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13th Mediterranean Conference on Medical and Biological engineering and Computing 2013, MEDICON 2013

作者： Ripka, W.L. Ulbricht, Leandra Neves, E.B. Gewehr, P.M. Federal University of Technology-Paraná Graduate Program in Electrical Engineering and Computer Science Curitiba Brazil Federal University of Technology-Paraná Graduate Program in Biomedical Engineering Curitiba Brazil

ISBN: (纸本)9783319008455

The aim of this study was to test the correlation between a two dimensional (2D) and three dimensional (3D) biomechanical photogrammetric model, with inspiratory and expiratory lung capacities in adolescents. This was a cross sectional study, in which 40 adolescents of both genders were assessed, aged between 14 and 17 years old. Anthropometric data were collected (body mass, height and thorax length). Then, three tests were performed for forced vital capacity (FVC) in the supine position, using a spirometer. For FVC proof assessments, the volunteers were subjected to a mapping of body structures with adhesive markers and, after the test, one was selected with better respiratory performance for computational analysis of photogrammetric thoracoabdominal mobility. The results showed average values of 15.40, 61.29, 1.66 and 25.54, for age (years), body mass (kg), height (m) and thorax length (cm), respectively. The analysis found respiratory values of: 3:22 l for forced expiratory volume in one second, 3.87 l for forced vital capacity, 6.50 l/s for peak expiratory flow and 3.59 l for deep inspiration. The correlation showed better values for 3D photogrammetry as compared to 2D. The inclusion of the third dimension has been applied in order to adequate photogrammetric methodology to existing kinematics analysis tools as videogrammetry computed and plethysmography. © Springer international Publishing Switzerland 2014.

关键词： Photogrammetry

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Power efficient error control for bluetooth-based sensor networks

Power efficient error control for bluetooth-based sensor net...

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IEEE Conference on Local computer Networks - 30th Anniversary, LCN 2005

作者： Kleinschmidt, João H. Borelli, Walter C. Pellenz, Marcelo E. School of Electrical and Computer Engineering State University of Campinas - UNICAMP Graduate Program in Computer Science Pontifical Catholic University of Paraná - PUCPR

ISBN: (纸本)0769524214

This paper- studies different error control schemes in wireless sensor networks with Bluetooth technology. The tradeoff between reliability and energy consumption of Bluetooth packets are analyzed, using different error control techniques, such as retransmission and channel coding The AUX1 packet is utilized for custom coding and also are proposed adaptive techniques based on the number of hops of the network. The wireless channel is modeled with Rayleigh fading. The results obtained may be used as references to determine the packet type in a sensor application. © 2005 IEEE.

关键词： Wireless telecommunication systems

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Carbon nanostructure field emission devices

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Journal of Vacuum science and Technology B: Microelectronics and Nanometer Structures 2006年第2期24卷 1008-1012页

作者： Wong, Y.M. Kang, W.P. Davidson, J.L. Soh, K.L. Choi, B.K. Hofmeister, W. Department of Electrical Engineering and Computer Science Vanderbilt University Nashville TN 37235 Interdisciplinary Graduate Program in Material Science Vanderbilt University Nashville TN 37235 Department of Chemical Engineering Vanderbilt University Nashville TN 37235

The microwave plasma chemical vapor deposition (MPCVD) method is capable of synthesizing various forms of carbon allotropes such as diamond, carbon nanotubes (CNTs), and diamondlike carbon (DLC) while varying the growth parameters or utilizing substrate coated with catalytic thin film. In this article, needlelike carbon nanostructures (CNs) were synthesized utilizing high power (5 kW) and high working pressure (120 Torr) MPCVD on a thin catalytic film of nickel with a diffusion barrier layer of titanium on a micropatterned silicon substrate. The effects of in situ doping by trimethylboron (TMB) on the morphologies and field emission properties of the CNs were also investigated. Straight, needlelike CNs with random orientation were obtained for 15 min growth time with or without TMB doping. However, the CNs appear to transform into diamondlike nanocrystals after prolonged growth of 30 min with TMB doping. Unlike MPCVD grown CNTs, it was found that the CNs have strong adhesion to the substrate and could withstand postgrowth processing such as ultrasonic agitation cleaning in solvents. Field emission tests performed show that in situ TMB doping of the patterned needlelike CNs lowers the turn-on field of the cathode. A vacuum field emission triode with self-aligned gate utilizing CNs as the field emitter is also fabricated. © 2006 American Vacuum Society.

