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Maximizing the Thermal Performance of Microheaters for Non-Volatile Phase Change Photonics: A Comparative Study of Pulse Width Parameter Effects 23

Maximizing the Thermal Performance of Microheaters for Non-V...

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23rd IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, ITherm 2024

作者： F., Vasquez-Aza H., Sun C., Lian Y.-S., Huang S.A., Vitale I., Takeuchi J., Hu N., Youngblood C.A.R., Ocampo G., Pavlidis University of Connecticut School of Mechanical Aerospace and Manufacturing Engineering StorrsCT06269 United States University of Maryland Department of Materials Science and Engineering College ParkMD20742 United States University of Maryland Institute for Research in Electronics and Applied Physics College ParkMD20742 United States Mit Lincoln Laboratory Advanced Materials and Microsystems Group LexingtonMA02139 United States University of Maryland Quantum Materials Center Department of Physics College ParkMD20742 United States Massachusetts Institute of Technology Department of Materials & Science Engineering CambridgeMA02139 United States The University of Pittsburgh Electrical & Computer Engineering Department PittsburghPA15213 United States

ISBN: (数字)9798350364330

ISBN: (纸本)9798350364330

Guiding, manipulating, and detecting light is integral to modern photonic systems that can be designed for displays, memory, data transfer, and sensors. The prospect of realizing programmable yet zero-static power photonics has recently gained attention thanks to the rise of optical phase change materials (PCMs). PCMs have a unique set of properties that make them desirable for this task: nonvolatile retention and reversible switching between their amorphous and crystalline states, which, in addition, display distinct optical and electronic properties. Nonvolatile photonic devices have primarily relied on either electrical or optical switching of PCMs. Electrical switching offers effortless CMOS integration;however, it is predominantly limited to binary output. In contrast, optical switching enables accurate multilevel control but is limited in scalability. This paper studies an alternative electro-thermal approach to nonvolatile photonics by controlling the PCM phase via doped silicon microheaters. The results highlight the significance of precise spatial and temporal temperature control to ensure optimal modulation of the PCM. Specifically, this paper investigates the impact of the electric pulse width (0.2 μs to 10 μs) on the maximum temperature for two different microheater designs. High resolution (≈410 nm) transient thermoreflectance imaging (TTI) is used to measure and analyze the surface temperature distribution across each microheater under pulsed biasing. To assess the impact of varying pulse widths, this study systematically examines their effects on maximum temperature, response time, and maximum power. Shorter pulse widths ( 1 μs) provide greater thermal stability but exhibit reduced maximum temperatures due to limited current levels. The experimental results demonstrate a nonlinear relationship between the peak temperature and the power supplied at sub-steady state thermalization time scales. For the specific microheater geometry, the optimal pulse wid

关键词： Photonic integration technology

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Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Cyber-Security Knowledge Graph Generation by Hierarchical No...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Ryan Barron Maksim E. Eren Manish Bhattarai Selma Wanna Nicholas Solovyev Kim Rasmussen Boian S. Alexandrov Charles Nicholas Cynthia Matuszek Theoretical Division Los Alamos National Laboratory Los Alamos USA Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Maryland USA Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos USA

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG.

关键词： Semantics Digital forensics Buildings Knowledge graphs Organizations Ontologies Data models

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Wireless,multimodal sensors for continuous measurement of pressure,temperature,and hydration of patients in wheelchair

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npj Flexible Electronics 2023年第1期7卷 479-493页

作者： Seokjoo Cho Hyeonseok Han Hyunwoo Park Sung-Uk Lee Jae-Hwan Kim Sung Woo Jeon Mengqiu Wang Raudel Avila Zhaoqian Xi Kabseok Ko Minsu Park Jungyup Lee Myungwoo Choi Je-Sang Lee Weon Gi Min Byeong-Ju Lee Soyeong Lee Jungrak Choi Jimin Gu Jaeho Park Min Seong Kim Junseong Ahn Osman Gul Chankyu Han Gihun Lee Seunghwan Kim Kyuyoung Kim Jeonghyun Kim Chang-Mo Kang Jahyun Koo Sung Soo Kwak Sungbong Kim Dong Yun Choi Seokwoo Jeon Hyung Jin Sung Yong Bae Park Minkyu Je Young Tae Cho Yong Suk Oh Inkyu Park Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Advanced 3D Printing Technology Development Division Korea Atomic Energy Research InstituteDaejeon 34057Republic of Korea Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian 116023P.R.China Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA DUT-BSU Joint Institute Dalian University of TechnologyDalian 116023China Ningbo Institute of Dalian University of Technology Ningbo 315016China Department of Electronics Engineering Kangwon National UniversityChuncheon 24341Republic of Korea Querrey-Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Sibel Health Inc NilesIL 60714USA Department of Materials Science and Engineering KAIST Institute for The Nanocentury(KINC)Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of planning and development Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of rehabilitation medicine Pusan national university hospitalBusan 49241Republic of Korea Department of AI Convergence Network Ajou UniversitySuwon 16499Republic of Korea Department of Electronic Convergence Engineering Kwangwoon UniversitySeoul 01897Republic of Korea Korea Photonics Technology Institute Gwangju 61007Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02841Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02841Republic of Korea Center

