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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Rossi, Henrique Souza Mitra, Karan Gavrell, Justin Åhlund, Christer Luleå University of Technology Department of Computer Science Electrical and Space Engineering Skellefteå Sweden

ISBN: (纸本)9798331508050

Cloud-based social eXtended Reality (XR) services are the cornerstone for realizing the promises of the Metaverse. These services hosted either on datacenters or edge, will demand stringent mobile network quality of service (QoS) to operate effectively and provide an acceptable user quality. It becomes fundamental to study how mobile networks QoS factors round-trip time (RTT), packet loss (PL), and jitter affect these services by measuring their effect on users' perceived quality of experience (QoE). Subjective QoE assessment involves carefully controlled laboratory environments to generate the desired conditions between a large set of users. The requirements for a cloud-based social XR service lab-setup are complex: Identify a reliable streaming service, a customizable VR application, emulate network conditions, define activities or tasks for users to perform, collect their data, label it;all while mitigating possible human mistakes. To address these requirements, we present an effective technical setup that can consistently repeat the same conditions between users and that can be easily replicated to other labs conducting cloud-based social XR research. © 2025 IEEE.

关键词： Network performance

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Three-Port DC-DC Converter with Galvanic Isolation Using Multi-Winding Transformer 3

Three-Port DC-DC Converter with Galvanic Isolation Using Mul...

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3rd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Paul, Preenu Jose, Babita R. Shahana, T.K. Abraham, Chikku Mathew, Jimson School of Engineering Cochin University of Science and Technology Division of Electronics Engineering Kerala India Muthoot Institute of Technology and Science Department of Electrical and Electronics Engineering Kerala India Indian Institute of Technology Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350387193

A three port DC-DC converter that employs push-pull topology with isolation is presented. The converter transfers power from two DC input sources using a multi-winding transformer to the load. The high gain of the converter is achieved using quasi - switched boost (qSB) network, switched capacitor (SC) topology, multi winding transformer turn's ratio and voltage doubler circuit (VDR). The converter operation, steady state analysis deriving output voltage and comparison with similar topologies are presented. MATLAB/ Simulink results justify the converter operation and analysis. The underlined features of proposed converter are (i) high voltage conversion ratio (ii) galvanic isolation (iii) reduced utilization of semiconductor devices and (iv) capability to combine multiple input sources. © 2024 IEEE.

关键词： Capacitor bank

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QoE Assessment of Cloud-Based Social Extended Reality Applications Over Heterogeneous Access Networks 22

QoE Assessment of Cloud-Based Social Extended Reality Applic...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Mitra, Karan Rossi, Henrique Souza Gavrell, Justin Åhlund, Christer Luleå University of Technology Department of Computer Science Electrical and Space Engineering Skellefteå Sweden

ISBN: (纸本)9798331508050

The next generation of immersive applications, such as eXtended reality (XR), will likely be cloud-based and streamed over mobile networks using myriad technologies such as WiFi and 6th-generation mobile networks. Mobile networks promise ubiquitous connectivity but are prone to stochastic network conditions that may be detrimental to end users' quality of experience (QoE). The impact of network conditions on QoE has been studied extensively by industry and academia regarding various multimedia services such as audio, video, and gaming. However, the impact of network conditions on users' QoE for XR-based social applications has yet to be thoroughly investigated. This paper presents novel results assessing the impact of network conditions (N=20) involving factors such as round trip time (RTT), jitter (RJ), and packet losses (PL) on users' QoE via realistic subjective tests (N=28) regarding social XR application. Our results show that social XR applications require stringent QoS conditions. In particular, our results show that increasing RTT values do not significantly affect users' QoE up to 77ms. Combined PL and RTT cases cause significant QoE degradation from 77ms onward with greater than 2% PL. Most importantly, results show that a very small jitter value with one standard deviation beyond 52 milliseconds can lead to significant QoE degradation. Further, jitter values beyond three standard deviations for 27ms RTT and beyond should be avoided. © 2025 IEEE.

