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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1777-1788页

作者： Yan, Wending Sharma, Aashish Tan, Robby T. National University of Singapore Electrical and Computer Engineering Department Singapore Singapore Agency for Science Technology and Research Singapore Singapore

Estimating optical flow in dense foggy scenes is a challenging task. The basic assumptions for computing flow such as brightness and gradient constancy become invalid. To address the problem, we introduce a semisupervised deep learning method that can learn from real fog images without requiring the corresponding optical flow ground-truths. Our method is a multitask network, integrating the domain transformation and optical flow networks in one framework. The domain transformation is performed between foggy and clean domains. Under our semisupervised training strategy, we train our method in a supervised manner with a pair of synthetic fog images, their corresponding clean images and optical flow ground-truths. Subsequently, given a pair of real fog images and a pair of clean images that are not corresponding to each other (unpaired), in the next training batch, we train our network in an unsupervised manner. The supervised and unsupervised training processes are alternated iteratively. Since our method relies on unsupervised learning for real data, we show that it can be used for test-time training. We show in our experiments that performing test-time training improves the results further on our test data. Extensive experiments show that our method outperforms the state-of-the-art methods in estimating optical flow in dense foggy scenes. © 2020 IEEE.

关键词： Optical flows

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Optimizing DDoS Detection in SDNs Through Machine Learning Models 16

Optimizing DDoS Detection in SDNs Through Machine Learning M...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Haque, Md. Ehsanul Hossain, Amran Alam, Md. Shafiqul Siam, Ahsan Habib Rabbi, Sayed Md Fazle Rahman, Md. Muntasir East West University Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798331505264

The emergence of Software-Defined Networking (SDN) has changed the network structure by separating the control plane from the data plane. However, this innovation has also increased susceptibility to DDoS attacks. Existing detection techniques are often ineffective due to data imbalance and accuracy issues;thus, a considerable research gap exists regarding DDoS detection methods suitable for SDN contexts. This research attempts to detect DDoS attacks more effectively using machine learning algorithms: RF, SVC, KNN, MLP, and XGB. For this purpose, both balanced and imbalanced datasets have been used to measure the performance of the models in terms of accuracy and AUC. Based on the analysis, we can say that RF and XGB had the perfect score, 1.0000, in the accuracy and AUC, but since XGB ended with the lowest Brier Score which indicates the highest reliability. MLP achieved an accuracy of 99.93%, SVC an accuracy of 97.65% and KNN an accuracy of 97.87%, which was the next best performers after RF and XGB. These results are consistent with the validity of SDNs as a platform for RF and XGB techniques in detecting DDoS attacks and highlights the importance of balanced datasets for improving detection against generative cyber attacks that are continually evolving. © 2024 IEEE.

关键词： Network security

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Improvement of Switching Frequency and Input Voltage Range in Single-Stage PFC Flyback Converter for LED Driver Applications 16

Improvement of Switching Frequency and Input Voltage Range i...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Khanmohammadi, Amir Hossein Sadeghian, Salar Hamzeh, Mohsen Abrishamifar, Adib University of Tehran School of Electrical and Computer Engineering Tehran Iran Iran University of Science and Technology School of Electrical Engineering Tehran Iran

ISBN: (纸本)9798331533946

The boundary conduction mode (BCM) flyback power factor correction (PFC) converter is well-suited for low to medium power-level applications. It isolates input and output voltages while improving the power factor (PF) by modifying the transformer turns ratio. However, the converter switching frequency varies throughout the line cycle, and the frequency range depends on the input voltage and load conditions. Consequently, the electromagnetic interference (EMI) spectra show significant variations, making the EMI filter design more challenging. This paper presents a constant on-time (COT) control strategy that employs a variable inductor (VI) to limit the range of switching frequency fluctuations across the universal input voltage range (85-264 Vrms). © 2025 IEEE.

