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IEEE Topical Meeting on electrical Performance of Electronic Packaging (EPEPS)

作者： Ram Krishna Ashita Victor Srujan Penta Xu Chen Muhannad S. Bakir Nam Sung Kim Elyse Rosenbaum School of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798350351231

ISBN: (纸本)9798350351248

This work presents an interposer design methodol-ogy for UCle die-to-die interfaces that maximizes yield given a set of signal integrity specifications. Four different routing configurations on a silicon interposer are considered, and the cost, performance, and yield tradeoffs are elucidated. The fabrication steps for the D2D interconnects are outlined and sources of yield-limiting variability are identified. The yield analysis is expedited by the use of a Gaussian Process Regression surrogate model.

关键词： Fabrication Runtime Gaussian processes Predictive models Routing Silicon Device-to-device communication Space exploration Signal integrity Testing

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Impact of Dataset Balancing on Machine Learning-Based Intrusion Detection Systems 10

Impact of Dataset Balancing on Machine Learning-Based Intrus...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Yusri, Muhammad Iqbal Habaebi, Mohamed Hadi Gunawan, Teddy Surya Mansor, Hasmah Kartiwi, Mira Nur, Levy Olivia International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Telkom University School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350365283

Intrusion Detection Systems (IDS) are indispensable for cybersecurity, as they safeguard networks from increasingly sophisticated and sophisticated cyberattacks. This paper assesses the influence of dataset balancing on the performance of machine learning-based IDS, thereby addressing the challenge of imbalanced data in detecting network intrusions. We concentrate on three IDS implementations: Tree-based Intelligent IDS, Multi-Tiered Hybrid IDS (MTH-IDS), and Leader Class and Confidence Decision Ensemble (LCCDE). We utilized the Synthetic Minority Over-Sampling Technique (SMOTE) to balance data and implemented feature selection and hyperparameter optimization to improve the model's performance using the CICIDS 2017 dataset. The LCCDE model exhibits the highest performance, as our comparative analysis demonstrates that the combination of SMOTE and feature selection enhances the F1 scores. The results underscore the significance of advanced ensemble techniques and data preprocessing in developing resilient IDS. This research emphasizes the necessity of ongoing optimization and evaluation of IDS models to guarantee effective protection against the development of cyber threats. © 2024 IEEE.

关键词： network intrusion

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Realtime Implementation of Multiple Fault Detection Methodology for Solar PV Farms using Hybrid AMH and Deep Learning Techniques

Realtime Implementation of Multiple Fault Detection Methodol...

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13th International electrical engineering Congress, iEECON 2025

作者： Pannela, Chandrakala Madichetty, Sreedhar Nb, Shanthi Kumar Pannela, Gowthami Ecole Centrale School of Engineering Mahindra University Electrical and Computer Engineering Hyderabad India

ISBN: (纸本)9798331543952

In this article, an intelligent fault detection mechanism is proposed to classify multiple electrical faults. Various electrical faults like line-line fault, open circuit fault, short circuit faults, and line-to-ground faults are studied in detail. Voltage and current sensors placed at each solar photovoltaic (SPV) array string collect the data during these fault conditions. Machine learning and Deep learning techniques are used to classify the type of fault and can even detect the exact location of the fault. A mismatch detection circuit consisting of a boost converter and optocoupler is designed to differentiate between mismatch faults and electrical faults;this circuit design also indicates the exact location of a mismatch fault. The proposed method is implemented in the Python platform and the results are analysed. © 2025 IEEE.

关键词： Deep learning

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Literature Survey on Face Recognition of Occluded Faces 7

Literature Survey on Face Recognition of Occluded Faces

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Anil, J. Suresh, L. Padma Muthukumar, P. Krishna Veni, S.H. Asha, P. Prasad, Rajesh Nooral Islam Centre for Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Thuckalay India Baselios Mathews II College of Engineering Sasthamcotta Department of Electrical and Electronics Engineering Kerala Kollam690 520 India Saveetha Institute of Medical and Technical Sciences Tiruvallur Saveetha School of Engineering Department of Electrical and Electronics Engineering Tamilnadu Chennai602105 India Baselios Mathews II College of Engineering Sasthamcotta Department of Computer Science and Engineering Kerala Kollam690 520 India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Stella Mary's College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu India

