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Highly efficient near-infrared thermally activated delayed fluorescence organic light-emitting diodes with emission beyond 800 nm

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Journal of Materials Chemistry C 2023年第21期11卷 6981-6988页

作者： Liang, Jing-Xing Tang, Yukun Wang, Xiaofei Zhang, Kai Shih, Yu-Wei Chen, Chia-Hsun Chiu, Tien-Lung Li, Pei Jin Lee, Jiun-Haw Wang, Chuan-Kui Wu, Chung-Chih Fan, Jian Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices Soochow University Jiangsu Suzhou215123 China Department of Electrical Engineering Graduate Institute of Electronics Engineering and Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei10617 Taiwan Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan250014 China Department of Chemistry National Taiwan University Taipei10617 Taiwan Department of Electrical Engineering Yuan Ze University Taoyuan32003 Taiwan Graduate Institute of Photonics and Optoelectronics and Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan State Key Laboratory of Structural Chemistry Fujian Institute of Research on Structure of Matter Chinese Academy of Sciences Fujian Fuzhou35002 China

Near-infrared (NIR) emitters are employed in a wide range of applications such as bio-sensors, optical communication devices, and organic light-emitting diodes (OLEDs). The development of NIR thermally activated delayed-fluorescence (TADF) materials lags significantly behind that of platinum complexes and quantum dots. To achieve cost reduction and environmental sustainability, TADF OLEDs have attracted extensive attention in both the academic community and industry in recent years. Herein, a conjugated planar acceptor was applied in a TADF emitter (TCN-TPA) to enhance the intermolecular through-space electronic coupling and redshift the emission spectra at high doping levels. In addition, multiple sub-acceptor units were integrated into a rigid acceptor to narrow the band gap. Importantly, the overall robust configuration of the TCN-TPA molecule led to high photoluminescence quantum yield (PLQY). Consequently, TCN-TPA demonstrated a record-high external quantum efficiency (EQE) of 2.4% at 802 nm and 1.1% at 841 nm among TADF OLEDs in this spectral range. © 2023 The Royal Society of Chemistry

关键词： Energy gap

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MGTN: Multi-scale Graph Transformer Network for 3D Point Cloud Semantic Segmentation

MGTN: Multi-scale Graph Transformer Network for 3D Point Clo...

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IEEE Visual communications and image processing (VCIP)

作者： Da Ai Siyu Qin Zihe Nie Hui Yuan Ying Liu Xi’an Key Laboratory of Image Processing Technology and Applications for Public Security Xi’an University of Posts and Telecommunications Xi’an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The structural similarity of point clouds presents challenges in accurately recognizing and segmenting semantic information at the demarcation points of complex scenes or objects. In this study, we propose a multi-scale graph transformer network (MGTN) for 3D point cloud semantic segmentation. First, a multi-scale graph convolution (MSG-Conv) is devised to address the limitations faced by existing methods when extracting local and global features of point cloud data with varying densities simultaneously. Subsequently, we employ a graph-transformer (G-T) module to enhance edge details and spatial position information in the point cloud, thereby improving recognition accuracy for small objects and confusing elements such as columns and beams. Extensive testing on ShapeNet parts and S3DIS datasets was conducted to demonstrate the effectiveness of MGTN. Compared to the baseline network DGCNN, our proposed MGTN achieves substantial performance improvements, as evidenced by notable increases in mIoU of 1.5% and 18.5% on the ShapeNet parts and S3DIS datasets respectively. Additionally, MGTN outperforms the recent CFSA- Net by 2.3% and 3.4% on OA and mIoU respectively.

关键词： Point cloud compression Three-dimensional displays Visual communication Convolution Semantic segmentation Semantics Transformers Feature extraction Data mining Testing

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NEAT-Net: An Unsupervised Cross-View Prior Inpainting Network for CBCT Metal Artifact Reduction

NEAT-Net: An Unsupervised Cross-View Prior Inpainting Networ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yang Yang Zhan Tong Xinyun Zhong Yijie Shi Yan Xi Wenxue Yu Zhan Wu Yang Chen Laboratory of Image Science and Technology the School of Computer Science and Engineering Southeast University Nanjing China First-Imaging Tech Shanghai China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Intraoperative Cone-Beam Computed Tomography (CBCT) facilitates intraoperative navigation for Minimally Invasive Spine Surgery (MISS). However, high-attenuation metal implants used in MISS often cause metal artifacts in the reconstructed CBCT images. Current algorithms do not consider the cross-view information in the projection-domain for metal artifact reduction (MAR). Inaccurate projection-domain inpainting results in CBCT MAR lead to tissue blurring and secondary artifacts, significantly compromising the accuracy of CBCT-guided MISS and increasing surgical risks. To address the above challenge, in this paper, we propose a novel unsupervised cross-view prior inpainting network for CBCT Metal Artifact Reduction named NEAT-Net. Firstly, a cross-view prior multi-scale inpainting module is constructed to learn the inter-view complementary information. Secondly, a hybrid feature attention module is proposed to adaptively fuse cross-view features. In addition, an unsupervised training approach is proposed to directly learn from metal-affected data. Extensive experiments are conducted to verify the effectiveness of our algorithm on a real clinical dataset.

