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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Yoon, Heeyong Chon, Kang-Wook Kim, Min-Soo Dgist Department of Electrical Engineering and Computer Science Daegu Korea Republic of School of Computer Science and Engineering Koreatech Cheonan Korea Republic of School of Computing Kaist Daejeon Korea Republic of

ISBN: (纸本)9798350364637

Owing to the explosive growth of the Internet of Things (IoT), there have been vast volumes of sensor-generated time series data in various locations. A lot of network usage occurs through these locations to process the sensor-generated data. To reduce network usage, the point of data processing gradually shifts from the central cloud to servers at the edge of the Internet, called edge servers. To cover the paradigm shift, there has been a challenging issue in training a neural network model, which aggregates the data generated by different locations and causes a massive amount of transmitted data across the network. Federated learning successfully resolves this issue by exchanging model parameters in the edge server through the central cloud. Meanwhile, Spatio-Temporal Graph Neural Networks (STGNNs) have gained much attention for analyzing time series data transmitted from several locations in IoT environments. Despite the increasing number of STGNN models that have been examined, the integration of STGNNs and federated learning remains underexplored. This paper presents FedSTGNN, a framework that seamlessly adapts arbitrary STGNN models to edge computing environments. The proposed method partitions the spatio-temporal graph with a mathematical definition and then trains each partitioned data with an arbitrary STGNN model on edge servers separately. Then, it aggregates all the model parameters in the central cloud with dramatically reduced network usage. Through experiments, we demonstrate that the FedSTGNN framework could train the model with a reduced amount of network communication by utilizing the advantages of edge computing and a slight loss in accuracy. © 2024 IEEE.

关键词： Spatio-temporal data

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Design of an RF-CMOS Switched-Capacitor Power Amplifier for NB-IoT RF Transceivers

Design of an RF-CMOS Switched-Capacitor Power Amplifier for ...

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2023 International Young Engineers Forum in electrical and computer engineering, YEF-ECE 2023

作者： Santos, Ana Isabel Pedro Oliveira, Joao NOVA School of Science and Technology Department of Electrical and Computer Engineering Caparica Portugal Centre of Technology and Systems CTS-UNINOVA Department of Electrical and Computer Engineering Caparica Portugal

ISBN: (纸本)9798350300420

The paper presents the implementation of a Switched Capacitor Power Amplifier (SCPA) to be integrated into a Narrowband Internet of Things (NB-IoT) Transceiver. The SCPA is designed to operate at a frequency of 0.9GHz and aiming the maximum output power allowed by the standard of 23dBm. All the blocks within the SCPA were developed using RF components of a standard 130nm CMOS process, with a 1.2V/2.4V supply voltage. Results show that the SCPA is able to produce a maximum output power of 15.61dBm with a maximum Power-Added Efficiency (PAE) of 26.52% and a Total Harmonic Distortion (THD) of 0.68%. The measured HD2 and HD3 are-70.23dBc and-43.41dBc, respectively. Additional Process, Voltage and Temperature (PVT) corners and Monte Carlo simulations indicate the SCPA operates properly when subject to different conditions and mismatches. Besides the SCPA, a 16 QAM modulation stage is designed in VerilogA in order to evaluate the performance of the RF PA when a sequence of symbols is transmitted. © 2023 IEEE.

关键词： Power amplifiers

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AtheroRisk: An Integrated computer Software System for Stroke Risk Stratification Utilizing Carotid Plaque Analysis in Ultrasound Videos

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SN computer science 2025年第2期6卷 1-16页

作者： Gemenaris, Michalis Liapi, Georgia D. Markides, Christos Constantinou, Kyriacos Loizou, Christos P. Neophytou, Michalis Kardoulas, Dimitrios Pattichis, Marios S. Griffin, Maura Nicolaides, Andrew Pattichis, Constantinos S. Kyriacou, Efthyvoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science and Engineering Frederick University Nicosia Cyprus Department of Computer Science University of Cyprus Nicosia Cyprus American Medical Center Nicosia Cyprus Vascular Surgery Clinic Attikon General University Hospital Athens Greece Department of Electrical and Comp. Engineering Univ. of New Mexico Albuquerque NM United States Vascular Screening and Diagnostic Center Nicosia Cyprus

