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Thermoelastic damping in MEMS gyroscopes at high frequencies

Microsystems & Nanoengineering 2023年第1期9卷 137-148页

作者： Daniel Schiwietz Eva M.Weig Peter Degenfeld-Schonburg Robert Bosch GmbH Corporate Research71272RenningenGermany Department of Electrical&Computer Engineering Technical University of Munich85748GarchingGermany Munich Center for Quantum Science and Technology(MCQST) 80799MunichGermany TUM Center for Quantum Engineering(ZQE) 85748GarchingGermany

Microelectromechanical systems(MEMS)gyroscopes are widely used,e.g.,in modern automotive and consumer applications,and require signal stability and accuracy in rather harsh environmental *** many use cases,device reliability must be guaranteed under large external loads at high *** sensitivity of the sensor to such external loads depends strongly on the damping,or rather quality factor,of the high-frequency mechanical modes of the *** this paper,we investigate the influence of thermoelastic damping on several high-frequency modes by comparing finite element simulations with measurements of the quality factor in an application-relevant temperature *** measure the quality factors over different temperatures in vacuum,to extract the relevant thermoelastic material parameters of the polycrystalline MEMS *** simulation results show a good agreement with the measured quantities,therefore proving the applicability of our method for predictive purposes in the MEMS design ***,we are able to uniquely identify the thermoelastic effects and show their significance for the damping of the high-frequency modes of an industrial MEMS *** approach is generic and therefore easily applicable to any mechanical structure with many possible applications in nano-and micromechanical systems.

关键词： uniquely generic quantities

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Guest Editorial: Special Issue on Knowledge-Infused Learning for Computational Social Systems

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IEEE Transactions on Computational Social Systems 2024年第3期11卷 3045-3048页

作者： Nguyen, Tu Piuri, Vincenzo Rodrigues, Joel Qi, Lianyong Mumtaz, Shahid Lee, Warren Huang-Chen Kennesaw State University Department of Computer Science MariettaGA30060 United States University of Milan MI Milano20122 Italy Piauí Teresina64049-550 Brazil Qufu Normal University School of Computer Science Shandong273165 China Instituto de Telecomunicações Aveiro3810-164 Portugal National Chung Cheng University Department of Communications Engineering & Electrical Engineering Chiayi County621301 Taiwan

This special issue comprises 12 articles, showcasing the latest advances in computational social systems research.

关键词： Special issues and sections Computational cultural modeling Social factors Learning systems Knowledge based systems Behavioral sciences Economics Social factors

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Silicon Carbide Power Devices: Evolution, Applications, and Future Opportunities

IEEE Electron Devices Magazine

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IEEE Electron Devices Magazine 2025年第4期2卷 30-35页

作者： Mikael Östling School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Kista-Stockholm Sweden

This review discusses the evolution and development of SiC Device technology over the past 30 years and shows opportunities in improving power efficiency by employing SiC devices in power electrical systems applicatio... 详细信息

关键词： Thermal management of electronics Fabrication Power system measurements Silicon carbide Reviews Green products Silicon Power electronics Reliability Material properties

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Quantifying Public Response to COVID-19 Events: Introducing the Community Sentiment and Engagement Index 15

Quantifying Public Response to COVID-19 Events: Introducing ...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Thakur, Nirmalya Patel, Kesha A. Poon, Audrey Cui, Shuqi Azizi, Nazif Shah, Rishika Shah, Riyan South Dakota School of Mines and Technology Department of Electrical Engineering and Computer Science Rapid CitySD57701 United States Emory University Department of Mathematics AtlantaGA30322 United States Emory University Department of Computer Science AtlantaGA30322 United States

ISBN: (纸本)9798331507695

This study introduces the Community Sentiment and Engagement Index (CSEI)., developed to capture nuanced public sentiment and engagement variations on social media, particularly in response to major events related to COVID-19. Constructed with diverse sentiment indicators, CSEI integrates features like engagement, daily post count, compound sentiment, fine-grain sentiments (fear, surprise, joy, sadness, anger, disgust, and neutral), readability, offensiveness, and domain diversity. Each component is systematically weighted through a multi-step Principal Component Analysis (PCA)-based framework, prioritizing features according to their variance contributions across temporal sentiment shifts. This approach dynamically adjusts component importance, enabling CSEI to precisely capture high-sensitivity shifts in public sentiment. CSEI's development showed statistically significant correlations with its constituent features, underscoring internal consistency and sensitivity to specific sentiment dimensions. CSEI's responsiveness was validated using a dataset of 4,510,178 Reddit posts about COVID-19. The analysis focused on 15 major events, including the WHO's declaration of COVID-19 as a pandemic, the first reported cases of COVID-19 across different countries, nationallockdowns, vaccine developments, and crucial public health measures. Cumulative changes in CSEI revealed prominent peaks and valleys aligned with these events, indicating significant patterns in public sentiment across different phases of the pandemic. Pearson correlation analysis further confirmed a statistically significant relationship between CSEI daily fluctuations and these events (p = 0.0428), highlighting CSEI's capacity to infer and interpret the shifts in public sentiment and engagement in response to major events related to COVID-19. © 2025 IEEE.