关键词： Carbon nanotubes

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Size-dependent infiltration and optical detection of nucleic acids in nanoscale pores

Size-dependent infiltration and optical detection of nucleic...

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作者： Lawrie, Jenifer L. Jiao, Yang Weiss, Sharon M. Department of Electrical Engineering and Computer Science Interdisciplinary Graduate Program in Materials Science Vanderbilt University Nashville TN 37235 United States

Experiments and complimentary simulations are presented to demonstrate the size-dependent infiltration and detection of variable length nucleic acids in porous silicon with controllable pore diameters in the range of 15-60 nm. The pore diameter must be tuned according to target molecule size in order to most effectively balance sensitivity and size-exclusion. A quantitative relationship between pore size (15-60 nm), nucleic acid length (up to ~5.3 nm), and sensor response is presented with smallermolecules detected more sensitively in smaller pores as long as the pore diameter is sufficient to enable molecular infiltration and binding in the pores. The density of probe molecules on the pore walls and subsequent hybridization efficiency for targetmolecule binding are also reported and are shown to depend strongly on the method of infiltration as well as the target molecule size.© 2010 IEEE.

关键词： Porous silicon

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SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

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IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 5122-5132页

作者： Huang, Shih-Chia Hoang, Quoc-Viet Le, Trung-Hieu National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology International Graduate Program in Electrical Engineering and Computer Science Taipei10608 Taiwan Hung Yen University of Technology and Education Faculty of Information Technology Hungyen 17000 Viet Nam

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed. © 2012 IEEE.

关键词： Feature extraction

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On Robust Cross-view Consistency in Self-supervised Monocular Depth Estimation

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Machine Intelligence Research 2024年第3期21卷 495-513页

作者： Haimei Zhao Jing Zhang Zhuo Chen Bo Yuan Dacheng Tao School of Computer Science University of SydneySydney2008Australia Shenzhen International Graduate School Tsinghua UniversityShenzhen518055China School of Information Technology&Electrical Engineering University of QueenslandBrisbane4072Australia

Remarkable progress has been made in self-supervised monocular depth estimation (SS-MDE) by exploring cross-view consistency, e.g., photometric consistency and 3D point cloud consistency. However, they are very vulnerable to illumination variance, occlusions, texture-less regions, as well as moving objects, making them not robust enough to deal with various scenes. To address this challenge, we study two kinds of robust cross-view consistency in this paper. Firstly, the spatial offset field between adjacent frames is obtained by reconstructing the reference frame from its neighbors via deformable alignment, which is used to align the temporal depth features via a depth feature alignment (DFA) loss. Secondly, the 3D point clouds of each reference frame and its nearby frames are calculated and transformed into voxel space, where the point density in each voxel is calculated and aligned via a voxel density alignment (VDA) loss. In this way, we exploit the temporal coherence in both depth feature space and 3D voxel space for SS-MDE, shifting the “point-to-point” alignment paradigm to the “region-to-region” one. Compared with the photometric consistency loss as well as the rigid point cloud alignment loss, the proposed DFA and VDA losses are more robust owing to the strong representation power of deep features as well as the high tolerance of voxel density to the aforementioned challenges. Experimental results on several outdoor benchmarks show that our method outperforms current state-of-the-art techniques. Extensive ablation study and analysis validate the effectiveness of the proposed losses, especially in challenging scenes. The code and models are available at https://***/sunnyHelen/RCVC-depth.

关键词： 3D vision depth estimation cross-view consistency self-supervised learning monocular perception

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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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Hybrid Beam Forming with Data Logging Scheduling(DLS) for wireless sensor network(WSN)

Hybrid Beam Forming with Data Logging Scheduling(DLS) for wi...