关键词： Multimedia services

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Topology Optimization in Three-Phase C-Core SRMs 50

Topology Optimization in Three-Phase C-Core SRMs

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Davarpanah, Gholamreza Mohammadi, Sajjad Kirtley, James L. Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Department of Electrical Engineering and Computer Science MIT Cambridge United States

ISBN: (纸本)9781665464543

This paper investigates the optimized combination of rotor and stator teeth in a three-phase switched reluctance motor featuring a connected C-core topology to attain a larger winding area and, thus, a higher electrical loading capability, leading to a higher torque density. The general formulation is discussed. Additionally, the shorter flux path within the design leads to a reduced core loss. The finite element method is employed in the design and field analysis. A comparison with a conventional motor shows the superiority of the proposed configuration. The optimized design is prototyped and tested. Both static and dynamic torques are extracted. A good correlation between simulation and experimental results is observed. © 2024 IEEE.

关键词： Topology

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Federated Multiarmed Bandits Under Byzantine Attacks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1488-1501页

作者： Saday, Artun Demirel, Ilker Yildirim, Yigit Tekin, Cem Bilkent University Department of Electrical and Electronics Engineering Ankara06800 Turkey Massachusetts Institute of Technology Computer Science & Artificial Intelligence Laboratory CambridgeMA02139 United States

Multiarmed bandits (MAB) is a sequential decision-making model in which the learner controls the trade-off between exploration and exploitation to maximize its cumulative reward. Federated multiarmed bandits (FMAB) is an emerging framework where a cohort of learners with heterogeneous local models play an MAB game and communicate their aggregated feedback to a server to learn a globally optimal arm. Two key hurdles in FMAB are communication-efficient learning and resilience to adversarial attacks. To address these issues, we study the FMAB problem in the presence of Byzantine clients who can send false model updates threatening the learning process. We analyze the sample complexity and the regret of β-optimal arm identification. We borrow tools from robust statistics and propose a median-of-means (MoM)-based online algorithm, Fed-MoM-UCB, to cope with Byzantine clients. In particular, we show that if the Byzantine clients constitute less than half of the cohort, the cumulative regret with respect to \beta-optimal arms is bounded over time with high probability, showcasing both communication efficiency and Byzantine resilience. We analyze the interplay between the algorithm parameters, a discernibility margin, regret, communication cost, and the arms’ suboptimality gaps. We demonstrate Fed-MoM-UCB's effectiveness against the baselines in the presence of Byzantine attacks via experiments. © 2020 IEEE.

关键词： Federated learning

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Enhancing Educational technology in Lectures for School Students with Learning Disabilities: A Comprehensive Analysis

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SN computer science 2024年第6期5卷 1-17页

作者： Adjiovski, Benjamin Bogatinoska, Dijana Capeska Ismajloska, Mersiha Malekian, Reza University of Information Science and Technology “St. Paul the Apostle” Department of Electrical Electronic and Computer Engineering University of Pretoria

Learning disabilities present formidable obstacles for children in their educational journeys, often overlooked or misunderstood. This research aims to address two critical issues: the differentiation between learning and intellectual disabilities, and the resistance of teachers to integrating technology into education. In response, a comprehensive approach was adopted. Firstly, a mobile application was developed to screen for potential learning disabilities, offering teachers insights into individual student challenges and personalized lecture adaptations. Secondly, a quasi-experimental pretest–posttest study was conducted, comparing student performance following traditional and technologically supported lectures. Results from the study revealed significant score improvements among students identified as potentially having learning disabilities when exposed to technologically supported lectures. Conversely, students not identified as potential cases remained within the normal range of performance. These findings underscore the potential of technology to enhance educational outcomes for children with learning disabilities while highlighting the importance of recognizing and addressing these challenges in educational settings. By bridging the gap between identification and intervention, this research contributes to a more inclusive and effective educational environment. © The Author(s) 2024.

关键词： Assistive technology Inclusive education Learning disabilities Mobile application screening technology in education

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AODS: Adaptive Obstacle Detection System for Smart Wheelchairs

AODS: Adaptive Obstacle Detection System for Smart Wheelchai...

