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arXiv 2021年

作者： Gradoni, Gabriele Renzo, Marco Di Díaz-Rubio, Ana Tretyakov, Sergei A. Caloz, Christophe Gradoni, Gabriele Peng, Zhen Alu, Andrea Lerosey, Geoffroy Fink, Mathias Galdi, Vincenzo Cui, Tie Jun Frazier, Benjamin W. Anlage, Steven M. Salucci, Marco Massa, Andrea Cheng, Qiang Wang, Jinghe Jin, Shi Dardari, Davide Decarli, Nicoló Yurduseven, Okan Matthaiou, Michail Kenney, Mitchell Gordon, George Georgiou, Orestis Nguyen, Cam Ly Martini, Enrica Maci, Stefano Wakatsuchi, Hiroki Phang, Sendy School of Mathematical Sciences Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Laboratoire des Signaux et Systémes CNRS CentraleSupélec Université Paris-Saclay Gif-sur-Yvette Paris France Department of of Electronics and Nanoengineering Aalto University Espoo Finland META Research Group KU Leuven Belgium School of Mathematical Sciences Department of Electrical and Electronic Engineering University of Nottingham University Park NG72RD United Kingdom Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign IL United States Advanced Science Research Center City University of New York United States Greenerwave 6 rue Jean Calvin Paris75005 France Institut Langevin 1 rue Jussieu Paris75005 France Fields & Waves Lab Department of Engineering University of Sannio Benevento Italy State Key Laboratory of Millimeter Waves Southeast University Nanjing China Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States Department of Electrical and Computer Engineering Department of Physics Quantum Materials Center University of Maryland College ParkMD20742 United States Trento Italy Chengdu China Beijing China Institute of Electromagnetic Space State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China University of Bologna Bologna Italy Bologna Italy Centre for Wireless Innovation Institute of Electronics Communications and Information Technology Queen’s University Belfast Belfast United Kingdom Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Wireless System Laboratory Corporate Research & Development Center Toshiba Corporation Kawasaki Japan Department of Information Engineering an

This Roadmap takes the reader on a journey through the research in electromagnetic wave propagation control via reconfigurable intelligent surfaces. Meta-surface modelling and design methods are reviewed along with physical realisation techniques. Several wireless applications are discussed, including beam-forming, focusing, imaging, localisation, and sensing, some rooted in novel architectures for future mobile communications networks towards 6G. © 2021, CC BY.

关键词： Electromagnetic wave propagation

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A platform for high performance photon correlation measurements

arXiv

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arXiv 2020年

作者： Zadeh, Iman Esmaeil Los, Johannes W.N. Gourgues, Ronan B.M. Chang, Jin Elshaari, Ali W. Zichi, Julien van Staaden, Yuri J. Swens, Jeroen Kalhor, Nima Guardiani, Antonio Meng, Yun Zou, Kai Dobrovolskiy, Sergiy Fognini, Andreas W. Schaart, Dennis R. Dalacu, Dan Poole, Philip J. Reimer, Michael E. Hu, Xiaolong Pereira, Silvania F. Zwiller, Val Dorenbos, Sander N. Optics Research Group ImPhys Department Faculty of Applied Sciences Delft University of Technology Delft2628 CJ Netherlands Single Quantum B.V. Delft2628 CJ Netherlands Optics Research Group Imphys Department Faculty of Applied Sciences Delft University of Technology Delft2628 CJ Netherlands Stockholm106 91 Sweden School of Precision Instrument and Optoelectronic Engineering Tianjin University Tianjin300072 China Key Laboratory of Optoelectronic Information Science and Technology Ministry of Education Tianjin300072 China Medical Physics & Technology Radiation Science & Technology Department Faculty of Applied Sciences Delft University of Technology National Research Council of Canada OttawaONK1A 0R6 Canada Institute for Quantum Computing Department of Electrical & Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

