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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Saibharath, S. Mishra, Sudeepta Arora, Gazal Sharma, Anamika Hota, Chittaranjan Dept. of Computer Science and Information systems BITS Pilani Hyderabad Campus India Dept. of Computer Science and Engineering IIT Ropar Punjab India

ISBN: (纸本)9783903176553

5G operates in the unlicensed spectrum to expand its capacity. This spectrum provides more uplink and downlink frequency bands in 5G to satisfy ultra-dense and scalability requirements. Wi-Fi is a prominent wireless technology that operates in unlicensed bands. Due to the proximity of cellular and Wi-Fi channels, utilization of both Wi-Fi and 5G New Radio (NR) can cause interference. The co-existence of 5G NR and Wi-Fi devices lacks the means of communication for negotiation and coordination among them. Listen-Before-Talk and Duty-cycling are standard Medium Access Control (MAC) mechanisms to enable co-existence. Due to a lack of coordination, network utilization becomes unfair with the existence of selfish devices. The selfish users could maximize throughput and affect other Wi-Fi and NR users. Here, we investigate the effects of selfish nodes on this Wi-Fi Co-existence with 5G. These nodes disobey exponential backoff to boost their channel acquisition. We study the effect of misbehavior through the Order Gain metric. Through standard CSMA/CA settings with overall channel occupancy of 99.75%, we demonstrate that selfish users profoundly impact fair co-existence. We study the side effects through throughput, channel sensing, and acquisition for various backoff window access patterns. Finally, we go through counteraction methods to overcome the selfish user impact. © 2023 IFIP.

关键词： Wi-Fi

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Optimizing Privacy-Accuracy Trade-Off in IoT Intrusion Detection: An Analysis of FedAvg and FedProx with Differential Privacy

Optimizing Privacy-Accuracy Trade-Off in IoT Intrusion Detec...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Anwar, Sayeda Suaiba Hoque, Mohammed Moshiul Chittagong University of Engineering & Technology Dept. of Computer Science and Engineering Bangladesh East Delta University Chattagram Bangladesh

ISBN: (纸本)9798331535476

Ensuring the privacy and accuracy of interconnected network devices is crucial in the Internet of Things (IoT) landscape, especially when deploying Intrusion Detection Systems (IDS) on devices with constrained resources. This study offers a method to improve intrusion detection in IoT environments by combining Federated Learning with Differential Privacy. The proposed approach builds an efficient intrusion detection model by fusing Bidirectional Long Short-Term Memory (BiLSTM) with Convolutional Neural Networks (CNN). We evaluate this model on the Bot-IoT dataset, meticulously curated by the University of New South Wales (UNSW). This work aims to balance high detection accuracy with robust privacy protection, a crucial yet often neglected aspect of IoT security. We use federated learning to distribute intrusion detection tasks across various IoT devices while protecting data privacy, using differential privacy algorithms to assess and limit information leakage. This study evaluates two aggregation strategies, Federated Averaging (FedAvg) and Federated Proximal (FedProx), and examines how varying epsilon values and noise multipliers affect model accuracy. The findings indicate t hat a noise multiplier o f roughly 0.5 yields a steady accuracy curve, with FedProx achieving higher accuracy than FedAvg. © 2024 IEEE.

关键词： Network intrusion

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Integrating Temporal Analysis with Hybrid Machine Learning and Deep Learning Models for Enhanced Air Quality Prediction 19

Integrating Temporal Analysis with Hybrid Machine Learning a...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Omer, Muhammad Ali, Sardar Jaffar Raza, Syed M. Le, Duc-Tai Choo, Hyunseung Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of James Watt School of Engineering University of Glasgow Glasgow United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

Air pollution remains a critical issue, adversely affecting public health and the environment. In this study, we utilize the Air Quality index dataset from Kaggle to analyze temporal and seasonal variations of key pollutants, specifically Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Benzene (C6H6 ). Building upon this analysis, we predict Absolute Humidity (AH), a vital meteorological factor influencing pollutant dispersion, using Machine Learning (ML) and Deep Learning (DL) techniques. Three ML techniques, Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR), and three DL techniques, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are employed for predicting AH. The results indicate that while the RF model achieved the lowest Mean Absolute Error (MAE) among ML methods (0.02), the CNN model, despite having an MAE of 0.04, demonstrated statistical superiority through paired t-tests and Wilcoxon signed-rank tests (p © 2025 IEEE.

关键词： Convolutional neural networks

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Machine Learning Methods to Classify Brain Tumor Cell: An Analysis 2

Machine Learning Methods to Classify Brain Tumor Cell: An An...

