检索结果-内蒙古大学图书馆

Antennas and Propagation Society International Symposium

作者： Shiwen Tang Yujie Zhang Junhui Rao Tianrui Qiao Chi-Yuk Chiu Ross Murch Department of Electronic and Computer Engineering Institute for Advanced Study The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

A dual-port 1×2 antenna array incorporating a parasitic pixel surface in front of the array is proposed. By optimizing the pixel surface, different radiation patterns can be provided by each port. To demonstrate the effectiveness of the approach, a dual-port design, with patterns directed to 112° and 66° and gains of 7.87 dBi and 6.95 dBi respectively at 28 GHz, is provided. A feature of the design is that no RF switches are needed in the pixel surface. The proposed antenna array can be suitable for future fifth-generation (5G) wireless communication systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

GANscan:continuous scanning microscopy using deep learning deblurring

引用

Light(Science & Applications) 2022年第10期11卷 2378-2387页

作者： Michael John Fanous Gabriel Popescu Quantitative Light Imaging Laboratory Beckman Institute for Advanced Science and TechnologyUniversity of Illinois at Urbana-ChampaignUrbanaIL 61801USA Department of Bioengineering University of Illinois at Urbana-Champaign306 N.Wright StreetUrbanaIL 61801USA Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign306 N.Wright StreetUrbanaIL 61801USA

Most whole slide imaging(WSI)systems today rely on the"stop-and-stare"approach,where,at each field of view,the scanning stage is brought to a complete stop before the camera snaps a *** procedure ensures that each image is free of motion blur,which comes at the expense of long acquisition *** order to speed up the acquisition process,especially for large scanning areas,such as pathology slides,we developed an acquisition method in which the data is acquired continuously while the stage is moving at high *** generative adversarial networks(GANs),we demonstrate this ultra-fast imaging approach,referred to as GANscan,which restores sharp images from motion blurred *** allows us to complete image acquisitions at 30x the throughput of stop-and-stare *** method is implemented on a Zeiss Axio Observer Z1 microscope,requires no specialized hardware,and accomplishes successful reconstructions at stage speeds of up to 5000 μm/*** validate the proposed method by imaging H&E stained tissue *** method not only retrieves crisp images from fast,continuous scans,but also adjusts for defocusing that occurs during scanning within+/-5 μ*** a consumer GPU,the inference runs at<20 ms/image.

关键词： hardware continuous image

来源：评论

学校读者我要写书评

暂无评论

Language models as science tutors 24

Language models as science tutors

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Alexis Chevalier Jiayi Geng Alexander Wettig Howard Chen Sebastian Mizera Toni Annala Max Jameson Aragon Arturo Rodríguez Fanlo Simon Frieder Simon Machado Akshara Prabhakar Ellie Thieu Jiachen T. Wang Zirui Wang Xindi Wu Mengzhou Xia Wenhan Xia Jiatong Yu Jun-Jie Zhu Zhiyong Jason Ren Sanjeev Arora Danqi Chen Princeton Language and Intelligence Princeton University School of Natural Sciences Institute for Advanced Study School of Mathematics Institute for Advanced Study Neuroscience Institute Princeton University Hebrew University of Jerusalem Department of Computer Science Oxford University FIM - Institute for Mathematical Research ETH Zürich Department of Computer Science University of Wisconsin-Madison and Meta FAIR Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment Princeton University

NLP has recently made exciting progress toward training language models (LMs) with strong scientific problem-solving skills. However, model development has not focused on real-life use-cases of LMs for science, including applications in education that require processing long scientific documents. To address this, we introduce TUTOREVAL and TUTORCHAT. TUTOREVAL is a diverse question-answering benchmark consisting of questions about long chapters from STEM textbooks, written by experts. TUTOREVAL helps measure real-life usability of LMs as scientific assistants, and it is the first benchmark combining long contexts, free-form generation, and multi-disciplinary scientific knowledge. Moreover, we show that fine-tuning base models with existing dialogue datasets leads to poor performance on TUTOREVAL. Therefore, we create TUTORCHAT, a dataset of 80,000 long synthetic dialogues about textbooks. We use TUTORCHAT to fine-tune Llemma models with 7B and 34B parameters. These LM tutors specialized in math have a 32K-token context window, and they excel at TUTOREVAL while performing strongly on GSM8K and MATH. Our datasets build on open-source materials, and we release our models, data, and evaluations publicly.