关键词： Training Minimally invasive surgery Accuracy Navigation Fuses Computed tomography Metals Implants Engineering in medicine and biology image reconstruction

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Privacy and Security in Ubiquitous Integrated Sensing and communication: Threats, Challenges and Future Directions

arXiv

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arXiv 2023年

作者： Qu, Kaiqian Ye, Jia Li, Xuran Guo, Shuaishuai The School of Control Science and Engineering Shandong University China Shandong Key Laboratory of Wireless Communication Technologies Shandong University China The School of Electrical Engineering Chongqing University Chongqing400044 China Shandong Key Laboratory of Medical Physics and Image Processing School of Physics and Electronics Shandong Normal University China

Integrated sensing and communication (ISAC) technology is at the forefront of next-generation communication, enhancing applications from intelligent transportation to unmanned aerial vehicle surveillance and healthcare through ubiquitous sensing capability. However, the advent of ISAC brings with it a dual-edged challenge. While it opens up new avenues for application, it also raises significant concerns regarding the privacy of sensitive data and the security of systems in an inherently open environment. To navigate these challenges and unlock ISAC’s full potential, this article starts with an analysis of the underlying factors that contribute to privacy and security vulnerabilities. It also identifies the technical obstacles that stand in the way of achieving a secure and private ISAC ecosystem within the confines of conventional network frameworks. As a solution, we introduce a security and privacy-preserving network (SPPN), designed to mitigate these potential threats and tackle the technical challenges through a secure framework for information handling and collaborative efforts among trusted parties, ISAC operators, and users. Central to SPPN’s security strategies is an artificial intelligence based system, equipped to detect unauthorized access and coordinate defense mechanisms, including deploying friendly jammers to thwart malicious devices and utilizing reconfigurable intelligent surfaces (RISs) to bolster the network’s defense mechanisms against potential attacks. Through detailed analysis and case studies, this paper demonstrates the effectiveness of the SPPN framework, which not only strengthens the network’s defenses but also significantly improves its sensing and communication capabilities. Copyright © 2023, The Authors. All rights reserved.

关键词： Sensitive data

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Low-light color image enhancement based on NSST

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The Journal of China Universities of Posts and Telecommunications 2019年第5期26卷 41-48页

作者： Wu Xiaochu Tang Guijin Liu Xiaohua Cui Ziguan Luo Suhuai Jiangsu Key Laboratory of Image Processing and Image Communication Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Electrical Engineering and Computing the University of NewcastleNSW 2308Australia

In order to improve the visibility and contrast of low-light images and better preserve the edge and details of images,a new low-light color image enhancement algorithm is proposed in this *** steps of the proposed algorithm are described as ***,the image is converted from the red,green and blue(RGB)color space to the hue,saturation and value(HSV)color space,and the histogram equalization(HE)is performed on the value ***,non-subsampled shearlet transform(NSST)is used on the value component to decompose the image into a low frequency sub-band and several high frequency ***,the low frequency sub-band and high frequency sub-bands are enhanced respectively by Gamma correction and improved guided image filtering(IGIF),and the enhanced value component is formed by inverse NSST ***,the image is converted back to the RGB color space to obtain the enhanced *** results show that the proposed method not only significantly improves the visibility and contrast,but also better preserves the edge and details of images.

关键词： non-subsampled shearlet transform guided image filtering low-light image enhancement the HSV color space

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhan Tong Zhan Wu Yang Yang Weilong Mao Shijie Wang Yinsheng Li Yang Chen Laboratory of Image Science and Technology Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Laboratory of Image Science and Technology School of Computer Science and Engineering and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics.

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Real-Time Student Behavior Analysis via YOLOv5 with Coordinate Attention 9th

Real-Time Student Behavior Analysis via YOLOv5 with Coordina...

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9th International Symposium on Artificial Intelligence and Robotics, ISAIR 2024

作者： Ma, Shuai Chang, Heyou Yang, Xinru Chen, Quan Han, Jian Key Laboratory of Intelligent Information Processing Nanjing XiaoZhuang University Nanjing China School of Information and Engineering Nanjing XiaoZhuang University Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9789819629138

The detection and recognition of student behavior play a pivotal role in the context of smart classrooms. However, conventional methods often encounter performance degradation due to challenges such as occlusion, data diversity, and complexity. To effectively addresses these issues, we propose a method that integrates YOLOv5 with coordinate attention mechanism and GhostConv for analyzing students' behavior. Specifically, the coordinate attention module enhances the localization ability of the proposed method by addressing problems related to target scale variations and uneven distribution. Additionally, we employ GhostConv to replace conventional convolutional layers in YOLOv5, reducing parameters while improving computational efficiency. To verify the validity of our method, a self-built challenging dataset for real teaching scenarios and Citypersons dataset are selected for the experiment. In comparison to the original YOLOv5, our method has achieved a 14.8% and 5.7% increase in mean average precision rates, with a 19.0% improvement in frames per second. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Human-Centered Financial Signal Analysis Based on Visual Patterns in Stock Charts

