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Heterogeneously integrated silicon-conductive oxide MOSCAP microring modulator array

Photonics Research 2025年第1期13卷 187-193页

作者： WEI-CHE HSU SAEED ABDOLHOSSEINI HAISHENG RONG RANJEET KUMAR BERND ZECHMANN ALAN X.WANG Department of Electrical and Computer Engineering Baylor UniversityWacoTexas 76798USA School of Electrical Engineering and Computer Science Oregon State UniversityCorvallisOregon 97331USA Intel Corporation Santa ClaraCalifornia 95054USA Center of Microscopy and Imaging Baylor UniversityWacoTexas 76798USA

In pursuit of energy-efficient optical interconnect,the silicon microring modulator(Si-MRM)has emerged as a pivotal device offering an ultra-compact footprint and capability of on-chip wavelength division multiplexing(WDM).This paper presents a 1×4 metal-oxide-semiconductor capacitor(MOSCAP)Si-MRM array gated by high-mobility titanium-doped indium oxide(ITiO),which was fabricated by combining Intel’s high-volume manufacturing process and the transparent conductive oxide(TCO)patterning with the university *** 1×4 Si-MRM array exhibits a high electro-optic(E-O)efficiency with V_(π)·L of 0.12 V·cm and achieves a modulation rate of(3×25+1×15)Gb/s with a measured bandwidth of 14 ***,it can perform on-chip WDM modulation at four equally spaced wavelengths without using thermal *** process compatibility between silicon photonics and TCO materials is verified by such an industry-university co-fabrication approach for the MOSCAP Si-MRM array and demonstrated enhanced performance from heterogeneous integration.

关键词： modulator conductive oxide

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Vulnerability Analysis of Deep Learning Model for OCTA Image Classification 5th

Vulnerability Analysis of Deep Learning Model for OCTA Ima...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Trivedi, Munesh Chandra Bharadwaj, Siddhant Department of Computer Science and Engineering Pandit Sunderlal Sharma Central Institute of Vocational Education Bhopal India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal India

ISBN: (纸本)9789819761029

The prevalence of Deep Learning in healthcare has revolutionized medical diagnostics, yet the vulnerability of these models to adversarial attacks threatens their security. This study investigates the susceptibility of a sophisticated deep learning model, trained to classify OCTA images as healthy or diabetic, to adversarial perturbations. Despite achieving high accuracy on unperturbed data, the model remains vulnerable to adversarial noise. The study employs techniques like Project Gradient Descent (PGD) and Fast Gradient Sign Method (FGSM) to generate adversarial examples and tests their efficacy against the model. Results show that even minor perturbations can lead to misclassification, emphasizing the need for adversarial robustness in healthcare models. As healthcare decisions are critical, incorporating adversarial training is must to mitigate the impact of adversarial vulnerabilities in deep learning-based medical diagnoses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Adversarial Machine Learning for Detecting Advanced Threats Inspired by StuxNet in Critical Infrastructure Networks 12

Adversarial Machine Learning for Detecting Advanced Threats ...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Ahmad, Hafiz Bilal Gao, HaiChang Latif, Naila Aziiz, Abdul Auraangzeb, Muhammad Khan, Muhammad Tanveer School of Computer Science and Technology Xidian University Shaanxi 710071 China School of Telecommunications and Engineering Xidian University Shaanxi 710071 China School of Computer Science and Engineering Southeast University Nanjing China School of Electrical Engineering Southeast University Nanjing China School of Software Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350330366

Inspired by the notorious StuxNet malware, this paper presents a unique and resilient architecture to address the imminent issue of identifying advanced threats within critical infrastructure networks. We use adversarial machine learning techniques to keep ahead of ever-changing threats. Our defense system's fundamental component is a Multi-Layer Perceptron (MLP) architecture that we've dubbed 'StuxNet.' This innovative method combines threat intelligence, anomaly detection, and deep learning to forecast and counter sophisticated attacks. The effectiveness of our method in bolstering the security of critical infrastructure networks is supported by empirical evaluations and real-world case studies. Importantly, the StuxNet-MLP architecture has a 98 percent detection accuracy for sophisticated threats. These results highlight the paramount significance of preventative cybersecurity measures in protecting mission-critical systems and provide useful insights for fending off sophisticated attacks. To ensure a safer and more resilient digital future, this study highlights the need to stay ahead in the continuous battle to secure key infrastructure. © 2024 IEEE.