Individuals who are unable to walk independently spend most of the day in a *** population is at high risk for developing pressure injuries caused by ***,early diagnosis and prevention of these injuries still remain ***,we introduce battery-free,wireless,multimodal sensors and a movable system for continuous measurement of pressure,temperature,and hydration at skin *** device design includes a crack-activated pressure sensor with nanoscale encapsulations for enhanced sensitivity,a temperature sensor for measuring skin temperature,and a galvanic skin response sensor for measuring skin hydration *** movable system enables power harvesting,and data communication to multiple wireless devices mounted at skin-cushion interfaces of wheelchair users over full body *** evaluations and numerical simulations of the devices,together with clinical trials for wheelchair patients,demonstrate the feasibility and stability of the sensor system for preventing pressure injuries caused by sitting.

关键词： injuries modal mounted

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Hyperdimensional Computing Versus Convolutional Neural Network: Architecture, Performance Analysis, and Hardware Complexity

Hyperdimensional Computing Versus Convolutional Neural Netwo...

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International Conference on Microelectronics, ICM

作者： Eman Hassan Meriem Bettayeb Baker Mohammad Yahya Zweiri Hani Saleh Electrical Engineering and Computer Science Department System-on-Chip (SoC) Lab Khalifa University Abu Dhabi UAE Department of Aerospace Engineering and Advanced Research and Innovation Center (ARIC) Khalifa University Abu Dhabi UAE

The interest in brain-inspired computing architectures has been growing, particularly in the context of edge devices with constrained resources for executing cognitive functions. One such approach is hyperdimensional computing (HDC), a novel concept that draws inspiration from the large representation of human neuronal activity. HDC has demonstrated effectiveness in one-dimensional tasks, like text identification and activity recognition, offering advantages in power consumption and response time over convolutional neural networks (CNNs). This paper compares HDC and CNN regarding architecture, accuracy, and hardware complexity, explicitly focusing on image classification tasks. Our findings indicate that CNNs generally outperform HDC in two-dimensional tasks but require significantly more computational resources. In contrast, HDC offers adequate results using just 16% of the data needed for training. Additionally, experiments conducted using a Raspberry Pi 4 show that HDC can enhance inference speed and energy efficiency by approximately 2.5 times relative to CNNs.

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Enhancing Performances of Deep Neural Networks with Ensemble Learning Methods for Complex Human Activity Recognition Using Wearable Sensors

Enhancing Performances of Deep Neural Networks with Ensemble...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Sensor-based human activity recognition (HAR) is a widely explored research area focused on identifying human behaviors by analyzing sensor-generated data, particularly one-dimensional time series signals. Such HAR systems have many real-world applications, including elderly health monitoring and athlete performance evaluation. This paper presents Ens-DeepNet, a novel ensemble learning approach designed for recognizing complex human activities using data from wearable sensors. While deep neural networks have proven effective for HAR tasks, their performance can be further improved through ensemble strategies. To this end, we propose an ensemble architecture that combines five distinct deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). These models are integrated using a majority voting scheme to boost classification accuracy. The proposed method is evaluated using the UTwente dataset, which includes sensor data collected from ten individuals via tri-axial accelerometers and gyroscopes. Experimental findings demonstrate that the ensemble model achieves an impressive accuracy of 99.28%, surpassing the performance of each standalone model, whose accuracies range from 92.77%to 97.34%. This performance gain is particularly notable in recognizing complex activities such as eating, drinking coffee, smoking, and conversing.