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2017 IEEE international Conference on Applied System Innovation, ICASI 2017

作者： Renu, Sakthidasan Yen, Lei Belay, Abebe Lin, Hsin-Piao Jeng, Shiann-Shiun Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taiwan Department of Electronic Engineering National Taipei University of Technology Taiwan Department of Electrical Engineering National Dong Hwa University Taipei Taiwan

ISBN: (纸本)9781509048977

Improve of the overall network efficiency between source and destination relay selection and interference free communication is important criteria in WSN. In this paper we are proposing SON based algorithm capable of simultaneous Scheduling and Routing in WSN. The proposes scheme contains the three stages, in first stage the sensor nodes are classified into different clusters and each nodes having different weight based on distance from base station. In second stage the IRSS (Improvised Received Signal Strength) based scheme used select Routing path based on the Node weight, Residual Energy and Probability of Link Loss values of Relay Sensors. On the other side the channel estimation algorithm executed simultaneously with previous two stages. In final we are implementing the Data Logging based Scheduling and Routing scheme (DLSR) for WSN with Energy and Maximized Throughput. We are applying path recovery algorithm in presence of Void path or node failures. The proposed scheme additionally will form the virtual relay path to make communication with nearly zero loss. © 2017 IEEE.

关键词： Scheduling

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Identifying Materials-Level Sources of Performance Variation in Superconducting Transmon Qubits

arXiv

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arXiv 2025年

作者： Murthy, Akshay A. Bal, Mustafa Bedzyk, Michael J. Cansizoglu, Hilal Chan, Randall K. Chandrasekhar, Venkat Crisa, Francesco Datta, Amlan Deng, Yanpei Matute Diaz, Celeo D. Dravid, Vinayak P. Garcia-Wetten, David A. Garattoni, Sabrina Ghimire, Sunil Goronzy, Dominic P. de Graaf, Sebastian E. Haeuser, Sam Hersam, Mark C. Isheim, Dieter Joshi, Kamal Kim, Richard Kolachina, Saagar Kopas, Cameron J. Kramer, Matthew J. Lachman, Ella O. Lee, Jaeyel Lim, Peter G. Lunin, Andrei Mah, William Marshall, Jayss Mutus, Josh Y. Oh, Jin-Su Pappas, David P. Park, Joong-Mok Prozorov, Ruslan dos Reis, Roberto Seidman, David N. Sung, Zuhawn Tanatar, Makariy Walker, Mitchell J. Wang, Jigang Wu, Haotian Zhou, Lin Zhu, Shaojiang Grassellino, Anna Romanenko, Alexander BataviaIL60510 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Rigetti Computing BerkeleyCA94710 United States Ames National Laboratory AmesIA50011 United States Department of Physics & Astronomy Iowa State University AmesIA50011 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States The NUANCE Center Northwestern University EvanstonIL60208 United States International Institute of Nanotechnology Northwestern University EvanstonIL60208 United States National Physical Laboratory TeddingtonTW11 0LW United Kingdom Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Applied Physics Graduate Program Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Iowa State University AmesIA50011 United States

The Superconducting Materials and Systems (SQMS) Center, a DOE National Quantum Information science Research Center, has conducted a comprehensive and coordinated study using superconducting transmon qubit chips with known performance metrics to identify the underlying materials-level sources of device-to-device performance variation. Following qubit coherence measurements, these qubits of varying base superconducting metals and substrates have been examined with various nondestructive and invasive material characterization techniques at Northwestern University, Ames National Laboratory, and Fermilab as part of a blind study. We find trends in variations of the depth of the etched substrate trench, the thickness of the surface oxide, and the geometry of the sidewall, which when combined, lead to correlations with the T1 lifetime across different devices. In addition, we provide a list of features that varied from device to device, for which the impact on performance requires further studies. Finally, we identify two low-temperature characterization techniques that may potentially serve as proxy tools for qubit measurements. These insights provide materials-oriented solutions to not only reduce performance variations across neighboring devices, but also to engineer and fabricate devices with optimal geometries to achieve performance metrics beyond the state-of-the-art values. © 2025, CC BY.

关键词： Qubits

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