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Integrated Photonics Research, Silicon and Nanophotonics, IPR 2022

作者： Ko, Joo Hwan Yoo, Young Jin Song, Young Min School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology 123 Cheomdan-gwagiro Buk-gu Gwangju61005 Korea Republic of

We present the tunable Fano resonator based on tailoring continuum states with a porosity control. The optical coating provides unique bi-directional responses in the visible-range, capable of dynamically reshaping th... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical coatings

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Combining Heuristic and Reinforcement Learning to Achieve the Low-latency and High-throughput Receiver-side Congestion Control

arXiv

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arXiv 2025年

作者： Jiang, Xianliang Gong, Guanghui Jin, Guang Faculty of Electrical Engineering and Computer Science of Ningbo University China Eastern Institute of Technology Ningbo China

Traditional congestion control algorithms struggle to maintain the consistent and satisfactory data transmission performance over time-varying networking condition. Simultaneously, as video traffic becomes dominant, the loose coupling between the DASH framework and TCP congestion control results in the un-matched bandwidth usage, thereby limiting video streaming performance. To address these issues, this paper proposes a receiver-driven congestion control framework named Nuwa. Nuwa deploys the congestion avoidance phase at the receiver-side, utilizing one-way queueing delay detection to monitor network congestion and setting specific target delays for different applications. Experimental results demonstrate that, in most cases, with appropriate parameter configuration, Nuwa can improve the throughput of TCP flows 4% to 15.4% and reduce average queueing delay by 6.9% to 29.4%. Furthermore, we also introduce the use of reinforcement learning to dynamically adjust Nuwa’s key parameter k, enhancing Nuwa’s adaptability to the unpredictable environment. © 2025, CC BY-NC-ND.

关键词： Video streaming

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Online Image Sensor Fault Detection for Autonomous Vehicles 15

Online Image Sensor Fault Detection for Autonomous Vehicles

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15th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022

作者： Chen, Yizhi Zhu, Wenyao Chen, Dejiu Lu, Zhonghai School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Stockholm Sweden School of Industrial Engineering and Management Kth Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781665464994

Automated driving vehicles have shown glorious potential in the near future market due to the high safety and convenience for drivers and passengers. Image sensors' reliability attract many researchers' interests as many image sensors are used in autonomous vehicles. We propose an online image sensor fault detection method based on comparing the historical variances of normal pixels and defective pixels to detect faults. For fault pixels without uncertainty, with a detecting window of more than 30 frames, we get 100% accuracy and 100% recall on realistic continuous traffic pictures from the KITTI data set. We also explore the influence of fault pixel values' uncertainty from 0% to 25% and study different fixed thresholds and a dynamic threshold for judgments. Strict threshold, which is 0.1, has a high accuracy (99.16%) but has a low recall (34.46%) for 15% uncertainty. Loose threshold, which is 0.3, has a relatively high recall (83.78%) but mistakes too many normal pixels with 18.17% accuracy for 15% uncertainty. Our dynamic threshold balances the accuracy and recall. It gets 100% accuracy and 58.69% recall for 5% uncertainty and 78.38% accuracy and 55.39% recall for 15% uncertainty. Based on the detected damage pixel rate, we develop a health score for evaluating the image sensor system intuitively. It can also be helpful for making decision about replacing cameras. © 2022 IEEE.

关键词： Autonomous vehicles

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Correction to: Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes (Neural Computing and Applications, (2025), 10.1007/s00521-025-10991-3)

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Neural Computing and Applications 2025年 1-2页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

In this article, the author’s name Stephen Ojo was incorrectly written as Stepehn Ojo and the affiliation details for author Stephen Ojo was incorrectly given as ‘Department of electrical and computer engineering, College of engineering Anderson, Anderson University, Anderson, SC 29621, USA’ but should have been ‘Department of electrical and computer engineering, College of engineering, Anderson University, Anderson, SC 29621, USA’. In addition, the affiliation details for author Gabriel Avelino Sampedro were incorrectly given as ‘College of computer Studies, De La Salle University, 2401 Taft Ave., Malate, 1004 Manila, Philippines and Faculty of Information and Communication Studies, University of the Philippines Open University, 4031 Los Baños, Philippines’ but should have been ‘Department of computer science, University of the Philippines Diliman, 1101 Quezon City, Philippines’. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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Performance Analysis of Random Forest Classifier in Extracting Features from the EEG signal

Performance Analysis of Random Forest Classifier in Extracti...