ISBN: (纸本)9798350372816

In this paper, a study of the recent advancements in occluded face recognition is made. Recently, this has become very relevant due to the outburst of corona virus. Because of the seriousness of the spread of the disease, every person wore a mask, and it became challenging for automated systems to identify the person. So, it became the need of the hour to find new techniques for occluded face detection. In the years 2021 to 2022, many papers have been published in this regard. Researchers all over the world have contributed significantly towards occluded face recognition. Also, the advancement of deep learning has helped researchers a lot in implementing innovative techniques to deal with the challenges in face recognition, especially occluded faces, which is considered one of the most difficult challenges to overcome. This paper intends to push the limits further and encourage researchers to find more innovative techniques for occlusion-independent face recognition. In this paper, the recent publications in this field are studied. This paper gives an insight into different algorithms which use Convolutional Neural networks, Deep Learning, Attention mechanism, Dictionary representation, Simultaneous segmentation, Joint segmentation and identification, etc., for occluded face detection and recognition. Incorporating these strategies in face recognition has substantially improved the recognition rate, specifically for occluded face recognition. Additionally, the databases for training occluded face recognition algorithms lack adequate data. This paper also gives an insight into the recent databases available for training occluded face recognition algorithms. Also, different methods for obtaining synthesised occluded faces are discussed. © 2024 IEEE.

关键词： Convolutional neural networks

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A Hybrid Distance Technique to Optimize the K-Nearest Neighbor Algorithm 2

A Hybrid Distance Technique to Optimize the K-Nearest Neighb...

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2nd International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Abdulmuttaleb Al-Musawi, Hussein Ali Aljaidi, Mohammad Sara, Massar Boostani, Reza School of Engineering Shiraz University Electrical and Computer Engineering Faculty Cse and It Dept. Shiraz Iran Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798350373363

Plenty of Bayesian-consistent, heuristic and statistical classifiers have been introduced. Although support vector machines, deep neural networks, and random forests exhibit acceptable performance in several fields, their primary challenge lies in the lengthy training phase, especially when dealing with big data. K-nearest neighbors (KNN) is one of the simplest, Bayesian consistent and most common algorithms for classification tasks which locally makes its decision and there is no need to any training phase, can be implemented by parallel processing algorithms which makes it suitable to deal with big datasets. The core of this classifier relies on measuring the distance between test samples and its neighbors. Therefore, designing a hybrid distance technique to better find relevant neighbors is of interest. In this research, a weighted combination five distances has been proposed to optimize the performance of the KNN algorithm on diverse 28 datasets. To select appropriate weights of distances for each dataset, particle swarm optimization algorithm was used due to its simplicity and high convergence speed. The proposed method was applied to the datasets, demonstrating acceptable performance compared to its rivals. © 2023 IEEE.

关键词： Classification (of information)

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Cascading Delays for the High-Speed Rail network Under Different Emergencies:A Double Layer network Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 2014-2025页

作者： Xingtang Wu Mingkun Yang Wenbo Lian Min Zhou Hongwei Wang Hairong Dong School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China School of Control and Computer Engineering North China Electric Power UniversityBeijing 100096China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China National Research Center of Railway Safety Assessment Beijing Jiaotong UniversityBeijing 100044China

High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.

关键词： Delay propagation double layer network high speed rail network max-plus algebra

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A UAV localization method based on RF TDOA and visual YOLO feature fusion 4

A UAV localization method based on RF TDOA and visual YOLO f...

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4th International Conference on Electronic Information and Communication engineering, EICE 2025

作者： Liu, Runjie Zhang, Yuankang Xu, Huina National Supercomputing Center in Zhengzhou Zhengzhou450001 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