In this study we propose and evaluate AtheroRisk, a standalone integrated computer software system for the analysis of carotid B-mode ultrasound (U/S) images and videos. Our goal was to provide a tool to help physicians stratify stroke risk. The presented system is based on outcomes from different research studies, as well as European guidelines for the evaluation and treatment of carotid artery disease. The goal is to enable reproducible analysis of atherosclerotic plaques in real time. AtheroRisk brings together analysis of U/S image and/or video of the carotid arteries facilitating anonymization, standardization, noise filtering and segmentation of plaques;followed by image and motion feature extraction to derive plaque-composition and stability. All the above steps are combined to determine the annual stroke risk rate and the 5-year stroke-free survival rate. The system is powered by an SQLite local database, in which the end user can save and manage data extracted from processing and analysis in an anonymized way. The first version of AtheroRisk was developed following an incremental software development process. The developed system was verified using 54 U/S videos (27 Asymptomatic, AS;27 Symptomatic, SY cases) while the clinician’s satisfaction and comments were collected through a questionnaire-based validation process. The results show that the integrated system proposed in this study can be successfully used for the automated image and video analysis of the CCA plaque in ultrasound videos. © The Author(s) 2025.

关键词： Carotid plaque analysis Motion analysis Plaque features Stroke risk stratification Ultrasound video

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Millimeter- Wave Vehicle-to-Infrastructure Communications for Autonomous Vehicles: Location-Aided Beam Forecasting in 6G 16

Millimeter- Wave Vehicle-to-Infrastructure Communications fo...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Makadia, Omikumar B. Patel, Dhaval K. Shah, Kashish D. Raval, Mehul S. Zaveri, Mukesh Merchant, S.N. School of Engineering and Applied Science Ahmedabad University India Svnit Department of Computer Science and Engineering Gujarat Surat India Iit Bombay Department of Electrical Engineering Mumbai Powai India

ISBN: (纸本)9798350383119

The rise of autonomous vehicles has generated substantial interest due to their potential to transform transportation, enhance safety, and reduce traffic congestion in urban areas. This research investigates strategies to facilitate communication among various elements in driving environments, such as vehicles, pedestrians, and emergency responders, primarily focusing on vehicle-to-infrastructure (V2I) communication. Furthermore, advancements in wireless technology, including 6G and millimetre waves, offer improved connectivity but face challenges in signal propagation. The study emphasizes the importance of beam-forming as a solution, exploring alternative, resource-efficient methods to predict optimal signal directions using user positions. Positive simulation results suggest real-world applicability, and the study addresses critical questions about utilizing future positions and their impact on overhead complexity. This research provides valuable insights into position-based approaches for enhancing wireless communication in autonomous driving scenarios. © 2024 IEEE.

关键词： 6G Autonomous vehicles beam prediction overhead complexity training overhead

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Evaluation of Federated Learning Techniques on Edge Devices Using Synthetic Medical Imaging Datasets

Evaluation of Federated Learning Techniques on Edge Devices ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Alhonainy, Ahmad Rao, Praveen University of Missouri Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350359527

Artificial intelligence (AI) holds great promise in healthcare as it can significantly advances disease diagnosis using diverse medical datasets. However, learning generalizable machine learning models requires data from multiple data providers and should not compromise patient privacy to abide by federal laws. Federated learning (FL) is an attractive approach of learning such models from different data providers without exchanging the actual data owned by the providers. In certain medical scenarios, there is limited labeled data to train models via FL. Therefore, in this paper, we explore the effectiveness of synthetic medical imaging datasets for training deep learning models using FL. We specifically focus on FL using edge devices that have limited computing and storage capacity. Our federated learning environment was implemented using the open source Flower framework and a network of edge devices. We selected a publicly available histopathology image dataset related to lung and colon cancer. We evaluated the performance of three well-known deep learning models, namely, DenseNet121, EfficientNetB0, and MobileNetV2 by training them on both real and synthetic images. We observed that DenseNet121 consistently performed the best. For example, it achieved an accuracy of 98% when trained on both real and synthetic images for colon cancer. Thus, synthetic data can be effectively used to train deep learning models via FL when real data are limited. © 2023 IEEE.

