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Energy Efficiency in Hybrid NOMA-MEC Networks

IEEE Transactions on Wireless Communications 2025年

作者： Huang, Wenqi Wang, Kaidi Lin, Yushen Ding, Zhiguo The University of Manchester School of Electrical and Electronic Engineering M13 9PL United Kingdom University of Manchester Department of Electronic and Electrical Engineering United Kingdom Khalifa University Department of Computer and Information Engineering United Arab Emirates

The combination of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) has recently received great attention for maximizing energy efficiency (EE) in wireless networks. Previous studies mainly focus on single-ratio EE maximization using pure orthogonal multiple access (OMA) or NOMA scheme in MEC networks. This paper proposes a dynamic hybrid NOMA scheme that dynamically transforms between pure OMA and pure NOMA under the general network status in the multi-user MEC network. An offloading multi-ratio and non-convex EE problem is formulated. Two iterative algorithms, i.e., the successive Dinkelbach method-based algorithm and the closed-form nested fractional programming (FP) algorithm, are proposed to efficiently solve the offloading EE over multiple users' time and power within polynomial time based on different FP techniques, which provides feasible ideas to solve such multi-ratio optimization problems. The Dinkelbach method-based algorithm decomposes and optimizes the offloading EE user-wise. The nested FP algorithm jointly maximizes multiple users' EE by transforming the multi-ratio problem into equivalent linear forms, where the closed-form power solution is obtained. Simulation results show the superiority of the dynamic hybrid NOMA strategy and the feasibility of the two proposed algorithms. Critical insights are obtained over the dynamic hybrid NOMA strategies in the general MEC network. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Relationship of IQ with Causal Connections Between Brain Salient Network Regions

Relationship of IQ with Causal Connections Between Brain Sal...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Mina Ghoorchian Hamid Soltanian-Zadeh Mir Mohsen Pedram Mohsen Mir Mohammad Sadeghi Department of Cognitive Science Institute for Cognitive Science Studies Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran

ISBN: (数字)9798331529710

ISBN: (纸本)9798331529727

IQ is one of the indicators that has always been of interest to psychiatrists, doctors and cognitive science researchers. Since this index plays a key role in people’s lives and also in the occurrence of brain abnormalities, many studies have been conducted on it. In this paper, we used the resting state magnetic resonance imaging data available in the HCP database to classify people into three groups with low, medium, and high IQ and applied machine learning methods on the matrix of causal connections between related network regions. We applied brain highlights. We calculated the matrix of causal relationships in this network by the spDCM algorithm and then obtained the differences of the three groups using the ANOVA statistical test and 6 edges out of 169 edges with p-value <0.05 were found to be significantly different, according to the order of the edges, this difference is in the relationship between rMCC-rvIPFC, rInsula-rvIPFC, rInsula-rPutamen, rInsulaIIPG, IIPG-rSFG, IIPG-rSFG. In the next step, we used the algorithms of support machines and nearest neighbors to classify these three groups. Finally, using the support vector regression algorithm, we predicted the IQ from the edge values with RMSE=5.41 and MAE=3.59. The results of this research indicate a significant relationship between IQ and the salient network of the brain.

关键词： Support vector machines Neuroimaging Visualization Accuracy Machine learning algorithms Medical treatment Prediction algorithms Classification algorithms Indexes Analysis of variance

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FlashMix: Fast Map-Free LiDAR Localization via Feature Mixing and Contrastive-Constrained Accelerated Training

FlashMix: Fast Map-Free LiDAR Localization via Feature Mixin...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Raktim Gautam Goswami Naman Patel Prashanth Krishnamurthy Farshad Khorrami Department of Electrical and Computer Engineering Control/Robotics Research Laboratory (CRRL) NYU Tandon School of Engineering Brooklyn NY USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Map-free LiDAR localization systems accurately localize within known environments by predicting sensor position and orientation directly from raw point clouds, eliminating the need for large maps and descriptors. However, their long training times hinder rapid adaptation to new environments. To address this, we propose FlashMix, which uses a frozen, scene-agnostic backbone to extract local point descriptors, aggregated with an MLP mixer to predict sensor pose. A buffer of local descriptors is used to accelerate training by orders of magnitude, combined with metric learning or contrastive loss regularization of aggregated descriptors to improve performance and convergence. We evaluate FlashMix on various LiDAR localization benchmarks, examining different regularizations and aggregators, and demonstrating its effectiveness for rapid and accurate LiDAR localization in real-world scenarios. The code is available at https://***/raktimgg/FlashMix.