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2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022

作者： Jamunadevi, C. Ragupathy, P. Sritha, P. Pandikumar, S. Deepa, S. Kongu Engineering College Department of Computer Technology-PG Erode India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Sathyamangalam India American College Department of Computer Science Madurai India

ISBN: (纸本)9781665495158

Epilepsy is a disorder and is identified by baseless seizures that have been associated with unexpected improper neural discharges which result in various health issues and also result in death. One of the most common methods in monitoring and detecting contraction seizures is an electroencephalogram. But it is highly affordable and requires increased temporal resolution. EEG (electroencephalogram) is a commonly used method for monitoring and detecting seizures. The prevalence of EEG seizure detection has increased due to the increasing number of researchers who are focused on developing automated methods to detect the abnormalities in the EEG signals. But, it requires higher temporal resolution and is typically only available for a limited amount of time. Through machine learning, it is possible to extract the details of EEG signals that can help detect seizures. In this paper, the performance analysis is performed under various classifiers such as Random Forest, Gaussian Boosting, and AdaBoost. The results show that Random Forest is the most accurate classifier for achieving high degree of accuracy. © 2022 IEEE.

关键词： Electroencephalography

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HASHL: Dynamic Hash Verification for Detecting and Preventing Eclipse Attacks

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IEEE Internet of Things Journal 2025年

作者： He, Daojing Gong, Wei Tu, Chen Chan, Sammy Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Jiangxi University of Science and Technology School of Information Engineering Jiangxi Ganzhou China City University of Hong Kong Department of Electrical Engineering Hong Kong

With the rapid development of blockchain technology, P2P networks are facing increasing security threats, among which Eclipse attacks, as a type of network isolation attack, have seriously affected the normal operation of the network and the integrity of data. To address this challenge, this study implements node authentication and dynamic reputation evaluation by leveraging a dynamic hash computation mechanism that integrates challenge strings, node identifiers, and the latest active time, ensuring the uniqueness of node identities and the authenticity of operations. Based on a dynamic hash chain behavior evaluation mechanism, node behaviors are quantified across three dimensions: integrity, consistency, and temporal consistency, enabling precise identification of anomalous nodes. Furthermore, a network prevention repository framework is proposed, which dynamically adjusts the trust index of nodes by combining historical behavior with real-time data, effectively detecting and defending against stealthy Eclipse attacks. In addition, extensive testing on both Bitcoin and Ethereum platforms has shown that the method proposed in this study not only can effectively coexist on these two platforms, but also significantly improves the security and stability of the network, effectively reducing the occurrence of Eclipse attacks. © 2014 IEEE.

关键词： Blockchain

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Harnessing Machine Learning for Increased Efficiency in Real-Time Data Analysis 3

Harnessing Machine Learning for Increased Efficiency in Real...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Dhiraj Kumar Umamaheswari, B. Junnarkar, Aparna A. Singh, Vaishali Singh, Manpreet Savitha, R. Vivekananda Global University Department of Electrical Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Electrical and Electronics Engineering Faculty of Engineering and Technology Kanataka Bangalore India

ISBN: (纸本)9798350319125

The goal of this research is to analyze the ability of device mastering (ML) to increase the efficiency of real-time records evaluation. Notably, the focus is on leveraging ML techniques to correctly pick out, examine, and categorize information streams as a way to offer more green selection-making possibilities. This study will explore present ML strategies, which include supervised and unsupervised learning, deep studying, and reinforcement learning, and their software for real-time statistics evaluation. Additionally, the studies will remember the feasibility of combining ML strategies with present information science strategies and technology, including streaming data infrastructure and facts engineering. Sooner or later, the research will compare the potential prices and benefits of enforcing the sort of device in phrases of computational requirements, statistics handling, and accuracy. Via presenting insight into the feasibility of device mastering for actual-time records analysis, this research aims to cope with the issue of scalability in present-day techniques and help tell future selections in statistics engineering and analytics. © 2023 IEEE.

关键词： Efficiency

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A Blockchain-enabled Intelligent Electronic Healthcare Platform

A Blockchain-enabled Intelligent Electronic Healthcare Platf...

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Blockchain Computing and Applications (BCCA), International Conference on

作者： Ismaeel Al Ridhawi Ali Abbas Akshay Agrawal Muder Almiani School of Electrical Engineering and Computer Science University of Ottawa Canada Gulf University for Science and Technology Kuwait Kuwait College of Science and Technology Kuwait

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

Healthcare frameworks have evolved over the decades with the advancements in technology. Patient disease prevention, diagnosis, treatment, and well-being in general have progressed tremendously with the support of Artificial Intelligence (AI), data science, and advances in networking and communication. The security of the healthcare system plays an important role in the robustness and integrity of patient healthcare records. The use of intrusion detection and prevention plays a pivotal role in securing not only healthcare systems but also electronic health records. Today, traditional healthcare security frameworks that rely on conventional Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) cannot cope with the vulnerability of complex cyber attacks that can jeopardize private patient information. Furthermore, healthcare frameworks are shifting towards distributed and decentralized architectures that conventional IDS and IPS solutions are incapable of protecting. With that said, in this paper we present a blockchain-enabled IDS framework to protect electronic healthcare systems, which uses a diversified set of centralized, distributed, and decentralized security measures to detect and prevent a plethora of cyber attacks. The system is designed to be adaptable and capable of finding new and developing threats. A data set is simulated on the basis of healthcare flows, access levels, and reason. The evaluation results show that the proposed IDS framework provides adequate results in improving security.