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2024 IEEE International Conference on Control science and Systems engineering, ICCSSE 2024

作者： Li, Lucy Chen, Wenyu Chen, Keyu Electrical and Computer Engineering The Ohio State University ColumbusOH United States School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331517199

The rising global demand for smart mobility solutions underscores the critical need for advanced technologies in power wheelchairs to ensure the safety and independence of users with mobility challenges. This work introduces a novel segmentation-based pipeline for obstacle detection, specifically tailored for outdoor environments, to enhance navigational safety in smart wheelchairs. The segmentation model within the pipeline addresses prevalent issues in current technologies-such as variable lighting conditions, scale changes, and distance sensitivity-that significantly affect detection accuracy. The proposed model incorporates innovative components, including a Distance Augmentation Module (DAM), Scale Aware Module (SAM), and an Illumination Simulation Module (ISM), ensuring robust performance under diverse operational conditions. Validation on a specially curated dataset from urban settings in Chengdu, China, demonstrates a segmentation accuracy of 86.0%, out-performing existing solutions. The findings suggest substantial improvements in detecting and navigating around obstacles, thereby significantly enhancing the operational efficiency and safety of smart wheelchairs. This work not only contributes a sophisticated algorithmic approach and a tailored dataset to the field but also verifies the practical efficacy of the model through extensive experimental validation. © 2024 IEEE.

关键词： Obstacle detectors

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Graph Topology Identification Based on Covariance Matching

Graph Topology Identification Based on Covariance Matching

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Han, Yongsheng Natali, Alberto Leus, Geert Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

ISBN: (纸本)9798350368741

This paper addresses graph topology identification for applications where the underlying structure of systems like brain and social networks is not directly observable. Traditional approaches based on signal matching and spectral templates have limitations, particularly in handling scale issues and sparsity assumptions. We introduce a novel covariance matching methodology that efficiently reconstructs the graph topology using observable data. For the structural equation model (SEM) using an undirected graph, we demonstrate that our method can converge to the correct result under relatively soft conditions. Furthermore, we extend our methodology to polynomial models and any known distribution of latent variables, broadening its applicability and utility in diverse graph-based systems. © 2025 IEEE.

关键词： covariance matching graph topology identification polynomial model structural equation model

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LLMaaS: Serving Large-Language Models on Trusted Serverless Computing Platforms

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 405-415页

作者： Cai, Zinuo Ma, Rongbo Fu, Yicheng Zhang, Weishan Ma, Ruhui Guan, Haibing Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Qingdao Institute of Software College of Computer Science and Technology Qingdao266555 China

In recent years, the emergence of large-language models (LLMs) has profoundly transformed our production and lifestyle. These models have shown tremendous potential in fields, such as natural language processing, speech recognition, and recommendation systems, and are increasingly playing crucial roles in applications such as human-computer interaction and intelligent customer service. Efficient inference solutions for LLMs in data centers have been extensively researched, with a focus on meeting users' quality of service requirements. In this article, we focus on two additional requirements that responsible LLM inference should meet under QoS conditions: security throughout the model execution process and low maintenance requirements for the inference system. Therefore, we propose LLMaaS, a trusted model inference platform based on a serverless computing platform aimed at providing inference as a service for LLMs. First, we design a trusted serverless computing platform based on software guard extension (SGX), which includes distributed identity verification and SGX device plugins to ensure the security and trustworthiness of the inference process. Additionally, to reduce the maintenance requirements of the system, we enhance the SGX-based deep learning computing framework, including replacing PyTorch and using a greedy algorithm for graph partitioning. We conduct tests on four typical large models, and the experimental results demonstrate that, with minimal overhead and user code modifications, we can ensure the security of model execution. © 2020 IEEE.

关键词： Quality of service

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SVTNet: Dual Branch of Swin Transformer and Vision Transformer for Monocular Depth Estimation

SVTNet: Dual Branch of Swin Transformer and Vision Transform...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jia, Shuwen Wang, Yongxiong Chen, Han Huang, Shuai School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798350368741

In monocular depth estimation, effective acquisition of global and local information is the key to improving accuracy. We introduce a novel dual branch network called Swin Vision Transformer Net (SVTNet), where the Swin Transformer and Vision Transformer are combined to learn features with global and local information. In Swin Transformer branch, we remove the Feature Pyramid Networks which reduces the noise introduced by interpolation and maintains the original characteristics of the hierarchical features extracted from each layer of Swin Transformer. In Vision Transformer Branch, we remove the multi-scale convolutional structure and keep the same-scale feature maps extracted by Vision Transformer for reducing the consumption of memory and extracting more global features. The experimental results on the NYU-Depth v2, KITTI, and CityScapes datasets show that compared with existing advanced methods, the SVTNet has achieved significant performance improvements in multiple evaluation metrics. © 2025 IEEE.

关键词： dual branch Feature Pyramid Networks monocular depth estimation Same-scale feature Swin Transformer Vision Transformer

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