IEEE Open Journal of the Computer Society最新文献

{"title":"Reconfigurable Production Lines for Industrial 5.0 Automation: An Intent-Based Approach","authors":"Engin Zeydan;Josep Mangues;Suayb S. Arslan;Yekta Turk;Tharaka Hewa;Madhusanka Liyanage;Aidan O'Mahony","doi":"10.1109/OJCS.2025.3599219","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3599219","url":null,"abstract":"Reconfigurable production lines empowered by an intent-based approach and next-generation wireless networks can be a cornerstone of the transformative Industry 5.0 paradigm. This article presents a novel framework for developing an intent-based reconfigurable production line enriched with blockchain technology. By using blockchain as an enabler for decentralization, the proposed approach aims to integrate security and efficiency into the fabric of industrial automation. Experimental evaluations confirm the effectiveness of the proposed approach (in terms of scalability and latency) and highlight its strengths and potential for improvement. To be more precise, the blockchain systems are 85 to 95 seconds faster than the cloud service for 50 simultaneous transactions and offer around 69% lower latency at maximum utilization with 10 000 events. Important challenges such as integration into the existing infrastructure, security, standardization and energy efficiency are highlighted and recommendations for overcoming these hurdles are also given at the end of the article.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1341-1352"},"PeriodicalIF":0.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11125895","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurological Disorder Recognition via Comprehensive Feature Fusion by Integrating Deep Learning and Texture Analysis","authors":"Najmul Hassan;Abu Saleh Musa Miah;Yuichi Okuyama;Jungpil Shin","doi":"10.1109/OJCS.2025.3594701","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3594701","url":null,"abstract":"Neurological disorders, including Brain Tumors (BTs), Alzheimer’s Disease (AD), and Parkinson’s Disease (PD), pose significant global health challenges. Early and accurate diagnosis is crucial for effective treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is a key diagnostic tool, but traditional Machine Learning (ML) approaches often rely on labor-intensive handcrafted features, leading to inconsistent performance. Recent advancements in Deep Learning (DL) enable automated feature extraction, which offers improved robustness and scalability. However, many existing methods face challenges in fully exploiting the complementary strengths of DL and handcrafted features across multiple disease types. This study proposes a novel hybrid DL model that integrates automated deep features with statistical textural descriptors for the classification of BTs, AD, and PD. The model employs a dual-stream architecture: (1) a modified VGG16 convolutional neural network (CNN), chosen for its favorable trade-off between performance and computational efficiency in medical imaging, to extract deep features from MRI slices, and (2) a sequential one dimensional (1D) CNN to process six gray-level co-occurrenc matrix (GLCM)derived handcrafted features, empirically validated for their superior discriminative power in neuroanatomical texture analysis. By integrating these complementary feature sets, the model leverages global patterns and fine-grained textural details, resulting in a robust and comprehensive representation for accurate and reliable medical image classification. Gradient-weighted class activation mapping (Grad-CAM) is incorporated to enhance interpretability by localizing diagnostically relevant brain regions. The fused features are passed through a fully connected layer for final classification. The proposed model was evaluated on four publicly available MRI datasets, achieving accuracies of 98.86%, 99.50%, 98.52%, and 99.13% on the CE-MRI (multi-class BT), Br35H (binary BT), AD, and PD datasets, respectively. The model achieved an average classification accuracy of 99.05% across the three neurological disorders. Our method outperforms recent state-of-the-art (SOTA) methods, which shows the effectiveness of the proposed model integrating DL and handcrafted features to develop interpretable, robust, and generalizable AI-driven diagnostic systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1366-1377"},"PeriodicalIF":0.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11106739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Few-Shot Segmentation Using Multi-Similarity and Attention Guidance","authors":"Ehtesham Iqbal;Sirojbek Safarov;Seongdeok Bang;Sajid Javed;Yahya Zweiri;Yusra Abdulrahman","doi":"10.1109/OJCS.2025.3592291","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3592291","url":null,"abstract":"Few-shot segmentation (FSS) methods aim to segment objects of novel classes with relatively few annotated samples. Prototype learning, a popular approach in FSS, employs prototype vectors to transfer information from known classes (support images) to novel classes(query images) for segmentation. However, using only prototype vectors may not be sufficient to represent all features of the support image. To extract abundant features and make more precise predictions, we propose a <bold>M</b>ulti-<bold>S</b>imilarity and <bold>A</b>ttention <bold>N</b>etwork (MSANet) including