关键词： Deep learning Performance evaluation Accuracy Time series analysis Bidirectional long short term memory Performance gain Human activity recognition Ensemble learning Older adults Wearable sensors

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Energy Minimization in STAR-RIS Assisted UAV Enabled SWIPT Systems with FHB Protocol

Energy Minimization in STAR-RIS Assisted UAV Enabled SWIPT S...

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IEEE International Conference on Communications (ICC)

作者： Ji Wang Lijuan Qin Wenwu Xie Xingwang Li Shouyin Liu Thippa Reddy Gadekallu Gautam Srivastava Department of Electronics and Information Engineering Central China Normal University Wuhan China School of Information Science and Engineering Hunan Institute of Science and Technology Yueyang China School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo China College of Information Science and Engineering Jiaxing University Jiaxing China Division of Research and Development Lovely Professional University Phagwara India Haiyan County Zhongda Group Jiaxing City Zhejiang Province China Department of Mathematics and Computer Science Brandon University Brandon Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

This paper investigates how to improve the energy efficiency of unmanned aerial vehicle (UAV)-enabled simultane-ous wireless information and power transfer (SWIPT) systems with multiple outdoor energy receivers (ERs) and multiple indoor wired-charging information receivers (IRs) by utilizing simul-taneously transmitting and reflecting reconfigurable intelligence (STAR-RIS), in which the UAV avoids flying over the indoor no-fly zone. Specifically, the total UAV energy consumption is minimized, while ensuring that the energy harvesting require-ment (EHR) of each ER and the communication throughput requirement (CTR) of each IR are met. To achieve this, the total UAV energy consumption is minimized by optimizing the STAR-RIS phase-shifts, the UAV trajectory, and hovering time using an iterative technique based on the fly-hover-broadcast (FHB) protocol. The technique allows the UAV to radiate energy-carrying information signals for the ERs and IRs at a limited number of hover positions. Simulation results demonstrate that the proposed design significantly outperforms other benchmark schemes, demonstrating its potential for improving the energy efficiency of UAV-enabled SWIPT systems while meeting the EHRs of each ER and the CTR of each IR.

关键词： Wireless communication Energy consumption Protocols Simulation Receivers Autonomous aerial vehicles Throughput

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Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

Binary Bleed: Fast Distributed and Parallel Method for Autom...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Ryan Barron Maksim E. Eren Manish Bhattarai Ismael Boureima Cynthia Matuszek Boian S. Alexandrov Theoretical Division Los Alamos National Laboratory Los Alamos USA Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Maryland USA Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos USA

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as nonnegative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter $k$ to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific $k$ . The score is calculated for different trials over a range of $k$ , and the ideal $k$ is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good $k$ value, visiting a range of $k$ can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the $k$ search space for these grid-search ML algorithms by truncating the target $k$ values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal $k$ and the accuracy of the Binary Bleed in identifying the correct $k$ for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub.

关键词： Instruction sets Computational modeling Heuristic algorithms Power system stability Stability analysis Elbow Distributed computing Optimization Overfitting Software development management

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Factors affecting the distribution of microplastics in soils of China

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Frontiers of Environmental science & engineering 2023年第9期17卷 157-176页

作者： Yin Liu Matthias CRillig Quan Liu Jingjing Huang Muhammad Amjad Khan Xiaohui Li Qin Liu Qingqing Wang Xuesong Su Linyi Lin Yang Bai Genmao Guo Yi Huang Yong Sik Ok Shan Hu Junfeng Wang Honggang Ni Qing Huang Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province/Center for Eco-Environmental Restoration Engineering of Hainan Province/State Key Laboratory of Marine Resource Utilization in South China Sea/College of Ecology and Environment Hainan UniversityHaikou 570228China Institute of Biology Ecology of PlantsFreie Universität Berlin/Berlin Brandenburg Institute of Advanced Biodiversity Research(BBIB)Berlin 14195Germany Hainan Inspection and Detection Center for Modern Agriculture Haikou 570100China Institute of Environment and Sustainable Development in Agriculture Chinese Academy of Agricultural SciencesBeijing 100081China China College of Management and Economics Tianjin UniversityTianjin 300072China College of Environmental Sciences and Engineering Peking UniversityBeijing 100871China Korea Biochar Research Center APRU Sustainable Waste Management Program&Division of Environmental Science and Ecological EngineeringKorea UniversitySeoul 02841Republic of Korea School of Urban Planning and Design Peking University Shenzhen Graduate SchoolShenzhen 518055China