关键词： Critical infrastructures

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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Adaptive Multi-Link Data Allocation for LEO Satellite Networks

Adaptive Multi-Link Data Allocation for LEO Satellite Networ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zheng, Jinkai Luan, Tom H. Zhao, Jinwei Li, Guanjie Zhang, Yao Pan, Jianping Cheng, Nan Dongguan University of Technology School of Electrical Engineering and Intelligentization Guangdong China Xidian University School of Cyber Engineering Shaanxi China University of Victoria Department of Computer Science VictoriaBC Canada Northwestern Polytechnical University School of Computer Science Shaanxi China Xidian University School of Telecommunications Engineering Shaanxi China

ISBN: (纸本)9798350351255

The rapid development of Low Earth Orbit (LEO) satellite networks has provided ubiquitous Internet access to users around the world, especially in areas where there are no terrestrial networks. However, a dish can only communicate with one of the available satellites when uploading data in the current framework, resulting in low communication efficiency. As the number of satellites continues to increase, the current framework cannot make full use of the user-satellite link resources. In this paper, we first conduct a measurement of Starlink's network performance and report some unique features. Then, we propose an adaptive multi-link data allocation framework for LEO satellite networks where a dish can communicate with multiple satellites at the same time to improve data transmission efficiency. With this framework, data can be split into chunks and uploaded simultaneously over multiple links. Our goal is to determine the data allocation strategies to jointly optimize the transmission latency and data processing costs. To this end, we propose a deep reinforcement learning-based algorithm integrated with the traffic prediction module to determine the optimal data allocation strategies in a dynamic network environment. Through extensive simulations, we demonstrate the effectiveness of our approach compared with baselines. © 2024 IEEE.

关键词： Satellite links

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Comparison of Popular CNN Architectures for Cervical Cancer Early Diagnosis

Comparison of Popular CNN Architectures for Cervical Cancer ...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Indra, Zul Jusman, Yessi Salambue, Roni Mahdiyah, Evfi Universitas Riau Departement of Computer Science Pekanbaru Indonesia Universitas Muhammadiyah Yogyakarta Departement of Electrical Engineering Bantul Indonesia

ISBN: (纸本)9798350382914

Cervical cancer has been considered by WHO as one of the deadliest diseases for women because of its high mortality and morbidity rates. It was reported that cervical cancer accounts for around 12% of all types of cancer in women worldwide. In fact, despite having very high morbidity and mortality rates, cervical cancer takes a relatively long time to mutate from normal cells to precancerous conditions. Therefore, it can be concluded that cervical cancer can be completely cured if it can be detected early. Therefore, research related to early detection of cervical precancerous cells is a must to prevent patient mortality and morbidity. This research is expected to be a method that can be used in diagnosing cervical cancer early. To achieve this goal, this study applies several architectures of the CNN algorithm such as MobileNetV2, RestNet50, InceptionV3, EfficientNetB0, Xception, and VGGNet16. As for the dataset for pattern recognition of cervical cancer, this study uses ThinPrep images which are considered as one of the best types of cervical cancer images. Based on the results obtained, of the 5 architectures used, this study succeeded in obtaining an accuracy value above 95%. Thus, it can be concluded that these 5 architectures are very feasible if applied to help early diagnosis of cervical cancer. © 2023 IEEE.

关键词： Diseases

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Performance Comparison of dSPACE and MCU MPC Implementation in PV Power Optimizer

Performance Comparison of dSPACE and MCU MPC Implementation ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Metry, Morcos Guler, Naki Rohouma, Wesam Balog, Robert S. Texas A&m University Department of Electrical and Computer Engineering Doha Qatar Gazi University Department of Electrical-Electronic Engineering Technology Faculty Ankara Turkey University of Doha for Science and Technology Department of Electrical Engineering Doha Qatar Texas A&m University Department of Electrical and Computer Engineering College StationTX United States

ISBN: (纸本)9798350316445

Finite control set model predictive control (MPC) can be computationally intensive especially when scaled up for more complicated converters since each state variable in the system is evaluated for all possible switching configurations. Multi-objective optimization of the cost function adds to the complexity of MPC. Much of the MPC literature in power electronics relies on expensive rapid prototyping (RP) platforms (RPP) such as dSPACE and OPAL RT. This paper aims to bridge the gap between expensive RPP and low-cost MCUs by exploring the feasibility of rapid prototyping (RP) on the low-cost F28379D launchpad. This paper is aimed toward the practitioner or researcher who desires to reduce to practice the MPC methods developed on a RPP to a lower-cost embedded controller. The suitability of a low-cost Texas Instrument (TI) launchpad to perform the same rapid-prototyping functions needed for MPC regulator development in a full-featured, high-end RPP is discussed. Experimental results using the dSPACE 1006 and the F28379D Launchpad showing steady-state performance and dynamic response are illustrated and compared. Comparative results show the F28379D launchpad and dSPACE 1006 offer similar performance markers. © 2023 IEEE.