关键词： Spatio-temporal data

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OFDM with Generalized Cascade Index Modulation for Secure Communications

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China Communications 2024年第10期21卷 210-225页

作者： Hu Zeng Jin Xiaopin Yao Yudong Chen Fangjiong Wan Dehuan Liu Yun College of Information Science and Technology Zhongkai University of Agriculture and EngineeringGuangzhou 510225China College of Information Engineering China Jiliang UniversityHangzhou 310018China Department Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ 07030USA School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Center for Data Science and Artificial Intelligence Guangdong University of FinanceGuangzhou 510521China

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error *** CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital ***, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

关键词： cascade index modulation low-complexity detector maximum likelihood OFDM physical layer security

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BlazePose-Based Action Recognition with Feature Selection Using Stochastic Fractal Search Guided Whale Optimization

BlazePose-Based Action Recognition with Feature Selection Us...

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2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023

作者： Alsawadi, Motasem S. Sandoval-Gastelum, Marcial Danish, Irfan Rio, Miguel University College London Department of Electronic and Electrical Engineering London United Kingdom Laxenburg Austria FAST - National University of Computer and Emerging Sciences Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350347074

The BlazePose, which models human body skeletons as spatiotemporal graphs, has achieved fantastic performance in skeleton-based action identification. A Spatial-Temporal Graph Convolutional Network can then be used to forecast the actions. This architecture performance can be improved by simply replacing the skeleton input data with a different set of joints that provide more information about the activity of interest. On the other hand, existing approaches require the user to manually set the graph's topology and then fix it across all input layers and samples. This research shows how to use Stochastic Fractal Search - Guided Whale Optimization Algorithm in conjunction with the BlazePose skeletal data to construct a novel implementation of this topology for action recognition. We utilized the NTU-RGB+D and the Kinetics datasets as benchmarks in our experiments. © 2023 IEEE.

关键词： Fractals

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Spectrum Transformer: Wideband Spectrum Sensing using Multi-Head Self-Attention 24

Spectrum Transformer: Wideband Spectrum Sensing using Multi-...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Zhang, Weishan Wang, Yue Chen, Xiang Tian, Zhi George Mason University Department of Electrical and Computer Engineering FairfaxVA22030 United States Georgia State University Department of Computer Science AtlantaGA30303 United States

ISBN: (纸本)9781665496261

Data-driven machine learning techniques have been advocated to detect the existence of target signals in complex wireless environments. However, wideband spectrum sensing has to deal with special challenges, including enlarged data dimensionality, insufficient training data, and implicit inter-band dependencies. Facing these issues, conventional deep models unfortunately suffer from high model complexity, inferior sensing accuracy, and notorious over-fitting issues due to ignorance of domain knowledge on spectrum occupancy patterns given limited training data. All these factors lead to ineffective learning model design in the most current literature. To fill this gap, this paper proposes a novel multi-task learning solution for wideband spectrum sensing. Empowered by the multi-head self-attention mechanism, we design an efficient Spectrum Transformer architecture to effectively learn both the inter-band spectrum occupancy correlations and the inner-band spectrum features of the wideband spectrum. Spectrum Transformer outperforms the existing methods based on convolutional neural networks especially in small-data regimes, by achieving higher sensing accuracy with 89% reduction in model complexity. © 2023 IEEE.

关键词： Cognitive radio

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Implementation of Microgrid for Rural Electrification to Cater on Side Load Demand 3

Implementation of Microgrid for Rural Electrification to Cat...

