检索结果-内蒙古大学图书馆

IEEE International Wireless Symposium (IWS)

作者： Peng Yang Zhe Chen Zongxiang Wang Yue Qi Pinqing Wang Peigen Zhou Lin Peng PinPin Yan Jixin Chen Wei Hong State Key Lab. of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE Corporation

ISBN: (数字)9798350389999

ISBN: (纸本)9798350390001

This paper presents a power amplifier (PA) operating in the frequency range of 115-140 GHz, implemented using a 0.13 μm germanium-silicon (SiGe) BiCMOS process. The single stage amplifier employs a cascode structure and integrates gmboosting technology to enhance gain. To ensure superior broadband performance at high frequencies, input and output matching are achieved through the utilization of a Marchand balun. The PA operates from 115 GHz to 140 GHz (3dB bandwidth) with a peak gain of 23.7 dB at 130GHz. At 116 GHz, the amplifier achieves a saturated output power of 16.2 dBm, with the peak power added efficiency (PAE) of 10.2%. The total chip size is 0.92×0.73 mm 2 .

关键词： Wireless communication Transmitters Baluns Millimeter wave technology Power amplifiers BiCMOS integrated circuits Power generation Gain Silicon germanium Periodic structures

来源：评论

学校读者我要写书评

暂无评论

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound optimization

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao ...

引用

2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Song, Xianxin Xu, Jie Liu, Fan Han, Tony Xiao Eldar, Yonina C. School of Science and Engineering Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence China Southern University of Science and Technology Department of Electrical and Electronic Engineering China Wireless Technology Lab 2012 Laboratories Huawei China Weizmann Institute of Science Faculty of Mathematics and Computer Science Israel

ISBN: (纸本)9781665459754

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. The AP aims to estimate the target's direction-of-arrival (DoA) with respect to the IRS, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the CramérRao lower bound (CRLB) on estimation error. Towards this end, we first obtain the CRLB expression for estimating the DoA in closed form. Next, we optimize the joint beamforming design to minimize the CRLB, via alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design based on CRLB minimization achieves improved sensing performance in terms of mean squared error, as compared to the traditional schemes with signal-to-noise ratio maximization and separate beamforming. © 2022 IEEE.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

Community discovery method based on complex network of data fusion based on the super network perspective

引用

International Journal of Computers and Applications 2021年第4期43卷 383-390页

作者： Pei, Li School of Computer Science & Technology Xi‘an University of Posts and Telecommunications Xi‘an China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi‘an University of Posts and Telecommunications Xi‘an China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi‘an Jiaotong University Xi‘an China

To enhance the computational efficiency and precision of community discovery, a community discovery algorithm with the mixed label based on the minimum description length (MDI) of information compression is proposed in this paper. Firstly, the community detection is converted to the information compression problem of seeking for an effective network structure and the quality evaluation function is constructed based on the MDL criterion. Secondly, the community discovery algorithm with heuristic mixed label movement is constructed based on the label node movement algorithm and Louvain community addition algorithm so as to reduce the quality evaluation function. Finally, the simulation experiment in the standard test set and API capture Sina microblog dataset shows that the proposed algorithm is superior to the selected comparison algorithm in computational efficiency and precision. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

