacturing processes grow in complexity, wafer maps may display a mixture of defect types, necessitating the utilization of more intricate deep learning… read more here.

Keywords: deep learning; framework; defect; type ... See more keywords

Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning

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recommendations! Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3196780

Abstract: Deep neural networks (DNNs) can achieve high accuracy when there is abundant training data that has the same distribution as the test data. In practical applications, data deficiency is often a concern. For classification tasks,… read more here.

Keywords: supervised learning; robust data; data efficient; deep supervised ... See more keywords
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OmniDRL: An Energy-Efficient Deep Reinforcement Learning Processor With Dual-Mode Weight Compression and Sparse Weight Transposer

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recommendations! Published in 2022 at "IEEE Journal of Solid-State Circuits"

DOI: 10.1109/jssc.2021.3138520

Abstract: In this article, we present an energy-efficient deep reinforcement learning (DRL) processor, OmniDRL, for DRL training on edge devices. Recently, the need for DRL training is growing due to the DRL’s distinct characteristics that can… read more here.

Keywords: energy; transposer; weight; deep reinforcement ... See more keywords

An efficient deep learning-based troposphere ZTD dataset generation method for massive GNSS CORS stations

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recommendations! Published in 2023 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2023.3276874

Abstract: Nowadays, a large number of Global Navigation Satellite System (GNSS) continuously operating reference stations (CORS) have been established around the world, which have already been and will continue to provide massive troposphere zenith total delay… read more here.

Keywords: series; ztd; ztd dataset; deep learning ... See more keywords

Efficient Deep-Learning-Driven Sparse-Target Imaging Method for Array Borehole Radar in Nonuniform Medium

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recommendations! Published in 2024 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2023.3346464

Abstract: In this study, an efficient deep-learning-driven sparse-target imaging (DLSTI) method was developed for array borehole radar to improve the accuracy of target localization in a subsurface nonuniform media. First, by making use of the linear… read more here.

Keywords: deep learning; sparse target; radar; learning driven ... See more keywords