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共 342 条记录，以下是31-40 订阅

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Standardization Review on Non-Terrestrial Networks 39

Standardization Review on Non-Terrestrial Networks

引用

39th International Conference on information Networking, ICOIN 2025

作者： Kyung, Yeunwoong Lee, Jaewook Ko, Haneul Oh, Seungmin Lee, Jaehwan Kim, Youngjun Kongju National University Div. of Info. & Comm. Eng Cheonan South Korea Pukyong National University Dep. of Info. and Comm. Engineering Busan South Korea Kyung Hee University Dep. of Electronic Eng Yongin-si South Korea Kongju National University Dep. of Computer Science and Eng Cheonan South Korea Kyungnam University School of Computer Science and Eng Changwon South Korea

ISBN: (纸本)9798331506940

As the demand for global connectivity increases, non-terrestrial networks (NTNs), particularly satellite-based systems, have gained significant attention in communication network standardization. The 3rd Generation Partnership Project (3GPP) has been at the forefront of developing standards that incorporate satellite communications into the broader terrestrial network framework. This paper reviews the progression of NTN standardization, focusing on three key stages: (1) the use of satellites as relays or the next generation NodeBs (gNBs), (2) the integration of user plane functions (UPF) within satellites, and (3) the inclusion of control plane network functions (NFs) within satellites. These developments in standardizations reflect a shift from basic relay functions to fully integrated satellite networks. © 2025 IEEE.

关键词： 3GPP Standards Control Plane Network Functions Non-Terrestrial Networks

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Personalized Stress Detection using a Lightweight Machine Learning Framework With Convenient Wrist-Worn Sensors

Personalized Stress Detection using a Lightweight Machine Le...

引用

IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： M. Hasan S. Mithun M. Mahmud S. Baral K. Poudel Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Dept. of Computer Science University of Memphis TN USA Dept. of Electrical and Computer Engineering University of Memphis TN USA Div. of General Internal Medicine Univ. of Tennessee Health Science Center Memphis TN USA Dept. of Computer Science Middle Tennessee State University Murfreesboro TN USA

ISBN: (数字)9798350388572

ISBN: (纸本)9798350388589

Over 70% Americans experience daily stress, making real-time, accurate stress detection crucial for timely intervention, and promoting physical and mental well-being. In this study, we detect stress using machine learning from publicly available wrist-worn sensor data. We examined both population and indiv.dual levels, focusing on simple, computation friendly statistical features for reduced processing to facilitate adoptions in real-life mHealth applications. Our framework demonstrates that stress detection achieves over 96% accuracy at both the population and indiv.dual levels, with the Extreme Gradient Boosting classifier achieving the highest predictive accuracy. We found significant variability between indiv.duals and achieved an average accuracy of 99.63% with area under the curve (AUC) 99.23%, F1-score 99.00%, precision, and recall 99.22%. This approach offers a promising solution for real-life stress detection due to its high accuracy and simplicity.

关键词： Analytical models Accuracy Focusing Human factors Signal processing Predictive models Feature extraction Boosting Real-time systems Biology

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Libpubl: Exploiting persistent user buffers as logs for write atomicity 21

Libpubl: Exploiting persistent user buffers as logs for writ...

引用

13th ACM Workshop on Hot Topics In Storage and File Systems, HotStorage 2021

作者： Jung, Jaewon Choi, Jungsik Han, Hwansoo DMC Engineering Sungkyunkwan University Mobile Div. Samsung Electronics Korea Republic of Computer Science and Eng. HPC Research Center Sungkyunkwan University Korea Republic of Computer Science and Eng. Human-AI Interaction Sungkyunkwan University Korea Republic of

ISBN: (纸本)9781450385503

With recent advancement in NVM technologies, non-volatile main memory(NVMM) has been highlighted as promising systems for large scale data centers. By utilizing memory-mapped IO, researchers have attempted to provide fast IOs without kernel mode switches and complex kernel IO layers. Even though they improve the performance by taking advantage of NVMM characteristics, memory-mapped IOs still require additional user-level logging techniques to guarantee write atomicity and file system integrity. We propose a user-level library file system, Libpubl, which is designed to minimize the write amplification overhead of traditional logging by exploiting persistent user buffers as logs. Libpubl ensures atomic updates of data and improves the performance and the scalability. Compared to the state-of-the-art NVM file systems, Libpubl increases the performance by 50∼120% in Fio benchmark. © 2021 Association for Computing Machinery.

