检索结果-内蒙古大学图书馆

CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Fang, Chengyu Kim, Minjeong Czaplewski, David A. Mei, Hongyan Deshpande, Sanket Yu, Zhaoning Xiao, Yuzhe Yang, Xuting Dibos, Alan M. Saffman, Mark Choy, Jennifer T. Kats, Mikhail A. Department of Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI53706 United States Center for Nanoscale Materials Argonne National Laboratory LemontIL60349 United States Department of Physics University of Wisconsin-Madison MadisonWI53706 United States Department of Physics University of North Texas DentonTX76201 United States Department of Material Science and Engineering University of Wisconsin-Madison MadisonWI53706 United States

We report optical metasurfaces that enable bottle-beam arrays for blue-detuned trapping of neutral atoms. Our approach only requires a single metasurface which significantly reduces the complexity of the trapping expe... 详细信息

关键词： Atoms

来源：评论

学校读者我要写书评

暂无评论

Indoor Navigation System for Visually Impaired People using computer vision

Indoor Navigation System for Visually Impaired People using ...

引用

2022 IEEE International Conference on Electro Information Technology, eIT 2022

作者： Fraga, Adrian Leyte Yu, Xinrui Yi, Won-Jae Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Department of Electrical and Computer Engineering Research Laboratory ChicagoIL United States Universidad Politécnica de Madrid E.T.S.I. Industriales Madrid Spain

ISBN: (纸本)9781665480093

This paper studies an indoor navigation guidance system for visually impaired people using Artificial Intelligence (AI) and computer vision techniques to guide users via optimal path based on quick response (QR) code markers and collision avoidance system supported by the monocular depth estimation algorithm. The proposed system utilizes a set of QR code markers, as location beacons, to generate an optimal path for the user to reach the destination. The identified QR code markers are used to correlate and compare information from the online database containing valuable information such as current location information, and the next possible locations from the current location. In addition, our system embraces a safety feature, a collision avoidance system, by utilizing the monocular depth estimation algorithm to identify obstacles in the path to the desired destination. All obtained information is provided to the user through a text-to-speech engine, where the system can direct the user to the optimal path via audio output. © 2022 IEEE.

关键词： Location

来源：评论

学校读者我要写书评

暂无评论

Multiuser Downlink NOMA Communication Enabled by Movable Antenna

Multiuser Downlink NOMA Communication Enabled by Movable Ant...

引用

IEEE Conference on Wireless Communications and Networking

作者： Zhe Li Lipeng Zhu Zhenyu Xiao Boyu Ning Daniel Benevides da Costa School of Electronic and Information Engineering Beihang University Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Movable antenna (MA) is an innovative technology that can enhance channel condition by altering antenna position within a local area. Integrating MA can further improve the performance of multiuser communications in non-orthogonal multiple access (NOMA) systems. In this paper, we investigate MA enabled NOMA for multiuser downlink communication, where the base station (BS) is equipped with a fixed-position antenna (FPA) array to serve multiple MA enabled user terminals (UTs). An optimization problem is formulated to maximize the minimum achievable rate among all the UTs by jointly optimizing the positions of MAs of each UT, the precoding matrix at BS, and the successive interference cancellation (SIC) decoding indicator matrix at UTs, subject to the limited movement area of the MAs, the maximum transmit power of the BS, and the SIC decoding condition. To solve this non-convex problem, we combine the hippo optimization (HO) method with the alternating optimization (AO) method to obtain a suboptimal solution efficiently. Simulation results show that the proposed algorithm can significantly improve the rate performance of the NOMA system compared to the conventional FPA system as well as other benchmark schemes.

关键词： NOMA Interference cancellation Precoding Simulation Transmitting antennas Adaptive arrays Benchmark testing Downlink Decoding Optimization

来源：评论

学校读者我要写书评

暂无评论

TEMPORAL UP-SAMPLING for ASYNCHRONOUS EVENTS

arXiv

引用

arXiv 2022年

作者： Xiang, Xijie Zhu, Lin Li, Jianing Tian, Yonghong Huang, Tiejun Department of Electrical and Computer Engineering Shanghai Jiao Tong University China School of Computer Science Peking University China Peng Cheng National Laboratory China

The event camera is a novel bio-inspired vision sensor. When the brightness change exceeds the preset threshold, the sensor generates events asynchronously. The number of valid events directly affects the performance of event-based tasks, such as reconstruction, detection, and recognition. However, when in low-brightness or slow-moving scenes, events are often sparse and accompanied by noise, which poses challenges for event-based tasks. To solve these challenges, we propose an event temporal up-sampling algorithm1 to generate more effective and reliable events. The main idea of our algorithm is to generate up-sampling events on the event motion trajectory. First, we estimate the event motion trajectory by contrast maximization algorithm and then up-sampling the events by temporal point processes. Experimental results show that upsampling events can provide more effective information and improve the performance of downstream tasks, such as improving the quality of reconstructed images and increasing the accuracy of object detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Luminance

来源：评论

学校读者我要写书评

暂无评论

Development of a Wildfire Detection and Monitoring Solution Using computer vision

Development of a Wildfire Detection and Monitoring Solution ...