In recent years, the rapid development of UAV technology has opened up a wide range of application prospects, but unauthorized UAV flights pose potential threats to low-altitude airspace security. Existing monitoring methods often rely on expensive high-precision equipment or single-sensor systems, making it difficult to achieve low-cost yet high-accuracy UAV localization and identification. A localization method based on a multi-sensor fusion strategy combining RF and visual data, that is, integrating RF TDOA localization technology with lightweight YOLO visual localization technology (TAYT, TDOA, and YOLO technology) is proposed in this paper. The method isolates the radio frequency bands used by the UAV, discovers the UAV through RF TDOA frequency scanning positioning, and combines Yolo images to locate the UAV. Experimental results demonstrate that this approach overcomes issues such as large errors caused by the susceptibility of spectrum sensors to environmental noise interference and non-line-of-sight effects. The minimum error of the positioning error is about 2.79 meters compared to the reference standard. A low-cost and high-efficiency positioning technology for effective low-altitude UAV monitoring is provided in this paper. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Light weight equipment

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Artificial Intelligence based network Security System to Predict the Possible Threats in Healthcare Data

Artificial Intelligence based Network Security System to Pre...

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2022 International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022

作者： Hailu, Tegegne Ayalew Viajiprabhu, G. Endris, Abdulkerim Seid Arappali, Nedumaran Kombolcha Institute of Technology Wollo University Electrical and Computer Engineering Ethiopia Pg Research Department Computer Science Erode-9 India School of Electrical and Computer Engineering Wollo University Kombolcha Institute of Technology Ethiopia

ISBN: (纸本)9781665478847

The world is filled with exciting technologies and ideas;scientists build machines to avoid human intervention in completing work. It is highly challenging to complete the task without the Artificial Intelligence (AI) Technology intervention. With the technological development, certain processes or consultations are performed with the aid of doctors available around the world. In this scenario, it could be noticed that health care is one of the world's expected domains that require the most incredible attention in data security while performing data transfer. Nodes in the network are considered based on the weakest link to overcome the cyber attacker's issues. Besides building the software for data storage, a better mechanism has to be incorporated to provide security to the stored data. This process is a delicate task for every network engineer. This paper will explain such concepts related to health prediction and health care by building the most robust network security systems. Finally, the discussion would cross over human-looping systems, which act as one of the common problems that are affected mentally for a person. The proposed optimized neural network model is compared with the existing data summarization method. From the results it is observed that the proposed model achieves an accuracy of 98.89% which is 4.76% higher than the existing model. © 2022 IEEE.

关键词： Health care

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Accurate and Fast Phase Compensation of Dynamic Samples in Digital Holographic Microscopy

Accurate and Fast Phase Compensation of Dynamic Samples in D...

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2023 Digital Holography and Three-Dimensional Imaging, DH 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Obando-Vásquez, Sofía Doblas, Ana Trujillo, Carlos Applied Optics Group School of Applied Science and Engineering Universidad EAFIT Medellín Colombia Department of Electrical and Computer Engineering The University of Memphis TN United States

We present a fast algorithm for accurate phase compensation of holographic videos of dynamic phenomena recorded in a digital holographic microscope. The proposal is tested with human sperm and red blood cell samples. ... 详细信息

ISBN: (纸本)9781957171289

关键词： Holography

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Cell-Free ISAC MIMO Systems: Joint Sensing and Communication Beamforming

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IEEE Transactions on Communications 2024年

作者： Demirhan, Umut Alkhateeb, Ahmed Arizona State University School Of Electrical Computer And Energy Engineering United States

This paper considers a cell-free integrated sensing and communication (ISAC) MIMO system, where distributed MIMO access points (APs) jointly serve the communication users and sense the target. For this setup, we derive a sensing SNR for multi-static sensing where both joint communication and sensing signals transmitted by different APs are utilized. With this sensing objective, we develop two baseline approaches that separately design the sensing and communication beamforming vectors, namely communication-prioritized sensing beamforming and sensing-prioritized communication beamforming. Then, we consider the joint sensing and communication (JSC) beamforming design and derive the optimal structure of these beamforming vectors based on a max-min fairness formulation. In addition, considering any pre-determined JSC beam design, we devise a power allocation approach. The results show that the developed JSC beamforming is capable of achieving nearly the same communication signal-to-interference-plus-noise ratio (SINR) of the communication-prioritized sensing beamforming solution with almost the same sensing SNR of the sensing-prioritized communication beamforming approach. The proposed JSC beamforming optimization also provides a noticeable gain over the power allocation with regularized zero-forcing beamforming, yielding a promising strategy for cell-free ISAC MIMO systems. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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