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4th International Symposium on Electrical, Electronics and information engineering, ISEEIE 2024

作者： Huang, He Huang, Wei College of Communication Technology Chengdu China School of Computer Science and Information Engineering Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei China

ISBN: (纸本)9798350355772

Although Cooperative Communications (CC) already exists in 4G/5G, performance improvement is just not enough. As 5G standard has gradually established recently, CC has been one of most critical communication technologies which plays a founding role in 6G IoV networks. Therefore, it is important to apply collaborative technology to achieve Internet of Everything (IoV) and improve communication quality significantly in 6G IoV ultra-mMTC networks. In this study, we review that collaborative ideology has been regarded as foundation theory which widely exists in future multiple hybrid scenarios, such as, Cognitive Internet of Things (C-IoT) networks, UAVs (Unmanned Aerial Vehicles) communications, air-space-ground integrated networks, underwater acoustic communication and so on. Besides, we discuss key communication technologies that are closely combined with unique communication scenes, and elaborate critical problems that shall be solved in 6G ultra-mMTC IoV networks. Furthermore, we discuss extreme low complexity and fast ultra-massive relays selection algorithm in all sorts of future 6G scenarios. At last, it is concluded that for arbitrary two communication points (source/destination devices, sensors, relays, IoT nodes and so on), ideology of nodes selection of collaborative communication is theory of foundation to realize IoV and optimize communication transmission performance in future networks. © 2024 IEEE.

关键词： Cooperative communication

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Enhancing STEM curriculum with virtual reality: electricity and magnetism simulations

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Multimedia Tools and Applications 2024年 1-20页

作者： Shinde, Nishant Sarma, Himangshu Human Computer Interaction Lab Computer Science & ampEngineering Indian Institute of Information Technology Sri City 630 Gnan Marg Andhra Pradesh Sri City517646 India

This study explores the use of virtual reality (VR) as an educational tool to enhance the understanding of electricity and magnetism among 10th-grade students in India. A VR application was developed using Unity and Meta Quest 2 (https://***/quest/products/quest-2/;Access Date: 22nd January 2024), featuring thirteen interactive activities-six on electricity and seven on the magnetic effects of electric current. Thirty participants engaged with the VR platform, which offers a hands-on learning experience not provided in the current curriculum. The methodology involved pre- and post-intervention assessments to evaluate the effectiveness of the VR application. Results indicate a significant improvement in students’ comprehension of electricity and magnetism, suggesting that VR can be a cost-effective and engaging supplement to traditional laboratory setups. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetoplasma

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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Computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Virtual Scanning: Unsupervised Non-line-of-sight Imaging from Irregularly Undersampled Transients 38

Virtual Scanning: Unsupervised Non-line-of-sight Imaging fro...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Cui, Xingyu Yue, Huanjing Li, Song Yin, Xiangjun Hou, Yusen Meng, Yun Zou, Kai Hu, Xiaolong Yang, Jingyu School of Electrical and Information Engineering Tianjin University China School of Precision Instrument and Optoelectronic Engineering Tianjin University China Key Lab. of Optoelectronic Information Science and Technology Ministry of Education China

Non-line-of-sight (NLOS) imaging allows for seeing hidden scenes around corners through active sensing. Most previous algorithms for NLOS reconstruction require dense transients acquired through regular scans over a large relay surface, which limits their applicability in realistic scenarios with irregular relay surfaces. In this paper, we propose an unsupervised learning-based framework for NLOS imaging from irregularly undersampled transients (IUT). Our method learns implicit priors from noisy irregularly undersampled transients without requiring paired data, which is difficult and expensive to acquire and align. To overcome the ambiguity of the measurement consistency constraint in inferring the albedo volume, we design a virtual scanning process that enables the network to learn within both range space and null space for high-quality reconstruction. We devise a physics-guided SURE-based denoiser to enhance robustness to ubiquitous noise in low-photon imaging conditions. Extensive experiments on both simulated and real-world data validate the performance and generalization of our method. Compared with the state-of-the-art (SOTA) method, our method achieves higher fidelity, greater robustness, and remarkably faster inference times by orders of magnitude. The code and model are available at https://***/XingyuCuii/Virtual-Scanning-NLOS. © 2024 Neural information processing systems foundation. All rights reserved.

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Adoption of LoRa-based Attendance System: An Empirical Investigation Using UTAUT Model in the IT Industry

Adoption of LoRa-based Attendance System: An Empirical Inves...

