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检索条件"任意字段=IEEE Conference on Local Computer Networks"

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Review of Intelligent Methods for Sign Language-to-Speech Translation 4

Review of Intelligent Methods for Sign Language-to-Speech Tr...

引用

4th ieee International conference on Advanced Learning Technologies for Education and Research, ICALTER 2024

作者： Trujillo, Edita Chafloque Cajusol Castillo, Luis Felipe Aquino, Juri Universidad Tecnológica del Perú Systenms and Computer Engineering Chiclayo Peru Universidad Tecnológica del Perú Dirección de Investigación Chiclayo Peru Universidad Tecnológica del Perú Docente of Systems and Computer Engineering Chiclayo Peru

ISBN: (纸本)9798331510244

In today's digital age, technology has significantly transformed communication. However, for the deaf community, the communication barrier remains a persistent challenge. This article addresses the critical need to develop intelligent systems that translate sign language into speech, highlighting its potential to improve the social inclusion of people with hearing impairment. The study conducts a systematic review of advanced methods, such as the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to identify promising techniques and persistent challenges in this field. The research is based on the analysis of 32 articles selected using strict inclusion and exclusion criteria, following the PRISMA statement. The results reveal that these systems can improve the autonomy of deafblind people, facilitating their access to essential services and promoting their social and occupational inclusion. Furthermore, it highlights the need for solutions adapted to specific local contexts due to the geographical variability in the prevalence of hearing impairment. This work not only contributes to scientific knowledge, but also lays the foundation for the development of more effective and accessible technologies, potentially transforming areas such as education, healthcare and employment for the deaf community. © 2024 ieee.

关键词： Intelligent systems

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A Cardiac MRI Segmentation Method Based on Context Cascade Attention 6

A Cardiac MRI Segmentation Method Based on Context Cascade A...

引用

6th International conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Hu, Panpan Liu, Ruixia Zhang, Shuaishuai Department of Mathematics and Artificial Intelligence Jinan250353 China Jinan250014 China

ISBN: (纸本)9798350353174

Cardiac Magnetic Resonance Imaging (MRI) segmentation plays a crucial role in clinical diagnosis, enabling accurate identification of cardiac lesions and abnormalities as well as quantification of various aspects of cardiac function by accurately segmenting structures such as the left ventricle, right ventricle, and myocardium. In recent years, numerous algorithms based on deep neural networks have been proposed for cardiac MRI segmentation. However, challenges still exist in refining features at each layer, preserving contextual local information, and enhancing global information. To address these issues, this study proposes a context cascade attention-based method for cardiac MRI segmentation. Firstly, PVTv2 is employed to capture remote dependencies within the encoder and facilitate feature extraction and representation. Secondly, a lightweight network incorporating a deep residual convolution module is constructed to fuse features using skip connections while leveraging cascade operations to refine layer-specific features through complementary information from different layers. Additionally, a contextual attention module is introduced to explore contextual information by retaining local details while compressing global information and suppressing unimportant areas to highlight salient features thereby achieving both retention of local information and enhancement of global information. Furthermore, model performance was evaluated using five-fold cross-validation demonstrating its robustness with improved accuracy in segmentation achieved through experimental results and visual analysis. © 2024 ieee.

关键词： Deep neural networks

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Automatic Modulation Classification Based on Decentralized Learning and Model Averaging

Automatic Modulation Classification Based on Decentralized L...

引用

2023 ieee INFOCOM conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Xu, Yunhe Ma, Min Fu, Xue Wang, Yu Gui, Guan Ohtsuki, Tomoaki College of Telecommunications and Information Engineering NJUPT Nanjing China Nanjing Vocational College of Information Technology Nanjing Nanjing China Keio University Department of Information and Computer Science Yokohama Japan

ISBN: (纸本)9781665494274

Automatic modulation classification (AMC) is a promising technology to identify the modulation mode of the received signal in non-cooperative communication scenarios. Recently, benefitting from the outstanding classification performance of deep learning (DL), various deep neural networks (DNNs) have been introduced into AMC methods. Most current AMC methods are based on local framework (localAMC) where there is only one device, or centralized framework (CentAMC) where multiple local devices (LDs) upload their data to the only one central server (CS). localAMC may not get ideal results due to insufficient data and finite computational power. CentAMC carries a significant risk of privacy leakage and the final data for training model in CS is quite massive. In this paper, we propose an AMC method based on decentralized learning with residual network (ResNet). Simulation results show that ResNet-based decentralized AMC (DecentAMC) method achieves similar classification performance to CentAMC while improving training efficiency and protecting data privacy. © 2023 ieee.

关键词： Deep neural networks

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Hotspotter: An Incentivized Crowdsensing System for WiFi and Cellular Network Coverage Visualization 29

Hotspotter: An Incentivized Crowdsensing System for WiFi and...

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29th ieee Asia Pacific conference on Communications, APCC 2024

作者： Ortiz, Sebastian Luis S. Enriquez, Justin Gabriel R. Tan, Wilson M. Festin, Cedric Angelo M. University of the Philippines - Diliman Networks and Distributed Systems Laboratory Department of Computer Science Metro Manila Philippines

ISBN: (纸本)9798331517816

Reliable internet access is a key enabler for economic growth. Although the Philippine government launched initiatives to improve connectivity, connection speeds remained below the global average, especially for mobile networks. This paper presents Hotspotter, a system that aims to aid implementers and stakeholders in quantifying and addressing network coverage issues. Hotspotter is an incentivized crowdsensing system that collects, maps, and visualizes WiFi and cellular data to pinpoint hotspots and dead zones for the effective deployment and relocation of WiFi access points. A mobile application for Android was developed to facilitate data collection on geolocation, nearby WiFi access points, and connected cellular networks. The user interface visualizes the aggregated data in a hexagon-grid map. Fieldwork was conducted in two sitios of Barangay San Lorenzo, Norzagaray, Bulacan to stress-test the Hotspotter system in a Geographically Isolated and Disadvantaged Area (GIDA). It was found that 2G and 4G had the widest coverage and strongest signals overall, with modal signal strengths of 4.0 and 3.0, respectively. Being at the cutting edge, 5G was not yet supported. In the end, the mobile application's passive sensing, collecting, and caching of data successfully operated even in the most isolated areas without an internet connection. © 2024 ieee.

关键词： Wireless local area networks (WLAN)

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Opportunistic Relay Strategy for Body Area networks

Opportunistic Relay Strategy for Body Area Networks

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2024 ieee International conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Wu, Hongbo Jia, Yukuan Yan, Jintao Zhou, Sheng Niu, Zhisheng Chang, Zheng Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350350548

In wireless body area networks (WBANs), the deep channel fading between the nodes and the hub significantly impairs the reliability of end-to-end signal transmission. However, some nodes in WBANs necessitate high-priority data transmission with stringent latency and accuracy requirements. Retransmission is ineffective against channel fading and can result in increased communication overhead and extended transmission delays. This paper proposes an opportunistic relay strategy tailored to the characteristics of WBAN nodes with varying priorities. This strategy converts suitable low-priority nodes as relays during high-priority time slots to forward high-priority data to the hub when deep fading occurs. The relay can be opportunistically selected based on the channel condition and the power usage. With Lyanupov optimization, we maximize the transmission reliability while ensuring the extra power consumption of low-priority nodes are acceptable. Subsequently, simulations are conducted in the Network Simulator 3 (NS3) to validate the proposed relay selection strategy, showing that the proposed strategy effectively improves the reliability from 90.2% to 99.1% with an additional power consumption of 30%. © 2024 ieee.

关键词： Wireless local area networks (WLAN)

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local Community Detection from Noisy Graph Streams 5

Local Community Detection from Noisy Graph Streams

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5th International conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Yang, Han Wang, Lingxiang Wang, Qingren Wang, Huabin Zhang, Yiwen Zhong, Hong Yan, Dengcheng School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9798331506612

The demand for real-time graph analysis has encouraged the research of online community detection methods. However, these methods often assume clean data, which is uncommon in reality. How to further improve the performance and robustness of online community detection for real-world networks has garnered the great attention. In this paper, we propose NgsLCD, an algorithm that identifies valid edges in noisy streams to expand communities and mitigate noise effects. It also uses a node-community similarity metric to filter out incorrectly added nodes. Tests on real networks show NgsLCD reduces noise impact and maintains robust detection performance across varying noise rate. © 2024 ieee.

关键词： local Community Detection Min-hash Noisy graph Stream

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Intelligent Optimization of Hybrid networks: Decision-Making with Integrated Approaches 15

Intelligent Optimization of Hybrid Networks: Decision-Making...

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15th International conference on Information and Communication Technology Convergence, ICTC 2024

作者： Huh, Jun-Hwan Arai, Maki Hasegawa, Mikio Tokyo University of Science Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350364637

The rapid expansion of heterogeneous networks necessitates sophisticated optimization techniques to enhance the overall network performance. Traditional network optimization approaches, such as the per-layer and narrow scope of cross-layer optimization, often fail to capture global network dynamics because of their limited scope. In this study, we propose a comprehensive integrated optimization framework that utilizes a neural network with a decision-making algorithm to optimize gateway selection in hybrid networks comprising local 5G (L5G) and Wi-Fi. Our approach is validated through simulations in EXata, demonstrating improvements in normalized through-put compared to random- and RSSI-based selection strategies. Quantitative results show that our proposed ML-based method enhances network performance by up to 13% compared to the RSSI-based method in dense network scenarios, suggesting its potential utility in next-generation networks. © 2024 ieee.

关键词： Gateways (computer networks)

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An Optimized-PSO Approach for Improving Elman Neural Network Performance in Time-Series Forecasting 2

An Optimized-PSO Approach for Improving Elman Neural Network...

引用

2nd ieee International conference on Electrical, Automation and computer Engineering, ICEACE 2024

作者： Yang, Yuxin Xinjiang University School of Mathematics and Systems Science Urumqi China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368208

The Elman Neural Network (ENN), a type of recurrent neural network, excels at modeling temporal dependencies and dynamic systems, making it well-suited for time-series prediction tasks. However, its performance heavily relies on the initialization of network parameters, which can result in slow convergence and suboptimal prediction accuracy. To address these limitations, we propose an Optimized-PSO-based Elman Neural Network (OP-ENN), where the traditional Particle Swarm Optimization (PSO) algorithm is enhanced to optimize the initial weights and thresholds of the Elman model. In our approach, several modifications are made to improve the convergence rate and avoid local optima: 1) Adaptive Inertia Weights and Learning Factors based on Nonlinear Variation, 2) Velocity Update Formula based on Second-order Oscillation Processing, and 3) Half-selection Logic. These improvements culminate in the Optimized-PSO algorithm, which is validated through two benchmark functions, showing significant improvements in convergence speed and accuracy over the conventional PSO. The OP-ENN model is then applied to a real-world problem of electrical load forecasting, demonstrating its superior performance in terms of both prediction accuracy and convergence speed. The results indicate that the proposed model is an effective solution for optimizing Elman networks, offering enhanced robustness and precision in time-series prediction tasks. © 2024 ieee.

关键词： Elman neural networks

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An Information System Project Using Augmented Reality for a Small local History Museum 18

An Information System Project Using Augmented Reality for a ...

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18th ieee International conference on computer Science and Information Technologies, CSIT 2023

作者： Lypak, Halyna Kunanets, Nataliia Veretennikova, Nataliia Matsiuk, Halyna Kramar, Taras Duda, Oleksii Ternopil Ivan Pului National Technical University The Department of Computer Science Ternopil Ukraine Lviv Polytechnic National University Information Systems and Networks Department Lviv Ukraine Ternopil Ivan Pului National Technical University Ukrainian and Foreign Languages Department Ternopil Ukraine

the essence of the augmented and virtual reality technologies is revealed, the advantages and prospects of their use in museums are analyzed. An information system project using augmented reality technologies was crea... 详细信息

ISBN: (纸本)9798350360462

关键词： Virtual reality

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On-Sensor Binarized Fully Convolutional Neural Network for localisation and Coarse Segmentation

On-Sensor Binarized Fully Convolutional Neural Network for L...

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ieee/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Liu, Yanan Lu, Yao Shanghai Univ Sch Microelect Shanghai Peoples R China Univ Bristol Visual Informat Lab Bristol Avon England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Current neural networks are compatible with high-performance GPU/CPUs. However, implementing neural networks on emerging embedded sensor for inference is challenging due to sensor's unique hardware architecture and stringent computing resources. With this in mind, this work presents new methods to implement fully convolutional neural networks (FCNs) on Pixel Processor Array (PPA) sensors with many techniques to fully use the limited resources on sensor. Specifically, we, for the first time, design and train binarized FCN for both binary weights and activations using batchnorm, group convolution, and learnable threshold for binarization, producing networks small enough to be embedded on the focal plane of the PPA, with limited local memory resources, and using parallel elementary add/subtract, shifting, and bit operations only. We demonstrate the first implementation of an FCN on a PPA device, performing three convolution layers entirely in the pixel-level processors. We use this architecture to demonstrate inference generating heat maps for object segmentation and localisation at over 280 FPS using the SCAMP-5 PPA vision chip.

关键词： Performance evaluation Heating systems Convolution Neural networks computer architecture Object segmentation Parallel processing

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