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Characterizing and controlling infrared phonon anomaly of bilayer graphene in optical-electrical force nanoscopy

Light(science & Applications) 2023年第12期12卷 2700-2710页

作者： Junghoon Jahng Sunho Lee Seong-Gu Hong Chang Jun Lee Sergey G.Menabde Min Seok Jang Dong-Hyun Kim Jangyup Son Eun Seong Lee Hyperspectral Nano-imaging Team Korea Research Institute of Standards and ScienceDaejeon 34113Republic of Korea Department of Aerospace Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Multiscale Mechanical Properties Measurement Team Korea Research Institute of Standards and ScienceDaejeon 34113Republic of Korea School of Mechanical Engineering Chonnam National UniversityGwangju 61186Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Functional Composite Materials Research Center Korea Institute of Science and TechnologyJeonbuk 55324Republic of Korea SKKU Advanced Institute of Nanotechnology Sungkyunkwan UniversitySuwon 16419Republic of Korea Division of Nano&Information Technology KIST SchoolUniversity of Science and TechnologySeoul 02792Republic of Korea

We,for the first time,report the nanoscopic imaging study of anomalous infrared(IR)phonon enhancement of bilayer graphene,originated from the charge imbalance between the top and bottom layers,resulting in the enhancement of E1u mode of bilayer graphene near 0.2 *** modified the multifrequency atomic force microscope platform to combine photo-induced force microscope with electrostatic/Kelvin probe force microscope constituting a novel hybrid nanoscale optical-electrical force imaging *** enables to observe a correlation between the IR response,doping level,and topographic information of the graphene *** the nanoscale spectroscopic image measurements,we demonstrate that the charge imbalance at the graphene interface can be controlled by chemical(doping effect via Redox mechanism)and mechanical(triboelectric effect by the doped cantilever)***,we can also diagnosis the subsurface cracks on the stacked few-layer graphene at nanoscale,by monitoring the strain-induced IR phonon *** approach provides new insights into the development of graphene-based electronic and photonic devices and their potential applications.

关键词： electrical force optical

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Efficient Lane Detection based on Feature Aggregation for advanced Driver Assistance Systems

Efficient Lane Detection based on Feature Aggregation for Ad...

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2023 International Conference on advanced Robotics and Intelligent Systems, ARIS 2023

作者： Jhong, Sin-Ye Ko, Ching-Hsiang Su, Yi-Feng Hua, Kai-Lung Chen, Yung-Yao National Cheng Kung University Dept. of Engineering Science Tainan Taiwan National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Automotive Research and Testing Center Research and Development Division Changhua Taiwan National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350302714

As we enter the era of smart cars and electric vehicles, the significance of autonomous driving systems, especially crucial components like lane detection technology, increases dramatically. However, advanced solutions often face daunting challenges, especially under adverse environmental and lighting conditions, yielding varying detection results. This variability prompts automakers to enforce numerous restrictions on auxiliary functions, often deterring potential customers from upgrading relevant services. This study addresses these limitations and introduces a sophisticated deep learning-based lane detection model to supersede existing technologies. The model fuses an efficient feature extraction network with a Recurrent Feature-Shift Aggregator (RESA) and a Bilateral Up-Sampling Decoder (BUSD). These elements work harmoniously, enabling enhanced global information extraction and fortifying detection capability under challenging conditions. The methodology of this study is segregated into three parts: Efficient Backbone and Neck for feature extraction, Global Feature Enhancement Decoder for maximizing feature map details, and the actual implementation of a Lane Assist System. These modules collectively help us calculate high-accuracy lane coordinates, thereby overcoming the limitations of previous lane detection methods. Following the successful creation and validation of the model, the study extends the implementation to the Hexagon vehicle control platform, where the Lane Departure and Lane Keeping assistance systems are deployed. The proposed technology is validated through rigorous experiments and analyses, utilizing the highly recognized TuSimple dataset, custom-collected Taiwanese scene data, and actual road tests. Results illustrate high and consistent detection performance under diverse environmental conditions, validating the technology's effectiveness and feasibility. © 2023 IEEE.

