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International Conference on Energy, Power, Environment, Control and Computing (ICEPECC 2025)

作者： Rao Behram Umer Farhan Mushtaq Faisal Hammad Khan Muhammad Nouman Usman Hassan Amanullah Muhammad Adnan Photonics And Communication Lab Department of Electrical Engineering University of Engineering & Technology Taxila Pakistan Electronics and Communication Systems Lab Department of Telecommunication Engineering University of Engineering & Technology Taxila Pakistan School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan

ISBN: (数字)9781837243150

Li-Fi is another term for Visible light communication (VLC) which operates by transmitting data through free space by using eith er Light Emitting Diodes (LEDs) or Laser Diodes (LDs). The technique finds substantial applications in scientific research as we ll as military applications when used underwater. VLC technology helps eliminate radio interference alongside providing replacement solutions for traditional underwater communication systems. The research develops and models a visible light wireless communication system through ambient light testing with two 550 nm and 450 nm LD light sources and Pin photodetector over 10 meters using Opti System software. The system uses Non-Return-to-Zero On-Off Keying (NRZ OOK) also known as Amplitude Shift Keying (ASK) modulation through Mach-Zehnder Modulator with WDM technology in NLOS environment. A 10 Gbps data transmission occurs through the clear ocean environment along a 10-meter channel with 0.0405 1/m absorption and 0.0025 1/m scattering. The 550 nm transmitted source emits its maximum Quality factor as 8.35 when BER analyzers operate under ambient light conditions and produce its minimum BER as 4.89e-11 under the same conditions. Examining similar ambient light influences reveals the BER analyzers demonstrate a maximum Quality factor of 8.62 and minimum BER of 18.11e-12 when operating under 450 nm transmitted light.

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Utilization of DenseNet201, EfficientNetB3, Resnet50, and VGG19 as Pre-Trained Convolutional Neural Network Models for Brain Tumour Classification

Utilization of DenseNet201, EfficientNetB3, Resnet50, and VG...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Kanwarpartap Singh Gill Rupesh Gupta Gunjan Shandilya Srinivas Aluvala Adel Sulaiman Hani Alshahrani Mana Saleh Al Reshan Asadullah Shaikh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Emerging Technologies Research Lab (ETRL) College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Information Systems College of Computer Science and Information Systems Najran University Najran Saudi Arabia

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

A major development in medical imaging diagnosis is the use of pretrained Convolutional Neural Network (CNN) models for brain tumour classification. This study uses pretrained CNN EfficientNetB3, ResNet50, and VGG19, to explore the crucial field of early brain tumour identification. The dataset used in the research consists of 7023 brain MRI images, which are divided into 2000 validation images, 1311 testing images, and 3712 training images. After preparing the data, we want to see which CNN designs work best for sorting images of gliomas, meningiomas, no tumors, and pituitaries into different groups. The study shows how important it is to find brain tumors early and how we can make brain imaging tests more accurate and helpful for diagnosing them. The EfficientNetB3 model is the best and wins with remarkable 99% accuracy. The results show the good and bad points of different models and give important information to help improve skills in diagnosing using neuroimaging.

关键词： Neuroimaging Training Accuracy Magnetic resonance imaging Brain tumors Brain modeling Convolutional neural networks Medical diagnostic imaging Residual neural networks Testing

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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge Systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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Friction in Processual Ethics: Reconfiguring Ethical Relations in Interdisciplinary Research 25

Friction in Processual Ethics: Reconfiguring Ethical Relatio...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Rachael Garrett Patrick Brundell Simon Castle-Green Kat Hawkins Paul Tennent Feng Zhou Airi Lampinen Kristina Höök Steve Benford Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden Mixed Reality Lab School of Computer Science University of Nottingham Nottingham United Kingdom Computer Science University of Nottingham Nottingham United Kingdom Centre for Dance Research Coventry University Coventry United Kingdom Computer Science / Mixed Reality Lab University of Nottingham Nottingham United Kingdom Department of Computer and Systems Sciences Stockholm University Stockholm Sweden

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Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design

arXiv

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arXiv 2025年

作者： Hu, Yongquan Tang, Jingyu Gong, Xinya Zhou, Zhongyi Zhang, Shuning Elvitigala, Don Samitha Mueller, Florian Hu, Wen Quigley, Aaron J. School of Computer Science and Engineering University of New South Wales Sydney Australia The University of Tokyo Tokyo Japan Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Tsinghua University Beijing China Department of Computer Science and Engineering South University of Science and Technology Shenzhen China CSIRO’s Data61 University of New South Wales Sydney Australia