来源：评论

学校读者我要写书评

暂无评论

Towards HDR and HFR Video from Rolling-Mixed-Bit Spikings

Towards HDR and HFR Video from Rolling-Mixed-Bit Spikings

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yakun Chang Yeliduosi Xiaokaiti Yujia Liu Bin Fan Zhaojun Huang Tiejun Huang Boxin Shi Institute of Information Science School of Computer Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University National Key Lab of General AI School of Intelligence Science and Technology Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The spiking cameras offer the benefits of high dynamic range (HDR), high temporal resolution, and low data redundancy. However, reconstructing HDR videos in high-speed conditions using single-bit spikings presents challenges due to the limited bit depth. Increasing the bit depth of the spikings is advantageous for boosting HDR performance, but the readout efficiency will be decreased, which is unfavorable for achieving a high frame rate (HFR) video. To address these challenges, we propose a readout mechanism to obtain rolling-mixed-bit (RMB) spikings, which involves inter-leaving multi-bit spikings within the single-bit spikings in a rolling manner, thereby combining the characteristics of high bit depth and efficient readout. Furthermore, we introduce RMB-Net for reconstructing HDR and HFR videos. RMB-Net comprises a cross-bit attention block for fusing mixed-bit spikings and a cross-time attention block for achieving temporal fusion. Extensive experiments conducted on synthetic and real-synthetic data demonstrate the superiority of our method. For instance, pure 3 -bit spikings result in 3 times of data volume, whereas our method achieves comparable performance with less than 2% increase in data volume.

关键词： Computer vision Computational modeling Redundancy Cameras Boosting Data models Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Type-Representation Enhanced Event Detection via Structure Encoder

Type-Representation Enhanced Event Detection via Structure E...

引用

Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yuming Feng Linmeng Huang Haozhe Liang Xiaolong Jin Saiping Guan Jiafeng Guo School of Computer Science and Technology University of Chinese Academy of Sciences Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Institute of Systems Engineering Academy of Military Science Beijing

Event detection, aiming to detect whether a sentence contains the type of events of interest, is one of the important tasks in information Extraction. There exist many methods for event detection, which usually treat event detection as a classification problem. These existing methods suffer from the imbalanced training data of different event types. To relieve this problem, this paper proposes a new Type-Representation-Enhanced Event Detection (TREED) method, which introduce hierarchical event type representations to enhance classification accuracy. Specifically, TREED first models the hierarchical relationship among event types in the dataset; Next, upon interactions between the representations of trigger words and event types, it obtains the new representations of trigger words integrating event type information. Finally, TREED classifies the events based on the obtained more accurate trigger word representations. Experimental results on a commonly used benchmark dataset, ACE-2005, demonstrate that the proposed model can improve both accuracy and recall of the event detection task.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Variational Zero-shot Multispectral Pansharpening

arXiv

引用

arXiv 2024年

作者： Rui, Xiangyu Cao, Xiangyong Li, Yining Meng, Deyu School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Computer Science and Technology Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Macao Institute of Systems Engineering Macau University of Science and Technology Taipa China

Pansharpening aims to generate a high spatial resolution multispectral image (HRMS) by fusing a low spatial resolution multispectral image (LRMS) and a panchromatic image (PAN). The most challenging issue for this task is that only the to-be-fused LRMS and PAN are available, and the existing deep learning-based methods are unsuitable since they rely on many training pairs. Traditional variational optimization (VO) based methods are well-suited for addressing such a problem. They focus on carefully designing explicit fusion rules as well as regularizations for an optimization problem, which are based on the researcher’s discovery of the image relationships and image structures. Unlike previous VO-based methods, in this work, we explore such complex relationships by a parameterized term rather than a manually designed one. Specifically, we propose a zero-shot pansharpening method by introducing a neural network into the optimization objective. This network estimates a representation component of HRMS, which mainly describes the relationship between HRMS and PAN. In this way, the network achieves a similar goal to the so-called deep image prior because it implicitly regulates the relationship between the HRMS and PAN images through its inherent structure. We directly minimize this optimization objective via network parameters and the expected HRMS image through alternating minimization. Extensive experiments on various benchmark datasets demonstrate that our proposed method can achieve better performance compared with other state-of-the-art methods. The codes are available at https://***/xyrui/PSDip. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Measurement-based entanglement purification for entangled coherent states

引用

Frontiers of physics 2022年第2期17卷 71-81页

作者： Pei-Shun Yan Lan Zhou Wei Zhong Yu-Bo Sheng Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Science Nanjing University of Posts and TelecommunicationsNanjing 210003China Institute of Signal Processing Transmission Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China

The entangled coherent states(ECSs)have been widely used to realize quantum information processing ***,the ECSs may suffer from photon loss and decoherence due to the inherent noise in quantum channel,which may degrade the fidelity of *** overcome these obstacles,we present a measurement-based entanglement purification protocol(MBEPP)for ECSs to distill some highquality ECSs from a large number of low-quality *** first show the principle of this MBEPP without considering the photon *** that,we prove that this MBEPP is feasible to correct the error resulted from the photon ***,this MBEPP only requires to operate the Bell state measurement without performing local two-qubit gates on the noisy pairs and the purified high-quality ECSs can be preserved for other *** MBEPP may have application potential in the implementation of long-distance quantum communication.

关键词： measurement-based entanglement purification entangled coherent state photon loss decoherence

来源：评论

学校读者我要写书评

暂无评论

Software Module Clustering Using the Hierarchical Clustering Combination Method 7

Software Module Clustering Using the Hierarchical Clustering...

引用

7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Xia, Hong Zhang, Yongkang Chen, Yanping Zhang, Hengshan Wang, Zhongmin Wang, Fengwei School of Computer Science and Technology Xi'An Univ. of P. and Telecom. Shaanxi Key Lab. of Network Data Analysis and Intelligent Processing Xi'an China Xi'An University of Posts and Telecommunications School of Computer Science and Technology Xi'an China ZTECorporation Shenzhen China

ISBN: (纸本)9781665487115

The scale of software applications has increased dramatically. Hierarchical clustering is a good method for modular recovery of software architecture. Because the different evaluation criteria of software types and clustering results, a single hierarchical clustering algorithm cannot integrate the number of clusters, arbitrary decision-making, recovery quality and other indicators on different evaluation standards, there is no comprehensive clustering algorithm selection method. We propose a hierarchical clustering combination algorithm and use principal component analysis to combine the results of multiple hierarchical clusters, the combined result retain the basic information of each clustering algorithm as much as possible. The experimental results show that compared with the single software hierarchical clustering method and other combined methods, the result of our method is closer to expert decomposition, and it has good performance on a variety of indicators. © 2022 IEEE.

关键词： Cloud computing Software architecture Clustering methods Software algorithms Decision making Clustering algorithms Big Data

来源：评论

学校读者我要写书评

暂无评论

MEFold: Memory-Efficient Optimization for Protein Language Models via Chunk and Quantization

MEFold: Memory-Efficient Optimization for Protein Language M...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Yanfeng Jiang Ning Sun Zhengxian Lu Shuang Peng Yi Zhang Fei Yang Tao Li College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Zhejiang Lab Hangzhou China Haihe Lab of ITAI Tianjin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Protein language models are currently experiencing a surge in demand owing to their remarkable accuracy in protein structure prediction. Nevertheless, their applications are hindered by the significant computation and memory requirements. The existing optimization strategies primarily focus on computational efficiency while often neglecting memory optimization, thereby restricting their suitability for devices with limited resources. In this paper, we propose MEFold, a novel memory-efficient optimization framework for protein language models that enables efficient inference on resource-constrained devices. MEFold consists of Look-up Table Chunk and Fine-grained Quantization. Look-up Table Chunk reduces the memory of intermediate activations by chunk and avoids the overhead of obtaining the optimal chunk size configuration through pre-computing. For the memory of model parameters, Fine-grained Quantization, delicately controls the scope of quantization to ensure that memory reduction is achieved while preventing declines in accuracy and computational speed. Experimental results show that, compared to the original model, for protein sequences ranging from 74 to 1024 in length, our method significantly reduces the peak memory during inference from 14.7-54.2GB to 6.0-14.4GB, while minimizing the impact on inference latency. On CASP14 and CAMEO datasets, the accuracy loss compared to the original model is below 1%. Moreover, our optimization provides various memory-saving alternatives. Our code is available at https://***/llwx593/MEFold.

关键词： Proteins Quantization (signal) Accuracy Computational modeling Memory management Neural networks Predictive models

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：