检索结果-内蒙古大学图书馆

Analysis of Negative Capacitance MOSFETs Characteristic with Spacer

学校读者我要写书评

暂无评论

Analysis of Negative Capacitance MOSFETs Characteristic with...

International Symposium on VLSI Technology, Systems and Applications

作者： Yu-Chun Lee Sekhar Reddy Kola Chieh-Yang Chen Min-Hui Chuang Yiming Li Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering EECS International Graduate Program Institute of Communications Engineering Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd Hsinchu Taiwan

ISBN: (数字)9781728142326

ISBN: (纸本)9781728142333

In this work, our study comprises of design and investigation on negative capacitance (NC), metal-oxide-semiconductor (MOS) field effects transistors (MOSFETs) with spacer and source/drain (S/D) overlap engineering. The scope of the work is to boost the performance and high-energy efficiency of the studied NC-MOSFETs by using the ferro electric material (FE). The NC-MOSFETs with the spacer technology can achieve the admirable I on /I off ratio and subthreshold swing (SS), compared with planar MOSFETs. It makes device scaling possible by eliminating the short channel effect (SCE). We further estimated the effect of FE thickness and spacer, which are another critical parameter of obtaining better electrical characteristics and reducing SS.

关键词： Capacitance Iron Semiconductor device modeling MOSFET Integrated circuit modeling Mathematical model Ferroelectric materials

Beneath the Bun: Food additives in fast-food burgers and the path to healthier choices

学校读者我要写书评

暂无评论

Food and Humanity

Food and Humanity 2025年 5卷

作者： Besedin, Darislav Carter, Sharayah Shah, Rohan Hao Van, Thi Thu Eri, Rajaraman School of Science STEM College RMIT University Melbourne 3001 VIC Australia Alliance for Research in Exercise Nutrition and Activity (ARENA) Allied Health & Human Performance University of South Australia Adelaide 5001 SA Australia School of Health and Biomedical Sciences STEM College RMIT University Melbourne 3083 VIC Australia Department of Chemistry and Biotechnology School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn 3122 VIC Australia

The modern Western diet is dominated by ultra-processed food (UPF), which is increasingly linked to adverse health outcomes. Previous reviews have already identified problematic components of UPF, such as added fats and sugars. Therefore, the aim of this review was to provide a nuanced understanding of another ubiquitous component of UPF, food additives, and to explore any potential health risks associated with their consumption, as well as strategies for mitigating those risks. To provide real-world contextualization, we first quantified the food additive profile a person would be exposed to when consuming a popular UPF, the burger. Here we show that the selected burgers contain up to thirty-six different additives, primarily emulsifiers, thickeners, preservatives, and colours. While some reviewed additives, such as resistant starch and natural colours, have beneficial effects, the majority have a deleterious effect on health. However, limitations of current studies and gaps in government regulations complicate the assessment of the long-term health implications of a diet high in food additives. This review underscores the need for greater transparency in food labelling and a re-evaluation of food additive safety standards. It advocates for both elimination of certain food additives and reformulation strategies to mitigate potential health risks associated with UPF consumption. However, since elimination poses multiple challenges, reformulation may be a more realistic strategy. This could include fortification with fibre, replacing synthetic colours with natural colours, and reducing the use of preservatives by utilising advancements in hurdle technology. © 2025 The Authors

关键词： Emulsifiers Food labeling Preservatives Synthetic colours UPF

Classification of electrical impedance tomography data using machine learning

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Pessoa, Diogo Rocha, Bruno Machado Cheimariotis, Grigorios-Aris Haris, Kostas Strodthoff, Claas Kaimakamis, Evangelos Maglaveras, Nicos Frerichs, Inéz de Carvalho, Paulo Paiva, Rui Pedro University of Coimbra Centre for Informatics and Systems The University of Coimbra Department of Informatics Engineering Coimbra3030-290 Portugal Laboratory of Computing Medical Informatics and Biomedical Imaging Technologies Aristotle University of Thessaloniki Thessaloniki54636 Greece Department of Anesthesiology and Intensive Care Medicine University Medical Centre Schleswig-Holstein Campus Kiel Kiel Germany 1st Intensive Care Unit "G. Papanikolaou" General Hospital Thessaloniki Greece

Patients suffering from pulmonary diseases typically exhibit pathological lung ventilation in terms of homogeneity. Electrical Impedance Tomography (EIT) is a noninvasive imaging method that allows to analyze and quantify the distribution of ventilation in the lungs. In this article, we present a new approach to promote the use of EIT data and the implementation of new clinical applications for differential diagnosis, with the development of several machine learning models to discriminate between EIT data from healthy and non-healthy subjects. EIT data from 16 subjects were acquired: 5 healthy and 11 non-healthy subjects (with multiple pulmonary conditions). Preliminary results have shown accuracy percentages of 66% in challenging evaluation scenarios. The results suggest that the pairing of EIT feature engineering methods with machine learning methods could be further explored and applied in the diagnostic and monitoring of patients suffering from lung diseases. Also, we introduce the use of a new feature in the context of EIT data analysis (Impedance Curve Correlation). © 2021, CC BY.

