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Annual International Symposium on Reliability Physics

作者： D. Nergui M. Hosseinzadeh Y. A. Mensah H. P. Lee D. G. Sam K. Li E. X. Zhang D. M. Fleetwood John D. Cressler School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA HUAWEI Shenzhen Guangdong China Department of Electrical and Computer Engineering University of Central Florida Orlando FL USA Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA

ISBN: (数字)9798350369762

ISBN: (纸本)9798350369779

The impact of device scaling on the effects of mixed-mode electrical stress and ionizing radiation is assessed for third-and fourth-generation silicon-germanium heterojunction bipolar transistors (SiGe HBTs). When the devices were operating in forward-active mode, the fourth-generation technology showed better radiation tolerance but worse hot carrier degradation due to mixed-mode stress. However, when the devices were operating in inverse-active mode, the trend was the opposite and the fourth-generation technology showed worse radiation tolerance but less mixed-mode degradation.

关键词： Degradation Ionizing radiation Hot carriers Market research Heterojunction bipolar transistors Reliability Stress

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Investigating the Potential of Machine Learning for Enhancing Real-Time Data Analysis 3

Investigating the Potential of Machine Learning for Enhancin...

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

作者： Singh, Khushboo Mahalakshmi, V. Chavhan, Pranali G. Garg, Prateek Sahoo, Girija Shankar Sukumar, R. Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Karnataka Ramnagar562112 India

ISBN: (纸本)9798350319125

This abstract discusses the capability of system learning to serve as a device for real-time facts evaluation and its ability to decorate the accuracy of the resulting insights. Specifically, it examines the advantages of computerized function engineering and the way system gaining knowledge of primarily based tools may be leveraged to offer businesses with actionable data insights extra efficaciously than conventional strategies. It additionally looks at a few rising technologies, inclusive of deep neural networks, and their capability to push the limits of information analysis in real-time, and also discusses how improvements in allotted computing can similarly supplement the impact of gadget studying gear. Eventually, this summary additionally affords some demanding situations associated with actual-time evaluation of massive volumes of facts, and some advised answers. © 2023 IEEE.

关键词： Deep neural networks

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Deep Learning based Synthetic Aperture Imaging in the Presence of Phase Errors via Decoding Priors

Deep Learning based Synthetic Aperture Imaging in the Presen...

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IEEE National Conference on Radar

作者： Samia Kazemi Bariscan Yonel Birsen Yazici Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

In this paper, we designed a deep learning (DL) based method for synthetic aperture imaging in the presence of phase errors. Random variations in the transmission medium resulting from unforeseen environmental changes, fluctuations in sensor locations, and multiple scattering effects in the background medium often amount to uncertainties in the assumed data models. Imaging algorithms that rely on back-projected estimates are susceptible to estimation errors under these circumstances. Moreover, under dynamic nature of the medium, collecting high volume of measurements under the same operating conditions may become challenging. Towards this end, our imaging network incorporates DL in three major steps: first, we implement a deep network (DN) for pre-processing the erroneous measurements; second, we implement a DL-based decoding prior by recovering an encoded version of the reflectivity vector associated with the scattering media to reduce sample complexity, which is then mapped to an image estimate by a decoding DN; finally, we consider a fixed step implementation of an iterative algorithm in the form of a recurrent neural network (RNN) by using the unrolling technique that leads to a model-based imaging operator. The parameters of all three DNs are learned simultaneously in a supervised manner. We verified the feasibility of our approach using simulated high fidelity synthetic aperture measurements.

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Coherent Dispersive Wave Generation of OAM Mode in Double-ring-core Fiber

Coherent Dispersive Wave Generation of OAM Mode in Double-ri...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Geng, Wenpu Fang, Yuxi Bao, Changjing Pan, Zhongqi Yue, Yang Institute of Modern Optics Nankai University Tianjin300350 China Department of Electrical Engineering University of Southern California Los AngelesCA90089 United States Department of Electrical & Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States School of Information and Communications Engineering Xi'an Jiaotong University Xi'an710049 China

Two dispersive waves (DWs) generation of OAM1,1 mode with coherent spectra can be realized in the designed double-ring-core fiber. The location of the two DWs in the output spectrum matched well with the phase-matchin... 详细信息

ISBN: (纸本)9781957171296

关键词： Fiber design Optical confinement Orbital angular momentum multiplexing Phase matching Refractive index Spectral properties

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A CT-based Prediction System for Determining Respiratory Support Level in COVID-19 Patients

