检索结果-内蒙古大学图书馆

19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Sadat, Mohammad Nazmus Vargas-Alfonso, Erwin Dai, Rui Bradley University Department of Computer Science and Information Systems PeoriaIL United States University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States

ISBN: (纸本)9781665431620

Edge computing has opened new doors for real-time video analytics applications due to its ability to offer significantly faster response times by processing videos near the source. However, the limited computing capabilities of edge devices can affect the quality of video analysis. While the existing literature has focused on minimizing latency, the quality aspect needs to be rigorously investigated to satisfy the high accuracy requirements of video analytics applications. This paper proposes a new quality-aware video analytics framework for edge networks. First, a real-time video analytics platform based on edge computing is designed and implemented. Then, the impacts of different factors on the quality and the overall latency of edge computing-based video analysis are investigated. Next, the trade-off between the video analysis quality and the latency, i.e., computation benefits at the edge vs. remote server, is studied. Finally, a quality-aware edge computing-based video analytics framework (QVAF) is designed to minimize the total latency while guaranteeing a required detection accuracy. Evaluation results show that QVAF could significantly improve response times across different detection accuracy requirements. © 2022 IEEE.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Low-cost contact-free screen control based on machine vision 4

Low-cost contact-free screen control based on machine vision

引用

4th Optics Frontier Conference, OFS 2024

作者： Lin, Yangbing Wang, Teng Yan, Wei Peng, Xiao Zhang, Xinhai Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China College of Physics and Optoelectronic Engineering Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen University Shenzhen518060 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9781510682269

Contact-free manipulation technology represents a novel approach that offers a fresh possibility for human-machine interaction, aiming to meet the evolving demands of this interface. In this paper, we introduce a contact-free planar display control technique based on imaging. In our research, we employ cameras as sensors and integrate infrared lasers with image processing techniques to detect and localize input signals. By establishing a coordinate mapping relationship between the operational plane and the display plane, we achieved contact-free control over the planar display. Our paper introduces two methods for establishing this coordinate mapping. The first involves constructing mapping functions between the rectified and transformed camera imagery and the display plane, thereby creating a point-to-point correspondence. The second method entails dividing both the camera imagery and the display plane into segments, establishing a one-to-one relationship between regions. We analyze the strengths and limitations of these two approaches. While the former boasts higher theoretical precision, it is more susceptible to equipment factors and exhibits slower response speeds. Conversely, the latter, despite its lower theoretical precision, offers faster response times. Through the setup of an experimental platform, we demonstrate the feasibility of our proposed. © 2024 SPIE.

关键词： Machine vision

来源：评论

学校读者我要写书评

暂无评论

Visual cognition of birds and its underlying neural mechanism:A review

引用

Avian Research 2022年第2期13卷 216-225页

作者： Xiaoke Niu Zhenyang Jiang Yanyan Peng Shuman Huang Zhizhong Wang Li Shi Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical EngineeringZhengzhou UniversityZhengzhou450001China Department of Automation Tsinghua UniversityBeijing100084China

Birds have acute vision and many remarkable visual cognition abilities,due to their unique living *** underlying neural mechanisms have also attracted interests of researchers in ***,we firstly summarize the visual cognition abilities of birds,and make a comparison with ***,the underlying neural mechanisms are presented,including histological structure of avian brain and visual pathways,typical experimental results and conclusions in electrochemistry and *** latter mainly focuses on several higher brain areas related to visual cognition,including mesopallium ventrolaterale,entopallium,visual Wulst,and nidopallium ***,we make a conclusion and provide a suggestion about future studies on revealing the neural mechanisms of avian visual *** review presents a detailed understanding of avian visual cognition and would be helpful in ornithology studies in the field of cognitive neuroscience.

关键词： Avian Electrophysiology Neural mechanism Visual cognition

来源：评论

学校读者我要写书评

暂无评论

Biomedical Osteosarcoma Image Classification Using Elephant Herd Optimization and Deep Learning

引用

computers, Materials & Continua 2022年第12期73卷 6443-6459页

作者： Areej A.Malibari Jaber S.Alzahrani Marwa Obayya Noha Negm Mohammed Abdullah Al-Hagery Ahmed S.Salama Anwer Mustafa Hilal Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Osteosarcoma is a type of malignant bone tumor that is reported across the *** advancements in Machine Learning(ML)and Deep Learning(DL)models enable the detection and classification of malignancies in biomedical *** this regard,the current study introduces a new Biomedical Osteosarcoma Image Classification using Elephant Herd Optimization and Deep Transfer Learning(BOIC-EHODTL)*** presented BOIC-EHODTL model examines the biomedical images to diagnose distinct kinds of *** the initial stage,Gabor Filter(GF)is applied as a pre-processing technique to get rid of the noise from *** addition,Adam optimizer with MixNet model is also employed as a feature extraction technique to generate feature ***,EHOalgorithm is utilized along with Adaptive Neuro-Fuzzy Classifier(ANFC)model for recognition and categorization of *** algorithm is utilized to fine-tune the parameters involved in ANFC model which in turn helps in accomplishing improved classification *** design of EHO with ANFC model for classification of osteosarcoma is the novelty of current *** order to demonstrate the improved performance of BOIC-EHODTL model,a comprehensive comparison was conducted between the proposed and existing models upon benchmark dataset and the results confirmed the better performance of BOIC-EHODTL model over recent methodologies.

关键词： Biomedical imaging osteosarcoma classification deep transfer learning parameter tuning fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

LLM4CP:Adapting Large Language Models for Channel Prediction

引用

Journal of Communications and Information Networks 2024年第2期9卷 113-125页

作者： Boxun Liu Xuanyu Liu Shijian Gao Xiang Cheng Liuqing Yang State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing 100871China Internet of Things Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Internet of Things Thrust and Intelligent Transportation Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Department of Electronic and Computer Engineering and Department of Civil and Environmental Engineering The Hong Kong University of Science and TechnologyHong KongChina

Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction(LLM4CP)method to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM,preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves state-of-the-art (SOTA) prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.

关键词： channel prediction massive multi-input multi-output(m-MIMO) large language models(LLMs) fine-tuning time-series

来源：评论

学校读者我要写书评

暂无评论

Robust Multiresolution and Multistain Background Segmentation in Whole Slide Images 23rd

Robust Multiresolution and Multistain Background Segmentatio...

引用

Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical engineering, PCBEE 2023

作者： Jurgas, Artur Wodzinski, Marek Atzori, Manfredo Müller, Henning Information Systems Institute Sierre Switzerland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Neuroscience University of Padova Padova Italy Medical Faculty University of Geneva Geneva Switzerland

ISBN: (纸本)9783031384295

Background segmentation is an important step in analysis of histopathological images. It allows one to remove irrelevant regions and focus on the tissue of interest. However, background segmentation is challenging due to the variability of stain colors and intensity levels across different images, modalities, and magnification levels. In this paper, we present a learning-based model for histopathology background segmentation based on convolutional neural networks. We compare two multiresolution approaches to deal with the variability of magnification in histopathology images: (i) model that uses upscaling of smaller patches of the image, and (ii) model simultaneously trained on multiple resolution levels. Our model is characterized by solid performance both in multiresolution and multistain dyes (H &E and IHC), achieving good performance on publicly available dataset. The quantitative scores are, in terms of the Dice score, close to 94.71. The qualitative analysis presents strong performance on previously unseen cases from different distributions and various dyes. We freely release the model, weights, and ground-truth annotations to promote the open science and reproducible research. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pathology

来源：评论

学校读者我要写书评

暂无评论

Assessing adversarial replay and deep learning-driven attacks on specific emitter identification-based security approaches

引用

Discover Internet of Things 2024年第1期4卷 25页

作者： Tyler, Joshua H. Fadul, Mohamed K. M. Hilling, Matthew R. Reising, Donald R. Loveless, T. Daniel Electrical Engineering Department The University of Tennessee at Chattanooga 735 Vine Street ChattanoogaTN37403 United States Department of Intelligent Systems Engineering Indiana University 700 N. Woodlawn Avenue BloomingtonIN47408 United States Department of Computer Science Tennessee Technological University 1 Williams L. Jones Drive CookevilleTN38505 United States

Specific Emitter Identification (SEI) has been put forward as an Internet of Things (IoT) Physical Layer Security (PLS) approach for its abilities to detect, characterize, and identify wireless emitters by exploiting distinct, inherent, and unintentional features in their transmitted signals. Since its introduction, a significant amount of work has been conducted;however, most assume the emitters are passive and their identifying signal features are immutable and challenging to mimic. This suggests the emitters are reluctant and incapable of developing and implementing effective SEI countermeasures;however, Deep Learning (DL) has been shown capable of learning emitter-specific features directly from their raw in-phase and quadrature signal samples, while Software-Defined Radios (SDRs) are capable of manipulating these signal samples. Based on these capabilities, it is fair to question the ease at which an emitter can effectively mimic the SEI features of another or manipulate its own to hinder or defeat SEI. This work considers SEI mimicry using three signal features mimicking countermeasures;"off-the-self" DL;two SDRs of different sizes, weights, power, and cost (SWaP-C);handcrafted and DL-based SEI processes, and assessment of an SEI mimicry countermeasure in a "coffee shop" deployment. Our results show "off-the-shelf" DL algorithms, and SDR enables SEI mimicry;however, adversary success is hindered by: (i) the use of decoy emitter preambles, (ii) the use of a denoising autoencoder and (iii) SDR SWaP-C constraints. © The Author(s) 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Efficient pipelined flow classification for intelligent data processing in IoT

引用

Digital Communications and Networks 2022年第4期8卷 561-575页

作者： Seyed Navid Mousavi Fengping Chen Mahdi Abbasi Mohammad R.Khosravi Milad Rafiee Department of Computer Engineering Engineering FacultyBu-Ali Sina UniversityHamedanIran Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang262700China Telecommunications Group Department of Electrical and Electronic EngineeringShiraz University of TechnologyShirazIran

The packet classification is a fundamental process in provisioning security and quality of service for many intelligent network-embedded systems running in the Internet of Things(IoT).In recent years,researchers have tried to develop hardware-based solutions for the classification of Internet *** to higher throughput and shorter delays,these solutions are considered as a major key to improving the quality of *** of these efforts have attempted to implement a software algorithm on the FPGA to reduce the processing time and enhance the *** proposed architectures,however,cannot reach a compromise among power consumption,memory usage,and throughput *** view of this,the architecture proposed in this paper contains a pipelinebased micro-core that is used in network processors to classify *** this end,three architectures have been implemented using the proposed *** first architecture performs parallel classification based on header *** second one classifies packets in a serial *** last architecture is the pipeline-based classifier,which can increase performance by nine *** proposed architectures have been implemented on an FPGA *** results are indicative of a reduction in memory usage as well as an increase in speedup and *** architecture has a power consumption of is 1.294w,and its throughput with a frequency of 233 MHz exceeds 147 Gbps.

关键词： Efficiency Intelligent flow processing IoT Packet classification Pipeline

来源：评论

学校读者我要写书评

暂无评论

Adaptive Uniform Performance Control of Strict-Feedback Nonlinear systems With Time-Varying Control Gain

引用

IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 451-461页

作者： Kai Zhao Changyun Wen Yongduan Song Frank L.Lewis IEEE the Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned SystemsSchool of AutomationChongqing UniversityChongqing 400044China the UTA Research Institute University of Texas at ArlingtonFort WorthTX 76118 USA

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following ***,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control ***,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer ***,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is ***,simulation results verify the effectiveness and benefits of the approach.

关键词： Adaptive control enhanced Nussbaum function(ENF) strict-feedback systems unified prescribed performance

来源：评论

学校读者我要写书评

暂无评论

A Universal Hybrid Precoding Scheme for Massive MIMO Communications

引用

China Communications 2022年第11期19卷 160-178页

作者： Yimeng Feng Yi Jiang Mahesh K.Varanasi Key Laboratory for Information Science of Electromagnetic Waves(MoE) School of Information Science and TechnologyFudan UniversityShanghai 200433China the Department of Electrical Computer and Energy EngineeringUniversity of ColoradoBoulderColorado 80309-0425

Based on an analog radio frequency(RF)network,hybrid precoding(HPC)for massive MIMO can achieve very high spectral efficiencies with moderate hardware cost and power *** the extensive research efforts in recent years,the practioners are still looking for HPCs that are efficient and *** this paper,we present a new method termed as the universal hybrid precoding(UHP),which is nearly optimal,computationally efficient,and applicable to various types of RF network(thus,the name universal):the components of the network can be phase shifters(with finite or infinite resolutions),switches,or their combinations;the topology of the network can be fully-connected or *** the standard UHP,we also propose a simplified version termed as sUHP to trade a negligible performance loss for much reduced computational *** analysis shows that the computational complexity of the proposed UHP/sUHP is one to two orders of magnitude lower than the state-of-theart *** results verify the(near-)optimality of the proposed UHP scheme for various forms of the analog networks.

关键词： hybrid beamforming massive MIMO phase-shifter network switch network

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：