关键词： Machine learning

Predicting risk of cardiovascular disease using retinal optical coherence tomography imaging

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Maldonado-Garcia, Cynthia Bonazzola, Rodrigo Ferrante, Enzo Julian, Thomas H. Sergouniotis, Panagiotis I. Ravikumar, Nishant Frangi, Alejandro F. Centre for Computational Imaging and Simulation Technologies in Biomedicine School of Computing University of Leeds Leeds United Kingdom CONICET-UNL Santa Fe Argentina Division of Evolution Infection and Genomics School of Biological Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom Manchester Royal Eye Hospital Manchester University NHS Foundation Trust Manchester United Kingdom Manchester Centre for Genomic Medicine Saint Mary’s Hospital Manchester University NHS Foundation Trust Manchester United Kingdom Wellcome Genome Campus Cambridge United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom School of Computer Science Faculty of Science and Engineering University of Manchester Kilburn Building Manchester United Kingdom Christabel Pankhurst Institute University of Manchester Manchester United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom

Cardiovascular diseases (CVD) are the leading cause of death globally. Non-invasive, cost-effective imaging techniques play a crucial role in early detection and prevention of CVD. Optical coherence tomography (OCT) has gained recognition as a potential tool for early CVD risk prediction, though its use remains underexplored. In this study, we investigated the potential of OCT as an additional imaging technique to predict future CVD events. We analysed retinal OCT data from the UK Biobank. The dataset included 612 patients who suffered a myocardial infarction (MI) or stroke within five years of imaging and 2,234 controls without CVD (total: 2,846 participants). A self-supervised deep learning approach based on Variational Autoencoders (VAE) was used to extract low-dimensional latent representations from high-dimensional 3D OCT images, capturing distinct features of retinal layers. These latent features, along with clinical data, were used to train a Random Forest (RF) classifier to differentiate between patients at risk of future CVD events (MI or stroke) and healthy controls. Our model achieved an AUC of 0.75, sensitivity of 0.70, specificity of 0.70, and accuracy of 0.70, outperforming the QRISK3 score (the third version of the QRISK cardiovascular disease risk prediction algorithm;AUC = 0.60, sensitivity = 0.60, specificity = 0.55, accuracy = 0.55). The choroidal layer in OCT images was identified as a key predictor of future CVD events, revealed through a novel model explainability approach. This study demonstrates that retinal OCT imaging is a cost-effective, non-invasive alternative for predicting CVD risk, offering potential for widespread application in optometry practices and hospitals. © 2024, CC BY-NC-ND.

关键词： Optical coherence tomography

Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Huang, Yuhao Yang, Xin Li, Rui Qian, Jikuan Huang, Xiaoqiong Shi, Wenlong Dou, Haoran Chen, Chaoyu Zhang, Yuanji Luo, Huanjia Frangi, Alejandro Xiong, Yi Ni, Dong School of Biomedical Engineering Shenzhen University Shenzhen China Lab Shenzhen University China Department of Ultrasound Luohu Peoples Hosptial Shenzhen China CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine School of Computing University of Leeds Leeds United Kingdom University Hospital Gasthuisberg Electrical Engineering Department KU Leuven Leuven Belgium

3D ultrasound (US) is widely used due to its rich diagnostic information, portability and low cost. Automated standard plane (SP) localization in US volume not only improves efficiency and reduces user-dependence, but also boosts 3D US interpretation. In this study, we propose a novel Multi-Agent Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D US simultaneously. Our contribution is two-fold. First, we equip the MARL with a one-shot neural architecture search (NAS) module to obtain the optimal agent for each plane. Specifically, Gradient-based search using Differentiable Architecture Sampler (GDAS) is employed to accelerate and stabilize the training process. Second, we propose a novel collaborative strategy to strengthen agents’ communication. Our strategy uses recurrent neural network (RNN) to learn the spatial relationship among SPs effectively. Extensively validated on a large dataset, our approach achieves the accuracy of 7.05◦/2.21mm, 8.62◦/2.36mm and 5.93◦/0.89mm for the mid-sagittal, transverse and coronal plane localization, respectively. The proposed MARL framework can significantly increase the plane localization accuracy and reduce the computational cost and model size. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

ADAM CHALLENGE: DETECTING AGE-RELATED MACULAR DEGENERATION FROM FUNDUS IMAGES

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Fang, Huihui Li, Fei Fu, Huazhu Sun, Xu Cao, Xingxing Lin, Fengbin Son, Jaemin Kim, Sunho Quellec, Gwenole Matta, Sarah Shankaranarayana, Sharath M. Chen, Yi-Ting Wang, Chuen-Heng Shah, Nisarg A. Lee, Chia-Yen Hsu, Chih-Chung Xie, Hai Lei, Baiying Baid, Ujjwal Innani, Shubham Dang, Kang Shi, Wenxiu Kamble, Ravi Singhal, Nitin Wang, Ching-Wei Lo, Shih-Chang Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The ADAM Challenge State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore CONICET Yatiris lab of Pladema Institute Tandil Argentina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Inserm UMR 1101 BrestF-29200 France Univ Bretagne Occidentale BrestF-29200 France Zasti India Pvt. Ltd. India Graphen.Inc Muen Biomedical and Optoelectronics Technologies Inc Taiwan Department of Electrical Engineering Indian Institute of Technology Rajasthan Jodhpur India Department of Electrical Engineering National United University Taiwan Institute of Data Science Department of Statistics National Cheng Kung University Taiwan Shenzhen University Shenzhen China SGGS Institute of Engineering and Technology Nanded India Suzhou Tisu Information Technology Co. Ltd China AIRA Matrix Mumbai India Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taiwan

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models. © 2022, CC BY-NC-ND.

关键词： Cost effectiveness

Review of e-learning service framework based on cloud computing

学校读者我要写书评

暂无评论

AIP Conference Proceedings 2023年第1期2706卷

作者： Purwono Hendradi Moh Khanapi Abd Ghani Siti Nur Mahfuzah Mohamad Karima Aissaoui Uky Yudatama Teknik Informatika Universitas Muhammadiyah Magelang Magelang Indonesia Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Interactive Media Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Artificial Intelligence Data Science and Emergent Systems Laboratory ENSAF Sidi Mohammed Ben Abdellah University FEZ Morocco

The impact of Covid-19 on education is the result of online learning from home as part of the Work from Home (WFH) protocol. There are at least three essential roles in WFH, namely Institutions, Teachers and Students, each of which uses personal resources. This literature begins with a review of the primary literature with the theme of e-learning frameworks and cloud computing. It is found that two crucial things are Integration and measuring the level of success. On that basis, in this literature review research, keywords are taken from the theme, including the word equation by using AND OR operators on the research database site over a span of the last five years. Each literature will be explored in stages focusing on the problem of Integration and or Measurement Level measurement. These results become material for systematic discussion so that an overview is obtained for the development of an E-learning Service Framework based on Cloud computing using a six-layer model with the Business Process layer being the layer used in system integration, both with other service systems and similar systems in the education ecosystem.

关键词：

A3DSegNet: Anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Lyu, Yuanyuan Liao, Haofu Zhu, Heqin Zhou, S. Kevin Z2Sky Technologies Inc. Suzhou China Department of Computer Science University of Rochester NY United States Group China School of Biomedical Engineering Suzhou Institute for Advance Research University of Science and Technology of China Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China

Spinal surgery planning necessitates automatic segmentation of vertebrae in cone-beam computed tomography (CBCT), an intraoperative imaging modality that is widely used in intervention. However, CBCT images are of low-quality and artifact-laden due to noise, poor tissue contrast, and the presence of metallic objects, causing vertebra segmentation, even manually, a demanding task. In contrast, there exists a wealth of artifact-free, high quality CT images with vertebra annotations. This motivates us to build a CBCT vertebra segmentation model using unpaired CT images with annotations. To overcome the domain and artifact gaps between CBCT and CT, it is a must to address the three heterogeneous tasks of vertebra segmentation, artifact reduction and modality translation all together. To this, we propose a novel anatomy-aware artifact disentanglement and segmentation network (A3DSegNet) that intensively leverages knowledge sharing of these three tasks to promote learning. Specifically, it takes a random pair of CBCT and CT images as the input and manipulates the synthesis and segmentation via different decoding combinations from the disentangled latent layers. Then, by proposing various forms of consistency among the synthesized images and among segmented vertebrae, the learning is achieved without paired (i.e., anatomically identical) data. Finally, we stack 2D slices together and build 3D networks on top to obtain final 3D segmentation result. Extensive experiments on a large number of clinical CBCT (21,364) and CT (17,089) images show that the proposed A3DSegNet performs significantly better than state-of-the-art competing methods trained independently for each task and, remarkably, it achieves an average Dice coefficient of 0.926 for unpaired 3D CBCT vertebra segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Musculoskeletal system

Understanding metric-related pitfalls in image analysis validation

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision