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

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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Advancing Functional Near-Infrared Spectroscopy (fNIRS) with Transformers: A PRISMA-driven systematic review

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IEEE Access 2025年 13卷 97794-97817页

作者： Abdollahpour, Neda Kakaraparthi, Abhinav Sai Valluru, Chakradhar Artan, N. Sertac New York Institute of Technology Department of Electrical and Computer Engineering New York NY United States New York Institute of Technology Department of Computer Science New York NY United States

Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising neuroimaging modality in various domains including cognitive neuroscience, brain-computer interfaces (BCIs), clinical diagnostics, mental workload assessment, and neurorehabilitation. Despite its growing applications, processing fNIRS signals presents unique challenges. The advent of deep learning, particularly transformers, has taken on an increasingly pivotal role in addressing these complexities. As transformers have revolutionized multiple fields, their application in fNIRS signal processing has gained significant traction recently. To support ongoing research, this study systematically reviews the advancements and applications of transformer models in fNIRS research, following PRISMA guidelines. A comprehensive analysis was conducted on 30 studies (N = 895), offering a structured evaluation of current trends, methodologies and outcomes. This review paper highlights transformer architectures in fNIRS-based studies, with a particular focus on advanced modifications in key stages such as the embedding and attention mechanisms. Multimodal studies analyzing fNIRS with other modalities, such as Electroencephalography (EEG), using transformers are also reviewed. Finally, the paper examines open-access and private datasets used in the literature and offers guidelines for future research, identifying open problems that warrant further exploration. © 2013 IEEE.

关键词： brain-computer interface (BCI) deep learning emotion recognition Functional near infrared spectroscopy (fNIRS) mental arithmetic motor imagery systematic review transformer architecture transformer models

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Behavior-based Feedback Loop for Attentive E-reading (BFLAe): A Real-Time computer Vision Approach 1

Behavior-based Feedback Loop for Attentive E-reading (BFLAe)...

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1st IJCAI Workshop and Challenge on Micro-Gesture Analysis for Hidden Emotion Understanding, MiGA 2023

作者： Lee, Yoon Migut, Gosia Specht, Marcus Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

This study is built upon a behavior-based framework for real-time attention evaluation of higher education learners in e-reading. Significant challenges in AI model developments for learning analytics have been 1) defining valid indicators and 2) connecting the analytics results to interventions, balancing the generalization and personalization needs. To address this, we utilized a public multimodal WEDAR dataset and trained a neural network model based on real-time features of learners, aiming at predicting learners' moment-to-moment distractions. Real-time features for model training include 30 learners' attention regulation behaviors annotated every second, reaction times to blur stimuli, and page numbers indicating various reading phases. Our preliminary model based on a neural network has achieved 66.26% accuracy in predicting self-reported distractions. Based on the model, we suggest a framework of a Behavior-based Feedback Loop for Attentive e-reading (BFLAe). It has text blur as feedback, a mechanism responsive to learners' distractions that also works as data for next-round feedback. The general feedback implementation rules are established on a statistical analysis conducted on all learners. In addition, we propose a strategy for personalizing feedback using a quartile analysis of individual data, promoting learner-specific feedback. Our framework addresses the high demand for an automated e-learning assistant with non-intrusive data collection based on real-world settings and intuitive feedback provision. The feedback system aims to help learners with longer attention spans and less frequent distractions, leading to more engaging e-reading. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Modal analysis

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Secured Entry Control: An IoT and Deep Learning based Mask Detection System for COVID-19 Suspected Patient Screening and Access Regulation 3

Secured Entry Control: An IoT and Deep Learning based Mask D...

