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A novel contact engineering method for transistors based on two-dimensional materials

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Journal of Materials Science & Technology 2021年第10期69卷 15-19页

作者： Yaochen Sheng LuFang Zhang Feng Li Xinyu Chen Zhijian Xie Haiyan Nan Zihan Xu David Wei Zhan Jianhao Chen Yong Pu Shaoqing Xiao Wenzhong Bao State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics&SystemsSchool of MicroelectronicsFudan UniversityShanghai 200433China Engineering Research Center of IoT Technology Applications(Ministry of Education) Department of Electronic EngineeringJiangnan UniversityWuxi 214122China International Center for Quantum Materials School of PhysicsPeking UniversityBeijing 100871China New Energy Technology Engineering Laboratory of Jiangsu Province&School of Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Six Carbon Tech ShenzhenShenzhen 518106China

Contact engineering is of critical importance for two-dimensional(2D)transition metal dichalcogenide(TMD)-based ***,there are only a few solutions to overcome this obstacle because of the complexity of the TMD-contact *** this work,we propose a novel method using a soft plasma treatment followed by the seamless deposition of a metal electrode to reduce the contact resistance of MoS_(2)field effect transistors(FETs).The treated FETs exhibit three times higher mobility than the control FETs without plasma *** soft plasma treatment can remove the facial sulfur atoms and expose the middle Mo atoms so that they come into direct contact with the metal electrode,thus greatly improving the contact ***-principles calculation is also performed to support the experimental *** potentially scalable strategy can be extended to the whole family of TMD based FETs to provide a possible route of device processsing technology for 2D device application.

关键词： Electrical contact resistance MoS_(2) Plasma

PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport

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arXiv 2025年

作者： Guo, Tian Yuan, Hui Liu, Qi Su, Honglei Hamzaoui, Raouf Kwong, Sam School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Ji’nan250061 China School of Electronic Information Qingdao University Qingdao266071 China School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom Department of Computing and Decision Science Lingnan University Hong Kong

Point cloud compression significantly reduces data volume but sacrifices reconstruction quality, highlighting the need for advanced quality enhancement techniques. Most existing approaches focus primarily on point-to-point fidelity, often neglecting the importance of perceptual quality as interpreted by the human visual system. To address this issue, we propose a generative adversarial network for point cloud quality enhancement (PCE-GAN), grounded in optimal transport theory, with the goal of simultaneously optimizing both data fidelity and perceptual quality. The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit. The LFE unit uses dynamic graph construction and a graph attention mechanism to efficiently extract local features, placing greater emphasis on points with severe distortion. The GSC unit uses the geometry information of neighboring patches to construct an extended local neighborhood and introduces a transformer-style structure to capture long-range global correlations. The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality reconstruction. Experimental results show that the proposed method achieves state-of-the-art performance. Specifically, when applying PCE-GAN to the latest geometry-based point cloud compression (G-PCC) test model, it achieves an average BD-rate of -19.2% compared with the PredLift coding configuration and -18.3% compared with the RAHT coding configuration. Subjective comparisons show a significant improvement in texture clarity and color transitions, revealing finer details and more natural color gradients. Copyright © 2025, The Authors. All rights reserved.

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Multiple Sclerosis Detection with Reinforcement Learning and Differential Evolution

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Multiple Sclerosis Detection with Reinforcement Learning and...

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Jing Yang, Jin Wu, Chenwei Jiang, Gaozhe Lv, Yaning Liu, Bingyan Xu, Feng Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis United States Evident Battery Inc. Massachusetts United States Institute of Operations Research and Analytics National University of Singapore Singapore School of Mathematics and Statistics Xi'an University of Electronic Science and Technology Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Engineering and Design Technical University of Munich Munich Germany

ISBN: (纸本)9798350368741

Multiple Sclerosis (MS) disrupts nerve communication, potentially leading to permanent damage. Convolutional Neural Networks (CNNs) are commonly recommended to accelerate magnetic resonance imaging (MRI) analysis for MS. Traditional CNN-based methods often face challenges with feature selection, imbalanced classification, and hyperparameter optimization, which can reduce their effectiveness. To address these challenges, this paper introduces a reinforcement learning (RL) strategy for tackling feature selection and class imbalance, complemented by an advanced Differential Evolution (DE) algorithm for hyperparameter tuning. The RL algorithm dynamically adapts, enhancing feature selection and class balance, and consistently outperforms traditional static methods by continually optimizing its response to the complex patterns in MS detection. Furthermore, the DE is enhanced with a novel mutation mechanism that utilizes k-means clustering to identify significant clusters. Tested on the SNAC-MS and ISBI datasets, our model demonstrates robust accuracy with average F-measures of 92.873% and 93.783%, respectively, highlighting its effectiveness and adaptability in diagnosing MS. © 2025 IEEE.