关键词： Low-cost controller Model Predictive Control Power Electronics rapid prototyping platforms

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Camera-based Recovery of Cardiovascular Signals from Unconstrained Face Videos using an Attention Network

Camera-based Recovery of Cardiovascular Signals from Unconst...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Deshpande, Yogesh Thapa, Surendrabikram Sarkar, Abhijit Abbott, A. Lynn Virginia Tech Bradley Department of Electrical and Computer Engineering United States Virginia Tech Department of Computer Science United States Virginia Tech Transportation Institute United States

ISBN: (纸本)9798350302493

This paper addresses the problem of recovering the shape morphology of blood volume pulse (BVP) information from a video of a person's face. Video-based remote plethysmography methods have shown promising results in estimating vital signs such as heart rate and breathing rate. However, recovering the instantaneous pulse rate signals is still a challenge for the community. This is due to the fact that most of the previous methods concentrate on capturing the temporal average of the cardiovascular signals. In contrast, we present an approach in which BVP signals are extracted with a focus on the recovery of the signal shape morphology as a generalized form for the computation of physiological metrics. We also place emphasis on allowing natural movements by the subject. Furthermore, our system is capable of extracting individual BVP instances with sufficient signal detail to facilitate candidate re-identification. These improvements have resulted in part from the incorporation of a robust skin-detection module into the overall imaging-based photoplethysmography (iPPG) framework. We present extensive experimental results using the challenging UBFC-Phys dataset and the well-known COHFACE dataset. The source code is available at https://***/yogeshd21/CVPM-2023-iPPG-Paper. © 2023 IEEE.

关键词： Recovery

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Integrating Temporal Analysis with Hybrid Machine Learning and Deep Learning Models for Enhanced Air Quality Prediction 19

Integrating Temporal Analysis with Hybrid Machine Learning a...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Omer, Muhammad Ali, Sardar Jaffar Raza, Syed M. Le, Duc-Tai Choo, Hyunseung Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of James Watt School of Engineering University of Glasgow Glasgow United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

Air pollution remains a critical issue, adversely affecting public health and the environment. In this study, we utilize the Air Quality index dataset from Kaggle to analyze temporal and seasonal variations of key pollutants, specifically Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Benzene (C6H6 ). Building upon this analysis, we predict Absolute Humidity (AH), a vital meteorological factor influencing pollutant dispersion, using Machine Learning (ML) and Deep Learning (DL) techniques. Three ML techniques, Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR), and three DL techniques, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are employed for predicting AH. The results indicate that while the RF model achieved the lowest Mean Absolute Error (MAE) among ML methods (0.02), the CNN model, despite having an MAE of 0.04, demonstrated statistical superiority through paired t-tests and Wilcoxon signed-rank tests (p © 2025 IEEE.

关键词： Convolutional neural networks

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Deep Learning for Predictive Maintenance in Power Systems 2

Deep Learning for Predictive Maintenance in Power Systems

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Thingom, Chintureena Kumar, N.M.G. Gogula, Sreenivasulu Mukherjee, Aparajita Bhosale, Supriya Sarojwal, Atul Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Karnataka Bengaluru562106 India Mohan Babu University Sree Vidyanikethan Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh 517102 India Telangana Hyderabad501218 India Iem Newtown Campus Department of Computer Science and Engineering West Bengal New Town700160 India Sandip University Department of Computer Science and Engineering Maharashtra Nashik422213 India Mahatma Jyotiba Phule Rohilkhand University Department of Electrical Engineering Uttar Pradesh Bareilly243006 India

ISBN: (纸本)9798350364699

The operation of deep learning ways for prophetic conservation in power systems, aiming to enhance trustability, effectiveness, and cost-effectiveness in conservation operations. Power systems, pivotal for icing nonstop electricity force, face challenges similar to growing structure, outfit declination, and unanticipated failures. Traditional conservation strategies frequently calculate on periodic examinations and preventative measures, which may be expensive and hamstrung. Deep learning offers a data-driven approach to prophetic conservation by using literal data from detectors and functional parameters. Convolutional Neural Networks( CNNs) and intermittent Neural Networks( RNNs) are employed to dissect time-series data and describe patterns reflective of implicit outfit failures or declination. By prognosticating conservation requirements in advance, serviceability can record interventions more effectively, reduce time-out, and optimize resource allocation. Through case studies and simulations, this exploration demonstrates the efficacity of deep learning models in perfecting prophetic conservation delicacy and trustability in power systems. The integration of AI-driven prophetic conservation holds a pledge for transubstantiating conservation practices, enhancing grid adaptability, and icing sustainable energy forces for unborn demands. © 2024 IEEE.

关键词： Predictive maintenance

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