IEEE Open Journal of the Computer Society最新文献_第2页

Quantum Computing-Accelerated Kalman Filtering for Satellite Clusters: Algorithms and Comparative Analysis

IEEE Open Journal of the Computer Society Pub Date : 2025-01-27 DOI: 10.1109/OJCS.2025.3535081

Shreyan Prakash;Raj Bhattacherjee;Sainath Bitragunta;Ashutosh Bhatia;Kamlesh Tiwari

{"title":"Quantum Computing-Accelerated Kalman Filtering for Satellite Clusters: Algorithms and Comparative Analysis","authors":"Shreyan Prakash;Raj Bhattacherjee;Sainath Bitragunta;Ashutosh Bhatia;Kamlesh Tiwari","doi":"10.1109/OJCS.2025.3535081","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3535081","url":null,"abstract":"The increasing demand for high-precision real-time data processing in satellite clusters requires efficient algorithms to manage inherent uncertainties in space-based systems. We propose an innovative framework that integrates Quantum Neural Network (QNN) architecture into Kalman filtering processes, specifically tailored for Low Earth Orbit satellite clusters. Our quantum computing-based approach achieves a significant improvement in prediction accuracy and a reduction in mean absolute error compared to classical Kalman filtering techniques. These advances significantly improve computational efficiency and error handling, making the method highly scalable under varying noise levels. A comparative analysis demonstrates the superior performance of the Quantum Kalman Filter in processing speed, resource utilization, and prediction accuracy, all evaluated within the constraints of LEO satellite constellations. These findings highlight the potential of quantum computing to optimize data processing strategies for future missions, including deep space explorations.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"307-316"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403867","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}

引用次数: 0

UVtrack: Multi-Modal Indoor Seamless Localization Using Ultra-Wideband Communication and Vision Sensors

IEEE Open Journal of the Computer Society Pub Date : 2025-01-20 DOI: 10.1109/OJCS.2025.3531442

Yi Xu;Zhigang Chen;Ming Zhao;Fengxiao Tang;Yangfan Li;Jiaqi Liu;Nei Kato

{"title":"UVtrack: Multi-Modal Indoor Seamless Localization Using Ultra-Wideband Communication and Vision Sensors","authors":"Yi Xu;Zhigang Chen;Ming Zhao;Fengxiao Tang;Yangfan Li;Jiaqi Liu;Nei Kato","doi":"10.1109/OJCS.2025.3531442","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3531442","url":null,"abstract":"High precision and robust indoor positioning system has a broad range of applications in the area of mobile computing. Due to the advancement of image processing algorithms, the prevalence of surveillance ambient cameras shows promise for offering sub-meter accuracy localization services. The tracking performance in dynamic contexts is still unreliable for ambient camera-based methods, despite their general ability to pinpoint pedestrians in video frames at fine-grained levels. Contrarily, ultra-wideband-based technology can continuously track pedestrians, but they are frequently susceptible to the effects of non-line-of-sight (NLOS) errors on the surrounding environment. We see a chance to combine these two most viable approaches in order to get beyond the aforementioned drawbacks and return to the pedestrian localization issue from a different angle. In this article, we propose UVtrack, a localization system based on UWB and ambient cameras that achieves centimeter accuracy and improved reliability. The key innovation of UVtrack is a well-designed particle filter which adopts UWB and vision results in the weight update of the particle set, and an adaptive distance variance weighted least squares method (DVLS) to improve UWB sub-system robustness. We take UVtrack into use on common smartphones and test its effectiveness in three different situations. The results demonstrated that UVtrack attains an outstanding localization accuracy of 7 cm.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"272-281"},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10845877","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107126","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}

引用次数: 0

2024 List of Reviewers* 2024 年审查员名单*

IEEE Open Journal of the Computer Society Pub Date : 2025-01-14 DOI: 10.1109/OJCS.2025.3527836

引用次数: 0

New Incoming EIC Editorial 新到来的EIC社论

IEEE Open Journal of the Computer Society Pub Date : 2025-01-10 DOI: 10.1109/OJCS.2025.3525947

Vincenzo Piuri

引用次数: 0

Comparative Analysis of Traditional and Modern NLP Techniques on the CoLA Dataset: From POS Tagging to Large Language Models