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2023 International Conference on Digital Business and technology Management, ICONDBTM 2023

作者： Seta, Anantya Khrisna Taufiq Abdulhafizh, Sayyid Syamsuar, Dedy Jacob, Deden Witarsyah Ulfa, Maria Shin, Soo Young Faculty of Industrial Engineering Telkom University Bandung Indonesia Bina Nusantara University Information Systems Department School of Information Systems Jakarta Indonesia Universitas Bina Darma Information Systems Department Faculty of Science and Technology Palembang Indonesia Kumoh National Institute of Technology School of Electronic Engineering Gyeongsangbuk-do Korea Republic of

ISBN: (纸本)9798350328028

The rapid development of IoT in the last few decades must also be accompanied by technological advances. One of them is wireless communication technology, which is applied to send and receive data on IoT devices. Among these technologies, LoRa has excellent potential as a solution for low-power and low-cost reliability. However, since LoRa is a new technology, it has not been widely applied and there is a lack of user experience with the application of the technology. Therefore, The purpose of this study was to ascertain whether LoraID will be accepted and used as an attendance system in an IT firm utilizing the UTAUT model. With the aid of the SmartPLS application, 40 questionnaire respondent data in total were analyzed using PLS-SEM. The findings showed that behavioral intention are positively influ-enced by Performance Expectation, performance expectations while facilitating conditions and behavioral intentions positively affect usage intentions, and the result showed that social influence and effort expectations negatively affect behavioral intentions. © 2023 IEEE.

关键词： Internet of things

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Towards more reliable evaluation in pedestrian detection by rethinking “ignore regions”

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Li, Gang Li, Xiang Zhang, Shanshan Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China VCIP Nankai University Tianjing China

It remains a challenging task to detect pedestrians in crowds and it needs more efforts to understand why the detectors fail. When we perform an error analysis based on the traditional evaluation strategy, we find that it produces many misleading false positives, which in fact cover occluded pedestrians. The reason for this is that we usually have two kinds of annotations in the dataset: regular pedestrians (detection targets) labeled by full-body boxes and ignored pedestrians (NOT detection targets) labeled by visible boxes. Ignored pedestrians are labeled as an additional category termed the “ignore region”. Nevertheless, our detectors always predict a full-body box for each pedestrian. This gap results in the following case: when a detector successfully predicts a full-body box for those ignored pedestrians, a false positive is triggered due to the low overlap between the predicted full-body box and the labeled visible box for the ignored pedestrian. This becomes even more harmful as the detector improves and becomes more capable of locating occluded pedestrians. To alleviate this issue, we devise a new pedestrian detection pipeline, which considers the additional visible box at both the detection and evaluation stages. During detection, we predict an extra visible box apart from the full-body box for every instance;during evaluation, we employ visible boxes instead of full-body boxes to match the “ignore region”. We apply the new pipeline to dozens of detection methods and validate the effectiveness of our pipeline in reducing the over-reporting of false positives and providing more reliable evaluation results. © The Author(s) 2024.

关键词： Evaluation metric Object detection Occlusion handling Pedestrian detection

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Strategic Customer Behaviors in Observable Multi-server Batch Service Queueing systems with Shared Fee and Server Maintenance Cost 15th

Strategic Customer Behaviors in Observable Multi-server Bat...

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15th International Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS 2022

作者： Nakamura, Ayane Phung-Duc, Tuan Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (纸本)9783031312335

We consider an observable multi-server batch service queueing model where a shared server admission fee and maintenance cost for the monopolist are considered. Specifically, customers observe the number of full batches and waiting customers for an uncompleted batch, and then afterward decide to join or balk depending on their utility function. We analyze strategic customer behavior and find the equilibrium strategy. We also show numerical results for social welfare and monopolist’s revenue in various settings depending on fee, batch size, and number of servers. Based on our results, we discuss the existence of optimal fee, batch size, and number of servers. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Costs

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Highly efficient flexible perovskite solar cells with vacuum-assisted low-temperature annealed SnO2 electron transport layer

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Journal of Energy Chemistry 2022年第4期31卷 1-7页

作者： Xiaoguo Li Zejiao Shi Fatemeh Behrouznejad Mohammad Hatamvand Xin Zhang Yaxin Wang Fengcai Liu Haoliang Wang Kai Liu Hongliang Dong Farhan Mudasar Jiao Wang Anran Yu Yiqiang Zhan Center for Micro Nano Systems School of Information Science and Technology(SIST)Fudan UniversityShanghai 200433China Academy for Engineering&Technology Fudan UniversityShanghai 200433China Center for High Pressure Science and Technology Advanced Research PudongShanghai 201203China

