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P-104: Corrugated Silicon Nitride Masks with Enhanced Mechanical Strength for Patterning RGB-Stripe OLED Microdisplays

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SID Symposium Digest of Technical Papers 2024年第1期55卷 1784-1785页

作者： Shou-Cheng Dong Jiye Yang Bryan Siu Ting Tam Ching W. Tang State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Institute for Advanced Study The Hong Kong University of Science and Technology Hong Kong WISPO Advanced Materials (Suzhou) Co. Ltd. Suzhou China Department of Chemical Engineering University of Rochester Rochester New York USA

Corrugated silicon nitride masks (SiNMs) with improved mechanical strength are developed for patterning OLED microdisplays. The corrugations in the nitride membrane significantly increase the rigidity of the mask and reduce sagging, enabling 3000-ppi slot apertures for patterning RGB-stripe pixel layouts that are normally not achievable by fine metal masks.

关键词： OLED microdisplays Direct patterning Shadow mask Silicon nitride Corrugation

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Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning

Adaptive Configuration for Heterogeneous Participants in Dec...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yunming Liao Yang Xu Hongli Xu Lun Wang Chen Qian School of Computer Science and Technology University of Science and Technology of China China Suzhou Institute for Advanced Study University of Science and Technology of China China Department of Computer Science and Engineering Jack Baskin School of Engineering University of California Santa Cru

Data generated at the network edge can be processed locally by leveraging the paradigm of edge computing (EC). Aided by EC, decentralized federated learning (DFL), which overcomes the single-point-of-failure problem in the parameter server (PS) based federated learning, is becoming a practical and popular approach for machine learning over distributed data. However, DFL faces two critical challenges, i.e., system heterogeneity and statistical heterogeneity introduced by edge devices. To ensure fast convergence with the existence of slow edge devices, we present an efficient DFL method, termed FedHP, which integrates adaptive control of both local updating frequency and network topology to better support the heterogeneous participants. We establish a theoretical relationship between local updating frequency and network topology regarding model training performance and obtain a convergence upper bound. Upon this, we propose an optimization algorithm, that adaptively determines local updating frequencies and constructs the network topology, so as to speed up convergence and improve the model accuracy. Evaluation results show that the proposed FedHP can reduce the completion time by about 51% and improve model accuracy by at least 5% in heterogeneous scenarios, compared with the baselines.

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OSM2Net: A Robust Road Network Extraction Framework from Noisy Indoor Parking OpenStreetMap

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IEEE Internet of Things Journal 2025年

作者： Cao, Yu Guo, Xiansheng Boateng, Gordon Owusu Ansari, Nirwan Si, Haonan Qian, Bocheng Liu, Xinhao Xia, Huang Liu, Yinong University of Electronic Science and Technology of China Department of Electronic Engineering Chengdu611731 China Xi'an Jiaotong-Liverpool University Department of Communications and Networking Suzhou215123 China New Jersey Institute of Technology Advanced Networking Lab Department of Electrical and Computer Engineering NewarkNJ07102 United States

Intelligent Transportation Systems (ITS) rely on high-precision road networks, which are particularly scarce in indoor parking. Existing methods depend on expensive hardware (e.g., LiDAR) or manual mapping, both of which are costly and inefficient. The rise of the Internet of Things (IoT) has enabled large-scale data collection and connectivity, offering new opportunities for automated road network extraction. OpenStreetMap (OSM), as a crowdsourced IoT-driven platform, provides multi-layer geospatial data, including the Road Network Layer (RNL), Lane Boundary Layer (LBL), and Turn Sign Layer (TSL). However, OSM data often suffers from incompleteness and noisy connectivity, affecting the continuity and accuracy of road networks. This paper introduces OSM2Net, a novel framework designed to extract road networks from individual layers and leverage multi-layer data to construct directed road networks. Specifically, OSM2Net rasterizes noisy OSM data into bitmaps for image processing and multi-layer fusion. By leveraging the topology relationship between lane boundaries and road networks, a Lane-Road Map Generator (LRMG) creates a simulated dataset for training. Then, utilizing the simulated dataset, a Lane2Net model is designed to extract road networks from sparse lane boundary images. The framework then vectorizes bitmaps into a lightweight, undirected road network and refines it into a directed network by extracting and matching turn sign information. Experimental results show that Lane2Net achieves Intersection over Union (IoU) of 93% and 92% using simulated and real-world datasets, respectively. Extensive experiments on real-world datasets confirm that OSM2Net delivers robust completeness and high-quality road network extraction. © 2014 IEEE.