关键词：

来源：评论

学校读者我要写书评

暂无评论

IoT Farm: A Robust Methodology Design to Support Smart Agricultural System Using Internet of Things with Intelligent Sensors Association 7

IoT Farm: A Robust Methodology Design to Support Smart Agric...

引用

7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Karthiga, R. Brindha Devi, C.L. Janaki, R. Gayathri, C. Samuthira Pandi, S. Shobana, D. R.M.K Engineering College Kavaraipettai Department of IT Tamilnadu Chennai India Department of Computer Science Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science & Humanities Tamilnadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamilnadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350340600

The term 'Smart Farming' has been increasingly popular in recent years due, in large part, to the proliferation of network-based, high-tech farm monitoring cycles. These cycles make extensive use of technologies for communication and information gathering in the form of machinery, equipment, and sensors. The Internet of Things, often known as IoT, is the wave of the future in every sector of the economy, and its impacts will be felt by everyone. It is a collection of different devices that are capable of configuring themselves into a fully operational network. Conventional farming practices are progressively being supplanted by smart farming, which takes use of advancements made possible by the internet of things. This makes conventional farming methods more efficient, which in turn saves farmers money and reduces the amount of food that is lost. This technology helps farmers conserve energy and time by evaluating data in actual time from sensors placed in crops and offering actionable information on how their crops are progressing. The data comes from the sensors themselves, which are planted in the crops. Data from the field is sent to the cloud and evaluated there so that procedures may be automated utilizing Internet of Things devices. The assessment of the farmer's contribution into the process of making choices is demonstrated with information regarding the temperature of the field, the soil moisture level, the humidity level, and the amount of water that is used. In addition, the technical knowledge presented here is valuable to producers at every stage of the production process, including planting, growing, harvesting, packaging and shipping. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

A Novel Deep Learning Assisted Lung Nodule Identification Mechanism with Intelligent Image Processing Principles

A Novel Deep Learning Assisted Lung Nodule Identification Me...

引用

2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Senthil, P. Pandi, V.Samuthira Shobana, D. Jayashankari, J. Priya, J. Lakshmi Kaushik, G. Department of Computer Science and Engineering Cmr College of Engineering & Technology India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350300857

Lung cancer has the greatest fatality rate of all cancers. Due to its efficacy, chest CT has become standard practice for lung cancer screening and diagnosis. Radiologists have a significant challenge when trying to detect nodules in 3D volume imaging due to the tiny size of their shadows. This research study has created a novel computer-Aided Design (CAD) system that can automatically identify tumors in the lungs in CT scans to help with this problem. When it comes to cancers that can be fatal even if treated late, lung cancer is by far the most frequent. At now, CT can be utilized to aid in the early detection of lung cancer. The identification of lung cancer frequently relies on the expertise of medical professionals, who may overlook some patients and so exacerbate the situation. In several fields of medical imaging diagnostics, deep learning has shown to be an effective and widely used technique. This research work proposes an Intelligent Learning based Nodule Detection (ILND), a revolutionary deep learning-based approach to detecting lung cancer. This research study adapts those networks for use in classifying CT images of both malignant and benign lung nodules. The CT imaging dataset was used to assess the performance of the networks. The results of the experiments demonstrate that the suggested Intelligent Learning based Nodule Detection model outperformed the other network models, with an accuracy of 95.67%, a sensitivity of 93.65% and a specificity of 94.82%. © 2023 IEEE.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

引用

第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

来源：评论

学校读者我要写书评

暂无评论

Spectroscopy characterization of quantum modes in an on-chip squeezed microcomb

Spectroscopy characterization of quantum modes in an on-chip...

引用

2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Jahanbozorgi, Mandana Yang, Zijiao Parnell, Emily A. Jeong, Dongin Sun, Shuman Pfister, Olivier Lee, Hansuek Yi, Xu University of Virginia Department of Electrical and Computer Engineering CharlottesvilleVA22904 United States University of Virginia Department of Physics CharlottesvilleVA22904 United States Graduate School of Nanoscience and Technology Daejeon34141 Korea Republic of Korea Advanced Institute of Science and Technology Department of Physics Daejeon34141 Korea Republic of

We characterized the spectrum of 40 quantum modes in an on-chip squeezed microcomb. A theoretical model is developed to explain how cavity dispersion affects the squeezing and the frequency equidistance of these quant...