A broad range of scientific and industrial disciplines require precise optical measurements at very low light levels. Single-photon detectors combining high efficiency and high time resolution are pivotal in such experiments. By using relatively thick films of NbTiN (8-11 nm) and improving the pattern fidelity of the nano-structure of the superconducting nanowire single-photon detectors (SNSPD), we fabricated devices demonstrating superior performance over all previously reported detectors in the combination of efficiency and time resolution. Our findings prove that small variations in the nanowire width, in the order of a few nanometers, can lead to a significant penalty on their temporal response. Addressing these issues, we consistently achieved high time resolution (best device 7.7 ps, other devices ∼10-16 ps) simultaneously with high system detection efficiencies (80 − 90%) in the wavelength range of 780-1000 nm, as well as in the telecom bands (1310-1550 nm). The use of thicker films allowed us to fabricate large-area multi-pixel devices with homogeneous pixel performance. We first fabricated and characterized a 100 × 100 µm2 16-pixel detector and showed there was little variation among individual pixels. Additionally, to showcase the power of our platform, we fabricated and characterized 4-pixel multimode fiber-coupled detectors and carried out photon correlation experiments on a nanowire quantum dot resulting in g2(0) values lower than 0.04. The multi-pixel detectors alleviate the need for beamsplitters and can be used for higher order correlations with promising prospects not only in the field of quantum optics, but also in bio-imaging applications, such as fluorescence microscopy and positron emission tomography. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics

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Dual Band CPW-Fed Double Monopole Antenna for 2.4/5.8 GHz ISM band Medical Applications

Dual Band CPW-Fed Double Monopole Antenna for 2.4/5.8 GHz IS...

引用

International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Chaïmaâ Kissi Mariella Särestöniemi Marko Sonkki Sami Myllymäki Mohamed Nabil Srifi Heli Jantunen Carlos Pomalaza-Raez Electronics and Telecommunication Systems Research Group National School of Applied Sciences (ENSA) Ibn Tofail University Kenitra Morocco Centre for Wireless Communications Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland Microelectronics Research Unit Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland Department of Electrical and Computer Engineering Purdue University Indiana USA

ISBN: (数字)9781728137292

ISBN: (纸本)9781728137308

The conception and realization of a dual-band coplanar waveguide fed double monopole antenna, working at 2.4 and 5.8 GHz Industrial, Scientific and Medical (ISM) bands, is presented in this paper. To operate effectively at the aforementioned bands, the designed antenna consists of a symmetrical folded long monopole and a short monopole which are responsible of the resonant frequencies at 2.45 and 5.8 GHz, respectively. Furthermore, both monopoles are fed by a coplanar waveguide. The symmetrical configuration allows the proposed antenna to obtain symmetrical radiation patterns. Hence, an omnidirectional radiation pattern is obtained at 2.45 and 5.8 GHz. A detailed parametric study and antenna parameters are reported and the proposed antenna is designed, fabricated and measured in free space. The proposed antenna has a compact size with the overall dimensions of 30 mm × 27 mm × 1.6 mm. It is concluded from the measured results, that the presented antenna could be considered for applications operating at 2.4 and 5.8 GHz ISM bands. Next, a metallic reflector was introduced to the planar structure in order to improve the gain and provide better directivity for onbody applications, for instance wireless capsule endoscopy systems. For this specific application, the antenna will act as receiving antenna and hence will be held by the patient, which requires a primary on/in-body investigation next to the small intestine region (complex organ of the gastrointestinal tract), as discussed in the paper using multi-layer and voxel models. It is concluded that the proposed antenna structure is a good receiving antenna for wireless capsule endoscopy systems.