关键词： Switching frequency

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Robust asymmetric Cherenkov radiation in tilted anisotropic medium

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science China(Physics,Mechanics & Astronomy) 2024年第1期67卷 46-51页

作者： Shuwen Xue Yadong Xu Linkang Han Zhi Ning Chen Qiaoliang Bao Shan Zhu Huanyang Chen Institute of Electromagnetics and Acoustics and Department of Physics College of Physical Science and TechnologyJiujiang Research InstituteXiamen UniversityXiamen 361005China School of Physical Science and Technology Institute of Theoretical and Applied PhysicsSoochow UniversitySuzhou 215006China Department of Electrical and Computer Engineering National University of SingaporeSingapore 8117583Singapore Institute of Energy Materials Science(IEMS) University of Shanghai for Science and TechnologyShanghai 200093China

Cherenkov radiation(CR)is available for a wide variety of terahertz(THz)radiation sources,but its efficiency is deeply affected by intrinsic *** find that if the tilted angle(α)of anisotropic material and radiation angle(θ)meet the condition ofθ+α=π/2,the intensity of radiation fields for the charged particle bunch(CPB)moving from left to right cannot be influenced by intrinsic losses,which means long-distance radiation can be ***,we observe an asymmetric CR when the CPB moves from the opposite *** addition,we select natural van der Waals(vd W)materialα-MoO3as an example,further confirming that the radiation field can reach the far field and the asymmetric CR radiation can also be *** wonderful properties with long-distance radiation will extend the application of CR to a certain extent for future design and fabrication.

关键词： asymmetric Cherenkov radiation intrinsic losses long-distance radiation

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UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

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China Communications 2024年第10期21卷 1-15页

作者： Li Zhongjie Yuan Weijie Guo Qinghua Wu Nan Zhang Ji Department of Electronic and Electrical Engineering Southern University of Science and TechonologyShenzhen 518055China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Mathematics and Statistics Henan University of Science and TechnologyLuoyang 471000China

Orthogonal time frequency space(OTFS)technique, which modulates data symbols in the delayDoppler(DD) domain, presents a potential solution for supporting reliable information transmission in highmobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM). The empirical state evolution(SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

关键词： channel estimation hidden Markov model(HMM) orthogonal time frequency space(OTFS) unitary approximate message passing(UAMP)

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KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation for Environmental Monitoring 4

KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Sikander, Saad Rehman, Hamood Ur Naqvi, Syed Fakhr Abbas Aslam, Waqar Perwaiz, Nazia Sir Syed CASE Institute of Technology Department of Electrical Engineering Islamabad Pakistan Sir Syed CASE Institute of Technology Department of Electrical Engineering and Computer Science Islamabad Pakistan National University of Sciences and Technology NUST School of Electrical Engineering and Computer Science Islamabad Pakistan

ISBN: (纸本)9798350391862

Monitoring the environment and managing water bodies are crucial for preserving ecosystems and ensuring sustainable resource utilization. This study aims to propose a robust approach for segmenting water bodies by combining various complementary attributes extracted from deep learning techniques. By leveraging deep contextual features learned from attention regions and enhanced edge detection, our method significantly improves the accuracy of detecting water bodies in low-resolution satellite imagery. Utilizing S1 and S2 Sentinel imagery data and exploring multi-band features, we enhance key attributes through advanced UNet architectures - specifically, Ki-UNet and Attention UNet - and integrate complementary features for downstream tasks. Additionally, we assembled a local dataset from the Khyber Pakhtunkhwa (KPK) region of Pakistan, facilitating precise water body segmentation from satellite or aerial views. Our ensemble model achieved remarkable accuracy and Intersection over Union (IoU) scores, reaching up to 99.01% and 96.2%, respectively, surpassing state-of-the-art models. This research provides automated, accurate segmentation techniques essential for environmental management and resource assessment, offering a promising solution for delineating water bodies. github link : (https://***/SaadBaloch96/Dataset/blob/main/***) © 2024 IEEE.