ISBN: (纸本)9781957171180

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Generative Adversarial Network for Occlusion Removal from a Single Image

arXiv

引用

arXiv 2024年

作者： Jonna, Sanakaraganesh Medhi, Moushumi Sahay, Rajiv Ranjan The department of Computer Science and Engineering Indian Institute of Technology Kharagpur721302 India The Advanced Technology Development Center Indian Institute of Technology Kharagpur721302 India Department of Electrical Engineering Indian Institute of Technology Kharagpur721302 India

Nowadays, the enhanced capabilities of in-expensive imaging devices have led to a tremendous increase in the acquisition and sharing of multimedia content over the Internet. Despite advances in imaging sensor technology, annoying conditions like occlusions hamper photography and may deteriorate the performance of applications such as surveillance, detection, and recognition. Occlusion segmentation is difficult because of scale variations, illumination changes, and so on. Similarly, recovering a scene from foreground occlusions also poses significant challenges due to the complexity of accurately estimating the occluded regions and maintaining coherence with the surrounding context. In particular, image de-fencing presents its own set of challenges because of the diverse variations in shape, texture, color, patterns, and the often cluttered environment. This study focuses on the automatic detection and removal of occlusions from a single image. We propose a fully automatic, two-stage convolutional neural network for fence segmentation and occlusion completion. We leverage generative adversarial networks (GANs) to synthesize realistic content, including both structure and texture, in a single shot for inpainting. To assess zero-shot generalization, we evaluated our trained occlusion detection model on our proposed fence-like occlusion segmentation dataset. The dataset can be found on GitHub. © 2024, CC BY.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer

High-Fidelity Document Stain Removal via A Large-Scale Real-...

引用

IEEE Workshop on Applications of computer Vision (WACV)

作者： Mingxian Li Hao Sun Yingtie Lei Xiaofeng Zhang Yihang Dong Yilin Zhou Zimeng Li Xuhang Chen School of Computer Science and Engineering Huizhou University Department of Computer Science and Engineering The Ohio State University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shenzhen Institute of Advanced Technology Chinese Academy of Sciences School of Electronic and Communication Engineering Shenzhen Polytechnic University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Document images are often degraded by various stains, significantly impacting their readability and hindering downstream applications such as document digitization and analysis. The absence of a comprehensive stained document dataset has limited the effectiveness of existing document enhancement methods in removing stains while preserving fine-grained details. To address this challenge, we construct StainDoc, the first large-scale, high-resolution (2145 x 2245) dataset specifically designed for document stain removal. StainDoc comprises over 5,000 pairs of stained and clean document images across multiple scenes. This dataset encompasses a diverse range of stain types, severities, and document backgrounds, facilitating robust training and evaluation of document stain removal algorithms. Furthermore, we propose StainRestorer, a Transformer-based document stain removal approach. StainRestorer employs a memory-augmented Transformer architecture that captures hierarchical stain representations at part, instance, and semantic levels via the DocMemory module. The Stain Removal Transformer (SRTransformer) leverages these feature representations through a dual attention mechanism: an enhanced spatial attention with an expanded receptive field, and a channel attention captures channel-wise feature importance. This combination enables precise stain removal while preserving document content integrity. Extensive experiments demonstrate StainRestorer's superior performance over state-of-the-art methods on the Stain-Doc dataset and its variants *** and ***, establishing a new benchmark for document stain removal. Our work highlights the potential of memory-augmented Transformers for this task and contributes a valuable dataset to advance future research.

关键词： Training Deep learning computer vision Text analysis Attention mechanisms Semantics Memory architecture Benchmark testing Transformers Image restoration

来源：评论

学校读者我要写书评

暂无评论

A multi-modal approach for identifying schizophrenia using cross-modal attention

arXiv

引用

arXiv 2023年

作者： Premananth, Gowtham Siriwardena, Yashish M. Resnik, Philip Espy-Wilson, Carol Department of Electrical and Computer Engineering University of Maryland College ParkMD United States Institute for Advanced Computer Studies University of Maryland College ParkMD United States

This study focuses on how different modalities of human communication can be used to distinguish between healthy controls and subjects with schizophrenia who exhibit strong positive symptoms. We developed a multi-modal schizophrenia classification system using audio, video, and text. Facial action units and vocal tract variables were extracted as low-level features from video and audio respectively, which were then used to compute high-level coordination features that served as the inputs from the audio and video modalities. Context-independent text embeddings extracted from transcriptions of speech were used as the input for the text modality. The multimodal system is developed by fusing a segment-to-session-level classifier for video and audio modalities with a text model based on a Hierarchical Attention Network (HAN), with cross-modal attention. The proposed multi-modal system outperforms the previous state-of-the-art multi-modal system by 8.53% in the weighted average F1 score. © 2023, CC BY.

关键词： Embeddings

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：