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3rd International Conference on Advancement in electrical and Electronic engineering, ICAEEE 2024

作者： Sultana, Abida Peyal, Hasibul Islam Islam, Md. Injamul Abdal, Md. Nazmul Miraz, Shafiun Kontho, Md. Mahin Rahaman Leion, Zannatul Mawa Mamun, Abdul Al Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh Tejgaon College Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

The new coronavirus SARS-CoV-2, which triggered the COVID-19 pandemic, has had an unparalleled effect on economies, cultures, and world health. In response to the critical need for strict COVID-19 screening systems in public areas, this study presents a creative Secured Entry Control system. Detecting and controlling possible COVID-19 carriers attempting to enter the country is made possible by this system, which makes use of deep learning algorithms and IoT technology. The mask detection algorithm, MobileNetV2 model, has an exceptional validation accuracy of 98.96\%. The model's reliability is supported by performance evaluations using ROC curves, confusion matrix analysis, and an AUC value of 98.96\%, which is close to the optimal AUC score of 100\%. Because MobileNetV2 is well-suited for low-processing devices, it is easy to deploy it on Raspberry Pi, which helps to create an affordable system. Furthermore, by spotting increased body temperatures, a contactless temperature sensor improves the system's ability to identify possible COVID-19 carriers. The functioning of the system is confirmed by the working prototype that is presented in the Experimental Results section. The main goal of this research is to create an autonomous system that is affordable and selectively allows access to people who are less likely to transmit COVID-19 in public areas. To achieve the overall objective of reducing the spread of COVID-19 in public spaces, this research highlights the successful integration of mask identification algorithms and IoT. © 2024 IEEE.

关键词： COVID-19

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Pulsed Electromagnetic Field Interaction With a Transmission Line: An Analytical Traveling-Wave Approach Based on Reciprocity

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3783-3791页

作者： Štumpf, Martin Antonini, Giulio Lager, Ioan E. Ekman, Jonas FEEC Brno University of Technology Lerch Laboratory of EM Research Department of Radio Electronics Brno616 00 Czech Republic EISLAB Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå971 87 Sweden University of L’Aquila UAq EMC Laboratory Department of Industrial and Information Engineering and Economics L’Aquila671 00 Italy Delft University of Technology Terahertz Sensing Group Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands

Pulsed electromagnetic (EM) field signal transfer from a general EM source distribution to a transmission line (TL) is analyzed with the aid of Lorentz’s reciprocity theorem. In this fashion, the transient voltage induced by the impulsive EM source is expressed through the EM fields as radiated by the TL. These transmitted EM fields are expressed in closed form using an analytical procedure that resembles the Cagniard-De Hoop (CdH) technique. The validity of the proposed reciprocity-based methodology is verified with the aid of an alternative analytical solution describing the EM field signal transfer excited by an impulsive vertical electric dipole (VED). Illustrative numerical examples are presented. © 1963-2012 IEEE.

关键词： Time domain analysis

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Adaptive Federated Blockchain-powered Security for MEC-assisted Industrial IoT Ecosystems 19

Adaptive Federated Blockchain-powered Security for MEC-assis...

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19th International Conference on Emerging Technologies, ICET 2024

作者： Gyamfi, Eric Ansere, James Adu Kamal, Mohsin Mir, Suleman Bonsu, Kusi Ankrah School of Computer Science University College Dublin Ireland Sunyani Technical University Department of Electrical/Electronics Engineering Ghana School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad Pakistan National University of Computer and Emerging Sciences Department of Computer Engineering Peshawar Pakistan

ISBN: (纸本)9798350379587

Secure communication is essential for the Industrial Internet of Things (IIoT), but most IIoT devices cannot run conventional security models due to resource constraints. Multi-access mobile edge computing (MEC) brings computational resources closer to the network edge but struggles with scalability and flexibility in centralized security solutions. Distributed data processing on MEC servers also raises privacy and security concerns. This paper proposes a novel IIoT network security using federated blockchain (FB) and machine learning-based (ML) verification. MEC optimizes the FB model to ensure data integrity and confidentiality between the IIoT's local network cluster and external devices. Data within the local network cluster are secured with public-key cryptography. The ML-based verification model ensures legitimate key-pair updates and device joining in MEC-assisted IIoT. This approach outperforms conventional security solutions in scalability, data privacy, and adaptability to IIoT network changes. We present a detailed implementation and evaluate its performance using a realistic IoT testbed, showing improved network security while maintaining the performance and scalability of MEC-assisted IIoT systems. © 2024 IEEE.