The recent surge in artificial intelligence, particularly in multimodal processing technology, has advanced human-computer interaction, by altering how intelligent systems perceive, understand, and respond to contextual information (i.e., context awareness). Despite such advancements, there is a significant gap in comprehensive reviews examining these advances, especially from a multimodal data perspective, which is crucial for refining system design. This paper addresses a key aspect of this gap by conducting a systematic survey of data modality-driven Vision-based Multimodal Interfaces (VMIs). VMIs are essential for integrating multimodal data, enabling more precise interpretation of user intentions and complex interactions across physical and digital environments. Unlike previous task- or scenario-driven surveys, this study highlights the critical role of the visual modality in processing contextual information and facilitating multimodal interaction. Adopting a design framework moving from the whole to the details and back, it classifies VMIs across dimensions, providing insights for developing effective, context-aware *** Codes 68U35, 68T07 Copyright © 2025, The Authors. All rights reserved.

关键词： Taxonomies

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Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Pang, Yan Li, Yunhao Huang, Teng Liang, Jiaming Wang, Zhen Dong, Changyu Kuang, Dongyang Hu, Ying Chen, Hao Lei, Tim Wang, Qiong The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The School of Artificial Intelligence Guangzhou University China The Zhejiang Lab Hangzhou China Sun Yat-sen University China The Department of Computer Science and Engineering The Department of Chemical and Biological Engineering Hong Kong University of Science and Technology China The Department of Electrical Engineering University of Colorado Denver United States

In the specialized domain of brain tumor segmentation, supervised segmentation approaches are hindered by the limited availability of high-quality labeled data, a condition arising from data privacy concerns, significant costs, and ethical issues. In response to this challenge, this paper presents a training framework that adeptly integrates a plug-and-play component, MOD, into current supervised learning models, boosting their efficacy in scenarios with limited data. The MOD consists of an Online Tokenizer and a Dense Predictor, which employs self-distillation and self-modeling on masked patches, promoting swift convergence and efficient representation learning. During the inference phase, the plug-and-play MOD component is excluded, preserving the computational efficiency of the original model without incurring extra processing costs. We substantiated the value of our approach through experiments on leading 3D brain tumor segmentation baselines. Remarkably, models augmented with the MOD consistently showcased superior results, achieving elevated Dice coefficients and HD95 scores on two datasets: BraTS 2021 and MSD 2019 Task-01 Brain Tumor. Code: https://***/aigzhusmart/MOD © 2013 IEEE.

关键词： Self-supervised learning

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HeadSonic: Usable Bone Conduction Earphone Authentication via Head-conducted Sounds

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IEEE Transactions on Mobile Computing 2025年

作者： He, Zhixiang Chen, Jing He, Kun Gu, Yangyang Deng, Qiyi Zhang, Zijian Du, Ruiying Zhao, Qingchuan Wu, Cong Wuhan University School of Cyber Science and Engineering Wuhan430072 China State Key Lab of Intelligent Transportation System Beijing China Beijing Institute of Technology School of Cyberspace Science and Technology China City University of Hong Kong Department of Computer Science Hong Kong The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

Earables (ear wearables) are rapidly emerging as a new platform encompassing a diverse of personal applications, prompting the development of authentication schemes to protect user privacy. Existing earable authentication methods are all specifically designed for air-conduction earphones, which are not suited for bone conduction earphones (BCEs) that rely on bone conduction mechanisms. In this paper, we propose HeadSonic, a usable BCE authentication system based on the unique head-conducted sounds, which can be acquired when the user wears the BCE device. Specifically, the system emits a millisecond-level sound to initiate the authentication session. The signal captured by the BCE microphone is propagated through the user’s head, which is unique in density, geometry, and bone-tissue ratio. It operates implicitly, while maintaining robustness across different behaviors. Extensive experiments involving 60 subjects demonstrate that HeadSonic achieves a commendable balanced accuracy of 96.59%, proving its efficacy and resilience against replay and synthesis attacks. Our dataset and source codes are available at https://***/r/HeadSonic1CE4. © 2025 IEEE. All rights reserved.

关键词： Authentication

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Compressing Inside Generating: A Latent Domain Codec for AI-Generated Images

Compressing Inside Generating: A Latent Domain Codec for AI-...