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3rd International Conference on Energy, Power and electrical engineering, EPEE 2023

作者： Singh, Nagendra Mishra, Anuprita Gupta, Manju Dwivedi, Kaustubh Swarnkar, Mamta Trinity College of Engineering & Technology Department of Electrical Engineering Karimnagar India Ies Engineering College Department of Electrical Engineering Bhopal India Oriental Institute of Science and Technology Department of Electrical and Electronics Engineering Bhopal India University Polytechnic Rgpv Department of Electrical Engineering Bhopal India Computer Science & Engineering Manit Bhopal India

ISBN: (纸本)9798350318180

The effects of electricity may be understood in a way that makes the idea of the world developing without it absurd. But even after more than 70 years since our independence, India still has a significant number of rural regions without electricity. Such rural locations are far apart from the development of the populace's fundamental needs. They required affordable and reliable electricity for development in their local areas. Demand-side development of renewable power plants is possible in these regions to meet both the needs of the populace for electricity and those of industry. The design of both a grid-connected and solar-powered stand-alone system is covered in this article. Such a proposed microgrid system can be used as a stand-alone system or a grid-connected system. This microgrid system is used for house and small industry electrification. For capturing the maximum sunlight radiation, we used maximum power point tracking technology in this article. To connect different types of loads, the suggested microgrid system is created using MATLAB and Simulink. The Simulink model demonstrates how well and efficiently the suggested microgrid system satisfies the locals' load requirements. The proposed microgrid system is environmentally friendly, has a very low operating and maintenance cost, and is a reliable system that can be used to fulfill on-load demand in rural areas. © 2023 IEEE.

关键词： Grid connected system micro-grid system On side load denad rural electrification Solar operated system

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Video quality enhancement using different enhancement and dehazing techniques

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16607-16635页

作者： Ayoub, Abeer Naeem, Ensherah A. El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. El-Rabaie, El-Sayed M. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Electrical Department Faculty of Technology and Education Suez University Suez43527 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Video in an outdoor environment suffers from the haze problem due to smoke, dust, and other particles in the atmosphere. The quality of video under these atmospheric conditions is clearly/dramatically degraded, leading to poor contrast and information leakage. The proposed algorithm in this paper depends on pre-processing of video frames prior to the dehazing process. Different enhancement techniques are used to remove noise and control the dynamic range, because each frame has some noise due to sensor measurement errors. This noise might be magnified during the haze removal process, if totally neglected. Previous dehazing algorithms enhance the video contrast and reduce frame degradation. We use optimized dehazing algorithms, namely a dual-transmission-map dehazing algorithm, and a recursive Deep Residual Learning (DRL) network, as dehazing tools. We modify the previous dehazing algorithms to generate new algorithms to be suitable not only for visible frames but also for Near Infrared (NIR) frames. In the optimized dehazing algorithms, the haze effect is reduced, according to the hazing model. In the dual-transmission-map dehazing algorithm, the effect of attenuation parameter R and attenuation weight U on the dehazed frames is investigated. A dual-transmission-map algorithm, depending on the Dark Channel Prior (DCP) technique, is studied. We study the effect of a regularization parameter Ω on the visible and NIR dehazed frames without and with enhancement techniques. For the recursive deep residual learning algorithm, we increase the number of iterations in the DRL network from three iterations in the traditional DRL algorithm without denoising techniques to nine iterations, in order to explore the impact of increasing the number of iterations on the output dehazed frames. Since elapsed time grows with the number of iterations, we terminate the operation after the ninth iteration. A comparison between traditional algorithms and the proposed dehazing algorithms is

关键词： Deep learning

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A novel approach to deriving adaboost classifier weights using squared loss function for overlapping speech detection

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Multimedia Tools and Applications 2025年 1-28页

作者： Asbai, Nassim Bounazou, Hadjer Zitouni, Sihem Department of Electronics Faculty of Sciences and Technology Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj City34030 Algeria Speech Com. & Signal Proc. Lab. Faculty of Electrical Engineering USTHB Bab Ezzouar City16111 Algeria Department of Computer Science Faculty of Mathematics and Computer Science Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj City34030 Algeria

In this paper, we address the complex problem of detecting overlapping speech segments, a key challenge in speech processing with applications in speaker diarization, automatic transcription, and multi-speaker recognition systems. Traditional approaches, often relying on exponential loss functions within the AdaBoost framework, struggle to maintain robustness in noisy or imbalanced data environments. To enhance detection accuracy, we propose a novel AdaBoost variant—SLFAdaBoost—utilizing a squared loss function specifically tailored for overlapping speech detection. This model demonstrates superior stability and convergence, significantly outperforming conventional methods. Experimental evaluation on the NIST 2005 corpus revealed that SLFAdaBoost achieves an accuracy range of 92.5% to 98.6%, with F1 scores between 95.4% and 99.2%, notably surpassing standard AdaBoost and Random Forest classifiers. Additionally, our model shows resilience in noisy conditions, maintaining precision and recall metrics at higher noise levels than other classifiers. These results underscore SLFAdaBoost's capacity to handle intricate data patterns, offering a more robust solution for real-world speech processing applications where overlapping segments are prevalent. This contribution provides an efficient, high-performing model, advancing the capabilities of ensemble methods in complex audio environments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Speech recognition

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