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Fully Printed Flexible and Reconfigurable Antenna With Novel Phase Change VO2 Ink Based Switch

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Fully Printed Flexible and Reconfigurable Antenna With Novel...

International Flexible Electronics Technology Conference (IFETC)

作者： Mohammad Vaseem Zhen Su Shuai Yang Atif Shamim IMPACT Lab Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Kingdom of Saudi Arabia

ISBN: (纸本)9781538633588

Vanadium dioxide (VO 2 ) is identified as an attractive phase change material which can be used for switchable or reconfigurable RF components. At present, VO 2 is deposited by expensive and complex thin film micro-fabrication techniques. With the surge in low cost, additively manufactured or printed components, it will be beneficial to print phase change materials or switches as well. However, these kinds of functional inks are not available commercially. In this work, we present, for the first time VO 2 based ink that changes its conductive properties based on temperature. Precisely, it displays insulating properties at room temperature (resistance of ~1.2KΩ in the off-state), but becomes conductive when heated around 70°C (resistance of 2 ink and a custom silver-organo-complex (SOC) ink supported on flexible kapton substrate. In a planar inversed F antenna (PIFA) configuration, when the switch is in the OFF state, the antenna operates at 3.5 GHz band for 5G communications, and when it is in the ON state, it operates at 2.4 GHz band suitable for WiFi, Bluetooth or Zigbee applications. The antenna performance is assessed in different bending conditions where it achieved a maximum gain of ~2 dBi at 3.2GHz with concave bending position.

关键词： Antennas Ink Resistance Antenna measurements Vanadium Optical switches

SmartAlert: Machine Learning-Based Patient-Ventilator Asynchrony Detection System in Intensive Care Units

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computer Methods and Programs in Biomedicine 2025年

作者： Jaroslav Pažout Milan Němý Jakub Mikeš Jan Jirman Jan Kubr Eliška Niebauerová Miroslav Macík Michal Pech Michal Štajnrt Jakub Vaněk Petr Waldauf Václav Zvoníček Lenka Vysloužilová Robert Babuška František Duška Department of Anesthesiology and Intensive Care 3rd Faculty of Medicine Charles University and Kralovske Vinohrady University Hospital in Prague Šrobárova 50 100 34 Prague Czech Republic Division of Clinical Geriatrics Center for Alzheimer Research Department of Neurobiology Care Sciences and Society Karolinska Institute Blickagången 16 14183 Huddinge Sweden Department of Cognitive Systems and Neurosciences Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánů 1580/3 160 00 Prague Czech Republic Department of Computer Graphics and Interaction Faculty of Electrical Engineering Czech Technical University Karlovo náměstí 13 121 35 Prague Czech Republic Department of Artificial Intelligence Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánů 1580/3 160 00 Prague Czech Republic Cognitive Robotics Faculty of 3mE Delft University of Technology Mekelweg 2 2628 CD Delft The Netherlands

Background and Objective Patient-ventilator asynchronies (PVA) are associated with ventilator-induced lung injury and increased mortality. Current detection methods rely on static thresholds, extensive preprocessing, or proprietary ventilator data. This study aimed to develop and validate a fully online, real-time system that detects and classifies PVAs directly from ventilator screen data while alerting clinicians based on severity. Methods The SmartAlert system was developed using ventilator screen recordings from ICU patients. It extracts pressure and flow waveforms from video recordings, converts them into time-series data, and employs deep neural networks to classify asynchronies and assign alarm levels from no urgency to most urgent. A dataset of 381,280 double-breath units was independently annotated by two expert intensivists. Two deep learning models were trained: one for alarm prediction and another for asynchrony classification (ineffective triggering, double cycling, high inspiratory effort, no asynchrony). Performance was evaluated using accuracy, sensitivity, specificity, and AUC-ROC, compared to expert consensus. Results SmartAlert demonstrated strong performance for alarm level prediction (overall accuracy: 83.8%, weighted AUC-ROC: 0.943 [95% CI: 0.941–0.945]) and PVA classification (weighted accuracy: 89.3%, weighted AUC-ROC: 0.951 [95% CI: 0.950–0.953]). It showed high specificity for urgent alarms (99.9% for level 3) and PVA types (98.5% for ineffective triggering, 96.9% for double cycling, 94.8% for high inspiratory effort). Conclusions We developed and internally validated SmartAlert, an automated system that detects PVAs, classifies severity, and alerts clinicians in real time. Its potential to reduce alarm fatigue, optimize ventilator settings, and improve patient outcomes remains to be tested in clinical trials.