关键词： Benchmarking

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A Context-Aware Exercise Facilitation System While Doing Other Tasks

A Context-Aware Exercise Facilitation System While Doing Oth...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Mizuki Kobayashi Airi Tsuji Kaori Fujinami Grad. School of Bio-Functions and Systems Science Tokyo University of Agriculture and Technology Tokyo Japan Div. of Advanced Information Technology and Computer Science Tokyo University of Agriculture and Technology Tokyo Japan

We proposed a context-aware exercise-facilitation system that can be used in parallel with various tasks in daily life. The offline-evaluation results show that the proposed system can recommend appropriate exercises ... 详细信息

ISBN: (纸本)9781665492331

关键词： Task analysis Consumer electronics

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Decoding Speech Categorization using Microstate Cortical EEG Signals and Machine Learning

Decoding Speech Categorization using Microstate Cortical EEG...

引用

IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： M. Mahmud M. Hasan M. Yeasin G. Bidelman Div. of General Internal Medicine Univ. of Tennessee Health Science Center Memphis TN USA Department of Electrical and Computer Engineering University of Memphis TN USA Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Department of Speech Language and Hearing Sciences Indiana University Bloomington IN USA

ISBN: (数字)9798350388572

ISBN: (纸本)9798350388589

Categorical perception (CP) is a perceptual phenomenon that refers to the tendency of humans to group speech sounds into discrete units. In this work, we used cortical event-related potential (ERP) signals, recorded during a speech identification task, as input to a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and machine learning (ML) classifiers to examine how neural signatures of the brain distinguish prototypical vs. ambiguous speech token categories. Particularly, we used extreme gradient boosting (XGBoost), support vector machine, and random forest classifiers. Our analysis shows that using source reconstructed whole-brain data, the XGBoost classifier yielded the best classification accuracy of 94.12% with (area under the curve (AUC) 94.13%, Precision 94.00%, F1-score 94.00%) in the 197-258 ms time window after the stimulus onset. We also identified 15 important brain regions that distinguished vowel classes with accuracy of 90.28% (AUC 90.17%, F1-score 90.00%, Precision 90.00%), 4% less than whole-brain data. Remarkably, out of these 15 critical brain regions, nine were from the left hemisphere, consistent with the left-brain dominance for language processing. The best speech decoding was obtained from early portions of the ERP (~180-320 ms), which corroborates notions that sensory encoding is critical for successful phonetic processing.

关键词： Support vector machines Accuracy Hidden Markov models Phonetics Signal processing Electroencephalography Real-time systems Decoding Speech processing Random forests

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Wide-Angle Vital Signs Measurements by Adaptive FMCW Radar

Wide-Angle Vital Signs Measurements by Adaptive FMCW Radar

引用

IEEE MTT-S International Microwave Bio Conference ((IMBIOC)

作者： Pouya Mehrjouseresht Oluwatosin J. Babarinde Reda El Hail Dominique M. M.-P. Schreurs Dept. of Electrical Engineering Div. ESAT-WAVECORE KU Leuven Leuven Belgium Dept. of Computer Science Flanders Make DTAI-FET Belgium DTAI Leuven.AI KU Leuven Geel Belgium

This paper demonstrates the use of a people tracking algorithm and beamforming technique in monitoring vital signs for smart home applications. A person's location and velocity are detected. When the person remains stationary within a specific area (distance and angle), the radar beam is steered towards the target, and vital signs are measured. The advantage of this approach is not only the physiological parameters can be monitored in different positions by steering the transmitted signals towards the target's location, but also the target's range bin can be recognized clearly from other clutter according to its amplitude. To extract vital signs, the received signals from two receiver channels are combined. An algorithm based on continuous wavelet transform (CWT) with different mother wavelets is applied to the phase of the combined signal. The experimental results demonstrate that the proposed algorithm can monitor breathing and heart rate with a maximum error of 10.7% and 11.51%, respectively, within a range of 1 to 2 meters and an angle from −40 to 40 degrees.

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Sonic Waves Travel-time Prediction: When Machine Learning Meets Geophysics

Sonic Waves Travel-time Prediction: When Machine Learning Me...