关键词： Medical imaging

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Smart Solar Forecasting: Machine Learning Approaches for Predicting Solar Power

Smart Solar Forecasting: Machine Learning Approaches for Pre...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Kumar, Madhav Borgohain, Santanu Panda, Kaibalya Prasad Thokchom, Surmila Panda, Gayadhar National Institute of Technology Department of Electrical Engineering Meghalaya India Pandit Deendayal Energy University Department of Electrical Engineering Gujarat Gandhinagar India National Institute of Technology Department of Computer Science and Engineering Meghalaya India Nitttr Department of Electrical Engineering Kolkata India

ISBN: (纸本)9798350350821

Solar energy is one of the most popular renewable energy sources in recent years. These solar systems not only reduce the need for electricity but also greatly improve the environment. Due to our excessive dependence on fossil fuels and their limited sources and the pollution caused by them, most people are turning to solar energy to power their homes and industries. It's critical to understand the location of the solar panel and the local weather since solar power efficiency is highly dependent on environmental factors. With the inherent unpredictability of photovoltaic power output, machine learning is a useful tool for predicting how weather conditions affect solar power plants, and there are various other viable prediction methods available. This research evaluates several meteorological characteristics for PV forecasting using numerous machine learning algorithms. Using the collected historical data set, a machine learning model is created. Necessary preprocessing techniques such as univariate and bivariate analysis are used. Machine learning algorithms benefit from data visualization as it makes it easier to understand features and create a robust classification model. Accuracy-related performance metrics such as MAE, MSE, and RMSE are used to compare the methods. We evaluate and contrast the various models' performances. This evaluation approach helps to choose the algorithm that best suits the task at hand. © 2024 IEEE.

关键词： Mean square error

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Multi-agent Simulation for Mass School Shootings

Multi-agent Simulation for Mass School Shootings

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Dai, Wei Singh, Yash Pratap Zhang, Rui Purdue University Northwest Department of Computer Science HammondIN United States Purdue University Northwest Department of Electrical and Computer Engineering HammondIN United States

ISBN: (纸本)9798350362480

The increasing frequency of school shootings in the United States has been raised as a critical concern. Active shooters kill innocent students and educators in schools. These incidents highlight the urgent need for effective strategies to minimize casualties. This study aims to address the challenge of simulating and assessing potential mitigation measures by developing a multi-agent simulation model. Our model is designed to estimate casualty rates and evacuation efficiency during active shooter scenarios within school buildings. The simulation evaluates the impact of a gun detection system on safety outcomes. By simulating school shooting incidents with and without this system, we observe a significant improvement in evacuation rates, which increased from 16.6% to 66.6%. Furthermore, the Gun Detection System reduced the average casualty rate from 24.0% to 12.2% within a period of six minutes, based on a simulated environment with 100 students. We conducted a total of 48 simulations across three different floor layouts, varying the number of students and time intervals to assess the system's adaptability. We anticipate that the research will provide a starting point for demonstrating that a gunshot detection system can significantly improve both evacuation rates and casualty reduction. © 2024 IEEE.

关键词： Students

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Investigation on radiated emissions of electric aircraft at airports

Investigation on radiated emissions of electric aircraft at ...

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2023 International Symposium on Electromagnetic Compatibility - EMC Europe, EMC Europe 2023

作者： Yan, Jiexiong Johansson, Jonny Ekman, Jonas Nilsson, Andreas Wisten, Ake Luleå University of Technology Department of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350324006

Electric aircraft are gaining popularity due to their zero-carbon emission, low noise level and low operational cost. Current study regarding electric aircraft centers on safety as well as battery durability and little light has been shed on electromagnetic compatibility (EMC). Electric aircraft, together with their charging stations, are complex systems and their existence at an airport might pose the risk for interference of other electric equipment, for example, communication systems. This paper presents preliminary measurements on electromagnetic emissions from an electric aircraft and its charging station at an airport. Radiated emission measurements were performed from 30 MHz to 1 GHz and the results suggest that the aircraft emits energy mainly below 200 MHz during the charging process and that the general radiation level at the test site challenges the limits for class B equipment in EN 55032. However, the electric aircraft and the charging station do not seem to have influence on the communication systems at the test site. The results could serve as a guidance in terms of charging station installations at airports and the establishment of EMC related regulations about electric aircraft. © 2023 IEEE.

