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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Khan, Omar Nguyen, Anh Nam, Hyeongil Kim, Kangsoo University of Calgary Department of Computer Science Canada University of Calgary Department of Electrical and Software Engineering Canada

ISBN: (纸本)9798331514846

Virtual experiences can significantly influence our perception and behavior in the real world, shaping how we interact with and navigate physical environments. In this paper, we examine the impact of learning navigation routes in an immersive virtual environment (IVE) on navigation performance and user experience in a corresponding real-world indoor setting. We developed a guide system with two distinct audiovisual representations: a human agent guide and a symbol-based guide. A preliminary user study (N = 10) was conducted to evaluate the system. While no significant differences were observed between the two guide conditions, the findings reveal valuable insights into user-perceived confidence and enjoyment during real-world navigation tasks. Contrary to our expectations, the symbol-based guide elicited slightly higher positive scores compared to the human agent guide. We discuss these findings and outline directions for future research. © 2025 IEEE.

关键词： Virtual environments

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Enhancing Cybersecurity: A Pipeline Approach for Efficient Machine Learning Intrusion Detection 14

Enhancing Cybersecurity: A Pipeline Approach for Efficient M...

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14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Berei, Ethan Oun, Ahmed Khan, M. Ajmal Ohio Northern University Department of Electrical & Computer Engineering and Computer Science OH United States

ISBN: (纸本)9798350304138

The escalating commonness of severe cyberattacks on a global scale has advanced the critical need for integrating machine learning into cybersecurity practices. Seven models, trained on a reduced feature inSDN dataset, aim to detect malicious SDN traffic. Testing involves diverse traffic types generated with Pyshark and CICFlowMeter. The study achieves its goals by creating effective intrusion detection models, developing new test datasets, implementing the pipeline, and validating it through testing in the real-time practical network in the cybersecurity lab. The use of double-feature selection is pivotal. Overall, this research accelerates the deployment of supervised machine learning in intrusion detection, strengthening cybersecurity against evolving threats. © 2023 IEEE.

关键词： Pipelines

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Improved MPPT algorithm: Artificial neural network trained by an enhanced Gauss-Newton method

AIMS Electronics and Electrical Engineering

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AIMS Electronics and electrical engineering 2023年第4期7卷 380-405页

作者： Dkhichi, Fayrouz Laboratory of Networks Computer Science Telecommunications & Multimedia Electrical Engineering Department Superior School of Technology Hassan II University of Casablanca Morocco

A novel approach defined by the artificial neural network (ANN) model trained by the improved Gauss-Newton in conjunction with a simulated annealing technique is used to control a step-up converter. To elucidate the superiority of this innovative method and to show its high precision and speed in achieving the right value of the Maximum Power Point (MPP), a set of three comparative Maximum Power Point Tracker (MPPT) methods (Perturbation and observation, ANN and ANN associated with perturbation and observation) are exanimated judiciously. The behavior of these methods is observed and tested for a fixed temperature and irradiance. As a result, the proposed approach quickly tracks the right MPP = 18.59 W in just 0.04382 s. On the other hand, the outstanding ability of the suggested method is demonstrated by varying the irradiance values (200 W/m , 300 W/m , 700 W/m , 1000 W/m , 800 W/m and 400 W/m ) and by varying the temperature values (15°C, 35°C, 45°C and 5°C). Therefore, the ANN trained by Gauss-Newton in conjunction with simulated annealing shows a high robustness and achieves the correct value of MPP for each value of irradiance with an efficiency 99.54% and for each value of temperature with an efficiency 99.98%;the three other methods sometimes struggle to achieve the right MPP for certain irradiance values and often remains stuck in its surroundings. © 2023 The Author(s).

关键词： Maximum power point trackers

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Wireless Mouse EEG Device: Novel Design for Easy Operation 46

Wireless Mouse EEG Device: Novel Design for Easy Operation

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Benomar, Mohamed Xia, Xing Zhang, Hai Huang, Mao-Hsiang Vishwanath, Manoj Xu, Xiangmin Cao, Hung University of California Irvine Department of Electrical Engineering and Computer Science IrvineCA92697 United States University of California Irvine Department of Anatomy and Neurobiology IrvineCA92697 United States University of California Irvine Department of Computer Science IrvineCA92697 United States University of California Irvine Department of Electrical Engineering and Computer Science Department of Computer Science Department of Biomedical Engineering IrvineCA92697 United States

ISBN: (纸本)9798350371499

Electroencephalogram (EEG) has been widely used in studies using rodent models to understand brain functions and neurological disorders. However, conventional EEG setups have limits as recording devices are bulky and tethered, causing discomfort and deviating from natural habitat conditions. To address these, we develop an innovative wireless EEG device for mice with a compact size, wireless recording via Bluetooth, and minimal invasiveness through screw mounting. The device enhances experimental setups while ensuring the well-being of the mice in a more realistic environment facilitating easier translation to human studies. In an experimental setting involving induced seizures, EEG signals were recorded and analyzed to discern differences across epilepsy phases in both anesthetized and awake stages. This device holds promise to revolutionize EEG research using mice, bridging the gap between laboratory conditions and real-world scenarios, thus advancing our understanding of neurological phenomena. © 2024 IEEE.