关键词： advanced public transportation systems

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Domain Adaptation for Semantic Segmentation of Cataract Surgical Images Based on Masked Image Consistency 2nd

Domain Adaptation for Semantic Segmentation of Cataract Surg...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Zhang, Yuzhu Pan, Yijie Ou, Mingyang Gong, Guanghui Zhang, Qinhu Li, Haojin Li, Heng Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Ningbo China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo China

ISBN: (纸本)9789819619061

Cataract surgery is a complex procedure requiring precise execution of multiple steps. To improve the accuracy and efficiency of cataract surgeries, we present a semantic segmentation model for cataract surgery scenes. Our model leverages Unsupervised Domain Adaptation (UDA) techniques to enhance segmentation performance in clinical surgical environments, addressing challenges such as domain shift, occlusions between surgical tools and tissues, and long tail problem. The model utilizes a Teacher-Student model, where a student model is trained in the target domain with pseudo-labels generated by an Exponential Moving Average (EMA) teacher model, ensuring robust learning across domains. Additionally, we utilize a Masked Image Consistency (MIC) module to improve the model’s understanding of occluded regions by enforcing consistency between masked and unmasked predictions. To mitigate class imbalance between anatomical structures and surgical tools, we employ a maximum squares loss, enabling the model to achieve balanced learning. Our results demonstrate that the proposed model improves segmentation accuracy and robustness in cataract surgery scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantic Segmentation

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Adaptive Vehicle Control System for Improved Safety With Image Processing

Adaptive Vehicle Control System for Improved Safety With Ima...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： N. Vijayalakshmi L. Priya S. Ramaraj Menakadevi Nanjundan Satheesh Kumar Palanisamy Ayodeji Olalekan Salau Sepiribo Lucky Braide Department of Computer Science and Engineering Alliance School of Advanced Compuitng Alliance University Bangalore Department of ECE Sri Eshwar College of Engineering (Autonomous) Kinathukadavu Coimbatore Tamilnadu India Department of Computer Science and Engineering Karpagam College of Engineering India Department of ECE Karpagam Institute of Technology Coimbatore Department of ECE BMS Institute of Technology and Management Bengaluru Karnataka India Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electrical and Electronics Engineering Rivers State University Port Harcourt Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

We propose the development of an Adaptive Vehicle Control (AVC) system using readily available components such as a Raspberry Pi microcontroller, a motor, a motor driver, and a Raspberry Pi camera. This system aims to enhance safety and convenience by automatically adjusting vehicle speed based on surrounding traffic conditions and recognizing relevant road signs like school zones, speed limits, and hospital zones. The process begins with real-time image capture using the Raspberry Pi camera module. These images are then analyzed using Python-based computer vision algorithms, primarily leveraging OpenCV. The image processing pipeline employs techniques such as object detection and template matching to identify specific road signs within the captured frames. Upon sign detection, the system interprets their meanings and determines the appropriate speed limits associated with them. Using the information from the detected signs and their corresponding speed limits, the system regulates the motor connected to the vehicle's throttle through GPIO pins on the Raspberry Pi. By adjusting the motor's speed, the AVC system ensures compliance with the specified speed limits across various road conditions. Safety measures are paramount, with fail-safe mechanisms implemented to prevent accidents in case of system errors or malfunctions. This AVC system offers a cost-effective and adaptable solution for integrating intelligent cruise control capabilities into vehicles. Leveraging the computational power of the Raspberry Pi and the versatility of image processing techniques, the system can adapt to diverse driving environments, thereby enhancing overall driving safety. This project contributes to the progression of smart transportation systems and lays the foundation for further advancements in autonomous driving technologies.

关键词： Adaptive systems Roads Pipelines Motors Cameras Real-time systems Safety Smart transportation Cruise control Vehicles

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Intriguing Effect of the Correlation Prior on ICD-9 Code Assignment 61

Intriguing Effect of the Correlation Prior on ICD-9 Code Ass...

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61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

作者： Yang, Zihao Zhang, Chenkang Wu, Muru Liu, Xujin Chris Jiang, Lavender Yao Cho, Kyunghyun Oermann, Eric Karl Center for Data Science New York University United States Department of Neurosurgery NYU Langone Health United States Department of Electrical and Computer Engineering NYU Tandon School of Engineering United States Courant Institute of Mathematical Sciences New York University United States Canadian Institute for Advanced Research Canada Prescient Design United States Department of Radiology NYU Langone Health United States Neuroscience Institute NYU Langone Health United States