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2nd International Conference on Pervasive Computing Advances and applications, PerCAA 2024

作者： Swarnkar, Shekhar Kumar Yadav, Surendra Kumar Dept. of Computer Science & Engineering JECRC University Rajasthan Jaipur India Dept. of Computer Science & Engineering JECRC University Rajasthan Jaipur India

ISBN: (纸本)9781837242627

Under the brain tumor situation major poses risk to human life, and the timely detection of this illness is to successfully save human life. The suitable conclusion about brain tumor is the practice of using precise and efficient approaches for segmentation and classification on Magnetic Resonance Imaging. Major categorization of aberrant and normal tumor MR images has been the focus of research that has already been conducted. When it comes to classifying brain tumor cells, this paper presents a relative examination of various machine learning approaches that are considered to be state-of-the-art. The paper present both supervised and unsupervised learning methods, such as Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), Random Forests (RF), and deep learning models. A comparative analysis are presented in this research that focus on the tactics that are now being used that have shown to be the most successful in their brain tumor detection. There is a wide range of classifiers that may be used to get feature extraction from MRI brain images. In order to define the abnormal developments in brain MRI scans, the main phase of these procedures comprises a few actions that must be carried out while applying different techniques. These actions must be carried out in order to define the abnormal developments. A variety of models that are associated with feature extraction framework are used by the majority of the approaches that are now in use to extract substantial information from MRI brain images. © The Institution of engineering & Technology 2024.

关键词： Artificial Intelligence Brain Tumor Machine Learning Prediction support Vector Machine

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Robust Exercise-Based Telerehabilitation for Elderly Healthcare Survives 19

Robust Exercise-Based Telerehabilitation for Elderly Healthc...

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19th International Conference on Emerging Technologies, ICET 2024

作者： Awan, Aftab Ahmad Najam, Shaheryar Jalal, Ahmad Riphah International University Dept. of Biomedical Engineering Islamabad Pakistan Riphah International University Dept. of Electrical & Computer Eng Islamabad Pakistan Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350379587

Improving motor function post-injury or surgery often relies on physical therapy, but long waiting lists can impede effective monitoring of patient progress. Tele-rehabilitation addresses this issue by providing equitable access to therapy without the need for travel or costly in-home treatments. This paper introduces a machine learning system capable of autonomously detecting exercise correctness during patient performance, thereby enhancing rehabilitation outcomes. Utilizing the IRDS dataset, which includes 29 participants performing nine exercises, extensive feature engineering was conducted, and five machine learning algorithms coupled with various dimensionality reduction techniques were evaluated such as Random Forests (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machines (SVM) and Decision Trees (DT). Random Forests combined with correlation-based feature selection emerged as the most effective model with 98% accuracy in exercise recognition, 91% accuracy in exercise correctness detection, and a combined accuracy of 95%. © 2024 IEEE.

关键词： Remote patient monitoring

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An Advanced MIMO Multicarrier Framework for NTN-Based 5G Broadcast/Multicast Transmissions 11

An Advanced MIMO Multicarrier Framework for NTN-Based 5G Bro...

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11th IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2024

作者： Rahman, Talha Faizur Marojevic, Vuk Sacchi, Claudio Mississippi State University Dept. of Electrical and Computer Engineering Mississippi StateMS United States University of Trento Dept. of Information Engineering and Computer Science Trento Italy The University of New Mexico Dept. of Electrical Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350385045

Satellites are teaming up with 5G, forming a non-terrestrial network (NTN), to support broadband applications over wide coverage areas. However, low latency and high reliability are the challenges in NTN which is addressed through the use of low-earth orbit (LEO) satellites. High transmission efficiency can be accomplished by employing multiple-input multiple-output (MIMO) antenna systems and multicarrier wave-forms. Conventional multicarrier techniques, such as Orthogonal Frequency Division Multiplexing (OFDM) and its variants, are sensitive to the nonlinear distortion of high power amplifiers (HPAs) and high Doppler environments. Therefore, we consider Constant Envelope OFDM (CE-OFDM) assisted with Space-Time Shift Keying (STSK) and Low-Density Parity Check (LDPC) encoding for 5G broadcast/multicast systems based on LEO satellites working in the L-band. We propose a low complexity receiver based on A Posteriori Probability (APP) STSK decoding integrated with LDPC decoding, in which we derive the APP of LDPC coded bits for the STSK-CE-OFDM transmission. We analyze the computational complexity of the proposed receiver scheme and conclude that it is effective in counteracting HPA nonlinearity and high Doppler effects inherent to LEO satellites. Numerical results show that the proposed framework outper-forms conventional multicarrier techniques with a gain as high as 16 dB in the presence of amplifier nonlinearity. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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A Study on the Efficient ESS Charging/Discharging Operation Algorithm in LVDC Microgrid Environment

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Transactions of the Korean Institute of Electrical Engineers 2024年第6期73卷 1063-1073页