关键词： Cluster computing

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Hybrid Technique-based Optimal Energy Management in Smart Home Appliances

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IEIE Transactions on Smart Processing and Computing 2024年第5期13卷 425-434页

作者： Shirley, C.P. Depaa, R.A.B. Priya, A. Sarala, R. Solanki, Rajdeep Singh Malini, K.V. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Civil Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Chennai95 India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Rameshwaram High Road Viraganoor Madurai625009 India Department of Computer Science Medi-Caps University Indore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Anekal India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad75 India

This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed approach combines the Ebola Optimization Search Algorithm (EOSA) with the performance of spiking neural networks (SNN). The key objective of the proposed strategy is cost reduction through day-ahead load scheduling while also considering the best demand response (DR), and self-consumption of photovoltaic (PV). The EOSA method manages air conditioning (AC) and ensures thermal comfort. In addition, the SNN method was used to predict the optimal system control signal. Comparative analysis with existing systems, such as PSO, RFA, and BCO, showed that the proposed strategy resulted in 13% cost savings. The proposed system considered parameters, such as solar radiation, occupant presence, and electricity prices. The proposed technique is implemented in the MATLAB software, and the performance is compared with existing approaches. The efficiency of the proposed approach is higher than the existing techniques. © 2024 The Institute of Electronics and Information Engineers.

关键词： Demand response

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental science and Pollution Research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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Hybrid Digital-Analog Semantic Communications

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IEEE Journal on Selected Areas in Communications 2025年第7期43卷 2478-2492页

作者： Xie, Huiqiang Qin, Zhijin Han, Zhu Letaief, Khaled B. Jinan University College of Information Science and Technology Guangzhou China Tsinghua University Department of Electronic Engineering Beijing100084 China State Key Laboratory of Space Network and Communications Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China University of Houston Department of Electrical and Computer Engineering Houston United States The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digitalanalog approach for multimedia transmission. This is achieved through the introduction of analog-digital allocation and fusion modules. To strike a balance between data rate and distortion, we design new loss functions that take into account long-distance dependencies in the semantic distortion constraint, essential information recovery in the channel distortion constraint, and optimal bit stream generation in the rate constraint. Additionally, we propose denoising diffusion-based signal detection techniques, which involve carefully designed variance schedules and sampling algorithms to refine transmitted signals. Through extensive numerical experiments, we will demonstrate that HDA-DeepSC exhibits robustness to channel variations and is capable of supporting various communication scenarios. Our proposed framework outperforms existing benchmarks in terms of peak signal-to-noise ratio and multi-scale structural similarity, showcasing its superiority in semantic communication quality. © 1983-2012 IEEE.

关键词： Digital communication systems

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Automation of Traffic Signal for Better Commutation of Vehicles 3

Automation of Traffic Signal for Better Commutation of Vehic...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Vaghela, Aarzoo Maruf, Padaya Meitei, Naorem Yaipharenba Singh, Laiphrakpam Dolendro National Institute of Technology Department of Computer Science and Engineering Assam Silchar India Indian Institute of Technology NineLabs Electronics & Electrical Engineering Assam Guwahati India

ISBN: (纸本)9798331523893

Traffic congestion poses a significant challenge in urban areas globally, resulting in wasted time, increased fuel consumption, and heightened pollution levels. Traditional traffic light systems often rely on fixed timers, failing to adjust to fluctuating traffic patterns and causing unnecessary delays. To tackle this issue, an adaptive traffic signal timer is designed to dynamically respond to real-time traffic conditions. The proposed system harnesses live camera feeds from traffic junctions and employs object detection techniques to analyze traffic density accurately. The proposed method represents a proactive approach to addressing urban congestion challenges, promising more efficient traffic management and improved commuting experiences for residents and commuters. A comprehensive simulation platform is developed to validate our system's efficacy and facilitate comparative analysis with existing static systems. © 2025 IEEE.

关键词： Vehicle detection

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