关键词： advanced driver assistance systems

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Multi-StudentNet: a Semi-supervised Deep-Learning Framework for Endometrial Segmentation in Transvaginal Ultrasound images

Multi-StudentNet: a Semi-supervised Deep-Learning Framework ...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Peng, Boyuan Liu, Yiyang Zhu, Xin Huang, Jie Wang, Wenwen Fang, Li Zhou, Qin Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Chiyodaku Tokyo Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350350548

Semi-supervised learning (SSL) is a promising solution for the problem of insufficient medical labeled data. However, it is still a challenging task to segment transvaginal ultrasound (TVUS) images because of their poor contrast and speckle noise. In this study, we propose Multi-StudentNet, a semi-supervised deep-learning framework for endometrial segmentation in TVUS images. This method employs multiple student models to effectively integrate labeled and unlabeled data, improving the reliability and performance of segmentation while significantly reducing the need for extensive manual labeling. By leveraging the weights of multiple models, our framework facilitates feature sharing, which enhances model robustness. Specifically, Multi-StudentNet achieves a Dice coefficient (DSC) of 0.81 and a specificity of 0.99. Our multi-student models consistently outperform single-student models across various conditions, including normal, polyp, and cancer cases. Extensive experiments demonstrate that Multi-StudentNet achieves state-of-the-art performance in both accuracy and robustness. © 2024 IEEE.

关键词： Self-supervised learning

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Learning Rich information for Quad Bayer Remosaicing and Denoising 17th

Learning Rich Information for Quad Bayer Remosaicing and D...

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17th European Conference on computer Vision, ECCV 2022

作者： Jia, Jun Sun, Hanchi Liu, Xiaohong Xiao, Longan Xu, Qihang Zhai, Guangtao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China Shanghai Transsion Information Technology Shanghai China

ISBN: (纸本)9783031250712

In this paper, we propose a DNNs-based solution to jointly remosaic and denoise the camera raw data in Quad Bayer pattern. The traditional remosaic problem can be viewed as an interpolation process that converts the Quad Bayer pattern to a normal CFA pattern, such as the RGGB one. However, this process becomes more challenging when the input Quad Bayer data is noisy. In addition, the limited amount of data available for this task is not sufficient to train neural networks. To address these issues, we view the remosaic problem as a bayer reconstruction problem and use an image restoration model to remove noises while remosaicing the Quad Bayer data implicitly. To make full use of the color information, we propose a two-stage training strategy. The first stage uses the ground-truth RGGB Bayer map to supervise the reconstruction process, and the second stage leverages the provided image Signal Processor (ISP) to generate the RGB images from our reconstructed bayers. With the use of color information in the second stage, the quality of reconstructed bayers is further improved. Moreover, we propose a data pre-processing method including data augmentation and bayer rearrangement. The experimental results show it can significantly benefit the network training. Our solution achieves the best KLD score with one order of magnitude lead, and overall ranks the second in Quad Joint Remosaic and Denoise @ MIPI-challenge. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image reconstruction

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Absolute longitudinal distance measurement verification of a standard polystyrene nanoparticle near a surface in water by means of multi-wavelength evanescent field 22

Absolute longitudinal distance measurement verification of a...

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22nd International Conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2022

作者： Blattler, Aran Khajornrungruang, Panart Suzuki, Keisuke Baba, Akiyoshi Goto, Daiki Graduate School of Computer Science and System Engineering Mechanical Information Science and Technology Course Kyushu Institute of Technology Fukuoka Japan Department of Intelligent and Control Systems Advanced Mechanical Division Kyushu Institute of Technology Fukuoka Japan Center of Microelectronic System Kyushu Institute of Technology Fukuoka Japan School of Computer Science and System Engineering Advanced Mechanical Course Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9781998999118