作者： Jung, Sang-Woo An, Yoon-Young Kim, Ki-Il Dept. of Computer Science and Engineering Chungnam National University Korea Republic of ICT Convergence Standards Research Division Electronics and Telecommunications Research Institute Korea Republic of

Modern power systems are developing in a sustainable and energy-efficient direction, and research on intelligent power grids that accommodate new and renewable energy is being actively conducted. Since new and renewable energy-based distributed power generation systems and ESS are output as DC power, the DC microgrid reduces the power conversion step and improves the stability and efficienc of the power distribution network. The core of this study is to compare and analyze the performance of the three charge/discharge operation algorithms based on AI prediction in the LVDC microgrid environment. The AI server learns real-time data through interworking with the EMS to predict future load usage and solar power generation, and performs the charge/discharge operation algorithm of ESS based on the collected ESS state data. This paper presents the configuration of the LVDC microgrid testbed, the integration method between EMS and electrical facilities, and analyzes the performance of the developed ESS charge/discharge operation algorithm. It also evaluates the effectiveness of each operation algorithm through actual operation data and simulation, analyzes how these strategies contribute to system load reduction and energy efficiency improvement, and provides guidelines for selecting the optimal ESS charge/discharge operation algorithm under specific conditions. © The Korean Institute of Electrical Engineers.

关键词： Solar energy

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Machine Learning-Based Estimation of Dynamic Travel Times Between Points of Interest

Machine Learning-Based Estimation of Dynamic Travel Times Be...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Kaji, Shoma Kajimoto, Dai Noguchi, Tatsuya Takayama, Toshinori Bao, Siya Togawa, Nozomu Dept. of Computer Science and Communications Engineering Waseda University Tokyo Japan ZENRIN DataCom Tokyo Japan

ISBN: (纸本)9798331521165

Due to the dynamic traffic conditions, effective estimation of travel times is crucial for the real-world travel planning service. Although APIs provide accurate travel information, the high response time poses a limitation. This paper proposes a machine learning-based method using XGBoost to predict dynamic travel times between Points of Interests (POIs). By utilizing features including latitude and longitude of locations, route direction, and departure time, the model achieves high prediction accuracy. We conducted hyper-parameter tuning using Optuna and evaluated the proposed method with POIs in Kyoto. The proposed method achieved a low RMSPE of −3s. © 2025 IEEE.

关键词： Travel time

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A Medium Voltage Grid-connected PV Inverter with a New Modular High Voltage Gain Converter Featuring Internal Modified Voltage Doubling Balancers 14

A Medium Voltage Grid-connected PV Inverter with a New Modul...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Kanathipan, Kajanan Cheema, Muhammad Ali Masood Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada Northern Transformer Toronto Canada

ISBN: (纸本)9798331516116

This work proposes a medium voltage grid-connected inverter with modular high voltage gain converters for PV energy applications. The proposed topology utilizes (1) PV arrays interfaced with modular DC-DC converters in an input independent output series (IIOS) configuration, (2) internal modified voltage doubling balancers to achieve balanced output voltage under power mismatch scenarios, and (3) a high voltage neutral point clamped (NPC) 5-level grid-connected inverter system. The presented DC-DC converters are able to perform maximum power point tracking operation through phase-shift control on their full-bridge switches and ensure balanced output voltage under all scenarios with duty ratio control on the internal voltage doublers. In addition, a high step-up gain and soft-switching operation on all module switches are achieved through the use of an LLC resonant stage. A five-level three-phase inverter interfaces the system with the MVAC grid. The steady-state and dynamic performance of the proposed modular system are validated through simulation results on a 27kVrms (L-L) 60Hz output, four-module 60kW system and preliminary experimental results on a two-module proof-of-concept 1kV DC-link laboratory prototype. © 2025 IEEE.

关键词： Electric power transmission networks

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Source Free Domain Adaptation with Pseudo-Labeling Quality Assessed by Sam in Fundus Image Segmentation 22

Source Free Domain Adaptation with Pseudo-Labeling Quality A...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Yaacovi, Bar Goldberger, Jacob Tel-Aviv University Computer Science Dept. Israel Bar-Ilan University Faculty of Engineering Israel

ISBN: (纸本)9798331520526

Precise segmentation of the optic cup and optic disc in fundus images is crucial for measuring the cup-to-disc ratio, a key indicator for glaucoma screening and diagnosis. When applying a network trained on data from one medical site to data from another site, performance declines due to domain shift problems. This study centers on the realistic scenario of source-free unsupervised domain adaptation. We propose a new pseudo-labeling strategy that takes advantage of the capabilities of the SAM segmentation foundation model by using it to assess the quality of the pseudo-label segmentation of the target images created by the source model. We implemented our method on publicly available retinal segmentation datasets collected from several medical sites and reported improved performance compared to previous methods. © 2025 IEEE.

关键词： Image segmentation

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