IEEE Access最新文献

{"title":"Optimization of the Backstepping Control Parameters of an Active Electrohydraulic Suspension to Improve Passenger Comfort and Road Handling","authors":"Rachid Fattah;Jean-Pierre Kenne;Khalid Benjelloun;Ahmed Chebak","doi":"10.1109/ACCESS.2025.3544867","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544867","url":null,"abstract":"This study introduces an innovative optimization strategy for Electro-Hydraulic Active Suspension Systems (EHASS), combining game theory with Particle Swarm Optimization (PSO) to tune backstepping control parameters. Unlike conventional approaches relying on manual tuning or trial-and-error, our method systematically optimizes these parameters, ensuring a well-balanced trade-off between ride comfort and road handling. The optimization process considers worst-case road disturbances, leading to a 79.5% reduction in tracking error, a 44.7% decrease in VDV, and a 51.2% improvement in Crest Factor, complying with ISO 2631 standards. Comprehensive validation across ten road profiles, including highly irregular terrains, confirms the robustness of the proposed method. Additionally, a comparison with Genetic Algorithm (GA)-based optimization highlights that PSO achieves superior convergence and performance. These findings establish a new benchmark for intelligent suspension control, making our approach a strong candidate for real-world automotive applications.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"35985-36005"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900385","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 450 mV Tunable Low-Frequency OTA-C Third-Order Elliptic Low-Pass Filter for Bio-Signal Applications","authors":"Montree Kumngern;Tomasz Kulej;Fabian Khateb","doi":"10.1109/ACCESS.2025.3544735","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544735","url":null,"abstract":"This paper introduces a tunable third-order elliptic low-pass OTA-C filter specifically designed for biological signal applications. The proposed filter leverages a multiple-input operational transconductance amplifier (MI-OTA) that operates at low voltage and low power consumption. This innovative approach simplifies the filter topology and significantly reduces the number of active and passive components required. By utilizing the multiple-input capability of the OTA, the voltage gains of the low-pass transfer function can be easily increased. The filter operates with a supply voltage of 450 mV and consumes only 40.5 nW of power while achieving a cutoff frequency of 112.9 Hz and a dynamic range (DR) of 66.7 dB at 1% total harmonic distortion (THD). The filter’s performance was validated through the effective filtering of interfering ECG signals. The design and simulations were carried out using the Cadence environment and TSMC’s <inline-formula> <tex-math>$0.18~mu $ </tex-math></inline-formula>m CMOS technology. Monte Carlo (MC) analysis and process, voltage, and temperature (PVT) corner analysis were conducted to ensure the robustness of the design.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"36224-36232"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automotive DNN-Based Object Detection in the Presence of Lens Obstruction and Video Compression","authors":"Gabriele Baris;Boda Li;Pak Hung Chan;Carlo Alberto Avizzano;Valentina Donzella","doi":"10.1109/ACCESS.2025.3544773","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544773","url":null,"abstract":"Recent advances in sensing, processing, machine learning, and communication technologies are accelerating assisted and automated functions development for commercial vehicles. Environmental perception sensor data streams are processed to generate a correct and complete situational awareness. It is of utmost importance to assess the robustness of the sensor data pipeline, particularly in the case of data degradation in a noisy and variable environment. Sensor data reduction and compression techniques are key for higher levels of driving automation, as there is an expectation that traditional automotive vehicle wired networks will not be able to support the needed sensor datarates (i.e. more than 10 perception sensors, including cameras, LiDARs, and RADARs, generating tens of Gb/s of data). This work proposes for the first time to consider video compression for camera data transmission on vehicle wired networks in the presence of highly noisy data, e.g. partially obstructed camera field of view. The effects are discussed in terms of machine learning vehicle detection accuracy drop, and also visualising how detection performance spatially varies on the frames using the recently introduced metric, the Spatial Recall Index (SRI). The presented parametric obstruction noise model is generated to emulate real-world patterns, whereas compression is based on the well-established AVC/H.264. While Deep Neural Networks’ (DNNs’) performance is stable with lossy compression (up to 70:1) of ‘ideal’ data, when noise is added there is a significant accuracy degradation, in the range of a 7%-90% decrease. The proposed compression and noise tuning of the DNN training improves the performance up to 35%, enhancing the noise and compression robustness of the system. However, in the presence of compression combined with extreme levels of noise (i.e. more than 80% of pixels affected), DNN performance significantly degrades, up to a 90% decrease, even with re-training. This issue needs to be carefully considered in the design phase of perception and communication networks used to transmit sensor data.