引用

IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)

作者： Leo Ramos Edmundo Casas Eduardo Bendek Cristian Romero Francklin Rivas-Echeverría Dunetchka Cerpa Pablo Hernández Gonzalo Orellana José Luis Ibarra Carlos Rosas Albrecht Natalia Cuevas Juan Carlos Gallardo Kauel Inc. Menlo Park CA USA Computer Vision Center Universitat Autò noma de Barcelona Bellaterra Spain Faculty of Engineering University of Deusto Bilbao Spain Jet Propulsion Laboratory NASA Pasadena CA USA

ISBN: (数字)9798350362138

ISBN: (纸本)9798350362145

In this work, a platform for the detection, monitoring, and alerting of wildfires is developed, with the aim of demonstrating that the integration of artificial intelligence and other current technologies allows for the development of robust solutions to address various challenges. For this purpose, different versions of YOLOv8 were trained for the detection of fire and smoke. The result of this yielded the best model, the large version of YOLOv8, with metrics exceeding 80% precision and mean Average Precision. This model is integrated into a platform developed to keep a record of all generated alerts and to take actions on them. The platform features functionalities to record the location, date, time, and risk level of each alert. Additionally, it allows for viewing these alerts on a map and includes visualization elements to track the status of all alerts.

关键词： Measurement Industries Visualization Technological innovation computer vision Wildfires Artificial intelligence Monitoring

来源：评论

学校读者我要写书评

暂无评论

LoMAE: Low-level vision Masked Autoencoders for Low-dose CT Denoising

arXiv

引用

arXiv 2023年

作者： Wang, Dayang Xu, Yongshun Han, Shuo Wu, Zhan Zhou, Li Morovati, Bahareh Yu, Hengyong The Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States The Laboratory of Image Science and Technology Southeast University Nanjing210096 China The Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China

Low-dose computed tomography (LDCT) offers reduced X-ray radiation exposure but at the cost of compromised image quality, characterized by increased noise and artifacts. Recently, transformer models emerged as a promising avenue to enhance LDCT image quality. However, the success of such models relies on a large amount of paired noisy and clean images, which are often scarce in clinical settings. In the fields of computer vision and natural language processing, masked autoencoders (MAE) have been recognized as an effective label-free self-pretraining method for transformers, due to their exceptional feature representation ability. However, the original pretraining and fine-tuning design fails to work in low-level vision tasks like denoising. In response to this challenge, we redesign the classical encoder-decoder learning model and facilitate a simple yet effective low-level vision MAE, referred to as LoMAE, tailored to address the LDCT denoising problem. Moreover, we introduce an MAE-GradCAM method to shed light on the latent learning mechanisms of the MAE/LoMAE. Additionally, we explore the LoMAE’s robustness and generability across a variety of noise levels. Experiments results show that the proposed LoMAE can enhance the transformer’s denoising performance and greatly relieve the dependence on the ground truth clean data. It also demonstrates remarkable robustness and generalizability over a spectrum of noise levels. © 2023, CC BY.

关键词： Image quality

来源：评论

学校读者我要写书评

暂无评论

ISGAN: Unsupervised Domain Adaptation With Improved Symmetric GAN for Cross-Modality Multi-Organ Segmentation

引用

IEEE Journal of Biomedical and Health Informatics 2024年第6期29卷 3874-3885页

作者： Jiapeng Li Yifan Zhang Lisheng Xu Yudong Yao Lin Qi College of Medicine and Biological Information Engineering Northeastern University Shenyang China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA

The differences between cross-modality medical images are significant, so several studies are working on unsupervised domain adaptation (UDA) segmentation, which aims to adapt a segmentation model trained on a labeled source domain to an unlabeled target domain. The conventional UDA segmentation strategy aims to integrate image generation and segmentation. However, conventional image generation modules only consider information from a single domain (source or target), resulting in visual inconsistencies. The image generation module may also lack anatomical constraints, leading to incorrect pseudo-label generation. To address these issues, we propose an improved symmetric generative adversarial network (ISGAN). Unlike conventional approaches that perform domain adaptation only in the source or target domain, ISGAN adopts a symmetric architecture using two-path domain adaptation to reduce the visual difference. In addition, ISGAN adopts a bidirectional training strategy to optimize the image generation and segmentation modules. The bidirectional training strategy introduces the anatomical constraints into the image generation module, thereby reducing the generation of incorrect pseudo labels. Finally, we validate ISGAN on two cross-modality datasets (the MMWHS cardiac dataset and Abdomen dataset). ISGAN delivers promising segmentation and generalization performance compared with state-of-the-art UDA methods.