关键词： Electromagnetic compatibility

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Advanced Dual-Axis Solar Tracking System with IoT-Driven Real-Time Monitoring for Optimized Efficiency 3

Advanced Dual-Axis Solar Tracking System with IoT-Driven Rea...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Shimul, Asadul Islam Hossain, Md Maruf Dipa, Safia Aktar Bangabandhu Sheikh Mujibur Rahman Science and Technology University Department of Electrical and Electronic Engineering Gopalganj8100 Bangladesh University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO65211 United States

ISBN: (纸本)9798331534400

Photovoltaic (PV) cells can transform solar energy into electrical energy. However, variations in light intensity and the changing position of the sun typically compromise their efficiency. Fixed solar panels experience reduced efficiency when not correctly oriented with the sun, resulting in decreased power generation. To optimize efficiency, it is necessary to implement a tracking system that ensures the panels remain aligned toward the sun. To achieve this objective, a prototype dual-axis solar tracking system was developed. This system utilizes Light Dependent Resistor (LDR) sensors and is controlled by an Arduino UNO microcontroller. It enables movement in both vertical and horizontal directions. Four Light Dependent Resistors (LDRs) and two servo motors enable the panels to track sunlight, achieving ideal orientation. Performance monitoring is achieved by integrating the system with the ThingSpeak IoT platform using the Node MCU ESP8266. This integration allows for the tracking of energy data obtained from the sensors. Compared to a fixed-axis device, the dual-axis tracker demonstrated a 21.83% increase in efficiency. A real-time monitoring system offers a practical means to assess the operation of the solar tracker. © 2024 IEEE.

关键词： Resistors

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Comparative Analysis of Spatial Human Mobility Parameters in 15 Most Populous U.S. Metropolitan Statistical Areas

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IAENG International Journal of Applied Mathematics 2024年第6期54卷 997-1012页

作者： Matloub, Zaid Kostanic, Ivica Senior Data Engineer at Better World Analytics MelbourneFL United States Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL United States

This research paper presents the results of two studies investigating human mobility patterns in the 15 largest Metropolitan Statistical Areas (MSAs) in the United States. It studied 14 daily mobility parameters aggregated at the MSA level, derived from four primary mobility parameters: Number of Visited Locations (N_LOC), Number of Unique Visited Locations (N_ULOC), Radius of Gyration (R_GYR), and Distance Traveled (D_TRAV) over a 30-day period. The first study was conducted on data from two large MSAs, one coastal and one inland (Boston and Atlanta, respectively). The aim was to examine associations between daily values of mobility parameters aggregated at the MSA level and identify those carrying similar or identical information. Results of factor analysis showed that these could be adequately described by two independent factors, pointing to one or two of the mobility parameters as sufficient to represent the whole set in analyses based on associations. These could either be D_TRAV, as it had high loadings on both factors, or N_LOC and R_GYR due to their high loadings on the two extracted factors. The second study was conducted on daily mobility datasets from the 15 MSAs. The aim was to compare daily mobility patterns of these MSAs and group them based on their mobility pattern similarities. Factor analysis of the aggregated mean daily distances (D_TRAV) across different MSAs over the studied period classified them into two distinct groups: one predominantly composed of inland MSAs and the other primarily of coastal MSAs. Strong weekly cycle trends emerged in these groups. Specifically, individuals from the inland MSA group tended to travel the furthest on Fridays and the least on Sundays, whereas those from the coastal MSA group traveled the most on Saturdays and the least on Mondays. This weekly pattern was robust, with 7-day lag autocorrelations of mean daily parameter values ranging between 0.81 to 0.99, excluding the mean daily N_LOC. These findings offer a

关键词： Big data

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