关键词： Antenna measurements Resonant frequency Dual band Antenna radiation patterns Wireless communication Receiving antennas

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Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

arXiv

引用

arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

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Context-Aware Misbehavior Detection Scheme for Vehicular Ad Hoc Networks using Sequential Analysis of the Temporal and Spatial Correlation of the Cooperative Awareness Messages

arXiv

引用

arXiv 2019年

作者： Ghaleb, Fuad A. Maarof, Mohd Aizaini Zainal, Anazida Rassam, Murad Saeed, Faisal Alsaedi, Mohammed Cybersecurity X Lap Information Assurance and Security Research Group School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor81310 Malaysia Department of Computer and Electronic Engineering Sana’a Community College Sana’a5695 Yemen Faculty of Engineering and Information Technology Taiz University Taiz6803 Yemen College of Computer Science and Engineering University of Taibah Medina Saudi Arabia

Vehicular ad hoc Networks (VANETs) are emerged mainly to improve road safety, traffic efficiency, and passenger comfort. The performance of most VANET applications and services relies on the availability of accurate and recent mobility-information, so-called Cooperative Awareness Messages (CAM), shared by neighboring vehicles. However, misbehaving in terms of sharing false mobility information can disrupt any potential VANET application. Because cryptographic solutions in VANET are expensive, complicated, and vulnerable to internal misbehavior, security lapses are inevitable. Therefore, misbehavior detection is an important security component. Unfortunately, existing misbehavior detection solutions lack considering the high dynamicity of vehicular context which leads to low detection accuracy and high false alarms. The use of predefined and static security thresholds are the main drawbacks of the existing solutions. In this paper, a context-aware misbehavior detection scheme (CAMDS) is proposed using sequential analysis of temporal and spatial correlation of neighboring vehicles’ mobility information. A dynamic context reference is constructed online and timely updated using statistical techniques. Firstly, the Kalman filter algorithm is used to track the mobility information received from neighboring vehicles. Then, the innovation errors of the Kalman filter are utilized to construct a temporal consistency assessment model for each neighboring vehicle using Box-plot. Then, the Hampel filter is used to construct a spatial consistency assessment model that represents the current context reference model. Similarly, plausibility assessment reference models are built online and timely updated using the Hampel filter and by utilizing the consistency assessment reference model of neighboring information. Finally, a message is classified as suspicious if its consistency and plausibility scores deviate much from the context reference model. The proposed context-aware scheme

关键词： Vehicular ad hoc networks

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Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

引用

Computational intelligence and neuroscience 2023年第1期2023卷 4776770页

作者： Sujatha Krishnamoorthy Yaxi Zhang Seifedine Kadry Muhammad Attique Khan Majed Alhaisoni Nasser Mustafa Weifeng Yu Abdullah Alqahtani Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Wenzhou Zhejiang Province China. Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Wenzhou Zhejiang Province China. Department of Neurology Wenzhou Central Hospital Medical Group Wenzhou 325000 China. Department of Applied Data Science Noroff University College Kristiansand Norway. Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 UAE. Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon. Department of Computer Science HITEC University Taxila Pakistan. Computer Sciences Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia. Wenzhou-Kean University School of Science and Technology Wenzhou Zhejiang Province China. College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia.

Malfunctions in the immune system cause multiple sclerosis (MS), which initiates mild to severe nerve damage. MS will disturb the signal communication between the brain and other body parts, and early diagnosis will help reduce the harshness of MS in humankind. Magnetic resonance imaging (MRI) supported MS detection is a standard clinical procedure in which the bio-image recorded with a chosen modality is considered to assess the severity of the disease. The proposed research aims to implement a convolutional neural network (CNN) supported scheme to detect MS lesions in the chosen brain MRI slices. The stages of this framework include (i) image collection and resizing, (ii) deep feature mining, (iii) hand-crafted feature mining, (iii) feature optimization with firefly algorithm, and (iv) serial feature integration and classification. In this work, five-fold cross-validation is executed, and the final result is considered for the assessment. The brain MRI slices with/without the skull section are examined separately, presenting the attained results. The experimental outcome of this study confirms that the VGG16 with random forest (RF) classifier offered a classification accuracy of >98% MRI with skull, and VGG16 with K-nearest neighbor (KNN) provided an accuracy of >98% without the skull.