关键词： feature selection hyperparameter optimization imbalanced classification Multiple sclerosis reinforcement learning

Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy

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arXiv 2024年

作者： Huo, Wei Liu, Changxin Ding, Kemi Johansson, Karl Henrik Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

This paper investigates the use of the cubic-regularized Newton method within a federated learning framework while addressing two major concerns that commonly arise in federated learning: privacy leakage and communication bottleneck. We introduce a federated learning algorithm called Differentially Private Federated Cubic Regularized Newton (DP-FCRN). By leveraging second-order techniques, our algorithm achieves lower iteration complexity compared to first-order methods. We also incorporate noise perturbation during local computations to ensure privacy. Furthermore, we employ sparsification in uplink transmission, which not only reduces the communication costs but also amplifies the privacy guarantee. Specifically, this approach reduces the necessary noise intensity without compromising privacy protection. We analyze the convergence properties of our algorithm and establish the privacy guarantee. Finally, we validate the effectiveness of the proposed algorithm through experiments on a benchmark dataset. © 2024, CC BY.

关键词： Federated learning

Quick unsupervised hyperspectral dimensionality reduction for earth observation: A comparison

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arXiv 2024年

作者： Lupu, Daniela Garrett, Joseph L. Johansen, Tor Arne Orlandic, Milica Necoara, Ion Automatic Control and System Engineering Department National University of Science and Technology Politehnica Bucharest Bucharest060042 Romania Center for Autonomous Marine Operations and Systems Norwegian University of Science and Technology Trondheim Norway Department of Engineering Cybernetics Norway Department of Electronic Systems Norway G. Mihoc-C. Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest050711 Romania

Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data volume exceeds the capacity of the computational resources, particularly in the case of remote sensing platforms (e.g., drones, satellites), but also in the case of multi-year datasets. Moreover, the computational strategies of unsupervised dimensionality reduction often provide the basis for more complicated supervised techniques. Seven unsupervised dimensionality reduction algorithms are tested on hyperspectral data from the HYPSO-1 earth observation satellite. Each particular algorithm is chosen to be representative of a broader collection. The experiments probe the computational complexity, reconstruction accuracy, signal clarity, sensitivity to artifacts, and effects on target detection and classification of the different algorithms. No algorithm consistently outperformed the others across all tests, but some general trends regarding the characteristics of the algorithms did emerge. With half a million pixels, computational time requirements of the methods varied by 5 orders of magnitude, and the reconstruction error varied by about 3 orders of magnitude. A relationship between mutual information and artifact susceptibility was suggested by the tests. The relative performance of the algorithms differed significantly between the target detection and classification tests. Overall, these experiments both show the power of dimensionality reduction and give guidance regarding how to evaluate a technique prior to incorporating it into a processing pipeline. © 2024, CC BY.

关键词： Remote sensing

GVI: Guideable Visual Interpretation on medical tomographic images to improve the performance of deep network

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Pattern Recognition Letters 2025年 196卷 162-168页

作者： Hui Liu Fan Wei Lixin Yan Sushan Wang Chongfu Jia Lina Zhang Jiansheng Peng Yi Xu School of Control Science and Engineering Dalian University of Technology Dalian China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian China Department of Cardiovascular Radiology The First Affiliated Hospital of Dalian Medical University Dalian China Department of Radiology The First Affiliated Hospital of Dalian Medical University Dalian China Department of Artificial Intelligence and Manufacturing Hechi University Hechi China

In medical image analysis, the demand for interpretable deep neural networks is rapidly growing. However, a major challenge is that most existing interpretative methods are applied after training, leading to a lack of integration with the model’s learning process. As a result, these methods often fail to highlight regions within complex medical images critical for decision-making, such as abnormal tissues or lesions, which are essential for accurate diagnoses and treatment planning. This paper introduces Guided Visual Interpretation (GVI), a framework designed to enhance both the performance and interpretability of deep networks. Building on a deep network model with image-level labels, GVI incorporates a small amount of pixel-level annotations combined with attention mechanisms. These mechanisms facilitate visual interpretation through forward propagation, directing the model’s focus to the most relevant regions. By aligning the network’s decision-making with human cognitive processes, GVI improves interpretability. In our study, an attention layer was added after the convolutional layers of a pre-trained classification network. GVI is trained using a mixed supervision approach that integrates pixel-level annotations with a large amount of image-level data. Experimental results on both private and public datasets show that GVI generates visual explanations consistent with human decision-making principles and achieves superior classification accuracy compared to traditional methods. These findings highlight GVI’s potential to improve interpretability and diagnostic performance in critical fields like medical imaging.

