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Compact deep-groove-assisted bends on SiN platform for monolithic integrated laser

Optics Express 2025年第3期33卷 4383-4395页

作者： Zhang, Ziyao Skandalos, Ilias Ma, Rui Chen, Minjia Sun, Bohao Zhong, Chuan Gardes, Frederic Cheng, Qixiang Penty, Richard Centre for Photonic Systems Electrical Engineering Division Department of Engineering University of Cambridge CambridgeCB3 0FA United Kingdom Optoelectronics Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

To build a photonic integration platform that incorporates monolithically integrated light sources which require minimized loss and reflection, a micro-meter scale passive layer is needed in addition to the III-V layer. Low temperature plasma-enhanced chemical vapor deposition (PECVD) silicon nitride (SiN) is a promising candidate, owing to its back-end-of-line (BEOL) integration capabilities, along with its amorphous structure that allows the growth of defect-free thick layers. In view of the III-V layer stack, the optimized SiN waveguides are thick and in a rib formation. However, bends on this thick SiN platform suffer from high radiation losses, resulting in the need for a bending radius as large as 800 µm that poses challenges for compact photonic integrated circuits (PICs). This paper describes and demonstrates a novel SiN bending structure with a deep etched groove along the outer side. This structure significantly reduces the bending radii to 37 µm with a bending loss of 0.1 dB/90°. A compact micro-ring resonator (MRR) with deep etched grooves is also investigated, exhibiting a substantial enhancement in the free spectral range (FSR) compared with the rib waveguide MRRs, while offering a passband of 39.1 GHz. These notable performance improvements pave the way for compact integrated emitters on a single substrate. © 2025 Optica Publishing Group (formerly OSA). All rights reserved.

关键词： Silicon nitride

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Arabic text sentiment analysis and emotion classification using transformers 11

Arabic text sentiment analysis and emotion classification us...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Mossad, Nada Mohamed, Youssef Fares, A. Zaky, Ahmed B. Alexandria21934 Egypt Benha University The Computer Systems Engineering Program Faculty of Engineering at Shoubra The Department of Electrical Engineering Egypt

ISBN: (纸本)9798350369328

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately. © 2023 IEEE.

关键词： Sentiment analysis

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Monitoring of Individual Household electrical Appliances and Prediction of Energy Consumption Using Machine Learning 5

Monitoring of Individual Household Electrical Appliances and...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Selvam, N. Seenivasan, D. Naveen, G. Sudahar, R. Janagan, P. Dhanushmathi, S. M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India M.Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India

ISBN: (纸本)9798350385649

The study suggests a scalable method for estimating residential power usage. To increase the forecasting accuracy, an SVM (support vector machine) based technique combines many machine learning models, such as random forests, decision trees, and linear regression. Real-world data from Dutch families were used in the study, and the findings demonstrate that the suggested strategy performs better than conventional single-model techniques and attains higher accuracy in predicting energy use. The method is suited for usage in real-world applications since it is scalable and applies to huge datasets. An approach for machine learning used in load forecasting is called SVM (Support Vector Machine). Applications for long-term individual household forecasting include calculating advance payments to customers. However, there is a dearth of research on this type of forecasting;most techniques either concentrate on long-term predictions at the aggregate level or on individual family-level short-term projections. In order to close this distance, we provide a technique that uses only a few months' worth of current year's consumption data to anticipate each monthly consumption throughout the next year. Even with little data, utility suppliers may use this technique to predict any customer's consumption for the upcoming year. Data power use in the future may be predicted using linear regression. The linear regression process is used to forecast data in the future. © 2024 IEEE.

关键词： Forecasting

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An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks

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computers, Materials & Continua 2022年第2期70卷 2923-2936页

作者： Abdul Kadir Hamid Fahd N.Al-Wesabi Nadhem Nemri Ammar Zahary Imran Khan Department of Electrical&Computer Engineering University of SharjahSharjahUnited Arab Emirates Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversitySanaaYemen Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Electrical Engineering University of Engineering and TechnologyPeshawarPaki

With the emergence of 5G mobile multimedia services,end users’demand for high-speed,low-latency mobile communication network access is *** them,the device-to-device(D2D)communication is one of the considerable *** D2D communication,the data does not need to be relayed and forwarded by the base station,but under the control of the base station,a direct local link is allowed between two adjacent mobile *** flexible communicationmode reduces the processing bottlenecks and coverage blind spots of the base station,and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless *** of the important factors which affects the quality-of-service(QoS)of D2D communications is co-channel *** order to solve this problem of co-channel interference,this paper proposes a graph coloring based *** main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user *** proposed algorithm also provides simpler power *** heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is *** results show that the proposed algorithm effectively reduced the co-channel interference,power consumption and improved the system throughput as compared with existing algorithms.