关键词： Electroencephalography

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FedRME: Federated Learning for Enhanced Distributed Radiomap Estimation 100

FedRME: Federated Learning for Enhanced Distributed Radiomap...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Zhang, Weishan Wang, Yue Liu, Lingjia Tian, Zhi George Mason University Department of Electrical and Computer Engineering FairfaxVA United States Georgia State University Department of Computer Science AtlantaGA United States Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA United States

ISBN: (纸本)9798331517786

For future intelligent communication systems, radiomap estimation (RME) is essential for acquiring panoramic awareness of spectrum spatial distribution in wireless environments. Recently, deep learning-based RME methods have been developed to reconstruct radiomaps from spectrum measurements collected at distributed sensors. However, these methods rely on gathering all input data at a central fusion center, resulting in large communication overheads, high computation costs, and privacy leakage concerns. To address these challenges, this work proposes a FedRME approach that makes federated learning applicable for distributed RME over a large-scale network, accommodating geographically heterogeneous transmitter locations and propagation environments. Specifically, we partition the large area into smaller regions to reduce the model complexity required for learning the radiomap in each region. Meanwhile, we incorporate the landscape map as an auxiliary input to induce a common learning model that adheres to the same propagation physics across all these heterogeneous regions. In doing so, fusion centers in all regions can collaborate through federated learning to enhance the overall RME performance. Simulation results indicate that our proposed method outperforms existing benchmarks, particularly under limited data, achieving higher learning accuracy with reduced model complexity and lower computational cost. © 2024 IEEE.

关键词： Federated learning

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Non Linear Homogenization of Laminate Magnetic Material by Computing Equivalent Magnetic Reluctivity 14

Non Linear Homogenization of Laminate Magnetic Material by C...

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19th International Conference on Software Technologies, ICSOFT 2024

作者： Yousfi, Ghania Mohellebi, Hassane Electrotechnics Department Faculty of Electrical Engineering and Computer Science Mouloud Mammeri University Tizi-Ouzou Algeria

ISBN: (纸本)9789897587061

In the present study we present a numerical modeling of a laminate magnetic material using an homogenization technique which exploit an inverse problem resolution. The laminate magnetic material is consisting of an alternate of magnetic and insulates layers. The equivalent magnetic reluctivity is computed for the homogenized domain by considering a non linear behavior of the magnetic layers. A finite element method is used to solve the 2D non-linear electromagnetic partial differential equation. An optimization problem is constructed and solved with the association of the 2D finite element resolution and a conjugate bi-gradient algorithm. The computation of the equivalent magnetic reluctivity is then performed for different excitation field value according to B-H curve. The comparison of the obtained B-H curves of the laminate and the homogenized domains to the theoretical B-H curve (experimental data) show a good agreement of laminate results. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Inverse problems

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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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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Hasani, Muhammad Fikri Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance. © 2023 IEEE.

关键词： Embeddings

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TangibleMoments: Embedding XR Memories onto Physical Objects

TangibleMoments: Embedding XR Memories onto Physical Objects

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Khan, Omar Ahmed, Zaid Nam, Hyeongil Kim, Kangsoo University of Calgary Department of Computer Science Canada Engineering University of Calgary Department of Electrical and Software Canada

ISBN: (纸本)9798331514846

The increasing prevalence of Extended Reality (XR) and head-mounted displays (HMDs), alongside rapid advancements in 3D reality capture technology, unlocks a new paradigm for capturing and reliving past memories/experiences through XR. Current methods for accessing and interacting with these "XR Memories"still lack the ability to fully leverage the range of capabilities afforded by XR and HMDs. We introduce TangibleMoments, a novel framework that enables users to embed XR memories onto physical objects, transforming those objects into "Moments"- tangible user interfaces for accessing and interacting with XR memories. We describe and illustrate five interaction methods as part of this framework: Creating Moments, Recalling Moments, Sharing Moments, Copying Moments, and Clearing Moments. We showcase an initial prototype and discuss possible extensions. © 2025 IEEE.

关键词： Mixed reality

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Finite-Time Synchronization of Complex Networks With Intermittent Couplings and Neutral-Type Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 2026-2028页

作者： Engang Tian Yi Zou Hongtian Chen School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor, This letter focuses on the finite-time synchronization(FTS) of neutral-type complex networks with intermittent couplings. Different from most of the existing references concerning neutral-type systems,a delay-independent dynamical event-triggering controller is considered, operating the same way as the intermittent coupling and excluding the Zeno behavior naturally.

关键词： neutral letter concerning

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