IEEE Open Journal of the Computer Society Pub Date : 2025-01-07 DOI: 10.1109/OJCS.2025.3526712

Abdessamad Benlahbib;Achraf Boumhidi;Anass Fahfouh;Hamza Alami

{"title":"Comparative Analysis of Traditional and Modern NLP Techniques on the CoLA Dataset: From POS Tagging to Large Language Models","authors":"Abdessamad Benlahbib;Achraf Boumhidi;Anass Fahfouh;Hamza Alami","doi":"10.1109/OJCS.2025.3526712","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3526712","url":null,"abstract":"The task of classifying linguistic acceptability, exemplified by the CoLA (Corpus of Linguistic Acceptability) dataset, poses unique challenges for natural language processing (NLP) models. These challenges include distinguishing between subtle grammatical errors, understanding complex syntactic structures, and detecting semantic inconsistencies, all of which make the task difficult even for human annotators. In this article, we compare a range of techniques, from traditional methods such as Part-of-Speech (POS) tagging and feature extraction methods like CountVectorizer with Term Frequency-Inverse Document Frequency (TF-IDF) and N-grams, to modern embeddings such as FastText and Embeddings from Language Models (ELMo), as well as deep learning architectures like transformers and Large Language Models (LLMs). Our experiments show a clear improvement in performance as models evolve from traditional to more advanced approaches. Notably, state-of-the-art (SOTA) results were obtained by fine-tuning GPT-4o with extensive hyperparameter tuning, including experimenting with various epochs and batch sizes. This comparative analysis provides valuable insights into the relative strengths of each technique for identifying morphological, syntactic, and semantic violations, highlighting the effectiveness of LLMs in these tasks.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"248-260"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829978","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106179","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}

引用次数: 0

Optimizing Energy Efficiency in UPA-Assisted SWIPT Massive MIMO Systems Over Rician Fading Channels

IEEE Open Journal of the Computer Society Pub Date : 2025-01-03 DOI: 10.1109/OJCS.2025.3525519

Mohammad Hassan Adeli;Dariush Abbasi-Moghadam;Hossein Fotouhi;S. Mohammad Razavizadeh

{"title":"Optimizing Energy Efficiency in UPA-Assisted SWIPT Massive MIMO Systems Over Rician Fading Channels","authors":"Mohammad Hassan Adeli;Dariush Abbasi-Moghadam;Hossein Fotouhi;S. Mohammad Razavizadeh","doi":"10.1109/OJCS.2025.3525519","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3525519","url":null,"abstract":"Massive Multiple Input Multiple Output (mMIMO) is a promising solution for enabling green communication in next-generation wireless networks. Integrating mMIMO with Simultaneous Wireless Information and Power Transfer (SWIPT) technology can further enhance the system efficiencies in terms of Energy Efficiency (EE) and spectral efficiency. This article studies the feasibility and energy-efficient design of a uniform planar antenna (UPA)-assisted mMIMO-enabled SWIPT system. The downlink transmission of the SWIPT mMIMO system over the Rician fading channels is investigated with terminals harvesting energy based on a nonlinear energy harvesting model. We derive approximate expressions for signal-to-interference-plus-noise Ratio (SINR) and harvested power. Additionally, we formulate an EE optimization problem considering user-level quality of service and total transmit power constraints. To solve this nonconvex problem, we jointly optimize the allocated power and Power Splitting (PS) ratios by exploiting the fractional programming and convex-concave procedure approaches. Results demonstrate the superiority of our proposed design compared to the conventional scenarios with equal power allocation and fixed PS ratio algorithms with about 2 to 5 times EE improvements. The Results also indicate a considerably higher growth rate on EE by increasing the number of antennas and Rician factors compared to the two other methods.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"236-247"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820514","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107127","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}

引用次数: 0

Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos

IEEE Open Journal of the Computer Society Pub Date : 2025-01-03 DOI: 10.1109/OJCS.2025.3525560