two novel modules, a multi-similarity module and an attention module. The multi-similarity module exploits multiple feature-map of support images and query images to estimate accurate semantic relationships. The attention module instructs the MSANet to concentrate on class-relevant information. We evaluated the proposed network on standard FSS datasets, PASCAL-<inline-formula><tex-math>$5^{i}$</tex-math></inline-formula> 1-shot, PASCAL-<inline-formula><tex-math>$5^{i}$</tex-math></inline-formula> 5-shot, COCO-<inline-formula><tex-math>$20^{i}$</tex-math></inline-formula> 1-shot, and COCO-<inline-formula><tex-math>$20^{i}$</tex-math></inline-formula> 5-shot. An MSANet model with a ResNet101 backbone achieved state-of-the-art performance for all four benchmark datasets with mean intersection over union (mIoU) values of 69.13%, 73.99%, 51.09%, and 56.80%, respectively. The code used is available at <uri>https://github.com/AIVResearch/MSANet</uri>.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1271-1282"},"PeriodicalIF":0.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11095423","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards seL4 for Enhanced System Isolation and Security on Embedded Devices","authors":"Everton de Matos;George Lawton;Conor Lennon","doi":"10.1109/OJCS.2025.3592377","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3592377","url":null,"abstract":"Embedded systems increasingly face security threats due to limited isolation and hardware constraints, creating a demand for robust solutions. The seL4 microkernel, recognized for its minimal footprint and strong security guarantees, is particularly promising for embedded applications requiring secure isolation. This article explores seL4’s capabilities, specifically focusing on its use as a hypervisor on ARM platforms and as a Trusted Execution Environment (TEE) on RISC-V hardware. We describe our implementation of these approaches, highlighting key challenges and presenting methods to simplify their development and deployment. Performance evaluations indicate that seL4 effectively delivers strong isolation with minimal impact on resource usage and overall system performance. In particular, our results demonstrate low overheads for CPU utilization, memory consumption, and network throughput, even under intensive workloads. Finally, the article discusses challenges and recommendations towards the adoption of seL4-based solutions, providing a valuable reference for researchers and practitioners working towards enhancing security in embedded and Internet of Things systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1329-1340"},"PeriodicalIF":0.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11095653","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"XPolypNet: A U-Net-Based Model for Semantic Segmentation of Gastrointestinal Polyps With Explainable AI","authors":"Arjun Kumar Bose Arnob;Muhammad Mostafa Monowar;Md. Abdul Hamid;M. F. Mridha","doi":"10.1109/OJCS.2025.3592204","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3592204","url":null,"abstract":"Automated segmentation of gastrointestinal polyps is a critical step in the early detection and prevention of colorectal cancer (CRC), which is one of the most common causes of cancer-related deaths worldwide. This article presents a U-Net-based model enhanced with Attention Mechanisms and Atrous Spatial Pyramid Pooling (ASPP) for accurate polyp segmentation. To address the challenges of varying polyp sizes, indistinct boundaries, and complex textures, the model used a combined loss function (Binary Cross-Entropy and Dice Loss). Additionally, Gradient-Weighted Class Activation Mapping (Grad-CAM) was integrated to provide visual explanations of the model’s decisions to increase trust and interpretability by clinical practitioners. The presented model was evaluated on five benchmark datasets, achieving a Dice Coefficient of 0.8378 and a Mean Intersection over Union (mIoU) of 0.8427. The comparative analysis highlighted its superiority when compared to state-of-the-art contemporary approaches, with a precision and accuracy of 97%. Qualitative analyses also underline the ability to accurately delineate polyps, even in difficult situations. Although the model exhibited satisfactory performance, it still faced challenges regarding boundary misclassification and reduced efficacy in datasets with high variability. The next steps of this research will focus on domain adaptation and integration of additional modalities to enhance generalizability. This study provides a step toward automated polyp detection and demonstrates the potential of explainable artificial intelligence (XAI) to change the accuracy of diagnosis and healthcare for patients.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1283-1293"},"PeriodicalIF":0.