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"36575-36589"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meta-RHDC: Meta Reinforcement Learning Driven Hybrid Lyrebird Falcon Optimization for Dynamic Load Balancing in Cloud Computing","authors":"Mallu Shiva Rama Krishna;D. Khasim Vali","doi":"10.1109/ACCESS.2025.3544775","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544775","url":null,"abstract":"Cloud computing offers a scalable and cost-effective platform by providing on-demand access to shared computational resources. Efficient load balancing is essential to maintain optimal performance and maximize resource utilization, ensuring an even distribution of network traffic across servers, preventing overload, enhancing response times, and improving system reliability. This paper proposes a Meta Reinforcement Learning Driven Hybrid Lyrebird Falcon Optimization for Dynamic Load Balancing in Cloud Computing (Meta-RHDC), a novel approach for dynamic load balancing in cloud environments. The Meta-RHDC model leverages convolutional and recurrent neural networks to predict virtual machine loads and dynamically classify them into overloaded and underloaded categories. By integrating reinforcement learning with advanced optimization techniques, Meta-RHDC significantly improves task scheduling and load balancing compared to existing methods such as Load Optimization Algorithm (LOA), Reinforcement Learning (RL), and Falcon Optimization Algorithm (FOA). Extensive experiments conducted on the CloudSim platform demonstrate that Meta-RHDC achieves substantial improvements in key performance metrics. For task counts, makespan is reduced by 19.51%, energy consumption by 22.75%, and balanced CPU utilization by 21.98%. Resource utilization increases by 32.52%, scalability efficiency improves by 49.03%, and the failure rate decreases by 19.72%. For virtual machine (VM) counts, makespan improves by 30.57%, energy consumption by 42.59%, and balanced CPU utilization by 36.85%. Resource utilization rises by 31.75%, scalability efficiency increases by 36.61%, and the failure rate drops by 38.19%. These results confirm the robustness and efficiency of Meta-RHDC in optimizing execution, resource management, and scalability across diverse workloads, making it a superior solution for dynamic load balancing in cloud computing.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"36550-36574"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging Remote Operations and Augmented Reality: An Analysis of Current Trends","authors":"Andres de la Rosa-Garcia;Ángel Israel Soto Marrufo;David Luviano-Cruz;Alma Rodriguez-Ramirez;Francesco Garcia-Luna","doi":"10.1109/ACCESS.2025.3544633","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544633","url":null,"abstract":"Teleoperation provides an innovative approach for completing tasks in environments where humans cannot be physically present, such as hazardous or difficult-to-reach areas. Augmented Reality (AR) enhances this process by overlaying critical information directly into the user’s field of view, streamlining task execution and spatial awareness. This paper presents a comprehensive topical review of recent trends in both teleoperation and AR, highlighting their integration to improve remote human-machine interaction. We analyze advancements in both technologies, identify practical applications, and offer insights into their convergence in fields such as aerospace, medical robotics, and industrial automation. By examining academic publications and statistical trends from major databases, we project the future trajectory of AR-teleoperation and discuss potential challenges in implementing these systems, particularly regarding latency, cognitive overload, and security. This topical review aims to provide a detailed analysis of the state of the art and offers insights for future research in this rapidly evolving field.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"36502-36526"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900348","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain and NFT-Based Solution for Genomic Data Management, Sharing, and Monetization","authors":"Ahmad Musamih;Khaled Salah;Raja Jayaraman;Samer Ellaham;Mohammed Omar;Ibrar Yaqoob","doi":"10.1109/ACCESS.2025.3544643","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544643","url":null,"abstract":"Non-fungible tokens (NFTs) are unique digital assets stored on blockchains. NFTs are ideally suited for tokenizing genomic data, as they empower individuals with complete control over them. Next-generation sequencing (NGS) technology creates repositories of sequenced data from individuals’ raw genomic data, which raises challenges related to data ownership, management, and secure sharing. In this paper, we propose a blockchain and NFT-based solution that addresses the challenges of managing, sharing, and monetizing genomic data while preserving privacy using Threshold Cryptography and Fully Homomorphic Encryption (FHE). We integrate the proposed solution with the Interplanetary File System (IPFS), a decentralized storage system, to handle the substantial amount of genomic data off-chain. We develop three smart contracts to facilitate genomic data management, sharing, and monetization. We introduce composable NFTs to ensure that sequenced genomic data (SGD) NFTs are always linked to the parent raw genomic data (RGD) NFTs to maintain traceability. We present various diagrams and algorithms to illustrate the functionality of our solution. Our testing and validation results demonstrate that smart contracts function as intended. The cost evaluation shows that implementing the solution on a private blockchain is more feasible and user-friendly. Our solution provides a comprehensive framework for genomic data management, sharing, and monetization, with privacy-preserving mechanisms and traceability. We provide guidelines for the generalizability of our solution beyond genomics and outline the challenges and limitations of the proposed solution. We make the source code of the smart contracts publicly available on GitHub.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"35780-35804"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900373","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Residential Building Energy Usage Prediction Using Bayesian-Based Optimized XGBoost Algorithm","authors":"Nabaa Riyadh Baqer;Parviz Rashidi-Khazaee","doi":"10.1109/ACCESS.2025.3544699","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544699","url":null,"abstract":"With the growing energy demand in residential buildings, selecting energy-efficient building designs is crucial for sustainable development. Therefore, engineers and designers try to find a reliable tool to help them analyze and predict the energy consumption of buildings in the early design stages before construction. The state-of-the-art eXtreme Gradient Boosting (XGB) algorithm was successfully used to estimate the Heating Load (HL) and Cooling Load (CL) energy usage based on building design characteristics. To enhance the performance of XGB and improve prediction reliability, we propose a novel Bayesian optimization approach using the Tree-Structured Parzen Estimator (TPE), leading to the development of the TPE-XGB model for HL and CL estimation. Also, a new fair evaluation method was proposed to have a fair evaluation. The evaluation results based on the proposed fair evaluation method showed that the proposed model has outperformed other state-of-the-art models and improved the prediction accuracy of HL and CL by 3.4% (from 0.175 to 0.167) and 10.4% (from 0.307 to 0.275), respectively. As a result, the new proposed model provides the opportunity to be used as a strong and reliable tool in the early stages of building construction and to evaluate different designs/plans regarding energy consumption efficiency to select the best design with high reliability.