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Holographic Video Communication: Principle, applications and Open Issues

ieee Internet of Things Magazine 2025年第2期8卷 104-110页

作者： Zhang, Junyao Huang, Gaojian Wang, Weidong Zeng, Ming Hao, Wanming Zhu, Zhengyu Du, Jianhe Henan Polytechnic University China Universit Laval Canada Zhengzhou University China Communication University of China China

Advancements in optics, computational algorithms, and display technologies have paved the way for holographic video communication (HVC), which surpasses the limitations of traditional communication methods and is poised to be a key component of the sixth generation mobile communication technology (6G) vision outlined by the international Mobile Telecommunications (IMT) for 2030. Achieving high bandwidth, low latency, strong security, and substantial computing power required by HVC is essential for its realization. On the basis of the development process and work principle of HVC, we point out the network conditions required for HVC in this work. We also innovatively depict typical application scenarios of HVC in future wireless communication, and propose current technologies that can assist HVC. Additionally, we identify the challenges and future development directions faced by HVC in the context of future wireless communication. © 2025 ieee.

关键词： Holographic displays

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Enhancing Cellular IoT Access with Multi-Hop Uplink NOMA, and Mobile Edge computing for Smart Cities 3

Enhancing Cellular IoT Access with Multi-Hop Uplink NOMA, an...

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3rd international Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ramkumar, G. Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331509675

An Internet of Things (IoT) network is made up of multiple connected devices to collect and exchange information over the Internet for remote monitoring and control. IoT has drawbacks such as data security vulnerabilities posed by networking devices, data protection issues associated with data collection and sharing, and interoperability challenges among different IOT platforms and devices. A Global Channel Matching (GCM) algorithm is integrated into the Enhancing Cellular IoT Access with Multi-Hop Uplink NOMA, and Mobile Edge computing for Smart Cities (ECMMS) in this article, which aims to optimize relay transmission links for use in IoT networks. In a smart city environment, the GCM algorithm is designed to mimic Firefly behavior and dynamically adjust relay placement for network efficiency and interference reduction. The network model includes IoT devices connected to a Virtual Mobile Edge computing (MEC) fabric to facilitate task offloading and distributed computing. The proposed framework aims to improve computational efficiency by creating virtual servers that use networked IoT devices based on task requirements. ECMMS has demonstrated through detailed simulations and evaluations that it is more effective in optimizing relay selection, reducing interference, and achieving optimal data rates, which supports the scalability and reliability of IoT applications in smart city infrastructure. Using these parameters with discussed the overcomes of our ECMMS model when compared to MNMD, SHBT, and IHAG models. Such as packet loss, average throughput, average end-to-end delay, energy efficiency, and data success rate where calculate. Here packet loss and data success rates are high rates of 99 % and 95.74 %. Were average throughput, energy efficiency, and average end-to-end delay archives low efficiency of 71.02, 254.06, and 254.06 are calculated. © 2025 ieee.

关键词： Mobile edge computing

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CGNet: Classification-Guided Multi-Task Interactive network for Hyperspectral and Multispectral Image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

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2025 ieee international Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lv, Guohua Xu, Yanlong Gao, Yongbiao Zhao, Guixin Wang, Guotao Sun, Xiangcheng Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Ministry of Education China Beihang University Beijing China

ISBN: (纸本)9798350368741

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia University datasets. © 2025 ieee.

关键词： Feature interaction Hyperspectral classification Hyperspectral image Image fusion

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ASTGNN: Adaptive Spatio-Temporal Graph Neural network for Motor Imagery Recognition

ASTGNN: Adaptive Spatio-Temporal Graph Neural Network for Mo...

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2024 international Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhao, Zheng Xue, Haixiao Huang, Yuwen Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Motor imagery electroencephalogram recognition is a key area in brain-computer interfaces, with applications in human-computer interaction, rehabilitation, and virtual reality. Traditional methods often overlook the brain's topological characteristics and the correlations between EEG channels, resulting in suboptimal decoding. To address this, we propose an adaptive spatio-temporal graph neural network (ASTGNN), which constructs a brain graph topology and adaptively learns the topological adjacency matrix, exploring both common and individual electrode connections. The spatial features are extracted using a graph convolutional network, while a gated position-aware self-attention mechanism captures movement information and global dependencies, enhancing temporal feature extraction. Experiments show that ASTGNN significantly improves recognition, achieving an average accuracy of 87.36%. © 2025 SPIE.

