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Light People:*** May Lau,newly elected US NAE member in Hong Kong,talks about future of photonics and women in science

Light(science & Applications) 2024年第4期13卷 602-608页

作者： Yating Wan Chenzi Guo Electrical and Computer Engineering the ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology23955-6900 ThuwalSaudi Arabia Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of SciencesChangchunChina

Editorial Photonics technology remains a driving force in today’s scientific landscape,marked by continuous innovation and crossdisciplinary *** an enlighting conversation with Light:science&Applications,*** May Lau,a pioneer in photonics research,shares her deep insights on the evolution of technologies of LEDs,lasers,challenges of hetero-epitaxy,and the future of micro-LEDs and quantum dot *** honored as a member of the US National Academy of Engineering(NAE)for her significant contributions to photonics and electronics using III-V semiconductors on silicon,*** stands out as the sole Hong Kong scholar inducted into the NAE this year,joining 114 new and 21 international *** this exclusive Light People interview,*** shares her journey as a pioneering woman in engineering,her commitment to mentorship and academia,and her perspective on advancing female representation in *** summary provided is distilled from ***’s thoughtful responses during the *** a deeper exploration of ***’s experiences and advice,the full interview is available in the Supplementary material.

关键词： interview lighting driving

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Light People:*** Dai,*** Lo,and *** Su-innovators in silicon photonics

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Light(science & Applications) 2024年第12期13卷 2880-2885页

In this edition of Light People,we are excited to feature *** Dai(Zhejiang University),*** Su(Shanghai Jiao Tong University),and *** Lo(Advanced Micro Foundry Pte Ltd,Singapore),three prominent researchers shaping the future of silicon *** collaborative work addresses critical issues in silicon photonics,including reducing propagation losses,enlarging the functionalities and enhancing building blocks,integrating efficient laser sources,expanding applications,and pushing the boundaries of optical and electronic *** this interview,we delve into their academic journeys,challenges,and future visions,offering insights into the ongoing evolution of silicon photonics and its potential to transform *** a deeper exploration of their experiences and advice,the full interview is available in the Supplementary material.

关键词： interview offering photonics

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SCADBIN: A Blockchain-based Security Framework for Electronic Health Record Management 14

SCADBIN: A Blockchain-based Security Framework for Electroni...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Fasila, K.A. Mathew, Sheena Division of Computer Engineering School of Engineering Cochin University of Science and Technology Kerala India Computer Science and Engineering Muthoot Institute of Technology and Science Kerala India

Tremendous increase in the use of distributed Internet of Things (IoT) based applications leads to the evolution of huge amount of data every second. As the volume of data and correspondingly the number of users increase, the need for protecting information also increases. Among various distributed IoT applications, healthcare industry is in the dominating position. The management of Electronic Health Record (EHR) is a serious matter of concern since this includes the patient’s personal as well as health related information. This work proposes the use of an efficient security framework for EHR management. SCADBIN stands for Security using Cellular Automata, Dynamic key and Blockchain for IoT Networks. This architecture provides multilevel protection to EHRs starting from the data acquisition phase till the data access phase by a requesting node. Thus, SCADBIN assures end to end protection to EHRs. The paper explains the methodology and implementation details. The comparison of this architecture with existing methods is provided at the end with valid experimental results. © Grenze Scientific Society, 2023.

关键词： Internet of things

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Can blockchain link the future?

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Digital Communications and Networks 2022年第5期8卷 687-694页

作者： Liang Qiao Shuping Dang Basem Shihada Mohamed-Slim Alouini Robert Nowak Zhihan Lv College of Computer Science&Technology Qingdao UniversityQingdao266071China Computer Electrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal23955-6900Saudi Arabia Institute of Computer Science Warsaw University of TechnologyNowowiejska 15/1900-665WarsawPoland

Recently,decentralization has been extensively explored by ***,as a representation of decentralized technology,has attracted attention with its unique characteristics,such as irrevocability and ***,herein,we introduce cutting-edge blockchain technologies from four directions:blockchain system,consensus algorithms,smart contract,and ***,we analyze the current lack of consensus mechanism,fault tolerance,and block capacity of the blockchain,and the integration of blockchain into 5G/6G mobile ***,we discuss the possible applications of blockchain in intellectual property protection,the Internet of Things,digital twins,standardization,and epidemic prevention and ***,explore the impacts and solutions of blockchain on human society beyond technology.