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Data Compression Conference (DCC)

作者： Yuxu Chen Zhenhao Sun Yuliang Huang Lei Deng Wei Han Bo Bai Shiqi Wang Theory Lab Central Research Institute 2012 Labs Huawei Technology Co. Ltd. Department of Computer Science City University of Hong Kong

ISBN: (数字)9798331534714

ISBN: (纸本)9798331534721

Latent diffusion models (LDMs) have emerged as a prominent framework for image generation, consisting of a diffusion model $\mathcal{M}$ and a VAE decoder $\mathcal{D}$ . High-quality image generation models are large and computationally intensive. As a result, image generation is typically performed on cloud servers, with the generated images then transmitted to edge devices.

关键词： Performance evaluation Image coding Codecs Image synthesis Image edge detection Computational modeling Data compression Diffusion models Decoding Servers

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Robust Training in Multiagent Deep Reinforcement Learning Against Optimal Adversary

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年

作者： Guo, Weiran Liu, Guanjun Zhou, Ziyuan Wang, Jiacun Tang, Ying Wang, Miaomiao Tongji University School of Computer Science and Technology Shanghai201804 China Monmouth University Department of Computer Science and Software Engineering West Long BranchNJ07764 United States Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Beijing Institute of Control Engineering Space Optoelectronic Measurement and Perception Lab Beijing100190 China

Industry 5.0 enhances manufacturing ability through efficient human-machine interaction, combining human resources and robots to complete tasks more accurately and effectively. Artificial intelligence (AI) plays an essential role in Industry 5.0. As a branch in AI, multiagent deep reinforcement learning (MADRL) attracts vast attention in both academia and industry. However, there is a gap between virtual and physical environments in terms of how clean an observed state is. In addition, state adversarial attacks can seriously impact the performance of MADRL. Hence, how to improve the robustness of MADRL algorithms is an important research topic. In this article, we propose an optimal policy-based state adversary attack method that would make the MADRL algorithm more robust when it is applied in the training process of agents. Two case studies related to Industry 5.0 and a general case study are presented in which robustness training against the optimal adversarial attack is tested. The MADRL algorithms involved in the experiments include centralized training and decentralized execution (CTDE) framework and shared experience actor-critic (SEAC) to demonstrate the universality of our method. © 2013 IEEE.

关键词： Deep reinforcement learning

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Efficient Optimization in RIS-Assisted UAV System Using Deep Reinforcement Learning for mmWave-NOMA 6G Communications

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IEEE Internet of Things Journal 2025年

作者： Sobhi-Givi, Sima Nouri, Mahdi Shayesteh, Mahrokh G. Behroozi, Hamid Kwon, Hyun Han Piran, Md. Jalil Urmia University Electrical and Computer Engineering Department Urmia Iran University of Mohaghegh Ardabili Electrical and Computer Engineering Department Ardabil Iran Sharif University of Technology Electrical Engineering Department Tehran Iran Sharif University of Technology Wireless Research Lab ACRI Electrical Engineering Department Tehran Iran Sejong University Department of Civil and Environmental Engineering Seoul05006 Korea Republic of Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of

In the evolving landscape of wireless communications for 5G, 6G, and beyond, the deployment of unmanned aerial vehicles (UAVs) has emerged as a groundbreaking strategy to expand coverage areas due to their flexibility and ease of deployment. Simultaneously, reflecting intelligent surfaces (RIS) have introduced a transformative paradigm aimed at improving key performance metrics, such as average sum-rate and energy efficiency (EE). The seamless integration of advanced technologies, including UAVs, RIS, and non-orthogonal multiple access (NOMA), presents a promising avenue for significantly boosting the performance and efficiency of next-generation communication systems. This study investigates EE maximization for two scenarios in a NOMA-enabled mmWave network: multi-UAV-mounted base stations (BS) and multi-UAV-mounted distributed RIS. In both cases, each UAV serves a NOMA cluster with imperfect successive interference cancellation (SIC), capturing the impact of hardware impairments in real-world NOMA systems. For each scenario, an optimization problem is formulated to maximize EE by jointly optimizing the beamforming matrix, phase shift matrix, NOMA power allocation, and UAV 3D placement. The non-convex problems are tackled using both model-based and model-free deep reinforcement learning (DRL) algorithms under constraints such as minimum quality of service (QoS), beamforming and phase shift limits, and UAV trajectory constraints. The simulation results demonstrate that the proposed DRL algorithms significantly enhance spectral efficiency (SE) and EE, showcasing their suitability for 6G communication systems. Furthermore, a comparative analysis with orthogonal multiple access (OMA) and spatial division multiple access (SDMA) confirms that NOMA outperforms both techniques, achieving substantial gains in efficiency and performance. © 2014 IEEE.

关键词： Deep reinforcement learning

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