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IEEE Transactions on Multimedia 2025年

作者： Luo, Ji-Feng Chen, Yuzhen Zhang, Kaixun An, Xudong Hu, Menghan Zhai, Guangtao Zhang, Xiao-Ping East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering Shanghai200241 China University of Chicago Committee on Computational and Applied Mathematics ChicagoIL60637 United States Beijing University of Posts and Telecommunications Beijing100876 China Shanghai Jiao Tong University Institute of Image Communication and Network Engineering Shanghai200240 China Tsinghua Shenzhen International Graduate School Guangdong Shenzhen518055 China

The study utilized a human-centered perspective to research the financial markets, focusing on identifying variations in eye movement patterns between professional and non-professional traders as they analyze a series of stock charts. We use eye movement data as the analysis target because we hypothesize that it represents a behavioral phenotype indicative of stock analysts' cognitive processes during market analysis. Disparities were identified by conducting variance analysis and Wilcoxon signed-rank test on statistical metrics derived from eye fixations and saccades. We provided psychological and economic interpretations to understand the underlying reasons for these observed patterns. To showcase the practical application potential of the human-centered perspective, eye movement data and human visual characteristics were used to construct visual saliency prediction models of professional stock analysts. Leveraging this human-centered model, we developed two practical application demonstrations specifically designed to support and instruct novice traders. Based on the above demonstrations, we designed a training program that demonstrates how, with ongoing training, the non-professional traders' ability to observe stock charts improves progressively. The experimental data and the analyzing code are publicly available at doi: 10.5281/zenodo.10362154. © 1999-2012 IEEE.

关键词： Financial markets

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Auxiliary Diagnosis of Papillary Thyroid Carcinoma Based on Spectral Phenotype

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Phenomics 2023年第5期3卷 469-484页

作者： Bailiang Zhao Yan Wang Menghan Hu Yue Wu Jiannan Liu Qingli Li Min Dai Wendell QSun Guangtao Zhai Shanghai Key Laboratory of Multidimensional Information Processing East China Normal UniversityShanghai 200241China School of Health Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Pathology The Second People’s Hospital of WuhuWuhu 241000AnhuiChina Ophthalmology Department Shanghai Ninth People’s HospitalShanghai Jiao Tong University School of MedicineShanghai 610101China Department of Oral Maxillofacial Head Neck Oncology Shanghai Ninth People’s HospitalShanghai Jiao Tong University School of MedicineShanghai 200011China Institute of Image Communication and Information Processing Shanghai Jiao Tong UniversityShanghai 200240China

Thyroid cancer,a common endocrine malignancy,is one of the leading death causes among endocrine *** diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating ***,we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma(PTC)diagnosis and characterize PTC *** alleviate any concerns pathologists may have about using the model,we conducted an analysis of the used bands that can be interpreted pathologically.A spectra acquisition system was first built to acquire spectra of pathological section images from 91 *** obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC *** data of the corresponding patients were collected for subsequent model interpretability *** experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal *** spectral preprocessing method was used to process the spectra,and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection *** PTC detection model using mean centering(MC)and multiple scattering correction(MSC)has optimal performance,and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test *** model interpretable analysis,the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak,and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC ***,the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC *** addition,the correlation analysis was performed between the selected wavelengths and the clinical data,and the results show:the ref

关键词： Interpretable pathologic model Pathological analysis Intraoperative detection Spectral phenotype

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Intra-Guided Dual-Energy Material Decomposition Method for Dual-Source CBCT Systems

Intra-Guided Dual-Energy Material Decomposition Method for D...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xinyun Zhong Tianling Lyu Zhan Wu Wei Gao Yan Xi Wei Zhao Wentao Zhu Yang Chen Laboratory of Image Science and Technology School of Computer Science and Engineering Southeast University Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Beihang University Beijing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system. Due to the specific geometry of dual-source CBCT, cone-beam (CB) artifacts seriously degrade the material decomposition quality when the direct inversion method is adopted. In this paper, a novel dual-source intra-guided material decomposition framework is proposed to suppress CB artifacts in the material maps. Firstly, a dual-source two-pass algorithm is developed for artifact pre-correction during image reconstruction. Then, the structure of small cone-angle regions from one X-ray source is utilized to guide cone beam artifact removal in large cone-angle regions from another source. Finally, direct matrix inverse material decomposition is applied to the filtered image. Extensive experiments have been conducted on the authentic human phantom and demonstrate that the proposed framework achieves excellent performance in cone beam artifact reduction compared to other methods, and significantly enhances the quality of the material maps.

关键词： Geometry Head Costs Imaging Phantoms Filtering algorithms Matrix decomposition Engineering in medicine and biology image reconstruction X-ray imaging

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