关键词： Blockchain Consensus algorithm Scalability Smart contract Internet of things Digital twins

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An intelligent impulsive noise mitigation with deep learning method

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International Journal of Mechanical System Dynamics 2024年第3期4卷 346-360页

作者： Guo Yang Yuwen Qian Zikun Wang Xiangwei Zhou Wen Wu The School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjingChina The Division of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLouisianaUSA

To enable message transmission among sensors and equipment,power line communication(PLC)is a widely adopted smart ***,due to the occurrence of impulsive noise(IN),reliable transmissions over PLC channels in the smart grid are ***,in this paper,we propose an adaptive noise mitigation scheme to clip the IN with the sliding window-based method,where the altitude of the received signal in the current time slots is obtained by computing the average altitude of signals in the previous and next time *** detect the states of IN and dynamically estimate the power threshold of signals for the IN mitigation scheme,we develop an intelligent algorithm based on the long short-term memory *** prevent the useful signals from being eliminated as IN signals,we propose the accelerated proximal gradient method(APGM)based on tone reservation to reduce the peak-to-average power ratio(PAPR)for the transmitting signals with low computational *** addition,the closed-form expression of the bit error rate(BER)is derived for the proposed sliding window-based IN mitigation scheme according to the probability density function of the *** results demonstrate that the proposed IN mitigation scheme achieves a better BER performance than the conventional IN mitigation *** addition,the APGM aided by IN mitigation can further improve BER performance due to the PAPR reduction.

关键词： impulsive noise deep learning smart grid

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Design,simulation,and testing of a tunable MEMS multi-threshold inertial switch

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Microsystems & Nanoengineering 2024年第2期10卷 37-46页

作者： Qiu Xu Rodrigo T.Rocha Yousef Algoos Eric Feron Mohammad I.Younis Faculty of Electronic Information Engineering Huaiyin Institute of TechnologyHuai’an223003PR China Computer Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal23955-6900Saudi Arabia Piezoelectric Microsystem Technologies Silicon Austria LabsVillach9524Austria Physical Sciences and Engineering Division King Abdullah University of Science and TechnologyThuwal23955Saudi Arabia Mechanical Engineering Department State University of New YorkBinghamtonNY13902USA

This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold *** demonstrated device combines the advantages of accelerometers in providing quantitative acceleration measurements and g-threshold switches in saving power when in the inactive state upon experiencing acceleration below the *** designed proof-of-concept device with two thresholds consists of a cantilever microbeam and two stationary electrodes placed at different positions in the sensing *** adjustable threshold capability and the effect of the shock duration on the threshold acceleration are analytically investigated using a nonlinear beam *** are shown for the relationships among the applied bias voltage,the duration of shock impact,and the tunable *** fabricated prototypes are tested using a shock-table *** analytical results agree with the experimental *** designed device concept is very promising for the classification of the shock and impact loads in transportation and healthcare applications.

关键词： tunable acceleration testing

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Facial Expressions and EEG Signals for Emotion Recognition Using Deep2 Face Algorithm 10

Facial Expressions and EEG Signals for Emotion Recognition U...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Swetha, S. Babu, Nirmal Varghese Karunya Institute of Technology and Science Division of Computer Science and Engineering Karunya Nagar Tamil Nadu Coimbatore India Karunya Institute of Technology and Science Division of Data Science and Cyber Security Karunya Nagar Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