关键词： Prevention and mitigation Intrusion detection Robustness Electronic healthcare Blockchains Security IP networks Electronic medical records Artificial intelligence Cyberattack

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Phase Shift Signaling Reduction for IRS-Aided Internet of Things Networks by Exploiting Global Attention 23

Phase Shift Signaling Reduction for IRS-Aided Internet of Th...

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23rd IEEE International Conference on Communication technology, ICCT 2023

作者： Yu, Xianhua Li, Dong Cao, Haotong Lin, Zhi An, Kang Wang, Jiangzhou Ai-Dhahir, Naofal Hong Kong Polytechnic University Department of Computing Hong Kong School of Computer Science and Engineering Macau University of Science and Technology China College of Electronic Engineering National University of Defense Technology Hefei China Sixty-third Research Institute National University of Defense Technology Nanjing China School of Engineering University of Kent Canterbury United Kingdom The University of Texas at Dallas Department of Electrical and Computer Engineering Richardson United States

ISBN: (纸本)9798350325959

The Internet of Things (IoT) has recently received considerable attention due to its ability to enhance high-data rate multiple access for massive IoT devices. However, these devices sometimes suffer from coverage and performance degradation when they are geographically far from each other. Moreover, as IoT devices often operate in power-saving mode, they require energy-efficient transmissions. In response to these challenges, the Intelligent Reflecting Surface (IRS) has been proposed and attracted increasing attention in recent years. Most existing researches on IRS-aided IoT networks assume that the IRS possesses full knowledge of the quantized phase shift (QPS). However, in practice, obtaining the optimized QPS poses significant challenges for the IRS, mainly due to constraints on signaling storm. To address the issue of QPS signaling overhead, we propose a novel deep learning (DL) model known as the global attention phase shift compression network (GAPSCN). Further-more, we introduce a global attention mechanism within the GAPSCN to enhance reconstruction accuracy. This mechanism exploits the correlation between the channel and spatial dimensions, emphasizing a larger number of meaningful features than in existing attention-based works. Simulation results demonstrate the effectiveness of the global attention mechanism compared to conventional attention mechanisms, as well as the proposed GAPSCN, which achieves reliable reconstruction performance relative to existing state-of-the-art compression models. © 2023 IEEE.

关键词： global attention Intelligent reflecting surface Internet of Things phase shift compression

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Fully Explicit Dynamic Gaussian Splatting

arXiv

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arXiv 2024年

作者： Lee, Junoh Won, Changyeon Jung, Hyunjun Bae, Inhwan Jeon, Hae-Gon School of Electrical Engineering and Computer Science AI Graduate School Gwangju Institute of Science and Technology Korea Republic of

3D Gaussian Splatting has shown fast and high-quality rendering results in static scenes by leveraging dense 3D prior and explicit representations. Unfortunately, the benefits of the prior and representation do not involve novel view synthesis for dynamic motions. Ironically, this is because the main barrier is the reliance on them, which requires increasing training and rendering times to account for dynamic motions. In this paper, we design a Explicit 4D Gaussian Splatting(Ex4DGS). Our key idea is to firstly separate static and dynamic Gaussians during training, and to explicitly sample positions and rotations of the dynamic Gaussians at sparse timestamps. The sampled positions and rotations are then interpolated to represent both spatially and temporally continuous motions of objects in dynamic scenes as well as reducing computational cost. Additionally, we introduce a progressive training scheme and a point-backtracking technique that improves Ex4DGS’s convergence. We initially train Ex4DGS using short timestamps and progressively extend timestamps, which makes it work well with a few point clouds. The point-backtracking is used to quantify the cumulative error of each Gaussian over time, enabling the detection and removal of erroneous Gaussians in dynamic scenes. Comprehensive experiments on various scenes demonstrate the state-of-the-art rendering quality from our method, achieving fast rendering of 62 fps on a single 2080Ti GPU. © 2024, CC BY-NC-ND.

关键词： Gaussian distribution

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