ISBN: (纸本)9781959429692

The Ninth Revision of the International Classification of Diseases (ICD-9) is a standardized coding system used to classify health conditions. It is used for billing, tracking individual patient conditions, and for epidemiology. The highly detailed and technical nature of the codes and their associated medical conditions make it difficult for humans to accurately record them. Researchers have explored the use of neural networks, particularly language models, for automated ICD-9 code assignment. However, the imbalanced distribution of ICD-9 codes leads to poor performance. One solution is to use domain knowledge to incorporate a useful prior. This paper evaluates the usefulness of the correlation bias: we hypothesize that correlations between ICD-9 codes and other medical codes could help improve language models' performance. We showed that while the correlation bias worsens the overall performance, the effect on individual class can be negative or positive. Performance on classes that are more imbalanced and less correlated with other codes is more sensitive to incorporating the correlation bias. This suggests that while the correlation bias has potential to improve ICD-9 code assignment in certain cases, the applicability criteria need to be more carefully studied. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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MIMO Beamforming and Signal Modulation Design for Federated Learning Optimization

MIMO Beamforming and Signal Modulation Design for Federated ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Cong Shen Changchuan Yin Christopher G. Brinton Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper, we consider the optimization of federated learning (FL) over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp). In such a system, MIMO devices transmit their locally trained FL models to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. AirComp enables efficient wireless model aggregation by the PS in bandwidth-limited settings. However, wireless channel fading can produce distortions in AirComp-based FL. To tackle this challenge, we develop a novel aggregation scheme that combines digital modulation with AirComp to mitigate wireless fading while ensuring communication efficiency. We formulate this as a joint transmit-receive beamforming design optimization problem which dynamically adjusts the beamforming matrices to minimize the FL training loss with transmission errors. To solve this problem based on limited information at the PS, we employ an artificial neural network (ANN) to estimate the local FL models of all devices. Then, we derive a closed-form optimal design of the transmit and receive beamforming matrices based on predicted FL models. Numerical evaluations validate the advantages of the proposed methodology in terms of model training performance compared with baselines.

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Enhanced elevated-metal metal-oxide thin-film transistors for gate-driver circuit fabricated on a flexible substrate 58th

Enhanced elevated-metal metal-oxide thin-film transistors fo...

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58th International Symposium on Digest of Technical Papers, ICDT 2021

作者： Shi, Runxiao Wang, Sisi Wang, Yuqi Zhou, Zhichao Xia, Zhihe Lu, Lei Wong, Man Department of Electronic and Computer Engineering The Jockey Club Institute for Advanced Study The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong School of Electronic and Computer Engineering Peking University Shenzhen China

The effects of fluorination on amorphous indium-gallium-zinc oxide thin-film transistors fabricated on polyimide (PI) at a maximal temperature of 300 °C were investigated. An excimer laser was used to lift-off the PI from a glass-based carrier substrate. The resulting TFTs exhibit more positive threshold voltage, steeper subthreshold swing, reduced apparent short-channel effects, and tighter distribution of device parameters. Various test circuit blocks and a 4-stage 15T1C gate driver were constructed to demonstrate the beneficial effects of fluorination on circuit applications. © 2021 SID.

关键词： Thin film transistors

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Skeleton-Based Multi-Stream Adaptive Graph Convolutional Network for Indoor Scene Action Recognition

Skeleton-Based Multi-Stream Adaptive Graph Convolutional Net...

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2023 China Automation Congress, CAC 2023

作者： Li, Jiazhuo Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru The School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland The Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey The Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798350303759

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results. © 2023 IEEE.

关键词： Convolution

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Cloud Energy Storage Management Including Smart Home Physical Parameters

Cloud Energy Storage Management Including Smart Home Physica...

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Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： Vikash Kumar Saini Srinivas Yelisetti Rajesh Kumar Ameena S. Al-Sumaiti Center for Energy and Environment Malaviya National Institute of Technology Jaipur Advanced Power and Energy Center Electrical Engineering and Computer Science Khalifa University UAE Department of Electrical Engineering Malaviya National Institute of Technology Jaipur

Consumption of green energy in residential communities is increasing compared to conventional supply. However, the variability in generation due to different weather parameters is a significant challenge to their growth rate. Energy storage has the potential to address this issue, and sharing economy-based cloud energy storage (CES) has gained popularity as a way to reduce energy consumption costs and increase revenue. This study analyzes the deployment of CES infrastructure operation in seven residential smart homes community. All seven homes utilized CES services based on their daily net load demand. In the first phase, the PSO algorithm optimized the load trajectory, taking into account all smart home parameters and physical components. Subsequently, the operational cost of CES in the community is evaluated in scenarios with and without home energy management systems. Simulation results indicated that the energy consumption cost of the residential community from the grid is zero, and revenue is 43.59% higher when home energy management systems are employed. The modeled home parameters significantly affected total community energy costs.

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