关键词： 5G device-to-device communication wireless networks spectrum

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Implantable UWB Microstrip Patch Antenna for Biomedical Applications 5

Implantable UWB Microstrip Patch Antenna for Biomedical Appl...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Ishraq, Sadman Imran, Md Al Alam, Khorshed Khan, Mohammad Monirujjaman North South University Department of Electrical and Computer Engineering Dhaka Bangladesh Lamar University Phillip M. Drayer Department of Electrical Engineering TX United States Lamar University Industrial and Systems Engineering Department TX United States

ISBN: (纸本)9798331530013

To satisfy the requirements of UWB (3.1-10.6 GHz), a novel implanted ultra-wideband (UWB) antenna with a frequency range of 3.07-13.7 GHz has been developed, targeting biomedical applications. The antenna's performance was analyzed at 4 GHz, 6 GHz, 8 GHz, and 10 GHz in both in-body and free-space environments to ensure optimal functionality. The primary focus of the design is to create a compact, cost-effective, and high-performance antenna tailored for medical and healthcare applications. The radiating element consists of a rectangular patch fabricated on an FR-4 substrate, with total dimensions of 30\times 27\times 1.6\ \textmm^3. A partial ground plane is incorporated to achieve ultra-wideband characteristics, enabling a 10.63 GHz bandwidth that spans frequencies from 3.07 GHz to 13.7 GHz in open space. To evaluate the antenna's performance under implantation conditions, three human tissue layers are incorporated into the design. Key features such as radiation characteristics, gain, and directivity validate its suitability for use within the human body. The entire design process, including simulations and performance evaluations, was carried out using the CST Studio Suite software. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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SPD Miscoordination in PV systems Due to Lightning Strikes 16

SPD Miscoordination in PV Systems Due to Lightning Strikes

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16th International Conference on Applied and Theoretical Electricity, ICATE 2024

作者： Baluja, Osmel Perez Leon Avila, Bernardo Yaser Garcia Vazquez, Carlos A. Cotfas, Daniel Tudor Cotfas, Petru Adrian Breffe, Orlys E. Torres Transilvania University of Braşov Faculty of Electrical Engineering and Computer Science Department of Electronics and Computers Brasov Romania Technological University of Havana Department of Electric Power Systems Faculty of Electrical Engineering Havana Cuba

ISBN: (纸本)9798350388107

This study deals with the coordination of surge protection devices (SPD) in photovoltaic systems (PV). A PV farm model was developed in MATLAB Simulink with all the main components, solar panels, converter, grounding system, power transformer, cables in the DC and AC branches with their capacitances, and surge protection device throughout the installation. It was found that when lightning strikes the grounding system or in the vicinity of the PV installation, the coordination among SPD types 1, 2, and 3 is lost. Miscoordination can cause damage to several elements of the PV system, mainly the inverters. Finally, solutions were proposed to avoid SPD miscoordination during lightning strikes. © 2024 IEEE.

关键词： DC transformers

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Robust Authentication: Leveraging Hardware Fingerprints and AI to Enhance Security Against Spoofing

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IEEE Access 2025年 13卷 86912-86926页

作者： Nimmy, K. Jain, Kurunandan Sachin, S.M. Abekaesh, P.A. Venkitasubramaniam, Parv Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri690525 India Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri Campus Kerala Amritapuri690525 India Lehigh University Department of Electrical and Computer Engineering BethlehemPA18015 United States

Fingerprint authentication blends biometric precision with user convenience, leveraging unique ridge patterns for fast, reliable, and tamper-resistant identity verification. However, it remains vulnerable to spoofing attacks, where adversaries create synthetic fingerprints using materials like gelatin. To mitigate this, multi-factor authentication (MFA) enhances security by verifying both the fingerprint and the user’s device, restricting access to authorized hardware and complicating attacks. Although Photo Response Non-Uniformity (PRNU) has been explored for device authentication, its reliability diminishes under varying lighting conditions. To address this, we propose an AI-driven approach to generate robust hardware fingerprints from fingerprint scanners, enabling high-accuracy device identification. Our method, trained on 2,000 fingerprint images from 10 scanners, achieves an impressive 98% accuracy in identifying the source scanner. By integrating fingerprint biometrics, hardware fingerprints, and passwords, our novel MFA protocol significantly enhances security against spoofing and forgery attacks. Through security analysis, we demonstrate its effectiveness in preventing unauthorized access. Finally, we validate our authentication framework using an AI-powered binary classifier model, showcasing its practicality for real-world security applications. © 2013 IEEE.