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11095343","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Robust Cross-Channel Image Watermarking Technique for Tamper Detection and its Precise Localization","authors":"Muhammad Ashraf;Adnan Nadeem;Oussama Benrhouma;Muhammad Sarim;Kashif Rizwan;Amir Mehmood","doi":"10.1109/OJCS.2025.3589948","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3589948","url":null,"abstract":"Several watermarking techniques have been suggested to safeguard the integrity of transmitted images in public video surveillance applications. However, these techniques have a critical drawback in their embedding schemes: the watermark is limited to residing in a narrow traceable space to avoid fidelity issues. Such a protection layer can be evaluated or forcefully removed to breach data security. Once the protection layer (watermark) is removed, a watermarking algorithm cannot pinpoint the falsified regions in affected images and gives a binary answer. Consequently, attackers can present the falsification of visual elements as a non-malicious perturbation. Such a type of attack poses a serious security challenge. This study introduces a novel cross-channel image watermarking technique that randomly scatters the watermark pattern across a 24-bit image structure so that no emergence of embedding signatures and fidelity issues occurs after the process. Chaotic systems are employed to leverage their sensitivity to initial conditions and control parameters, resulting in high confusion and diffusion properties in the proposed scheme. The protection layer is completely intractable as it is randomly scattered in the entire RGB space, making it very hard to remove without leaving a clear footprint in affected images. This method creates a good balance between security and imperceptibility, it effectively detects and localizes falsified regions in tampered images, and maintains this ability until clear evidence of a removal attempt emerges in histograms. This property makes proposed algorithm a preferred choice for data integrity protection; it achieved an average F1-score of 0.97 for tamper detection.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1202-1213"},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11081476","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection","authors":"Safa Ben Atitallah;Maha Driss;Wadii Boulila;Anis Koubaa","doi":"10.1109/OJCS.2025.3587486","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3587486","url":null,"abstract":"The Industrial Internet of Things (IIoT) faces significant cybersecurity threats due to its ever-changing network structures, diverse data sources, and inherent uncertainties, making robust intrusion detection crucial. Conventional machine learning methods and typical Graph Neural Networks (GNNs) often struggle to capture the complexity and uncertainty in IIoT network traffic, which hampers their effectiveness in detecting intrusions. To address these limitations, we propose the Fuzzy Graph Attention Network (FGATN), a novel intrusion detection framework that fuses fuzzy logic, graph attention mechanisms, and GNNs to deliver high accuracy and robustness in IIoT environments. FGATN introduces three core innovations: (1) fuzzy membership functions to explicitly model uncertainty and imprecision in traffic features; (2) fuzzy similarity-based graph construction with adaptive edge pruning to build meaningful graph topologies that reflect real-world communication patterns; and (3) an attention-guided fuzzy graph convolution mechanism that dynamically prioritizes reliable and task-relevant neighbors during message passing. We evaluate FGATN on three public intrusion datasets, Edge-IIoTSet, WSN-DS, and CIC-Malmem-2022, achieving accuracies of 99.07%, 99.20%, and 99.05%, respectively. It consistently outperforms state-of-the-art GNN (GCN, GraphSAGE, FGCN) and deep learning models (DNN, GRU, RobustCBL). Ablation studies confirm the essential roles of both fuzzy logic and attention mechanisms in boosting detection accuracy. Furthermore, FGATN demonstrates strong scalability, maintaining high performance across a range of varying graph sizes. These results highlight FGATN as a robust and scalable solution for next-generation IIoT intrusion detection systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1065-1076"},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11075530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survey and Evaluation of Converging Architecture in LLMs Based on Footsteps of Operations","authors":"Seongho Kim;Jihyun Moon;Juntaek Oh;Insu Choi;Joon-Sung Yang","doi":"10.1109/OJCS.2025.3587005","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3587005","url":null,"abstract":"Large language models (LLMs), which have emerged from advances in natural language processing (NLP), enable chatbots, virtual assistants, and numerous domain-specific applications. These models, often comprising billions of parameters, leverage the Transformer architecture and Attention mechanisms to process context effectively and address long-term dependencies more efficiently than earlier approaches, such as recurrent neural networks (RNNs). Notably, since the introduction of Llama, the architectural development of LLMs has significantly converged, predominantly settling on a Transformer-based decoder-only architecture. The evolution of LLMs has been driven by advances in high-bandwidth memory, specialized accelerators, and optimized architectures, enabling models to scale to billions of parameters. However, it also introduces new challenges: meeting compute and memory efficiency requirements across diverse deployment targets, ranging from data center servers to resource-constrained edge devices. To address these challenges, we survey the evolution of LLMs at two complementary levels: architectural trends and their underlying operational mechanisms. Furthermore, we quantify how hyperparameter settings influence inference latency by profiling kernel-level execution on a modern GPU architecture. Our findings reveal that identical models can exhibit varying performance based on hyperparameter configurations and deployment contexts, emphasizing the need for scalable and efficient solutions. The insights distilled from this analysis guide the optimization of performance and efficiency within these converged LLM architectures, thereby extending their applicability across a broader range of environments.