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Green Energy, Computing and Sustainable Technology (GECOST), International Conference on

作者： W. K. Wong Yohanes Nuwara Filbert H. Juwono Foad Motalebi Dept. of Electrical and Computer Eng Curtin University Malaysia Miri Malaysia Corporate Information Technology Div. Asia Pulp and Paper Sinarmas Jakarta Indonesia Computer Science Program University of Southampton Malaysia Johor Malaysia

ISBN: (纸本)9781665486644

Sonic wave travel-time prediction is an important task in oil and gas exploration as it provides important information on the content and lithography of the rocks. Travel-time data, however, are not always accessible due to practical considerations. Currently, machine learning methods have been used to infer these values. In this paper, we look at the application of machine learning in predicting sonic wave travel-time, specifically in terms of challenges, benchmarks, and datasets. In addition, we present some preliminary results of sonic wave travel-time prediction using existing machine learning regression methods, namely curve fitting artificial neural network and multiple linear regression. Finally, this paper is aimed to act as a ”bridge” between machine learning practitioners and domain-specific oil and gas engineers.

关键词： Drilling Machine learning algorithms Oils Lithography Machine learning Artificial neural networks Geophysics

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Standardization Review on Non-Terrestrial Networks

Standardization Review on Non-Terrestrial Networks

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International Conference on information Networking

作者： Yeunwoong Kyung Jaewook Lee Haneul Ko Seungmin Oh Jaehwan Lee Youngjun Kim Div. of Info. & Comm. Eng Kongju National University Cheonan Korea Dep. of Info. and Comm. Engineering Pukyong National University Busan Korea Dep. of Electronic Eng Kyung Hee University Yongin-si Korea Dep. of Computer Science and Eng Kongju National University Cheonan Korea School of Computer Science and Eng Kyungnam University Changwon Korea

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

关键词： Satellites 3GPP Standards Reviews Trajectory 3GPP Relays Low latency communication Satellite communications Standards Next generation networking

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Research on Intelligent Network Operation Management System Based on Anomaly Detection and Time Series Forecasting Algorithms

Research on Intelligent Network Operation Management System ...

引用

Telecommunications, Optics and computer science (TOCS), IEEE Conference on

作者： Jian Guo Hua Guo Zhong Zhang Dept. of Information Management and Information System Tianjin University of Technology Tianjin China Dept. of Communication Engineering Tianjin Polytechnic University Tianjin China Div. of Science & Technology Tianjin University Tianjin China

ISBN: (纸本)9781665470544

The research try to implements an intelligent network operation management system for enterprise networks. First, based on Flask-state software architecture, the system adapt to Phytium CPU and Galaxy Kylin operating system successfully. Second, using the Isolation Forest algorithm, the system implements the anomaly detection of host data such as CPU usage. Third, using the Holt-winters seasonal prediction model, the system can predict time series data such as network I/O. The results show that the system can realizes anomaly detection and time series data prediction more precisely and intelligently.

关键词： Intelligent networks Software architecture Operating systems Time series analysis Software algorithms Prediction algorithms Telecommunications

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Characterization of Glioblastoma Organoid Bio-Responses Upon 30.5 GHz Continuous Wave Exposure

Characterization of Glioblastoma Organoid Bio-Responses Upon...

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IEEE MTT-S International Microwave Bio Conference ((IMBIOC)

作者： Elena Rampazzo Nissar Karim Luca Persano Rosanna Pinto Arianna Casciati Mirella Tanori George Hodgkins Ilan W. Davies Alessandro Zambotti Cristopher Hancock Cristiano Palego Gimapietro Viola Mariateresa Mancuso Caterina Merla Dep. of Women's and Children's Health (SDB) University of Padova Padova Italy School of Computer Science and Electronic Engineering Bangor University Bangor UK Div. Health Protection Technologies ENEA Rome Italy Group R&D CREO Medical Bath UK Group R& School of Computer Science and Electronic Engineering D CREO Medical Bath UK

This paper reports on the effects of 30.5 GHz continuous wave exposure on cancer stem cells of glioblastoma organoids. The exposure setup was completely developed and the dosimetry carried on based on both numerical and experimental activities. Our results showed modification in cell death and proliferation, suggesting that presented exposure at a power level of 0.1 W (RMS) seems useful for targeting glioblastoma cells, and improving future treatment of this complex pathology.

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