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2022 International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2022

作者： Nguyen, Phuong-Dung Nguyen, Hong-Quan Nguyen, Thuy-Binh Le, Thi-Lan Tran, Thanh-Hai Vu, Hai Huu, Quynh Nguyen Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi Viet Nam Viet-Hung Industrial University Faculty of Information Technology Hanoi Viet Nam University of Transport and Communications Faculty of Electrical-Electronic Engineering Hanoi Viet Nam Thuyloi University Faculty of Computer Science and Engineering Hanoi Viet Nam

ISBN: (数字)9781665474108

ISBN: (纸本)9781665474108

Student activity, a non-verbal modality, is an important cue to imply the students' learning state and engagement level during class. Quantitative observation of the student activities will contribute to innovating teaching methods. However, traditional methods that rely on manual observation are time-consuming. Nowadays, surveillance cameras have been widely deployed in the classroom. This results in a massive amount of classroom videos. However, the exploitation of these videos is limited due to the lack of annotated information. This paper has two main contributions. First, a fully annotated dataset dedicated to student activity analysis has been proposed. To this end, five activities in the classroom context including sitting, standing, sleeping, raising-hand and using-phone have been defined and collected in different class sessions. Second, three deep learning models have been fully assessed for the activities recognition. © 2022 IEEE.

关键词： Object detection

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Conflict Resolution Strategy in Handover Management for 4G and 5G Networks

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computers, Materials & Continua 2022年第9期72卷 5215-5232页

作者： Abdulraqeb Alhammadi Wan Haslina Hassan Ayman A.El-Saleh Ibraheem Shayea Hafizal Mohamad Yousef Ibrahim Daradkeh Communication Systems and Networks Research Lab Malaysia-Japan International Institute of TechnologyUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia College of Engineering A’sharqiyah University(ASU)Ibra400Oman Department of Electronics and Communication Engineering Faculty of Electrical and Electronics EngineeringIstanbul Technical University(ITU)Istanbul34467Turke Faculty of Engineering and Built Environment Universiti Sains Islam MalaysiaBandar Baru Nilai71800Malaysia Department of Computer Engineering and Networks College of Engineering at Wadi AddawasirPrince Sattam Bin Abdulaziz UniversityAl Kharj11991KSA

Fifth-generation(5G)cellular networks offer high transmission rates in dense urban ***,a massive deployment of small cells will be required to provide wide-area coverage,which leads to an increase in the number of handovers(HOs).Mobility management is an important issue that requires considerable attention in heterogeneous networks,where 5G ultra-dense small cells coexist with current fourth-generation(4G)*** mobility robustness optimization(MRO)and load balancing optimization(LBO)functions have been introduced in the 3GPP standard to address HO problems,non-robust and nonoptimal algorithms for selecting appropriate HO control parameters(HCPs)still exist,and an optimal solution is subjected to compromise between LBO and MRO ***,HO decision algorithms become *** paper proposes a conflict resolution technique to address the contradiction between MRO and LBO *** proposed technique exploits received signal reference power(RSRP),cell load and user speed to adapt HO margin(HM)and time to trigger(TTT).Estimated HM and TTT depend on a weighting function and HO type which is represented by user status during *** proposed technique is validated with other existing algorithms from the *** results demonstrate that the proposed technique outperforms existing algorithms overall performance *** proposed technique reduces the overall average HO ping-pong probability,HO failure rate and interruption time by more than 90%,46%and 58%,respectively,compared with the other schemes overall speed scenarios and simulation time.

关键词： Mobility management handover 5G heterogeneous networks

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Self-Supervised Graph Transformer with Contrastive Learning for Brain Connectivity Analysis Towards Improving Autism Detection 22

Self-Supervised Graph Transformer with Contrastive Learning ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Leng, Yicheng Anwar, Syed Muhammad Rekik, Islem He, Sen Lee, Eung-Joo University of Arizona Department of Electrical and Computer Engineering Tucson United States Children's National Hospital WashingtonDC United States Imperial College London Department of Computing London United Kingdom University of Arizona Department of Systems and Industrial Engineering Tucson United States

ISBN: (纸本)9798331520526

Functional Magnetic Resonance Imaging (fMRI) provides useful insights into the brain function both during task or rest. Representing fMRI data using correlation matrices is found to be a reliable method of analyzing the inherent connectivity of the brain in the resting and active states. Graph Neural Networks (GNNs) have been widely used for brain network analysis due to their inherent explainability capability. In this work, we introduce a novel framework using contrastive self-supervised learning graph transformers, incorporating a brain network transformer encoder with random graph alterations. The proposed network leverages both contrastive learning and graph alterations to effectively train the graph transformer for autism detection. Our approach, tested on Autism Brain Imaging Data Exchange (ABIDE) data, demonstrates superior autism detection, achieving an AUROC of 82.6 and an accuracy of 74%, surpassing current state-of-the-art methods. © 2025 IEEE.