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Perceptual Quality Assessment of Trisoup-Lifting Encoded 3D Point Clouds

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arXiv 2024年

作者： Long, Juncheng Su, Honglei Liu, Qi Yuan, Hui Gao, Wei Song, Jiarun Wang, Zhou The College of Electronics and Information Qingdao University Qingdao266071 China The School of Control Science and Engineering Shandong University Ji'nan250061 China The School of Electronic and Computer Engineering Peking University Shenzhen518055 China The Peng Cheng Laboratory Shenzhen518066 China The School of Telecommunications Engineering Xidian University Xi'An710071 China The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

No-reference bitstream-layer point cloud quality assessment (PCQA) can be deployed without full decoding at any network node to achieve real-time quality monitoring. In this work, we develop the first PCQA model dedicated to Trisoup-Lifting encoded 3D point clouds by analyzing bitstreams without full decoding. Specifically, we investigate the relationship among texture bitrate per point (TBPP), texture complexity (TC) and texture quantization parameter (TQP) while geometry encoding is lossless. Subsequently, we estimate TC by utilizing TQP and TBPP. Then, we establish a texture distortion evaluation model based on TC, TBPP and TQP. Ultimately, by integrating this texture distortion model with a geometry attenuation factor, a function of trisoupNodeSizeLog2 (tNSL), we acquire a comprehensive NR bitstream-layer PCQA model named streamPCQ-TL. In addition, this work establishes a database named WPC6.0, the first and largest PCQA database dedicated to Trisoup-Lifting encoding mode, encompassing 400 distorted point clouds with both 4 geometric multiplied by 5 texture distortion levels. Experiment results on M-PCCD, ICIP2020 and the proposed WPC6.0 database suggest that the proposed streamPCQ-TL model exhibits robust and notable performance in contrast to existing advanced PCQA metrics, particularly in terms of computational cost. The dataset and source code will be publicly released at https://***/qdushl/Waterloo-Point-Cloud-Database-6.0. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

Perceptual Quality Assessment of Octree-RAHT Encoded 3D Point Clouds

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arXiv 2024年

作者： Duan, Dongshuai Su, Honglei Liu, Qi Yuan, Hui Gao, Wei Song, Jiarun Wang, Zhou College of Electronics and Information Qingdao University Qingdao266071 China School of Control Science and Engineering Shandong University Ji’nan250061 China School of Electronic and Computer Engineering Peking University Shenzhen518055 China Peng Cheng Laboratory Shenzhen518066 China School of Telecommunications Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

No-reference bitstream-layer point cloud quality assessment (PCQA) can be deployed without full decoding at any network node to achieve real-time quality monitoring. In this work, we focus on the PCQA problem dedicated to Octree-RAHT encoding mode. First, to address the issue that existing PCQA databases have a small scale and limited distortion levels, we establish the WPC5.0 database which is the first one dedicated to Octree-RAHT encoding mode with a scale of 400 distorted point clouds (PCs) including 4 geometric multiplied by 5 attitude distortion levels. Then, we propose the first PCQA model dedicated to Octree-RAHT encoding mode by parsing PC bitstreams without full decoding. The model introduces texture bitrate (TBPP) to predict texture complexity (TC) and further derives the texture distortion factor. In addition, the Geometric Quantization Parameter (PQS) is used to estimate the geometric distortion factor, which is then integrated into the model along with the texture distortion factor to obtain the proposed PCQA model named streamPCQ-OR. The proposed model has been compared with other advanced PCQA methods on the WPC5.0, BASICS and M-PCCD databases, and experimental results show that our model has excellent performance while having very low computational complexity, providing a reliable choice for time-critical applications. To facilitate subsequent research, the database and source code will be publicly released at https://***/qdushl/Waterloo-Point-Cloud-Database-5.0. Copyright © 2024, The Authors. All rights reserved.

关键词： Database systems

A Study for UAV Autonomous Safe Landing-Site Selection on Rough Terrain 2021

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A Study for UAV Autonomous Safe Landing-Site Selection on Ro...

2nd International Conference on Computing and Data Science, CONF-CDS 2021

作者： Zheng, Wenlong Yi, Jianjun Xiang, Haojie Zhou, Bo Wang, Danwei Zhao, Changchun Department of Mechanical Engineering East China University of Science and Technology Shanghai200237 China Intelligent Unmanned System Control Technology Joint Lab Shanghai201109 China School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Shanghai Aerospace Control Technology Institute Shanghai201109 China

ISBN: (纸本)9781450389570

Autonomous safe landing of UAV is an important function in many scenarios such as force landing and delivery. This paper proposes a method to autonomously select a safe landing site for vertical take-off and landing (VTOL) UAV based on point cloud, which can minimize combined risks posed during touch down at the chosen landing site. The most suitable landing site of a landing zone is selected according to the terrain complexity. In this paper, (1) fine-grained grid elevation map converted from the terrain point cloud is used to calculate the potential risk such as slope, roughness and maximum height difference. (2) A comprehensive risk model is designed to consider all above risks to recognize obstacles and risk areas, and combine the flight distance factors to obtain the final cost map. (3) We process cost map as image by OpenCV to accelerate the processing and reduce reaction time. Terrain point clouds of simulation scene and real world are used for experiments and experimental results show that the selected landing sites can meet the safety requirements, which demonstrate the effectiveness and feasibility of our proposed method. © 2021 ACM.

关键词： Unmanned aerial vehicles (UAV)