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Towards Malware Detection via CPU Power Consumption: Data Collection Design and Analytics (Extended Version)

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arXiv 2018年

作者： Bridges, Robert Jiménez, Jarilyn Hernández Nichols, Jeffrey Goseva-Popstojanova, Katerina Prowell, Stacy Computational Science and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Lane Department of Computer Science and Electrical Engineering West Virginia University MorgantownWV26506 United States

This a er resents an ex erimental desi n and data analytics approach aimed at power-based malware detection on general-purpose computers. Leveraging the fact that malware executions must consume power, we explore the postulate that malware can be accurately detected via power data analytics. Our experimental design and implementation allow for programmatic collection of CPU power profiles for fixed tasks during uninfected and infected states using five different rootkits. To characterize the power consumption profiles, we use both simple statistical and novel, sophisticated features. We test a one-class anomaly detection ensemble (that baselines non-infected power profiles) and several kernel-based SVM classifiers (that train on both uninfected and infected profiles) in detecting previously unseen malware and clean profiles. The anomaly detection system exhibits perfect detection when using all features and tasks, with smaller false detection rate than the supervised classifiers. The primary contribution is the proof of concept that baselining power of fixed tasks can provide accurate detection of rootkits. Moreover, our treatment presents engineering hurdles needed for experimentation and allows analysis of each statistical feature individually. This work appears to be the first step towards a viable power-based detection capability for general-purpose computers, and presents next steps toward this goal. Copyright © 2018, The Authors. All rights reserved.

关键词： General purpose computers

Joint shape/texture representation learning for cardiovascular disease diagnosis from magnetic resonance imaging

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European heart journal. Imaging methods and practice 2024年第1期2卷 qyae042页

作者： Xiang Chen Yan Xia Erica Dall'Armellina Nishant Ravikumar Alejandro F Frangi School of Computing University of Leeds Woodhouse LS2 9JT Leeds UK. School of Medicine University of Leeds Woodhouse LS2 9JT Leeds UK. Christabel Pankhurst Institute The University of Manchester Oxford Rd M13 9PL Manchester UK. Department of Computer Science School of Engineering The University of Manchester Oxford Rd M13 9PL Manchester UK. Division of Informatics Imaging and Data Sciences School of Health Sciences The University of Manchester Oxford Rd M13 9PL Manchester UK. NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Oxford Rd M13 9PL Manchester UK. Medical Imaging Research Center (MIRC) University Hospital Gasthuisberg UZ Herestraat 49 - bus 7003 3000 Leuven Belgium. Department of Cardiovascular Sciences KU Leuven UZ Herestraat 49 - box 911 3000 Leuven Belgium. Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 postbus 2440 3001 Leuven Belgium. Alan Turing Institute British Library 96 Euston Rd. NW1 2DB London UK.

Aims:Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Cardiac image and mesh are two primary modalities to present the shape and structure of the heart and have been demonstrated to be efficient in CVD prediction and diagnosis. However, previous research has been generally focussed on a single modality (image or mesh), and few of them have tried to jointly consider the image and mesh representations of heart. To obtain efficient and explainable biomarkers for CVD prediction and diagnosis, it is needed to jointly consider both representations. Methods and results:We design a novel multi-channel variational auto-encoder, mesh-image variational auto-encoder, to learn joint representation of paired mesh and image. After training, the shape-aware image representation (SAIR) can be learned directly from the raw images and applied for further CVD prediction and diagnosis. We demonstrate our method on data from UK Biobank study and two other datasets via extensive experiments. In acute myocardial infarction prediction, SAIR achieves 81.43% accuracy, significantly higher than traditional biomarkers like metadata and clinical indices (left ventricle and right ventricle clinical indices of cardiac function like chamber volume, mass, and ejection fraction). Conclusion:Our mesh-image variational auto-encoder provides a novel approach for 3D cardiac mesh reconstruction from images. The extraction of SAIR is fast and without need of segmentation masks, and its focussing can be visualized in the corresponding cardiac meshes. SAIR archives better performance than traditional biomarkers and can be applied as an efficient supplement to them, which is of significant potential in CVD analysis.