关键词： Location awareness Training Measurement Point cloud compression Laser radar Accuracy Pose estimation Benchmark testing Mixers Convergence

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GPR-SCSANet: Unequal-Length Time Series Normalization with Split-Channel Residual Convolution and Self-Attention for Brain Age Prediction

GPR-SCSANet: Unequal-Length Time Series Normalization with S...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Sun, Fangling Liang, Chuang Adali, Tülay Zhang, Daoqiang Jiang, Rongtao Calhoun, Vince D. Qi, Shile Nanjing University of Aeronautics and Astronautics Department of Computer Science and Technology Nanjing China University of Maryland Department of Computer Science and Electrical Engineering BaltimoreMD United States Yale School of Medicine Department of Radiology and Biomedical Imaging New Haven United States AtlantaGA United States

ISBN: (纸本)9798350386226

Functional magnetic resonance imaging (fMRI), as a non-invasive method to reveal brain function alterations, frequently yields time series with unequal lengths in real-world scenarios, which may arise from factors such as motion artifacts, participant state, and differing scan protocols. This variability conflicts with the traditional methods relying on isometric inputs, which poses a significant challenge for the downstream applications such as brain age prediction. To address this challenge, we introduced Gaussian Process Regression (GPR) to normalize the length of time series and proposed split-channel residual convolution (SC) and self-attention mechanisms (SA) to perform brain age estimation, called GPR-SCSANet. Results showed that the proposed framework, GPR-SCSANet, is able to fully utilize the inherent information and learn richer feature representations from unequal-length fMRI time courses, which significantly improved the prediction accuracy across 3 brain atlases and 5 prediction models. The results demonstrated the effectiveness and robustness of the proposed GPR-SCSANet, showcasing the potential for broader applications in brain age prediction task. © 2024 IEEE.

关键词： Time series

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Quantifying the Effectiveness of Student Organization Activities using Natural Language Processing

arXiv

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arXiv 2024年

作者： Taruc, Lyberius Ennio F. De La Cruz, Arvin R. Polytechnic University of the Philippines-Graduate School FEU Institute of Technology-Computer Engineering Department Philippines Polytechnic University of the Philippines-Graduate School Philippines

Student extracurricular activities play an important role in enriching the students' educational experiences. With the increasing popularity of Machine Learning and Natural Language Processing, it becomes a logical step that incorporating ML-NLP in improving extracurricular activities is a potential focus of study in Artificial Intelligence (AI). This research study aims to develop a machine learning workflow that will quantify the effectiveness of student-organized activities based on student emotional responses using sentiment analysis. The study uses the Bidirectional Encoder Representations from Transformers (BERT) Large Language Model (LLM) called via the pysentimiento toolkit, as a Transformer pipeline in Hugging Face. A sample data set from Organization C, a Recognized Student Organization (RSO) of a higher educational institute in the Philippines, College X, was used to develop the workflow. The workflow consisted of data preprocessing, key feature selection, LLM feature processing, and score aggregation, resulting in an Event Score for each data set. The results show that the BERT LLM can also be used effectively in analyzing sentiment beyond product reviews and post comments. For the student affairs offices of educational institutions, this study can provide a practical example of how NLP can be applied to real-world scenarios, showcasing the potential impact of data-driven decision making. © 2024, CC BY-SA.

关键词： Students

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Protection Elements for Self-Healing Microgrids Using Only Local Measurements

Protection Elements for Self-Healing Microgrids Using Only L...

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2022 North American Power Symposium, NAPS 2022

作者： Silva, Elijah Lavrova, Olga Ropp, Michael New Mexico State University Klipsch School of Electrical and Computer Engineering Electrical and Computer Engineering Department Las CrucesNM88003 United States Sandia National Laboratories AlbuquerqueNM87185 United States

ISBN: (纸本)9781665499217

There are unique challenges associated with protection and self-healing of microgrids energized by multiple inverterbased distributed energy resources. In this study, prioritized undervoltage load shedding and undervoltage-supervised overcurrent (UVOC) for fault isolation are demonstrated using PSCAD. The PSCAD implementations of these relays are described in detail, and their operation in a self-healing microgrid is demonstrated. © 2022 IEEE.