Shadi Jaradat;Mohammed Elhenawy;Huthaifa I. Ashqar;Alexander Paz;Richi Nayak

{"title":"Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos","authors":"Shadi Jaradat;Mohammed Elhenawy;Huthaifa I. Ashqar;Alexander Paz;Richi Nayak","doi":"10.1109/OJCS.2025.3525560","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3525560","url":null,"abstract":"Near-miss traffic incidents, positioned just above \"unsafe acts\" on the safety triangle theory, offer crucial predictive insights for preventing crashes. However, these incidents are often underrepresented in traffic safety research, which tends to focus primarily on actual crashes. This study introduces a novel AI-based framework designed to detect and analyze near-miss and crash events in crowdsourced dashcam footage. The framework consists of two key components: a deep learning model to segment video streams and identify potential near-miss or crash incidents and a multimodal large language model (MLLM) to further analyze and extract narrative information from the identified events. We evaluated three deep learning models—CNN, Vision Transformers (ViTs), and CNN+LSTM—on a dataset specifically curated for three-class classification (crashes, near-misses, and normal driving events). CNN achieved the highest accuracy (90%) and F1-score (89%) at the frame level. At the event level, ViTs delivered a strong performance with a test accuracy of 77.27% and an F1-score of 67.37%, while CNN+LSTM, although lower in overall performance, demonstrated significant potential with a test accuracy of 78.1% and an F1-score of 68.69%. For a deeper analysis, we applied GPT-4o to process critical safety events (near-misses and crashes), utilizing both zero-shot and few-shot learning for narrative generation and feature extraction. The zero-shot learning method performed better, achieving an accuracy of 81.2% and an F1-score of 81.9%. This study underscores the potential of combining deep learning with MLLMs to enhance traffic safety analysis by integrating near-miss data as a key predictive layer. Our approach highlights the importance of leveraging near-miss incidents to proactively enhance road safety, thereby reducing the likelihood of crashes through early intervention and better event understanding.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"223-235"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106743","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}

引用次数: 0

Annual Report Text Information and Credit Rating Based on the Perspective of Readability

IEEE Open Journal of the Computer Society Pub Date : 2024-12-31 DOI: 10.1109/OJCS.2024.3523699

Yu Gong;Muhan Shi;Dongli Han

引用次数: 0

Statistical Validity of Neural-Net Benchmarks 神经网络基准的统计有效性

IEEE Open Journal of the Computer Society Pub Date : 2024-12-26 DOI: 10.1109/OJCS.2024.3523183

Alain Hadges;Srikar Bellur

引用次数: 0

Energy Efficiency of Kernel and User Space Level VPN Solutions in AIoT Networks AIoT网络中内核和用户空间级VPN解决方案的能源效率

IEEE Open Journal of the Computer Society Pub Date : 2024-12-26 DOI: 10.1109/OJCS.2024.3522566

ALEKSANDAR JEVREMOVIC;Zona Kostic;Ivan Chorbev;Dragan Perakovic;Andrii Shalaginov;Ivan Cvitic

{"title":"Energy Efficiency of Kernel and User Space Level VPN Solutions in AIoT Networks","authors":"ALEKSANDAR JEVREMOVIC;Zona Kostic;Ivan Chorbev;Dragan Perakovic;Andrii Shalaginov;Ivan Cvitic","doi":"10.1109/OJCS.2024.3522566","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3522566","url":null,"abstract":"The ability to process data locally using complex algorithms is becoming increasingly important in Internet of Things (IoT) contexts. Numerous factors contribute to this trend, including the requirement for immediate response, the need to protect data privacy/security, a lack of adequate infrastructure, and the desire to reduce costs. Due to the extensive hardware requirements (in terms of required computing power, memory, and other resources) for handling various scenarios, edge devices are typically configured to utilize general-purpose operating systems, primarily GNU/Linux. However, energy efficiency remains a critical requirement for this devices, especially in battery-powered scenarios (where energy inefficiency could make the device completely inoperable). Local data processing usually minimizes, but not entirely eliminates, data exchange with the environment. Along with energy costs of data processing, it is critical to also consider the energy efficiency of data protection when communicating with the environment. In this article, we evaluate the energy efficiency of kernel-level and user-space-level communication protection solutions: WireGuard and OpenSSL. These systems are evaluated on a range of hardware platforms, including Raspberry Pi 3, Nvidia Jetson NANO, Nvidia Jetson TX2, and Nvidia Jetson AGX Xavier. The energy efficiency of these systems was determined by examining long transfer streams with maximum channel/CPU utilization. We discovered that determining the energy efficiency of a device or protocol is difficult due to the high reliance on factors such as communication speed and direction.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"199-210"},"PeriodicalIF":0.0,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993268","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}

引用次数: 0