关键词： Environmental monitoring

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A parametric rate-distortion model for video transcoding

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Multimedia Tools and Applications 2025年 1-35页

作者： Jamali, Maedeh Karimi, Nader Samavi, Shadrokh Shirani, Shahram Department of Electrical and Computer Engineering McMaster University HamiltonL8S 4L8 Canada Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Computer Science Department Seattle University Seattle98122 United States

Over the past two decades, the rise in video streaming has been driven by internet accessibility and the demand for high-quality video. To meet this demand across varying network speeds and devices, transcoding is essential. This paper introduces a parametric rate-distortion (R-D) transcoding model that predicts transcoding distortion at different bitrates without the need for re-encoding. Experimental results validate the model’s effectiveness in predicting rate-distortion behavior for diverse video content. Using our model, visual quality (measured by PSNR and VMAF) of transcoded video can be improved through trans-sizing. Moreover, our model can identify visually lossless bitrate ranges. This allows service providers to adjust target bitrates with minimal quality loss. Experimental results validate the model’s effectiveness in predicting rate-distortion behavior for diverse video content. By using the VMAF measure, our model achieves a quality improvement of up to 2.55 and bitrate savings of up to 79.10%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Video streaming

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LiDUT-Depth: A Lightweight Self-supervised Depth Estimation Model Featuring Dynamic Upsampling and Triplet Loss Optimization 27th

LiDUT-Depth: A Lightweight Self-supervised Depth Estimation ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Jiang, Hao Fang, Zhijun Shao, Xuan Jiang, Xiaoyan Hwang, Jenq-Neng School of Computer Science and Technology Donghua University Shanghai China School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China Department of Electrical and Computer Engineering University of Washington SeattleWA United States

ISBN: (纸本)9783031784439

Lightweight yet reliable depth estimation models that can deployed on edge devices are crucial for the practical application of fields such as autonomous driving, robot navigation, and augmented reality. However, previous research often fails to balance accuracy and complexity well. Existing lightweight models still have relatively high error rates in specific scenarios, which makes them unsuitable for industrial applications. Hence, we propose LiDUT-Depth, a lightweight architecture for self-supervised monocular depth estimation that is enhanced through dynamic upsampling and triplet loss optimization, which achieves competitive results with smaller model sizes and lower computational complexity. Specifically, an efficient dynamic upsampling module (EDU Module) is employed to preserve detailed local features, thereby obtaining more accurate depth estimation results. In addition, an improved semantic-aware triplet loss (SaTri Loss) is integrated into the training loss, significantly improving depth estimation accuracy in gradient-rich regions. Experiments show that our architecture achieves a considerably enhanced accuracy compared with previous works with a much lower model size. Our codes and models are available here. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Augmented reality

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in engineering & sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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Advancing Sustainable IoT Appliance Load Monitoring Through Edge-Enabled Federated Transfer Learning

Advancing Sustainable IoT Appliance Load Monitoring Through ...

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2024 International Conference on Green Energy, Computing and Sustainable technology, GECOST 2024

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Sri Preethaa, K.R. Sundaram, Karthic Rama Abirami, K. Kpr Institute of Engineering and Technology Computer Science and Engineering Coimbatore641407 India Dresden01062 Germany Curtin University Faculty of Engineering and Science Department of Electrical and Computer Engineering Malaysia

ISBN: (纸本)9798350357905

Non-intrusive Appliance Load Monitoring (NALM) is essential for efficient electricity consumption tracking in households, promoting eco-friendly practices, and cost reduction. Privacy concerns in real-world NALM implementations can be addressed using federated learning. However, challenges persist, such as limited training data, edge model customization, and resource constraints. We introduce a practical federated learning framework for NALM, leveraging cloud-based model compression, collaborative data collection, and personalized edge and multi-task learning models through unsupervised transfer learning. Our advanced load monitoring model offers precise energy disaggregation through real-world experimentation, making it a leading solution for non-intrusive appliance load monitoring on edge devices. It maintains excellent learning performance and user privacy. Future research should aim to enhance federated learning efficiency and tackle remaining challenges in practical NALM implementations © 2024 IEEE.

关键词： Cost reduction

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