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提升大口径反射面天线服役性能的未来研究趋势

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engineering 2021年第8期 30-37页

作者：连培园王从思薛松王艳严粤飞许谦段宝岩王娜段玉虎伍洋 School of Electromechanical Engineering Xidian University School of Information and Control Engineering Xi'an University of Architecture and Technology Xinjiang Astronomical Observatory Chinese Academy of Science China Electronics Technology Group Corporation No.39 Research Institute China Electronics Technology Group Corporation No.54 Research Institute

深空测控网在卫星通信、载人航天、深空探测等人类空间活动中具有重要地位，而大口径反射面天线是深空测控网的重要组成[1]。该类天线的主要特点是口径极大，通常需要严格的表面精度以保障高增益和高指向精度等电性能要求[2-3]。根据反...

深空测控网在卫星通信、载人航天、深空探测等人类空间活动中具有重要地位，而大口径反射面天线是深空测控网的重要组成[1]。该类天线的主要特点是口径极大，通常需要严格的表面精度以保障高增益和高指向精度等电性能要求[2-3]。根据反射面天线是否全向可动，可分为两大类：非全向可动天线和全向可动天线。中国贵州500 m口径球面射电望远镜是典型的非全向可动天线，反射面面板只能在小角度范围内转动，该天线的口径超级大，

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Roadmap on Deep Learning for Microscopy

arXiv

引用

arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Developing an unsupervised real-time anomaly detection scheme for time series with multi-seasonality

arXiv

引用

arXiv 2019年

作者： Wu, Wentai He, Ligang Lin, Weiwei Su, Yi Cui, Yuhua Maple, Carsten Jarvis, Stephen Department of Computer Science University of Warwick United Kingdom School of Computer Science and Engineering South China University of Technology China Research Institute of Worldwide Byte Information Security Warwick Manufacturer Group University of Warwick United Kingdom College of Engineering and Physical Sciences University of Birmingham United Kingdom

On-line detection of anomalies in time series is a key technique used in various event-sensitive scenarios such as robotic system monitoring, smart sensor networks and data center security. However, the increasing diversity of data sources and the variety of demands make this task more challenging than ever. Firstly, the rapid increase in unlabeled data means supervised learning is becoming less suitable in many cases. Secondly, a large portion of time series data have complex seasonality features. Thirdly, on-line anomaly detection needs to be fast and reliable. In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data. Further, we propose a novel metric, Local Trend Inconsistency (LTI), and an efficient detection algorithm that computes LTI in a real-time manner and scores each data point robustly in terms of its probability of being anomalous. We have conducted extensive experimentation to evaluate our algorithm with several datasets from both public repositories and production environments. The experimental results show that our scheme outperforms existing representative anomaly detection algorithms in terms of the commonly used metric, Area Under Curve (AUC), while achieving the desired efficiency. © 2019, CC BY-NC-ND.

关键词： Anomaly detection

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Identifying the Effectiveness of Arabica Drip Coffee on Individual Human Brainwave

Identifying the Effectiveness of Arabica Drip Coffee on Indi...

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

作者： Cui Chenghu Hu Jinna Arnon Visavakitcharoen Punnarumol Temdee Roungsan Chaisricharoen Brain Science and Engineering Innovation Research Group School of Information Technology Mae Fah Luang University Chiang Rai Thailand The State Key Laboratory of Integrated Services Network Xidian Univerisity Shaanxi China Department of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

Caffeine is one of the core substances in coffee. It can directly influence human brain nerve status. Arabica drip coffee is a widely known beverage; drinker often drinks it to keep the brain awake, sensitive, and focus on mental activity. Previous studies have shown that the level of attention in the human brain is controlled by Beta frequency and caffeine can affect the level of Beta frequency. Therefore, this study focuses on the Influence of attention level of local Arabica coffee to the individual human brainwave, thus proving the value of the local Arabica coffee. The research applied twenty participants: ten males and ten females which age between 19 to 22. This research performed human brainwave detection via Electroencephalography (EEG) and interpreted the data through the k-means algorithm. The t-Test study verified that the individual Beta frequency increases significantly after drinking the coffee. Additionally, the classification of k-mean showed that the individual effectiveness could be classified into three groups including low, medium, and high-efficiency level for both before and after drinking the coffee.

关键词： Electroencephalography Sleep Clustering algorithms Digital art Media Telecommunications Classification algorithms

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