关键词： CVD diagnosis CVD prediction cardiac biomarker mesh reconstruction multi-channel VAE

Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics

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arXiv 2019年

作者： Wang, Jiachen Gao, Riqiang Huo, Yuankai Bao, Shunxing Xiong, Yunxi Antic, Sanja L. Osterman, Travis J. Massion, Pierre P. Landman, Bennett A. Vanderbilt University Computer Science NashvilleTN37235 United States Vanderbilt University Electrical Engineering NashvilleTN37235 United States Department of Medicine Division of Pulmonary and Critical Care Medicine Vanderbilt University Medical Center NashvilleTN37235 United States Department of Biomedical Informatics Vanderbilt University Medical Center NashvilleTN37235 United States

Early detection of lung cancer is essential in reducing mortality. Recent studies have demonstrated the clinical utility of low-dose computed tomography (CT) to detect lung cancer among individuals selected based on very limited clinical information. However, this strategy yields high false positive rates, which can lead to unnecessary and potentially harmful procedures. To address such challenges, we established a pipeline that co-learns from detailed clinical demographics and 3D CT images. Toward this end, we leveraged data from the Consortium for Molecular and Cellular Characterization of Screen-Detected Lesions (MCL), which focuses on early detection of lung cancer. A 3D attention-based deep convolutional neural net (DCNN) is proposed to identify lung cancer from the chest CT scan without prior anatomical location of the suspicious nodule. To improve upon the non-invasive discrimination between benign and malignant, we applied a random forest classifier to a dataset integrating clinical information to imaging data. The results show that the AUC obtained from clinical demographics alone was 0.635 while the attention network alone reached an accuracy of 0.687. In contrast when applying our proposed pipeline integrating clinical and imaging variables, we reached an AUC of 0.787 on the testing dataset. The proposed network both efficiently captures anatomical information for classification and also generates attention maps that explain the features that drive performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Pipelines

Reconstructing compound objects by quantum imaging with higher-order correlation functions

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arXiv 2019年

作者： Mikhalychev, A.B. Bessire, B. Karuseichyk, I.L. Sakovich, A.A. Unternoahrer, M. Lyakhov, D.A. Michels, D.L. Stefanov, A. Mogilevtsev, D. B. I. Stepanov Institute of Physics National Academy of Sciences of Belarus Nezavisimosti Ave. 68 Minsk220072 Belarus University of Bern Nstitute of Applied Physics Sidlerstrasse 5 BernCH-3012 Switzerland Electrical and Mathematical Science and Engineering Division 4700 King Abdullah University of Science and Technology Computer Thuwal 23955-6900 Saudi Arabia

Quantum imaging has a potential of enhancing precision of the object reconstruction by using quantum correlations of the imaging field. This is especially important for imaging requiring low-intensity fields up to the level of few-photons. However, quantum imaging generally leads to nonlinear estimation problems. The complexity of these problems rapidly increases with the number of parameters describing the object. We suggest a way to drastically reduce the complexity for a wide class of problems. The key point of our approach is connecting the features of the Fisher information with the parametric locality of the problem, and building the eficient iterative inference scheme reconstructing only a subset of the whole set of parameters in each step. This iterative scheme is linear on the total number of parameters. This scheme is applied to quantum near-field imaging, the inference procedure is developed resulting in super-resolving reconstruction of grey compound transmission objects. The functionality of the method is demonstrated with experimental data obtained by measurements of higher-order correlation functions for imaging with entangled twin-photons and pseudo-thermal light sources. By analyzing the informational content of the measurement, it becomes possible to predict the existence of optimal photon correlations providing for the best image resolution in the super-resolution regime. This prediction is experimentally confirmed. It is also shown how an estimation bias stemming from image features may drastically improve the resolution. Copyright © 2019, The Authors. All rights reserved.

关键词： Fisher information matrix

Twist angle dependent interlayer exciton lifetimes in van der waals heterostructures

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arXiv 2020年

作者： Choi, Junho Florian, Matthias Steinhoff, Alexander Erben, Daniel Tran, Kha Sun, Liuyang Quan, Jiamin Claassen, Robert Majumder, Somak Hollingsworth, Jennifer A. Taniguchi, Takashi Watanabe, Kenji Ueno, Keiji Singh, Akshay Moody, Galan Jahnke, Frank Li, Xiaoqin Department of Physics and Center for Complex Quantum Systems University of Texas at Austin AustinTX78712 United States Institute for Theoretical Physics University of Bremen Bremen28334 Germany Materials Physics & Applications Division Center for Integrated Nanotechnologies Los Alamos National Laboratory Los AlamosNM87545 United States International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki TsukubaIbaraki305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki TsukubaIbaraki305-0044 Japan Department of Chemistry Graduate School of Science and Engineering Saitama University Saitama338-8570 Japan Department of Physics Indian Institute of Science BengaluruKarnataka560012 India Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA93106 United States