关键词： Electric inverters

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Temporal Multiplexing of Heralded Photons Based on Thin Film Lithium Niobate Photonics

Temporal Multiplexing of Heralded Photons Based on Thin Film...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Ekici, Cagin Yu, Yonghe Adcock, Jeremy C. Muthali, Alif Laila Zahidy, Mujtaba Tan, Heyun Lin, Zhongjin Li, Hao Oxenløwe, Leif K. Cai, Xinlun Ding, Yunhong Department of Electrical and Photonics Engineering Technical University of Denmark Lyngby Denmark State Key Laboratory of Optoelectronic Materials and Technologies School of Electronics and Information Technology Sun Yat-sen University Guangzhou510275 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Heralded photons from a silicon source are temporally multiplexed utilizing thin film lithium niobate photonics. The time-multiplexed source, operating at a rate of R = 62.2 MHz, enhances single photon probability by ... 详细信息

ISBN: (纸本)9781957171395

关键词： Silicon photonics

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Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot Setting 2

Empirical Evaluation of ChatGPT on Requirements Information ...

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2nd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2023

作者： Zhang, Jianzhang Chen, Yiyang Liu, Chuang Niu, Nan Wang, Yinglin Hangzhou Normal University Alibaba Business School Hangzhou China University of Cincinnati Department of Electrical Engineering and Computer Science Cincinnati United States Shanghai University of Finance and Economics Department of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350329070

Recently, various illustrative examples have shown the impressive ability of generative large language models (LLMs) to perform NLP related tasks. ChatGPT undoubtedly is the most representative model. We empirically evaluate ChatGPT's performance on requirements information retrieval (IR) tasks to derive insights into designing or developing more effective requirements retrieval methods or tools based on generative LLMs. We design an evaluation framework considering four different combinations of two popular IR tasks and two common artifact types. Under zero-shot setting, evaluation results reveal ChatGPT's promising ability to retrieve requirements relevant information (high recall) and limited ability to retrieve more specific requirements information (low precision). Our evaluation of ChatGPT on requirements IR under zero-shot setting provides preliminary evidence for designing or developing more effective requirements IR methods or tools based on generative LLMs. © 2023 IEEE.

关键词： Information retrieval

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WIP: Federated Learning for Routing in Swarm Based Distributed Multi-Hop Networks

arXiv

引用

arXiv 2023年

作者： Cash, Martha Murphy, Joseph Wyglinski, Alexander Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA United States

Unmanned Aerial Vehicles (UAVs) are a rapidly emerging technology offering fast and cost-effective solutions for many areas, including public safety, surveillance, and wireless networks. However, due to the highly dynamic network topology of UAVs, traditional mesh networking protocols, such as the Better Approach to Mobile Ad-hoc Networking (B.A.T.M.A.N.), are unsuitable. To this end, we investigate modifying the B.A.T.M.A.N. routing protocol with a machine learning (ML) model and propose implementing this solution using federated learning (FL). This work aims to aid the routing protocol to learn to predict future network topologies and preemptively make routing decisions to minimize network congestion. We also present an FL testbed built on a network emulator for future testing of the proposed ML aided B.A.T.M.A.N. routing protocol. © 2023, CC BY.

关键词： Machine learning

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Wireless Sensor Networks for Disaster Management and Emergency Response using SVM Classifier 2

Wireless Sensor Networks for Disaster Management and Emergen...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Meenakshi, B. Gopi, B. Ramalingam, L. Vanathi, A. Sangeetha, S. Murugan, S. Department of Electrical and Electronics Engineering Sri Sairam Engineering College Tamil Nadu Chennai India Tamil Nadu Rasipuram India Tamil Nadu Chennai India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Wireless Sensor Networks (WSNs) are essential for IoT-enabled crisis management and executive response. This paper explores WSNs' function in catastrophe situations and proposes new ways to improve emergency response operations. The article begins with a discussion of disaster management and emergency response scenarios and the necessity for real-time data collecting, monitoring, and analysis. WSNs are suitable for such applications due to their dense and dispersed sensing, tolerance to hostile conditions, and low power consumption. The research provides unique WSN deployment, data aggregation, and resource optimization methodologies for disaster management. These solutions will improve network coverage, dependability, and sensor longevity. The research also examines the synchronization of AI and human brainpower calculations with WSNs to enable smart navigation and robotized crisis response. The research study addresses WSN security and privacy in disaster management. It identifies flaws and suggests ways to classify, verify, and share data. A reenacted fiasco is used to evaluate the proposed alternatives. Situational awareness, response coordination, and emergency operations efficiency increase with the provided techniques. This study sheds light on WSNs' application in IoT-enabled emergency response and disaster management. The proposed solutions advance WSN technology and enable more effective and efficient emergency response systems. © 2023 IEEE.

关键词： Disaster Management Internet of Things Machine learning SVM Classifier Wireless Sensor Networks

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