关键词： Functional neuroimaging

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Thermodynamics in Stochastic Conway Game of Life

arXiv

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arXiv 2023年

作者： Pomorski, Krzysztof Kotula, Dariusz Cracow University of Technology Poland Faculty of Electrical and Computer Engineering Faculty of Computer Science and Telecommunications Quantum Hardware Systems

Cellular automata can simulate many complex physical phenomena using the power of simple rules. The presented methodological platform expresses the concept of programmable matter in which Newton’s laws of motion are one of examples. Energy has been introduced as the equivalent of the"Game of Life" mass, which can be treated as first level of approximation. The temperature presence and propagation was calculated for various lattice topology and boundary conditions by using the Shannon entropy measure. The conducted study provides strong evidence that despite not fulfillment the principle of mass and energy conservation, the entropy, mass distribution and temperatures approaches thermodynamic equilibrium. In addition, the described cellular automata system transits from positive to a negative temperatures that stabilizes and can be treated as a signature of system dynamical equilibrium. Furthermore, the system dynamics was presented in case of few species of cellular automata competing for maximum presence on given lattice with different boundary conditions. © 2023, CC0.

关键词： Cellular automata

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Investigating Factors Affecting Electric Vehicle Users’ Procrastination Charging Behavior

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Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2023年第1期67卷 1295 - 1301页

作者： Mehrsa Khaleghikarahrodi Jeremy Herrera Santos Fatima Lynch-Smith Gretchen A. Macht Department of Mechanical Industrial & Systems Engineering University of Rhode Island Kingston RI USA Department of Electrical Computer & Biomedical Engineering University of Rhode Island Kingston RI USA

The existence of electric vehicle users who procrastinate their charging activities – delay in charging until a critically low state of charge (i.e., almost empty battery) – is acknowledged in the previous survey studies and data-driven analyses. However, the underlying reasons affecting such behavior have not been explored. This study attempts to justify whether procrastination in charging is due to larger battery capacities and users’ close proximity to charging stations. The results indicate that battery sizes can have a significant effect on EV users’ procrastination charging behavior. This study found that users with battery capacities greater than 21kWh tend to procrastinate charging since they can travel longer distances. The findings will be able to serve a broad range of EV industry stakeholders due to the emerging market and continuous improvement for achieving mass EV adoption.

关键词： Electric Vehicle User Behavior Procrastination Battery Size

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ITNR: Inversion Transformer-based Neural Ranking for cancer drug recommendations

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computers in Biology and Medicine 2024年 172卷 108312-108312页

作者： Sotudian, Shahabeddin Paschalidis, Ioannis Ch. Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA United States Department of Biomedical Engineering and Faculty of Computing and Data Sciences Boston University BostonMA United States

Personalized drug response prediction is an approach for tailoring effective therapeutic strategies for patients based on their tumors’ genomic characterization. While machine learning methods are widely employed in the literature, they often struggle to capture drug-cell line relations across various cell lines. In addressing this challenge, our study introduces a novel listwise Learning-to-Rank (LTR) model named Inversion Transformer-based Neural Ranking (ITNR). ITNR utilizes genomic features and a transformer architecture to decipher functional relationships and construct models that can predict patient-specific drug responses. Our experiments were conducted on three major drug response data sets, showing that ITNR reliably and consistently outperforms state-of-the-art LTR models. © 2024 Elsevier Ltd

关键词： Cell culture

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Automated Warehouse 5G Infrastructure Modeling Using Variational Autoencoders

Automated Warehouse 5G Infrastructure Modeling Using Variati...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Rahul Gulia Amlan Ganguly Andres Kwasinski Michael E. Kuhl Ehsan Rashedi Clark Hochgraf Department of Computer Engineering Rochester Institute of Technology Rochester NY Department of Industrial and Systems Engineering Rochester Institute of Technology Rochester NY Department of Electrical and Computer Engineering Technology Rochester Institute of Technology Rochester NY

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

The next decade is poised for a transformative shift in wireless communication technologies, driven by the increasing demand for data-intensive applications. Innovations in signal processing, network architecture estimation and design, and spectrum utilization will be critical in realizing the potential of 5G and beyond. These advancements will enable the seamless integration of emerging technologies and empower the digital transformation of industries and society. In this paper, we introduce a Variational Autoencoder (VAE) based model for indoor radio propagation modeling within an automated Industry4.0 warehouse or factory floor in the 5G wireless bands. We detail the creation of training data tensors, the architecture of our proposed VAE model, and its training using tensors that capture the impact of various interacting objects within a 5G-enabled smart warehouse infrastructure. The model is trained on multiple warehouse configurations to predict the signal-to-interference-plus-noise ratio (SINR) for both the validation dataset and unknown warehouse configurations. We present reconstruction error heatmaps to demonstrate the accuracy of our model and analyze its performance in different complex warehouse environments.