In van der Waals (vdW) heterostructures formed by stacking two monolayers of transition metal dichalcogenides, multiple exciton resonances with highly tunable properties are formed and subject to both vertical and lateral confinement. We investigate how a unique control knob, the twist angle between the two monolayers, can be used to control the exciton dynamics. We observe that the interlayer exciton lifetimes in MoSe2/WSe2 twisted bilayers (TBLs) change by one order of magnitude when the twist angle is varied from 1◦ to 3.5◦. Using a low-energy continuum model, we theoretically separate two leading mechanisms that influence interlayer exciton radiative lifetimes. The shift to indirect transitions in the momentum space with an increasing twist angle and the energy modulation from the moiré potential both have a significant impact on interlayer exciton lifetimes. We further predict distinct temperature dependence of interlayer exciton lifetimes in TBLs with different twist angles, which is partially validated by experiments. While many recent studies have highlighted how the twist angle in a vdW TBL can be used to engineer the ground states and quantum phases due to many-body interaction, our studies explore its role in controlling the dynamics of optically excited states, thus, expanding the conceptual applications of "twistronics". Copyright © 2020, The Authors. All rights reserved.

关键词： Van der Waals forces

Repair and resource scheduling in unbalanced distribution systems using neighborhood search

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arXiv 2018年

作者： Arif, Anmar Wang, Zhaoyu Chen, Chen Wang, Jianhui Department of Electrical and Computer Engineering Iowa State University AmesIA50011 United States Energy Systems Division Argonne National Laboratory LemontIL60439 United States Department of Electrical Engineering Southern Methodist University DallasTX75205 United States

This paper proposes an optimization strategy to assist utility operators to recover power distribution systems after large outages. Specifically, a mixed-integer linear programming (MILP) model is developed for co-optimizing crews, resources, and network operations. The MILP model coordinates damage isolation, network reconfiguration, distributed generator re-dispatch, and crew/resource logistics. In addition, a framework for integrating different types of photovoltaic (PV) systems in the restoration process is developed. We consider two different types of crews, namely, line crews for damage repair and tree crews for obstacle removal. We also model the repair resource logistic constraints. Furthermore, a new algorithm is developed for solving the distribution system repair and restoration problem (DSRRP). The algorithm starts by solving DSRRP using an assignment-based method, then a neighborhood search method is designed to iteratively improve the solution. The proposed method is validated on modified IEEE 123- and 8500-bus distribution test systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Scheduling

Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7:9 Wenchuan Earthquake

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arXiv 2019年

作者： Zhu, Lijun Peng, Zhigang McClellan, James Li, Chenyu Yao, Dong Dong Li, Zefeng Fang, Lihua School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Earth and Atmospheric Sciences Georgia Institute of Technology AtlantaGA30332 United States Seismological Lab. Division of Geological and Planetary Sciences California Institute of Technology PasadenaCA91125 United States Institute of Geophysics China Earthquake Administration Beijing100081 China

The increasing volume of seismic data from long-Term continuous monitoring motivates the development of algorithms based on convolutional neural net-work (CNN) for faster and more reliable phase detection and picking. However, many less studied regions lack a significant amount of labeled events needed for traditional CNN approaches. In this paper, we present a CNN-based Phase-Identification Classifier (CPIC) designed for phase detection and picking on small to medium sized training datasets. When trained on 30,146 labeled phases and applied to one-month of continuous recordings during the aftershock se-quences of the 2008 MW 7.9 Wenchuan Earthquake in Sichuan, China, CPIC detects 97.5% of the manually picked phases in the standard catalog and pre-dicts their arrival times with a five-Times improvement over the ObsPy AR picker. In addition, unlike other CNN-based approaches that require millions of training samples, when the ofi-line training set size of CPIC is reduced to only a few thousand training samples the accuracy stays above 95%. The on-line implementation of CPIC takes less than 12 hours to pick arrivals in 31-day recordings on 14 stations. In addition to the catalog phases manually picked by analysts, CPIC finds more phases for existing events and new events missed in the catalog. Among those additional detections, some are confirmed by a matched filter method while others require further investigation. Finally, when tested on a small dataset from a different region (Oklahoma, US), CPIC achieves 97% accuracy after fine tuning only the fully connected layer of the model. This result suggests that the CPIC developed in this study can be used to identify and pick P/S arrivals in other regions with no or minimum labeled phases. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning