检索结果-内蒙古大学图书馆

Unsupervised detection of epileptic seizures from EEG signals: A channel-specific analysis of long-term recordings

学校读者我要写书评

暂无评论

Unsupervised detection of epileptic seizures from EEG signal...

2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018

作者： Tsiouris, Kostas M. Konitsiotis, Spiros Markoula, Sofia Rigas, Georgios Koutsouris, Dimitrios D. Fotiadis, Dimitrios I. Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens AthensGR15773 Greece Department of Neurology Medical School University of Ioannina IoanninaGR45110 Greece University Hospital of Ioannina IoanninaGR45110 Greece Unit of Medical Technology and Intelligent Information Systems Dept. of Material Science and Engineering University of Ioannina IoanninaGR45110 Greece Department of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH University of Ioannina IoanninaGR45110 Greece

ISBN: (纸本)9781538624050

As long-term EEG recordings are becoming more and more frequent in clinical practice, the volume of EEG data that require annotation by clinical experts is growing exponentially, highlighting the need for automated epileptic seizure detection systems. Supervised methodologies based on machine learning techniques have been proposed as a solution, but they require annotated EEG data from multiple patients for training, while unsupervised methodologies, which do not have such limitations, are scarce in the literature. Thus, an unsupervised seizure detection methodology is developed to offer high seizure detection performance with substantial reduction in the time and effort required to inspect large volumes of EEG signals. The ictal rhythmical activity is detected by analyzing the spectral information of each EEG channel independently in order to find intensive accumulation of signal energy over the fundamental delta, theta and alpha frequency bands. The ictal rhythmical activity that is expressed during a seizure is detected using a set of four simple seizure detection conditions. The proposed methodology is validated using the public CHB-MIT EEG database, and the results suggest that an average sensitivity of 95.1% can be obtained with a false detection rate of 10.13 FP/h, while 95% of the each patient's EEG recordings is automatically rejected as non-ictal. © 2018 IEEE.

关键词： Neurophysiology

Microwave photonic fractional Hilbert transformer with an integrated optical soliton crystal micro-comb

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Tan, Mengxi Xu, Xingyuan Corcoran, Bill Wu, Jiayang Boes, Andreas Nguyen, Thach G. Chu, Sai T. Little, Brent E. Morandotti, Roberto Mitchell, Arnan Moss, David J. Centre for MicroPhotonics Swinburne University of Technology HawthornVIC3122 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia Department of Physics City University of Hong Kong Tat Chee Avenue Hong Kong State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Science Xi'an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada ITMO University St. Petersburg Russia Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China

We report a photonic microwave and RF fractional Hilbert transformer based on an integrated Kerr micro-comb source. The micro-comb source has a free spectral range (FSR) of 50GHz, generating a large number of comb lines that serve as a high-performance multiwavelength source for the transformer. By programming and shaping the comb lines according to calculated tap weights, we achieve both arbitrary fractional orders and a broad operation bandwidth. We experimentally characterize the RF amplitude and phase response for different fractional orders and perform system demonstrations of real-time fractional Hilbert transforms. We achieve a phase ripple of Copyright © 2019, The Authors. All rights reserved.

关键词： Bandwidth

Effects of RapidEye Imagery's Red-edge Band and Vegetation Indices on Land Cover Classification in an Arid Region

学校读者我要写书评

暂无评论

Chinese Geographical science 2017年第5期27卷 827-835页

作者： LI Xianju CHEN Gang LIU Jingyi CHEN Weitao CHENG Xinwen LIAO Yiwei College of Computer Science and Hubei Key Laboratory of Intelligent Geo-Information Processing China University of GeosciencesWuhan 430074China College of Marine Science and Technology China University of GeosciencesWuhan 430074China Laboratory of Geographic Information and Spatial Analysis Department of Geography and PlanningQueen's UniversityKingston ON K7L3N6Canada Faculty of Information Engineering China University of GeosciencesWuhan 430074China School of Electrical and Electronic Engineering Huazhong University of Science and TechnologyWuhan 430074China

Land cover classification(LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidE ye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidE ye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows: 1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement(3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.

关键词： arid region land cover classification RapidEye red-edge band vegetation indices random forest Dunhuang Basin

Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

Distributed Least Squares Solver for Network Linear Equations

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Yang, Tao George, Jemin Qin, Jiahu Yi, Xinlei Wu, Junfeng State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China U.S. Army Research Laboratory AdelphiMD20783 United States Department of Automation University of Science and Technology of China Hefei230027 China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden College of Control Science and Engineering Zhejiang University Hangzhou310027 China

In this paper, we study the problem of finding the least square solutions of over-determined linear algebraic equations over networks in a distributed manner. Each node has access to one of the linear equations and holds a dynamic state. We first propose a distributed least square solver over connected undirected interaction graphs and establish a necessary and sufficient on the step-size under which the algorithm exponentially converges to the least square solution. Next, we develop a distributed least square solver over strongly connected directed graphs and show that the proposed algorithm exponentially converges to the least square solution provided the step-size is sufficiently small. Moreover, we develop a finite-time least square solver by equipping the proposed algorithms with a finite-time decentralized computation mechanism. The theoretical findings are validated and illustrated by numerical simulation examples. Copyright © 2018, The Authors. All rights reserved.

关键词： Linear equations

Knowledge and Awareness of Generative Artificial Intelligence Use in Medicine Among International Stakeholders: A Cross-Sectional Study

学校读者我要写书评

暂无评论

Journal of evidence-based medicine 2025年第2期18卷 e70034页

作者： Xufei Luo Bingyi Wang Yule Li Shuang Liu Haodong Li Yaxuan Ren Wah Yang Kyle Lam Stephen R Ali Gemma Sharp Fabio Ynoe Moraes Ye Wang Di Zhu Zhenhua Yang Daher Mohammad Robert Fruscio Maged N Kamel Boulos Zhicheng Lin Kazuki Ide Xuping Song Lu Zhang Yih Chung Tham Hui Liu Long Ge Yaolong Chen Zhaoxiang Bian Evidence-Based Medicine Center School of Basic Medical Sciences Lanzhou University Lanzhou China. Research Unit of Evidence-Based Evaluation and Guidelines Chinese Academy of Medical Sciences (2021RU017) School of Basic Medical Sciences Lanzhou University Lanzhou China. World Health Organization Collaboration Center for Guideline Implementation and Knowledge Translation Lanzhou China. Institute of Health Data Science Lanzhou University Lanzhou China. Key Laboratory of Evidence Based Medicine of Gansu Province Lanzhou University Lanzhou China. Editorial Office of Medical Journal of Peking Union Medical College Hospital Peking Union Medical College Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China. Chinese Journal of Hematology National Clinical Research Center for Blood Diseases Haihe Laboratory of Cell Ecosystem Institute of Hematology & Blood Diseases Hospital Chinese Academy of Medical Sciences & Peking Union Medical College Tianjin China. Tianjin Institutes of Health Science Tianjin China. School of Public Health Lanzhou University Lanzhou China. School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China. Department of Metabolic and Bariatric Surgery The First Affiliated Hospital of Jinan University Guangzhou China. Department of Surgery and Cancer Imperial College London London UK. Reconstructive Surgery and Regenerative Medicine Research Centre Institute of Life Sciences Swansea University Medical School Swansea UK. School of Psychology University of Queensland Brisbane Australia. Department of Oncology Queen's University Kingston Canada. School of Chinese Medicine Vincent V.C. Woo Chinese Medicine Clinical Research Institute Hong Kong Baptist University Hong Kong China. Orthopedic Department Hôtel Dieu de France Beirut Lebanon. UO Gynecology Fondazione IRCCS San Gerardo dei Tintori Monza Italy. Department of Medicine and Surgery University of Milan-Bicocca Milan Italy.

OBJECTIVE:To assess the knowledge, attitudes, and practices (KAP) of medical stakeholders regarding the use of generative artificial intelligence (GAI) tools. METHODS:A cross-sectional survey was conducted among stakeholders in medicine. Participants included researchers, clinicians, and medical journal editors with varying degrees of familiarity with GAI tools. The survey questionnaire comprised 40 questions covering four main dimensions: basic information, knowledge, attitudes, and practices related to GAI tools. Descriptive analysis, Pearson's correlation, and multivariable regression were used to analyze the data. RESULTS:The overall awareness rate of GAI tools was 93.3%. Participants demonstrated moderate knowledge (mean score 17.71 ± 5.56), positive attitudes (mean score 73.32 ± 15.83), and reasonable practices (mean score 40.70 ± 12.86). Factors influencing knowledge included education level, geographic region, and attitudes (p < 0.05). Attitudes were influenced by work experience and knowledge (p < 0.05), while practices were driven by both knowledge and attitudes (p < 0.001). Participants from outside China scored higher in all dimensions compared to those from China (p < 0.001). Additionally, 74.0% of participants emphasized the importance of reporting GAI usage in research, and 73.9% advocated for naming the specific tool used. CONCLUSION:The findings highlight a growing awareness and generally positive attitude toward GAI tools among medical stakeholders, alongside the recognition of their ethical implications and the necessity for standardized reporting practices. Targeted training and the development of clear reporting guidelines are recommended to enhance the effective use of GAI tools in medical research and practice.

关键词： ChatGPT cross‐sectional survey generative artificial intelligence knowledge and awareness

A deep learning-based remaining useful life prediction approach for bearings

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Cheng, Cheng Ma, Guijun Zhang, Yong Sun, Mingyang Teng, Fei Ding, Han Yuan, Ye The Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China The College of Control Science and Engineering Zhejiang University Hangzhou310007 China The Department of Electrical & Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs is of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct the health index. In this paper, a novel data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RULs of bearings. More concretely, raw vibrations of training bearings are first processed using the Hilbert-Huang transform to construct a novel nonlinear degradation energy indicator which can be used as the training label. The CNN is then employed to identify the hidden pattern between the extracted degradation energy indicator and the raw vibrations of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings’ RULs are predicted through using a ǫ-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by performance test on other bearings undergoing different operating conditions. Copyright © 2018, The Authors. All rights reserved.

关键词： Hilbert-Huang transform

An automated pulmonary parenchyma segmentation method based on an improved region growing algorithm in PET-CT imaging

学校读者我要写书评

暂无评论

Frontiers of computer science 2016年第1期10卷 189-200页

作者： Juanjuan ZHAO Guohua JI Xiaohong HAN Yan QIANG Xiaolei LIAO College of Computer Science and Technology Taiyuan University of Technology Taiyuan 030024 China Key Laboratory of Advanced Transducers and Intelligent Control Systems Taiyuan University of Technology Taiyuan 030024 China

To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages： image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography （PET-CT） data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately.

关键词： pulmonary parenchyma segmentation bot-tom region of lung image binarization iterative threshold seeded region growing four-corner rotating and scanning,denoising contour refining PET-CT

Graphene-loaded metal wire grating for deep and broadband THz modulation in total internal reflection geometry

学校读者我要写书评

暂无评论

Photonics Research 2018年第12期6卷 1151-1157页

作者： YIWEN SUN RICCARDO DEGLJINNOCENTI DAVID A.RITCHIE HARVEY E.BEERE LONG XIAO MICHAEL RUGGIERO J.AXEL ZEITLER RAYKO I.STANTCHEV DANNI CHEN ZHENGCHUN PENG EMMA MACPHERSON XUDONG LIU National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound ImagingDepartment of Biomedical EngineeringSchool of MedicineShenzhen UniversityShenzhen 518060China Cavendish Laboratory University of CambridgeJ J Thomson AvenueCambridge CB30HEUK Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridgeMassachusetts 02139USA Department of Chemistry University of Vermont82 University PlaceBurlingtonVermont 05405USA Department of Chemical Engineering and Biotechnology University of CambridgePembroke StreetCambridge CB2 3RAUK Department of Electronic Engineering The Chinese University of Hong KongHong KongChina Key Laboratory of Ministry of Education for Optoelectronic Devices and Systems College of Optoelectronic EngineeringShenzhen University Shenzhen 518060China Department of Physics University of WarwickGibbet Hill RoadCoventry CV4 7ALUK

We employed a metallic wire grating loaded with graphene and operating in total internal reflection (TIR) geometry to realize deep and broadband THz modulation. The non-resonant field enhancement effect of the evanescent wave in TIR geometry and in the subwavelength wire grating was combined to demonstrate a ～77% modulation depth (MD) in the frequency range of 0.2–1.4 THz. This MD, achieved electrically with a SiO_2∕Si gated graphene device, was 4.5 times higher than that of the device without a metal grating in transmission geometry. By optimizing the parameters of the metallic wire grating, the required sheet conductivity of graphene for deep modulation was lowered to 0.87 mS. This work has potential applications in THz communication and real-time THz imaging.

关键词： modulation depth (MD) graphene Wire grating