关键词： Authentication Protocol

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TopoEEG: A Timesformer-Based Topographic Image Representation Method for Single-Trial Early Detection of P300 22

TopoEEG: A Timesformer-Based Topographic Image Representatio...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Hong, Jiazhen Najafizadeh, Laleh Rutgers University Integrated Systems and NeuroImaging Laboratory Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331520526

Detecting P300 event-related potentials (ERPs) from electroencephalography (EEG) finds applications in several domains including brain computer interfaces (BCIs). Due to the low signal-to-noise ratio (SNR), however, detecting P300 from single trials has been challenging. As a result, multiple repetitions are typically needed, extending the BCI decision time. To address this problem, in this paper, we present TopoEEG, a novel method that generates a sequence of topography-based images from EEG, to capture both spatial and temporal information. These sequences are subsequently processed using the state-of-the-art video classification technique, TimeSformer. Evaluated using the BCI Competition II (IIb) dataset, results demonstrate that TopoEEG achieves single-trial P300 detection with F1-scores of 0.6830 and 0.7134 within the first 500 ms and 600 ms, respectively. These results highlight TopoEEG's potential to enhance the efficiency and speed of BCIs by reducing the need for multiple repetitions and the time required for decision-making in single-trial P300 detection. © 2025 IEEE.

关键词： Brain computer Interfaces (BCIs) Electroencephalography (EEG) P300 TimeSformer

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Neurosymbolic Reinforcement Learning and Planning: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 1939-1953页

作者： Acharya, Kamal Raza, Waleed Dourado, Carlos Velasquez, Alvaro Song, Houbing Herbert University of Maryland Department of Information Systems Baltimore County BaltimoreMD21250 United States Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Daytona BeachFL32114 United States University of Colorado Department of Computer Science BoulderCO80309 United States

The area of neurosymbolic artificial intelligence (Neurosymbolic AI) is rapidly developing and has become a popular research topic, encompassing subfields, such as neurosymbolic deep learning and neurosymbolic reinforcement learning (Neurosymbolic RL). Compared with traditional learning methods, Neurosymbolic AI offers significant advantages by simplifying complexity and providing transparency and explainability. Reinforcement learning (RL), a long-standing artificial intelligence (AI) concept that mimics human behavior using rewards and punishment, is a fundamental component of Neurosymbolic RL, a recent integration of the two fields that has yielded promising results. The aim of this article is to contribute to the emerging field of Neurosymbolic RL by conducting a literature survey. Our evaluation focuses on the three components that constitute Neurosymbolic RL: neural, symbolic, and RL. We categorize works based on the role played by the neural and symbolic parts in RL, into three taxonomies: learning for reasoning, reasoning for learning, and learning-reasoning. These categories are further divided into subcategories based on their applications. Furthermore, we analyze the RL components of each research work, including the state space, action space, policy module, and RL algorithm. In addition, we identify research opportunities and challenges in various applications within this dynamic field. © 2023 IEEE.

关键词： Heuristic algorithms

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WIP: Integrating Cybersecurity Education: Implementation of an Undergraduate Course on Malicious Thermal Sensor Defense 54

WIP: Integrating Cybersecurity Education: Implementation of ...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Malek, Amin Patooghy, Ahmad Badawy, Abdel-Hameed California State University Department of Computer and Electrical Engineering BakersfieldCA United States North Carolina A&t State University Department of Computer Systems Technology Greensboro United States New Mexico State University Department of Electrical and Computer Engineering Las CrucesNM United States

ISBN: (纸本)9798350351507

Contribution: This work-in-progress innovative practice paper presents the inception of an undergraduate course focusing on the Ensemble of Countermeasures for Malicious Thermal Sensors Attacks (ECMTA), representing a novel endeavor at this academic level. Rooted in prior research across academic and industrial domains, this ongoing initiative embodies a journey of exploration and discovery in an emerging field, awaiting feedback from students and our industrial advisory board. Additionally, this proposal advocates for a flipped classroom (FC) model, prioritizing pre-class materials for theoretical understanding and in-class sessions for practical application and collaboration. Simultaneously, this project is an ongoing investigation into the effectiveness of FC methodologies for Hispanic/Latino populations in cybersecurity education. Recognizing the current dearth of consensus in the literature regarding this demographic's response to FC, the project aims to address this gap through a comprehensive assessment of Hispanic/Latino students' attitudes and academic outcomes, with findings expected by the end of 2024. Background: The surge in IoT devices revolutionized industries, offering convenience and connectivity. Yet, they pose substantial cybersecurity challenges, notably in thermal sensor vulnerabilities. Attackers' exploitation of these sensors to manipulate the temperatures of IOT devices underscores the urgent necessity for robust cybersecurity in IoT ecosystems. Intended outcome: This course's intended outcome is multifaceted. Students will grasp thermal sensor vulnerabilities and learn varied countermeasures to mitigate risks. Practical skills will be honed through hands-on lab exercises and simulations. Additionally, they'll foster a mindset of continual learning, which is vital for evolving cybersecurity careers. Following this study, we aim to assess the effectiveness of flipped classroom methodologies for Hispanic/Latino populations in cybersecurity educ

关键词： Temperature sensors

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