关键词： Analytical models Adaptation models Accuracy 5G mobile communication Indoor radio communication Training data Interference Predictive models Data models Signal to noise ratio

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An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

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computers, Materials & Continua 2022年第4期71卷 305-320页

作者： T.Gopalakrishnan Periakaruppan Sudhakaran K.C.Ramya K.Sathesh Kumar Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering SRM TRP Engineering CollegeTiruchirappalli621105India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific ***, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.

关键词： Muscle magnetic resonance imaging XGBoost synergic deep learning roI detection kapur’s entropy muscular dystrophies

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Triple Band Antenna Using Superstate with Double Substrate for Wireless Applications 26

Triple Band Antenna Using Superstate with Double Substrate f...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Uddin, Md. Kutub Hossen, Shawon Alam, Touhidul Islam, Mohammad Tariqul Alam, Mohammed Shamsul Hakim, Mohammad Lutful Institut Perubahan Iklim Selangor Bangi Malaysia International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong4318 Bangladesh Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Electrical Electronic and Systems Engineering Selangor Bangi Malaysia International Islamic University Chittagong Department of Computer Science and Engineering Chittagong4318 Bangladesh

ISBN: (纸本)9798350359015

A multiband antenna is a good choice to keep the device as compact as possible. This study introduces a tripleband antenna that is well-suited for 5G New Radio applications, satellite communications, and WiMAX. The antenna design has a two-layer substrate, including a ring-slotted rectangular superstate. To meet the design requirements, a rectangular superstate was used to provide the antenna with its triple-band capabilities. The antenna's triband capabilities come from its rectangular superstate, constructed using a ring slot at every antenna's four patch corners. In addition, it is advised and used to employ two FR-4 substrates that are 1.5 millimeters each to improve the antenna's efficacy substantially. The three frequencies shown by the results of the antenna modeling cover the frequency range used by 5G NR service operations: 4.780 GHz, 5.50 GHz, and 10.75 GHz. In each operational range, the measured reflection coefficient has a value below -10 dB at the frequency center, while the peak gains surpass 7 dBi. Nevertheless, the antennas provided in this context exhibit a broad spectrum of applications within the realm of wireless communication. © 2023 IEEE.

关键词： 5G Double substrate Multiband superstate WiMAX

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Office Building Energy Community: A Case Study on Optimal Market Operation and Grid Impact 9

Office Building Energy Community: A Case Study on Optimal Ma...

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9th IEEE International Smart Cities Conference, ISC2 2023

作者： Lepisto, Juhani Forcan, Jovana Forcan, Miodrag Heine, Pirjo Helen Electricity Network Helsinki Finland University of East Sarajevo Faculty of Philosophy Department of Computer Science and Systems East Sarajevo Bosnia and Herzegovina University of East Sarajevo Faculty of Electrical Engineering Department of Electroenergetics East Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350397758

In this paper, we study an office building connected to an urban distribution network from an energy community perspective. The office building and its electrical equipment have a real-world counterpart from which we construct synthetic time series based on the historical measurement data. The office building involves three stakeholders and their assets. The central stakeholders are the owner and the tenant of the building. The third stakeholder is the operator of flexible assets. The smartness of the office building is related to the control capabilities of its assets. These controllable assets are the battery energy storage system (BESS) and the electric vehicles (EV) charging system (EVCS) that both can operate in a flexible manner in response to energy price fluctuations and participate in a reserve market. We proposed different cases where either the owner or the tenant is paying the EV charging, and where the flexibility of the EV charging is either active or not. We show that, when the owner pays for the energy usage of the EVCS and the tenant is using it, there is a beneficial case to form a coalition between them and share the benefits from the flexibility, as otherwise there is no incentive for the tenant to be flexible in the first place. We conducted operative cost-benefit calculations where we shared the community benefits according to the Shapley value and simulated the power flows at the building's connection point based on the market operations. We found that harnessing benefits from the two market operations had significant effects on the power flows at the connection point. This was true when operating under the variable energy prices and in the reserve market. © 2023 IEEE.

关键词： Office buildings

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