关键词： Electroencephalography

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Dual-Environment Dehazing Using Enhanced U-Net for Indoor and Outdoor Scenarios

Dual-Environment Dehazing Using Enhanced U-Net for Indoor an...

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2025 ieee international Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Bhowmik, Swaroop Sharma, Ajay Dangi, Ramraj Vit Bhopal University School of Computing Science and Engineering M.P. Sehore466114 India

ISBN: (纸本)9798331529833

In this paper, we propose an Enhanced U-Net architecture designed to achieve 99 percent dehazing efficiency for both indoor and outdoor images. The core enhancement involves replacing standard convolution layers in the encoder with dilated convolution layers, which expand the receptive field without increasing the number of parameters. This modification allows the model to capture broader contextual information, crucial for dehazing images with complex environmental variations. Additionally, we replace the conventional ReLU activation function with Leaky ReLU, which prevents neuron inactivity by allowing a small gradient for negative inputs. This choice enhances the network's learning capability, especially in scenarios involving low-intensity regions or darker images, which are common in dehazing tasks. Experimental results demonstrate that our architecture outperforms traditional U-Net models, achieving superior performance in terms of PSNR, SSIM, and visual clarity, particularly for challenging haze conditions in diverse lighting and scene settings. The architecture efficiently balances model complexity and dehazing precision, making it a robust solution for real-world applications. © 2025 ieee.

关键词： network architecture

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Integration of Traffic Shaping with Time-Sensitive networking for Effective Traffic Management 3

Integration of Traffic Shaping with Time-Sensitive Networkin...

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3rd international Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Penumada, Archana Reddy Gundapaneni, Divya Jyothi Chennupati, Akshitha Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Time-Sensitive networking (TSN) has recently become an important standard to support time-sensitive reliable low-latency data transmission in industrial and real-time applications. This work explores the use of different traffic shaping algorithms, namely, credit-based shaping (CBS) and time-aware shaping (TAS) alongside frame preemption—a TSN instance which gives precedence to high-importance frames by means of lower-priority traffic interruption—to extend TSN’s traffic management capabilities. By controlling data flow and allowing for high priority packets to pass through unimpeded, traffic shaping algorithms serve an essential function in making the network more efficient and reducing congestion rates. The work compares these algorithms in isolation and also in combination over a network model, comprising of four switches and three end users for each switch, using the OMNeT++ simulation environment. This study aims to contribute insights into optimal shaping techniques for TSN, enhancing its suitability for critical network applications. © 2025 ieee.

关键词： Traffic congestion

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Optimizing Machine Learning for Malicious Blockchain Node Detection Using Modified Metaheuristic 1

Optimizing Machine Learning for Malicious Blockchain Node De...

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1st international Conference on AIML-applications for Engineering and Technology, ICAET 2025

作者： Bacanin, Nebojsa Vicentijevic, Marko Jovanovic, Luka Njegus, Angelina Radomirovic, Branislav Zivkovic, Miodrag Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia

ISBN: (纸本)9798350355611

The decentralized architecture of blockchain tech-nology introduces various challenges, particularly in Proof of Stake (PoS) networks, where malicious nodes can undermine the consensus process and threaten the integrity of the network. Nodes with significant stakes may disproportionately influence validation, weakening the network's decentralization. Despite ongoing advancements in blockchain security, there remains a notable gap in research on the integration of artificial intelli-gence (AI) and optimization techniques for enhancing blockchain security. This paper proposes an approach, utilizing a CatBoost classifier optimized with a modified Particle Swarm Optimization (PSO) algorithm, designed specifically for this study. A compar-ative analysis, performed on a real-world dataset, demonstrates the effectiveness of the proposed method, with the most accurate models achieving an accuracy of 87.13%. The findings highlight the potential of AI -driven optimization in improving blockchain security. Future research will focus on the application of AI and optimization techniques to further refine blockchain protocols, enhance detection and prevention of security threats, and improve overall network performance. © 2025 ieee.

关键词： Particle swarm optimization (PSO)

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Real-Time Estimation of Shoulder Motion Intention with a Neuromusculoskeletal Model and a Neural network

Real-Time Estimation of Shoulder Motion Intention with a Neu...