A CT-based Prediction System for Determining Respiratory Sup...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sharafeldeen, Ahmed Balaha, Hossam Magdy Farahat, Ibrahim Shawky Ghazal, Mohammed Connelly, James van Bogaert, Eric El-Baz, Ayman Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Department of Computer Science Mansoura University Mansoura Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Department of Radiology Faculty of Medicine University of Louisville LouisvilleKY United States

ISBN: (纸本)9798350368741

A novel diagnostic system is introduced for assessing the required level of respiratory support for COVID-19 patients. It bases its assessments on the correlation between detected COVID-19 lesions and the respiratory support levels administered to the patients. The correlation with computed tomography (CT) scans will focus on three respiratory support levels, including minimal support (Level 0), non-invasive support (Level 1, such as soft oxygen), and invasive support (Level 3, e.g., mechanical ventilation). The system initially outlines the pulmonary regions from CT scans, then identifies COVID-19 lesions within these segmented lung regions. Subsequently, three second-order texture features are extracted from the identified COVID-19 lesion regions to detect differences and abnormalities across varying severity levels in COVID-19 cases. To obtain the suitable level of respiratory support for each patient, a fusion mechanism based on backpropagation neural network is utilized to integrate the diagnosis of these three features, individually generated by the support vector machine (SVM) classifier. The system's performance is evaluated on 307 COVID-19 patients using a hold-out validation approach. This evaluation included various metrics, such as sensitivity, specificity, F1-score, Cohen's kappa, and accuracy, demonstrating impressive results. Specifically, it achieved a score of 97.25%, 98.56%, 97.26%, 95.79%, and 97.25%, respectively. The results demonstrate the effectiveness of the integrated system, which uses various second-order features, in predicting respiratory support needs for COVID-19 patients, outperforming both its individual components and other machine learning-based classification systems. © 2025 IEEE.

关键词： computer-Aided Diagnosis (CAD) GLCM GLRLM Machine Learning (ML) Neural Network (NN) SFM

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VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction

arXiv

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arXiv 2024年

作者： Tran, Khai Phan Hua, Wen Li, Xue School of Electrical Engineering and Computer Science The University of Queensland Australia Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

Document-level Relation Extraction (DocRE) aims to identify relationships between entity pairs within a document. However, most existing methods assume a uniform label distribution, resulting in suboptimal performance on real-world, imbalanced datasets. To tackle this challenge, we propose a novel data augmentation approach using generative models to enhance data from the embedding space. Our method leverages the Variational Autoencoder (VAE) architecture to capture all relation-wise distributions formed by entity pair representations and augment data for underrepresented relations. To better capture the multi-label nature of DocRE, we parameterize the VAE’s latent space with a Diffusion Model. Additionally, we introduce a hierarchical training framework to integrate the proposed VAE-based augmentation module into DocRE systems. Experiments on two benchmark datasets demonstrate that our method outperforms state-of-the-art models, effectively addressing the long-tail distribution problem in DocRE. Our code is released at: https://***/khaitran22/VaeDiff © 2024, CC BY-NC-SA.

关键词： Hierarchical systems

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A Survey on the Landscape of Machine Learning Solutions for Detecting Phishing Attacks 2

A Survey on the Landscape of Machine Learning Solutions for ...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Das, Shijon Ibrahem, Mohamed I. Fouda, Mostafa M. Augusta University School of Computer and Cyber Sciences AugustaGA30912 United States Idaho State University Department of Computer Science PocatelloID83209 United States Idaho FallsID United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States

ISBN: (纸本)9798331529529

Despite the advancements in cybersecurity, phishing attacks continue to pose significant threats to organizations and individuals worldwide. With the advent of time, phishing techniques have become more robust and thus demand more enhanced solutions than the existing ones. This survey paper explores how machine learning (ML) techniques are applied for detecting phishing attacks, offering a comprehensive overview of the challenges faced and the advancements made in the field. The paper discusses the fundamentals of phishing attacks and how ML can be applied to detect those attacks. The paper also discusses the applications of deep learning models and anomaly detection techniques which offer precise results for detecting modern phishing strategies. Finally, this survey addresses the challenges faced by current detection systems and suggests potential areas for future research. The goal of this survey is to provide a comprehensive overview that not only discusses the existing machine learning techniques but at the same time inspires innovation in this domain. © 2024 IEEE.

关键词： Phishing

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Multiuser Capacity Enhancement in B5G Networks using Hybrid CDMA-NOMA Scheme

Multiuser Capacity Enhancement in B5G Networks using Hybrid ...

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Green Energy, Computing and Sustainable Technology (GECOST), International Conference on

作者： Sabahat Sherien Haleema Sadia Hafsa Iqbal Ridha Alaa Ahmed Hablulmateen Department of Electrical Engineering CEME NUST Islamabad Pakistan School of Electrical Engineering and Computer Science NUST Islamabad Pakistan Department of Electronics and Communication Engineering University of Kufa Najaf Iraq

The rapid advancement of beyond fifth-generation (B5G) networks has opened a new era of high throughput demands, necessitating novel access schemes to overcome the associated challenges. Non-orthogonal multiple access (NOMA) has emerged as a promising technique, outperforming conventional orthogonal multiple access (OMA) in heterogeneous wireless networks. This paper introduces a new hybrid code division multiple access (CDMA)-NOMA scheme to improve multi-user capacity and power allocation (PA) efficiency in wireless networks. By combining the strengths of CDMA and NOMA, our proposed scheme forms NOMA clusters, each accommodating multiple users with varying channel characteristics. Intra-cluster interference is mitigated through successive interference cancellation (SIC), while inter-cluster interference is eliminated using CDMA spreading codes. We address the challenge of PA, a critical aspect of NOMA networks, by assigning distinct codes to all users within a cell. Through rigorous analysis, we evaluate the bit error rate performance of our hybrid scheme in diverse channel conditions, including the NYUSIM, AWGN, Rician, and Rayleigh channel models. The results presents an innovative solution to enhance B5G network capacity, demonstrating the potential of our hybrid CDMA-NOMA scheme to support more users efficiently while reducing SIC complexity.

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Optimal Control of Discrete-Time Multivariate Point Processes with Finite-Time Steady State

Optimal Control of Discrete-Time Multivariate Point Processe...

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IEEE Conference on Decision and Control

作者： A. Agung Julius Yunshi Wen Ruixuan Yan Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy New York

Multivariate point processes (MPP) are widely used to model the occurrence of multiple interrelated events in complex systems. They are used in a variety of fields to analyze data and define models that can make predictions about future events. In this paper, we consider finite-state MPP, which are products of finite state automata (FSA) and MPP. Specifically, the events in the MPP trigger state transitions in the FSA, while the intensities of the point processes are defined as functions of the FSA state and the history of the MPP. Further, we assume that some of the event types are controllable, i.e., they are not random but can be triggered. We formulate an optimal control problem for such system, which can then be expressed as optimal control for a Markov Decision Process (MDP) with infinite states. When the system has appropriate finite-time steady state properties, we use the concept of stochastic bisimulation of MDP to reduce the MDP into a finite state one, thereby allowing us to use standard optimal control techniques to calculate the optimal policy. We demonstrate the effectiveness of our method on a simplified sleep-wake cycle model, for the problem of optimally scheduling naps to maximize the length of wakefulness intervals.

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Improving the Performance of Job Backfilling Based on Job's Weight and Back Propagation Neural Network

Improving the Performance of Job Backfilling Based on Job's ...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hazem Alnajjar Nadia Al-Rousan Syed Sahal Nazli Alhady Syed Hassan Junita Mohamad-Saleh Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of computer engineering College of Engineering AlBalqa Applied University Salt Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University Amman Jordan Colleage of Engineering and Architecture School of Electrical & Electronic Engineering Engineering Branch Campus Universiti Sains Malaysia Malaysia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Nowadays, the grid computing environment faces many difficulties executing new jobs, especially jobs requiring large resource requirements and long execution times. This motivates researchers and scholars to find cheap and fast methods to improve the efficiency of grid environments. One of the cheap and fast methods is to implement job scheduling algorithms based on cheap and fast techniques. This paper proposes a new job ranking backfilling algorithm based on the job's weight and back propagation neural network. To define the weight of the job, first, the proposed model will use a clustering algorithm to cluster the job's dataset into groups, and then the groups will be ranked using an experimental ranking equation. A discrete event simulator is used to validate the proposed algorithm's capability and robustness. The average results revealed that the new algorithm outperforms previous algorithms. The improvement of the studied metrics is between 1.19 and 6.30, respectively. The results proved that the proposed model is efficient and can be used with low overhead in a real environment.

关键词： Backpropagation Measurement Scheduling algorithms Computational modeling Neural networks Clustering algorithms Grid computing Mathematical models Robustness Faces

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