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1214-1226"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072851","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144782051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Robust Image Encryption Protocol for Secure Data Sharing in Brain Computer Interface Applications","authors":"Sunil Prajapat;Pankaj Kumar;Kashish Chaudhary;Kranti Kumar;Gyanendra Kumar;Ali Kashif Bashir","doi":"10.1109/OJCS.2025.3587014","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3587014","url":null,"abstract":"Brain-computer interface (BCI) technology has emerged as a transformative means to link human neural activity with electronic devices. BCIs, which facilitate bidirectional communication between the brain and computers, are categorized as invasive, semi-invasive, and non-invasive. EEG (electroencephalography), a non-invasive technique recorded via electrodes placed on the scalp, serves as the primary data source for BCI systems. P300, a component of the human brain’s event-related potential, has gained prominence for detecting cognitive responses to stimuli. However, the susceptibility of BCI data to tampering during transmission underscores the critical need for robust security and privacy measures. To address security issues in P300-based BCI systems, this article introduces a novel elliptic curve-based certificateless encryption (CLE) technique integrated with image encryption protocols to safeguard the open communication pathway between near control and remote control devices. Our approach, unique in its exploration of ECC-based encryption for these systems, offers distinct advantages in security, demonstrating high accuracy in preserving data integrity and confidentiality. The security of our proposed scheme is rigorously validated using the Random Oracle Model. Simulations conducted using MATLAB evaluate the proposed image encryption protocol both theoretically and statistically, showing strong encryption performance against recent methods. Results include an entropy value of 7.98, Unified Average Changing Intensity (UACI) of 33.4%, Normalized Pixel Change Rate (NPCR) of 99.6%, and negative correlation coefficient values, indicating efficient and effective encryption and decryption processes.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1190-1201"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072718","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144782052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DriftShield: Autonomous Fraud Detection via Actor-Critic Reinforcement Learning With Dynamic Feature Reweighting","authors":"Jialei Cao;Wenxia Zheng;Yao Ge;Jiyuan Wang","doi":"10.1109/OJCS.2025.3587001","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3587001","url":null,"abstract":"Financial fraud detection systems confront the persistent challenge of concept drift, where fraudulent patterns evolve continuously to evade detection mechanisms. Traditional rule-based methods and static machine learning models require frequent manual updates, failing to autonomously adapt to emerging fraud strategies. This article presents DriftShield, a novel adaptive fraud detection framework that addresses these limitations through four key technical innovations: (1) the first application of Soft Actor-Critic (SAC) reinforcement learning with continuous action spaces to fraud detection, enabling simultaneous fine-grained optimization of detection thresholds and feature importance weights; (2) a dynamic feature reweighting mechanism that automatically adapts to evolving fraud patterns while providing interpretable insights into changing fraud strategies; (3) an adaptive experience replay buffer combining sliding windows with prioritized sampling to balance catastrophic forgetting prevention with rapid concept drift adaptation; and (4) an entropy-driven exploration framework with automatic temperature tuning that intelligently balances exploitation of known fraud patterns with discovery of emerging threats. Experimental evaluation demonstrates that DriftShield achieves 18% higher fraud detection rates while maintaining lower false positive rates compared to static models. The system demonstrates 57% faster adaptation times, recovering optimal performance within 280 transactions after significant concept drift compared to 650 transactions for the next-best reinforcement learning approach. DriftShield attains a cumulative detection rate of 0.849, representing a 7.7% improvement over existing methods and establishing the efficacy of continuous-action reinforcement learning for autonomous adaptation in dynamic adversarial environments.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1166-1177"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072929","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}