In the wet semiconductor process, abrasive nanoparticles are widely used in the chemical mechanical polishing (CMP) process to polish wafer surfaces for high planarization. During polishing, nanoparticles move with a high-speed three-dimensional (3D) position in the solution. In the 3D particles's moving positions, Z is a perpendicular longitudinal distance between the surface to the nanoparticle that plays a significant role in polishing, which could not be measured by general microscopy without longitudinal scanning. To understand nanoparticle phenomena near-surface, we have proposed and developed the apparatus with real-time fastmotion tracking on optical multi-wavelength evanescent fields microscopy to measure the 3D motion of the nanoparticle in water without scanning. In euspen's 2020, we could verify the relative distance ?Z of nanoparticles in water, but the absolute distance Z is unknown yet. In this manuscript, we have therefore developed an invisible nano-step height in water to verify the absolute height Z of an individual nanoparticle. The invisible nano-step height in water is made from an optical resin with a refractive index of 1.35 that is close to a refractive index of water to avoid unnecessary light during experimental verification. The nano-step height was less than 100 nm on a glass surface, fabricated using nanoimprint lithography. Polystyrene f100 nm standard particles in water were used to verify the height Z. The results of height Z of nanoparticles could be measured less than 100 nm with the measurement uncertainty ±10 nm (2s), which is related to the interferometer result of the nano-step height. © European Society for Precision engineering and Nanotechnology, Conference Proceedings - 22nd International Conference and Exhibition, EUSPEN 2022. All rights reserved.

关键词： Nanoparticles

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Traffic Pattern Sharing for Federated Traffic Flow Prediction with Personalization 24

Traffic Pattern Sharing for Federated Traffic Flow Predictio...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhou, Hang Yu, Wentao Wan, Sheng Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China SKLSDE Beihang University China Jinan University Engineering Research Center of Trustworthy AI Ministry of Education China

ISBN: (纸本)9798331506681

Accurate Traffic Flow Prediction (TFP) is crucial for enhancing the efficiency and safety of transportation systems, so it has attracted intensive researches by exploiting spatial-temporal dependencies within road networks. However, existing works only consider the case of centralized data collection with all traffic data observed, which may raise privacy concerns as each region of a city may have its own traffic administration department and the traffic data is not allowed to distribute. Therefore, this paper proposes to use Federated Learning (FL) to address this issue by allowing all clients (i.e., traffic administration departments in all regions in our problem) to collaboratively train TFP models without exchanging raw data, thereby offering a solution in maintaining data privacy. Nevertheless, most existing FL methods aim to learn a global model that performs well universally, so they cannot well handle the non-Independent and Identically Distributed (non-IID) traffic data naturally over different regions. To cope with this problem, this paper develops a new FL framework termed 'personalized Federated learning with Traffic Pattern Sharing' (FedTPS) to solve federated TFP problem. Our FedTPS critically exploits the underlying common traffic patterns (e.g., morning and evening rush hours) shared across different city regions and meanwhile maintaining the region-specific data characteristics in a personalized FL manner. Specifically, to extract the common traffic patterns, we decompose the traffic data in each client via using discrete wavelet transform, where the low-frequency components uncover the stable traffic dynamics of different regions and thus can be considered as the common traffic patterns. These common patterns are then shared among different clients through traffic pattern repositories on the server side to aid the global collaborative traffic flow modeling. Moreover, the model components capturing spatial-temporal dependencies in traffic data are r

关键词： Highway administration

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Incremental Learning with Pseudo Labels for Enhanced Glomeruli Segmentation Using SAM

Incremental Learning with Pseudo Labels for Enhanced Glomeru...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Liu, Xinkai Peng, Boyuan Zhu, Xin Iwasaki, Tsuyoshi Sato, Atsuya Kazama, Junichiro James Huang, Jie Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan Department of Nephrology and Hypertension Fukushima Medical University Fukushima Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan

ISBN: (纸本)9798350350548

The advent of the Segment Anything Model (SAM) marks a significant milestone in deep learning semantic segmentation tasks. In this study, an incremental learning model based on pseudo labels is proposed. The model generates bounding box prompts for SAM, enhancing the accuracy of glomeruli segmentation in renal pathology images. The model uses DenseNet-121 along with contrastive learning to enable self-supervised pre-training using mouse glomeruli images. Transfer learning is then conducted using labeled human renal pathology images to develop a binary classification model for glomeruli. Unlabeled images of human renal pathology are subsequently introduced for incremental learning. Class activation maps (CAMs) generated by the incremental learning model are post-processed to extract bounding rectangles for connected regions. These rectangles are then used as prompts for SAM segmentation. The proposed approach resulted in an average Dice similarity coefficient (DSC) of 0.825 in the glomeruli segmentation task. This result demonstrates the effectiveness of the incremental learning model and post-processing algorithms in improving SAM’s segmentation accuracy for renal pathology. © 2024 IEEE.