关键词： Image segmentation Training Biomedical imaging Image synthesis Magnetic resonance imaging Computed tomography Feature extraction Generative adversarial networks Generators Diffusion models

来源：评论

学校读者我要写书评

暂无评论

Performance Optimization of Semantic Communications for Users with Heterogeneous Knowledge

Performance Optimization of Semantic Communications for User...

引用

IEEE International Conference on Communications (ICC)

作者： Jiantong Zhang Mingzhe Chen Yujiao Zhu H. Shihao Tao Luo Zhilong Zhang Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, a semantic communication framework for a scenario where the transmitter and the receiver have different knowledge is proposed. In the proposed framework, the transmitter extracts semantic information from the data according to its knowledge and sends it to the receiver. The receiver requires to process the received semantic information based on its knowledge. Here, the knowledge implies a method which the transmitter or the receiver can use to process the semantic information. Since the transmitter or the receiver have different knowledge, they may have different understandings for the same information. To ensure the receiver can understand the semantic information, the transmitter requires to estimate the receiver's knowledge by asking a series of questions about the transmitted data. By evaluating the difference between the answers of the receiver and the answers of the transmitter, the transmitter can adjust the method of semantic information extraction. Since the size of the extracted semantic information and the number of the questions that the transmitter can ask are limited, the transmitter must adjust the method of semantic information extraction and select appropriate questions to transmit. This problem is formulated as an optimization problem whose goal is to maximize the similarity of answers of the transmitter and the receiver by determining semantic information while minimizing the semantic similarity of answers by determining the questions to be transmitted. To solve this problem, an adversarial reinforcement learning (ARL) algorithm is proposed. The proposed algorithm, which consists of a semantic information RL and a question RL, can find the optimal semantic information generation scheme and question selection scheme by a competitive game between two agents. Simulation results demonstrate that the proposed framework can improve the semantic similarity of answers by up to 3.7% gain and can achieve up to 15.2 % gain in terms of the aver

关键词： Transmitters Simulation Semantics Receivers Games Transforms Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Once read is enough: domain-specific pretraining-free language models with cluster-guided sparse experts for long-tail domain knowledge 24

Once read is enough: domain-specific pretraining-free langua...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Fang Dong Mengyi Chen Jixian Zhou Yubin Shi Yixuan Chen Mingzhi Dong Yujiang Wang Dongsheng Li Xiaochen Yang Rui Zhu Robert Dick Qin Lv Fan Yang Tun Lu Ning Gu Li Shang China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath and China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Microsoft Research Asia Shanghai China School of Mathematics & Statistics University of Glasgow Bayes Business School City St George's University of London Department of Electrical Engineering and Computer Science University of Michigan Department of Computer Science University of Colorado Boulder School of Microelectronics Fudan University

ISBN: (纸本)9798331314385

Language models (LMs) only pretrained on a general and massive corpus usually cannot attain satisfying performance on domain-specific downstream tasks, and hence, applying domain-specific pretraining to LMs is a common and indispensable practice. However, domain-specific pretraining can be costly and time-consuming, hindering LMs' deployment in real-world applications. In this work, we consider the incapability to memorize domain-specific knowledge embedded in the general corpus with rare occurrences and "long-tail" distributions as the leading cause for pretrained LMs' inferior downstream performance. Analysis of Neural Tangent Kernels (NTKs) reveals that those long-tail data are commonly overlooked in the model's gradient updates and, consequently, are not effectively memorized, leading to poor domain-specific downstream performance. Based on the intuition that data with similar semantic meaning are closer in the embedding space, we devise a Cluster-guided Sparse Expert (CSE) layer to actively learn long-tail domain knowledge typically neglected in previous pretrained LMs. During pretraining, a CSE layer efficiently clusters domain knowledge together and assigns long-tail knowledge to designate extra experts. CSE is also a lightweight structure that only needs to be incorporated in several deep layers. With our training strategy, we found that during pretraining, data of long-tail knowledge gradually formulate isolated, "outlier" clusters in an LM's representation spaces, especially in deeper layers. Our experimental results show that only pretraining CSE-based LMs is enough to achieve superior performance than regularly pretrained-finetuned LMs on various downstream tasks, implying the prospects of domain-specific-pretraining-free language models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Metasurfaces That Enable Bottle-Beam Arrays for Neutral Atom Trapping

Metasurfaces That Enable Bottle-Beam Arrays for Neutral Atom...

引用

2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Atom lasers

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：