The demand for lightweight, flexible, and high-performance portable power sources urgently requires high-efficiency and stable flexible solar cells. In the case of perovskite solar cells(PSCs), most of the common electron transport layer(ETL) needs to be annealed for improving the optoelectronic properties,while conventional flexible substrates could barely stand the high temperature. Herein, a vacuumassisted annealing SnO_(2) ETL at low temperature(100℃) is utilized in flexible PSCs and achieved high efficiency of 20.14%. Meanwhile, the open-circuit voltage(V_(oc)) increases from 1.07 V to 1.14 V. The flexible PSCs also show robust bending stability with 86.8% of the initial efficiency is retained after 1000 bending cycles at a bending radius of 5 mm. X-ray photoelectron spectroscopy(XPS), atomic force microscopy(AFM), and contact angle measurements show that the density of oxygen vacancies, the surface roughness of the SnO_(2) layer, and film hydrophobicity are significantly increased, respectively. These improvements could be due to the oxygen-deficient environment in a vacuum chamber, and the rapid evaporation of solvents. The proposed vacuum-assisted low-temperature annealing method not only improves the efficiency of flexible PSCs but is also compatible and promising in the large-scale commercialization of flexible PSCs.

关键词： Flexible perovskite solar cells Vacuum-assisted Electron transport layer Trap-assisted recombination

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Discovering Frequency Bursting Patterns in Temporal Graphs 39

Discovering Frequency Bursting Patterns in Temporal Graphs

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zhang, Qianzhen Guo, Deke Zhao, Xiang Yuan, Long Luo, Lailong National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China National University of Defense Technology Laboratory for Big Data and Decision China Nanjing University of Science and Technology China

ISBN: (纸本)9798350322279

A frequency bursting pattern (FBP) in temporal graphs represents some interaction behavior that accumulates its frequency at the fastest rate. Mining FBPs is essential to early warning of emergencies. However, existing studies on frequency-based pattern mining in graphs do not consider the temporal information and bursting features of a subgraph pattern. As a result, they may not provide effective and efficient mining algorithms for FBP discovery. In this paper, we study the problem of discovering top-k FBPs in temporal graphs. We present a novel model, referred to as maximal (m, θ)-bursting pattern, to describe FBPs in a temporal graph, which is a subgraph with a size larger than m that accumulates its frequency at the fastest rate during a time interval of length no less than θ. A naive solution for top-k FBPs discovery is to use the best-first search algorithm, where the burstiness threshold changes as more patterns are mined. However, this method will result in huge search space since we need to check every possible time interval for a candidate pattern in the temporal graph. To tackle this problem, we devise an online top-k framework in which k candidate results are maintained from the initial timestamp to the end in the temporal graph. Under the new framework, we further conceive two optimization strategies by exploiting incremental subgraph matching and Evolutionary Game Theory to boost the performance. Extensive experiment results on five real temporal graphs show that our algorithm has higher efficiency, effectiveness and scalability. © 2023 IEEE.

关键词： Game theory

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Efficient Brain Tumor Classification Leveraging Modified UNet and ResNet-50 Model 6th

Efficient Brain Tumor Classification Leveraging Modified UNe...

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6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Yuganesan, R. Rajkumar, S. Allen Gilchrist, J. Gowseelan, C. Varun Manickam, S. Soy, Theja School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789819619801

Identification and classification of brain tumors have become a concern for the medical community as the treatment planning and patients’ prognosis is highly dependent on their classification. In the past few years, deep learning models have become the most discussed and widely used tools to achieve higher accuracy and efficiency in this area. This study gives a comparison of the two widely used deep learning networks, the modified UNet and ResNet-50 for the task of brain tumor classification. The study assesses the efficiency of these architectures on the benchmark dataset and indicates the strengths and limitations of each. The evaluation process is made of extremely rigorous testing and validation procedures, which are aimed at making the data reliable. The outcomes of our study indicate that both modified UNet and ResNet-50 can reasonably be applied to the brain tumor classification. Modified UNet surpasses ResNet-50 in overall accuracy, which implies its effectiveness in high-precision classifications. This comparison revealed the feasibility of the architectures for classifying brain tumors. It helps both researchers and practitioners in the field by providing them with information to find the model appropriate for their specific needs. Moreover, the results of the study could be used to build the discussion on the application of the deep learning models in the diagnosis and classification of brain tumors as a part of the medical image analysis. Therefore, this paper provides a thorough guidance for individuals who want to use deep learning to enhance healthcare outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electrotherapeutics

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