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2025 ieee/SICE international symposium on System Integration, SII 2025

作者： Orellana Wong, Jorge Andree Nishikawa, Satoshi Kiguchi, Kazuo Kyushu University Department of Mechanical Engineering 744 Motooka Nishi-ku Fukuoka Japan

ISBN: (纸本)9798331531614

The development of robotic exoskeletons for human motion assistance and rehabilitation has intensified the need for efficient methods to discern users' motion intentions, particularly for complex upper limb movements. The shoulder joint poses the most significant challenge due to its anatomical complexity, multi-degree-of-freedom (DoF) movements, and variable muscle function. Consequently, real-time estimation of shoulder joint motion intentions remains the least explored. Existing prediction methods often fail to address the shoulder's range of motion, as many models are restricted to below the shoulder level or rely on computationally intensive physiological parameters unsuitable for real-time applications. The shoulder's varying moment arm further complicates signal normalization and accuracy. To overcome these challenges, we propose a novel approach combining a Neuromusculoskeletal Model with a Multilayer Perceptron (MLP). This method leverages biological insights from the model to improve neural network performance, focusing exclusively on regression for real-time predictions. The method inputs include sEMG signals from shoulder muscles and shoulder angle data. These sEMG signals are preprocessed and integrated into the Neuromusculoskeletal Model, which incorporates activation dynamics, contraction dynamics, and musculoskeletal measurements. By correlating skeletal measurements with humeral elevation, the method reduces computational complexity and calculates muscle torques, which are then used by the MLP to estimate humeral acceleration. This approach effectively addresses normalization issues by correlating shoulder angle, sEMG data, and estimated torques with humeral acceleration. © 2025 ieee.

关键词： Degrees of freedom (mechanics)

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File system performance comparison with Proxmox VE and KVM as type-1 Linux-based hypervisors 24

File system performance comparison with Proxmox VE and KVM a...

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24th international symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Dordević, Borislav Janjić, Kristina Kraljević, Nenad Davidović, Nikola Belgrade Serbia Academy of Technical and Art Applied Studies School of Electrical and Computing Engineering of Applied Studies Belgrade Serbia University of East Sarajevo Faculty of Electrical Engineering RS East Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798331515799

This paper provides a comparative analysis of file system performance between type-1 Linux-based hypervisors. Proxmox VE and KVM were selected as representative examples of type-1 Linux-based hypervisors. Though both hypervisors initially appear to have similar Linux-based and QEMU-based architectures, significant differences become apparent upon closer examination. For our experiments, we utilized Filebench due to its exceptional flexibility and adaptability, which allow it to emulate practical applications within realistic server environments. CentOS 9 was chosen as the guest operating system, serving as a common example of Linux distributions. Tests were performed with one, two, three, and four virtual machines running concurrently. Furthermore, this paper includes a mathematical model of the type-1 virtual environment, supplemented by a real-world experiment that serves as a case study. The mathematical model is used to interpret the performance results obtained from the experiments. © 2025 ieee.

关键词： Linux

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Scalable High-Performance Community Detection Using Label Propagation in Massive networks 16th

Scalable High-Performance Community Detection Using Label Pr...

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16th international Conference on Social networks Analysis and Mining

作者： Boddu, Sharon Khan, Maleq Texas A&M Univ Dept Elect Engn & Comp Sci Kingsville TX 78363 USA

ISBN: (纸本)9783031785405;9783031785412

Community detection is the problem of finding naturally forming clusters in networks. It is an important problem in mining and analyzing social and other complex networks. Community detection can be used to analyze complex systems in the real world and has applications in many areas, including network science, data mining, and computational biology. Label propagation is a community detection method that is simpler and faster than other methods such as Louvain, InfoMap, and spectral-based approaches. Some real-world networks can be very large and have billions of nodes and edges. Sequential algorithms might not be suitable for dealing with such large networks. This paper presents distributed-memory and hybrid parallel community detection algorithms based on the label propagation method. We incorporated novel optimizations and communication schemes, leading to very efficient and scalable algorithms. We also discuss various load-balancing schemes and present their comparative performances. These algorithms have been implemented and evaluated using large high-performance computing systems. Our hybrid algorithm is scalable to thousands of processors and has the capability to process massive networks. This algorithm was able to detect communities in the Metaclust50 network, a massive network with 282 million nodes and 42 billion edges, in 654 s using 4096 processors.

关键词： Community detection parallel graph algorithms network analysis graph mining

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