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"36036-36049"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900361","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143526629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the First-Order Statistical Properties of Vehicle-to-Vehicle Rician Fading Channel","authors":"Sylvester T. Akiishi;Ebenezer Esenogho;Ahmed Ali;Modisane Cameron","doi":"10.1109/ACCESS.2025.3544436","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544436","url":null,"abstract":"Vehicle-to-vehicle (V2V) communication channels have distinct characteristics compared to fixed-to-mobile channels (F2M). Both the transmitter and receiver in V2V systems use low-elevation antennas and are in motion, and the surrounding environment and traffic patterns influence the channel characteristics. To effectively understand and model V2V communication systems, it is essential to characterize the channel using first-order statistical properties, precisely the space time frequency correlation function (STF-CF) and space Doppler frequency power spectral density (SDF-PSD). These metrics enable a comprehensive channel characterization, dynamic environmental adaptation, understanding of mobility effects, spatial variations, and detailed insights into signal power distribution across different frequencies. This paper presents a simplified geometric two-ring (SGTR) model of a V2V channel with demystified and concise mathematical analysis for STF-CFs and SDF-PSD characteristics. The analysis yields closed-form solutions consistent with established models. Additionally, the numerical results demonstrate excellent agreement with theoretical predictions, offering valuable insights into the statistical properties of V2V Rician fading channels.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"36359-36373"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900542","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CQS-Attention: Scaling Up the Standard Attention Computation for Infinitely Long Sequences","authors":"Yiming Bian;Arun K. Somani","doi":"10.1109/ACCESS.2025.3544550","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544550","url":null,"abstract":"Transformer models suffer from unaffordable high memory consumption when the sequence is long and standard self-attention is utilized. We developed a sequence parallelism scheme called CQS-Attention that can break the limit of sequence length. A long sequence is divided into multiple overlapping subsequences. The attention of each subsequence is independently computed and gathered as the final exact attention of the original long sequence. CQS-Attention is a fork-join parallel model comprising three components: Scheduler, Workers, and Tiler. The Scheduler equally partitions computation responsibility in a completely mutually exclusive manner and ensures the local subsequence length is minimum. Each worker independently computes the standard attention of the assigned subsequence and transfers local results to the Tiler, which produces the final attention. CQS-Attention makes attention computation embarrassingly parallel. Hence, it enjoys great performance regarding single-device memory and computation time consumption, mathematical stability and scalability. More importantly, it is fully compatible with all state-of-the-art attention optimizations. Our code and supplementary information (SI) are available at <uri>https://github.com/CQS-Attention/CQS_Attention</uri>.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"35527-35538"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900388","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Techno-Economic Modeling Approach to 6G Network Deployment: Exploring Costs and Use Case Feasibility","authors":"Dimitris Kokkinis;Nikolaos Ioannou;Dimitrios Katsianis;Dimitris Varoutas","doi":"10.1109/ACCESS.2025.3544890","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3544890","url":null,"abstract":"In recent years, significant research has explored 6G vision, enabling technologies, business models, and potential applications. However, the economic feasibility of these advanced and intelligent services must be addressed before standardization. To support the successful realization of 6G promises, we quantified the technical requirements and assessed the economic viability of the proposed solutions using current 5G data and appropriate multipliers. An example application of our analysis shows that the required performance improvements and network densification lead to significantly higher infrastructure costs, with 6G investments estimated to be 200%–840% higher than those of 5G, depending on the use case. In addition, both revenue and Average Revenue Per User (ARPU) are projected to increase considerably. For example, implementing 6G in the case of Pervasive Connectivity & Smart Cities requires an ARPU growth of 10% and a 479% increase in revenue compared with today’s 5G, making it highly feasible. However, use cases such as AR/VR applications present challenges, with ARPU requiring a 582% increase to achieve 407% revenue growth. The proposed approach represents a significant contribution that offers economic insights to stakeholders by quantifying and assessing various 6G deployment scenarios. It does so in a structured and replicable manner, fostering an open dialogue on the economic potential of 6G within a well-defined framework.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"35597-35608"},"PeriodicalIF":3.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10900370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}