Emotions, the essence of human connection and understanding, take center stage in this pioneering research on emotion recognition. We look at identifying common emotions including joy, sorrow, neutral, anger, fear, and surprise. Introducing an innovative approach, we seamlessly blend advanced computer vision algorithms and intricate signal processing techniques to concurrently analyze facial expressions and EEG signals. The heart of our methodology lies in the utilization of the state-of- the-art Deep Face Analyzer, guided by the deep2face algorithm for meticulous interpretation of facial features, coupled with a spectrographic analyzer translating EEG signals into spectrograms. The overarching goal is to enhance the accuracy and resilience of emotion recognition, with the added dimension of a real-time Waveform Extractor for dynamic evaluation of audio signals. Within the landscape of diverse algorithms and modalities, the Hybrid Model (CNN + LSTM) emerges as a standout performer, achieving an impressive 88.2% accuracy by synergizing facial expressions and EEG signals. The strategic integration of long short-term memory (LSTM) networks with convolutional neural networks (CNNs) underscores the model's exceptional capacity to capture the nuanced intricacies of human emotions. Beyond mere technological advancement, this study underscores the importance of embracing multiple modalities for a holistic understanding of emotions, positioning the Hybrid Model as a powerful and nuanced tool for precise and multiclass emotion identification across diverse real-world scenarios. In essence, our research charts a path where the realm of emotions converges with technological innovation, fostering a deeper understanding of the human experience © 2024 IEEE.

关键词： Emotion Recognition

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Optimal Stopping Theory Based Online Node Selection in IoT Networks for Multi-Parameter Federated Learning

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 659-676页

作者： Dogan-Tusha, Seda Bouanani, Faissal El Qaraqe, Marwa Hamad Bin Khalifa University Qatar Foundation Division of Information and Computing Technology College of Science and Engineering Doha Qatar Mohammed V University Department of Computer Science Rabat10000 Morocco

Federated Learning (FL) has attracted the interest of researchers since it hinders inefficient resource utilization by developing a global learning model based on local model parameters (LMP). This study introduces a novel optimal stopping theory (OST) based online node selection scheme for low complex and multi-parameter FL procedure in IoT networks. Global model accuracy (GMA) in FL depends on the accuracy of the LMP received by the central entity (CE). It is therefore essential to choose trusty nodes to guarantee a certain level of global model accuracy without inducing additional system complexity. For this reason, the proposed technique in this study utilizes the secretary problem (SP) approach as an OST to perform node selection considering both received signal strength (RSS) and local model accuracy (LMA) of available nodes. By leveraging the SP, the proposed technique employs a stopping rule that maximizes the probability of selecting the node with the best quality, and thereby avoids testing all candidate nodes. To this end, this work provides a mathematical framework for maximizing the selection probability of the best node amongst candidate nodes. Specifically, the developed framework has been used to calculate the weighting coefficients of the RSS and LMA to define the node quality. Comprehensive analysis and simulation results illustrate that the OST based proposed technique outperforms state-of-the-art methods including the random node selection and the offline node selection (exhaustive search methods) in terms of GMA and computational complexity, respectively. © 2023 CCBY.

关键词： Federated learning

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Weed detection in precision agriculture: leveraging encoder-decoder models for semantic segmentation

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3547-3561页

作者： Thiagarajan, Shreya Vijayalakshmi, A. Grace, G. Hannah Shell India Markets Private Limited Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Division of Mathematics School of Advanced Sciences Vellore Institute of Technology Chennai India

Precision agriculture uses data gathered from various sources to improve agricultural yields and the effectiveness of crop management techniques like fertiliser application, irrigation management, and pesticide application. Reduced usage of agrochemicals is a key step towards more sustainable agriculture. Weed management robots which can perform tasks like selective sprinkling or mechanical weed elimination, contribute to this objective. A trustworthy crop/weed classification system that can accurately recognise and classify crops and weeds is required for these robots to function. In this paper, we explore various deep learning models for achieving reliable segmentation results in less training time. We classify every pixel of the images into different categories using semantic segmentation models. The models are based on an encoder-decoder architecture, where feature maps are extracted during encoding and spatial information is recovered during decoding. We examine the segmentation output on a beans dataset containing different weeds, which were collected under highly distinct environmental conditions, including cloudy, rainy, dawn, evening, full sun, and shadow. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

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