Microplastics(MPs)are found worldwide in high abundance,posing a potential threat to *** the ubiquity of MPs in the environment,very little is known about the regional distribution of MPs and underlying factors affecting this distribution in the field,which likely include human activity,but also features of the environment ***,out of a total of 1157 datapoints investigated in 53 Chinese studies,9.68%datapoints were removed as outliers in the heterogeneity *** review revealed that the abundance of MPs was highly *** addition,microplastic(MP)distribution maps based on China demonstrated that the highest abundance of MPs tended to occur near large rivers and central land affected by the intersection of two *** model-fitting and previous studies showed that MP abundance in China was correlated with longitude,agricultural mulching film usage per capita,temperature,and ***,due to the heterogeneity of MPs and the low matching degree between the current environmental data and the sampling points,this pattern was not as evident as reported in any single *** affecting the distribution of MPs can not be captured by linear relationships alone,and systematic selection of suitable environmental factors and further model optimization are needed to explore the cause of MP pollution in ***,this review revealed an uneven distribution of MPs and serves as a reference for model prediction to assess and control plastic pollution in natural soil environments.

关键词： Microplastic distribution Microplastic heterogeneity Effecting factors Agriculture Socio-economic factors

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Enabling technology and core theory of synthetic biology

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science China(Life sciences) 2023年第8期66卷 1742-1785页

作者： Xian-En Zhang Chenli Liu Junbiao Dai Yingjin Yuan Caixia Gao Yan Feng Bian Wu Ping Wei Chun You Xiaowo Wang Tong Si Faculty of Synthetic Biology Shenzhen Institute of Advanced TechnologyShenzhen 518055China Institute of Synthetic Biology Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China National Laboratory of Biomacromolecules Institute of BiophysicsChinese Academy of SciencesBeijing 100101China Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin UniversityTianjin 300072China State Key Laboratory of Plant Cell and Chromosome Engineering Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of Microbial Metabolism Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Microbial Resources Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Tianjin Institute of Industrial Biotechnology Chinese Academy of SciencesTianjin 300308China Ministry of Education Key Laboratory of Bioinformatics Center for Synthetic and Systems BiologyBioinformatics DivisionBeijing National Research Center for Information Science and TechnologyDepartment of AutomationTsinghua UniversityBeijing 100084China

Synthetic biology provides a new paradigm for life science research(“build to learn”)and opens the future journey of biotechnology(“build to use”).Here,we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology,including synthesis and assembly of a genome,DNA storage,gene editing,molecular evolution and de novo design of function proteins,cell and gene circuit engineering,cell-free synthetic biology,artificial intelligence(AI)-aided synthetic biology,as well as *** also introduce the concept of quantitative synthetic biology,which is guiding synthetic biology towards increased accuracy and predictability or the real rational *** conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

关键词： synthetic biology quantitative synthetic biology genome synthesis and assembly DNA storage molecular evolution de novo design computer-aided design cell engineering gene circuit chassis cell artificial intelligent(AI) biofoundry

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Enhancing Performances of Deep Neural Networks with Ensemble Learning Methods for Complex Human Activity Recognition Using Wearable Sensors 10

Enhancing Performances of Deep Neural Networks with Ensemble...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Sensor-based human activity recognition (HAR) is a widely explored research area focused on identifying human behaviors by analyzing sensor-generated data, particularly one-dimensional time series signals. Such HAR systems have many real-world applications, including elderly health monitoring and athlete performance evaluation. This paper presents Ens-DeepNet, a novel ensemble learning approach designed for recognizing complex human activities using data from wearable sensors. While deep neural networks have proven effective for HAR tasks, their performance can be further improved through ensemble strategies. To this end, we propose an ensemble architecture that combines five distinct deep learning models - convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). These models are integrated using a majority voting scheme to boost classification accuracy. The proposed method is evaluated using the UTwente dataset, which includes sensor data collected from ten individuals via tri-axial accelerometers and gyroscopes. Experimental findings demonstrate that the ensemble model achieves an impressive accuracy of 99.28%, surpassing the performance of each standalone model, whose accuracies range from 92.77%to 97.34%. This performance gain is particularly notable in recognizing complex activities such as eating, drinking coffee, smoking, and conversing. © 2025 IEEE.

关键词： Deep neural networks

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