关键词： Contrastive Learning

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Facial Expression Recognition With Vision Transformer Using Fused Shifted Windows

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IEEE Transactions on Affective Computing 2024年

作者： Sun, Xiao Wang, Rui Chen, Shaokai Wang, Meng Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machines School of Computer Science and Information Engineering China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China University of Science and Technology of China Institute of Advanced Technology China Hefei University of Technology School of Computer Science and Information Engineering Hefei China

Facial expressions contain massive affective information. Previous methods have focused on using diverse models based on CNN or Transformer to handle the facial expression recognition(FER) task. However, most of them treat the FER task as a general image classification task and neglect the impact of regions of interest (ROIs) for the performance of FER. To verify the influence of different ROIs, in this paper, we propose a vision Transformer based on Fused Shifted windows (FSwin), called FSwin Transformer. The semantic information of the face, obtained by ROIs, guides the FSwin Transformer to focus more on the key regions. The fused shifted windows enable the model to perform global semantic interactions, allowing it to concentrate on key regions without losing the topological structural information of the entire face. Additionally, learnable parameters are introduced to learn the feature weights expressed by each ROI, helping the model dynamically adjust the attention distribution. We have conducted controlled experiments to quantitatively verify the impact of ROIs. And the experimental results show that with the increase of the number of ROIs, the accuracy of FER is significantly improved, demonstrating that the key ROIs play an important role in feature extraction. The results on Jaffe, CK+, FER2013, AffectNet have reached 99.8%, 99.0%, 74.8%, and 68.9%, respectively, which all set new state-of-the-art. Extensive cross-dataset experiments also show that the FSwin Transformer has good generalization ability, proving that our proposed model has a beneficial effect on FER tasks. © 2010-2012 IEEE.

关键词： Feature extraction

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A Function Fitting System Based on Genetic Algorithm 1

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Sun, Qiuhong Wang, Jiaqi Zhou, Xiaokang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China The Faculty of Data Science Shiga University Hikone522-8522 Japan Department of Computer Science North China Electric Power University Baoding071003 China The RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan

ISBN: (数字)9789819998937

ISBN: (纸本)9789819998920

With the development of science and technology, function fitting has penetrated into various fields of scientific research, scientific and technological innovation. For the function of fitting analysis of a given function image, there is no public software on the market that uses the idea of genetic algorithm to solve the problem of function fitting. In order to make up for the insufficiency of the existing software and seize the opportunity of function fitting in various industries, a function fitting system based on genetic algorithm was proposed. It is a low-threshold software with a wide range of applications. When designing the function of fitting and analyzing a given function image, the genetic algorithm is used. With the application of ray detection in Unity, the closest expression of function fitting is obtained. At the same time, the line graph of fitness reduction during genetic algorithm iteration is given. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Tensor MIMO Perfect-Equalizer Design Using New Projection-Based Method and Approximation-Error Analysis

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IEEE Systems Journal 2024年第1期18卷 668-679页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Kao, Yi-Chih San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Chungli32003 Taiwan National Yang Ming Chiao Tung University Network and System Division Information Technology Service Center Hsinchu30010 Taiwan

As real environments are quite complex, multiple-input-multiple-output (MIMO) channels should be characterized by the multidimensional-multirelational models. Tensor linear algebra has been emerging as a powerful and promising tool for such MIMO channel modeling and estimation. However, to the best of our knowledge, there hardly exists any algebraic framework of MIMO channel equalization when the channel is modeled as an arbitrary-dimensional transfer tensor (a tensor whose entries are all rational z-functions). In this work, we first propose a new method based on the projection, namely, 'projection-based method,' to determine whether a given transfer tensor is a full-rank tensor or not and carry out the inversion of an invertible transfer tensor. Our idea is employing the projection subspace of the system transfer tensor to simplify the pertinent inversion process. Second, we design the finite-impulse-response (FIR) realization to approximate the inverse of an arbitrary transfer tensor such that all entries of the approximated inverse transfer tensor are finite-degree polynomials of z. Third, we also analyze the computational and memory complexities required by our proposed new projection-based method for checking the full-rank condition of a given transfer tensor and for undertaking the tensor inversion. Our developed new theories and analysis in this work can be applied to determine the FIR model-orders of an approximated inverse MIMO transfer tensor often encountered in signal processing, control, communications, etc. © 2007-2012 IEEE.

关键词： FIR filters

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