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Statistical modeling and analysis of laser-evoked potentials of electrocorticogram recordings from awake humans

Computational Intelligence and Neuroscience 2007年第1期2007卷 10479页

作者： Chen, Zhe Ohara, Shinji Cao, Jianting Vialatte, François Lenz, Fred A. Cichocki, Andrzej Laboratory for Advanced Brain Signal Processing RIKEN Brain Science Institute Wako-shi 351-0198 Saitama Japan Neuroscience Statistics Research Lab Massachusetts General Hospital Harvard Medical School Boston MA 02114 United States Department of Neurosurgery Johns Hopkins Hospital Baltimore MD 21287-7274 United States Department of Neurosurgery Kyoto Kizugawa Hospital Hirakawa Joyo 610-0101 Kyoto 26-1 Nishi-Rokutan Japan Department of Electronics and Information Engineering Saitama Institute of Technology Fukaya-shi 369-0293 Saitama Japan

This article is devoted to statistical modeling and analysis of electrocorticogram (ECoG) signals induced by painful cutaneous laser stimuli, which were recorded from implanted electrodes in awake humans. Specifically, with statistical tools of factor analysis and independent component analysis, the pain-induced laser-evoked potentials (LEPs) were extracted and investigated under different controlled conditions. With the help of wavelet analysis, quantitative and qualitative analyses were conducted regarding the LEPs' attributes of power, amplitude, and latency, in both averaging and single-trial experiments. Statistical hypothesis tests were also applied in various experimental setups. Experimental results reported herein also confirm previous findings in the neurophysiology literature. In addition, single-trial analysis has also revealed many new observations that might be interesting to the neuroscientists or clinical neurophysiologists. These promising results show convincing validation that advanced signal processing and statistical analysis may open new avenues for future studies of such ECoG or other relevant biomedical recordings.

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical Engineering 2022年 808 LNEE卷 v页

作者： Liu, Qi Liu, Xiaodong Chen, Bo Zhang, Yiming Peng, Jiansheng School of Computer and Software Nanjing University of Information Science and Technology Nanjing Jiangsu China School of Computing Edinburgh Napier University Edinburgh United Kingdom State Key Laboratory of Radar Signal Processing Xidian University Xi’an Shaanxi China School of Civil Engineering and Transportation Hebei University of Technology Tianjin Tianjin China Hechi Universtiy Guangxi Hechi China

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X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data

arXiv

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arXiv 2020年

作者： Hong, Danfeng Yokoya, Naoto Xia, Gui-Song Chanussot, Jocelyn Zhu, Xiao Xiang Remote Sensing Technology Institute German Aerospace Center Wessling82234 Germany Signal Processing in Earth Observation Technical University of Munich Munich80333 Germany Graduate School of Frontier Sciences University of Tokyo Chiba277-8561 Japan Geoinformatics Unit RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan School of Computer Science Wuhan University Wuhan430072 China Institute of Artificial Intelligence Wuhan University Wuhan430072 China State Key Laboratory for Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Univ. Grenoble Alpes INRIA CNRS Grenoble INP LJK Grenoble38000 France Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global urban scenes through remote sensing imagery. However, their ability in identifying materials (pixel-wise classification) remains limited, due to the noisy collection environment and poor discriminative information as well as limited number of well-annotated training images. To this end, we propose a novel cross-modal deep-learning framework, called X-ModalNet, with three well-designed modules: self-adversarial module, interactive learning module, and label propagation module, by learning to transfer more discriminative information from a small-scale hyperspectral image (HSI) into the classification task using a large-scale MSI or SAR data. Significantly, X-ModalNet generalizes well, owing to propagating labels on an updatable graph constructed by high-level features on the top of the network, yielding semi-supervised cross-modality learning. We evaluate X-ModalNet on two multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a significant improvement in comparison with several state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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NTIRE 2021 Challenge on Image Deblurring

arXiv

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arXiv 2021年

作者： Nah, Seungjun Son, Sanghyun Lee, Suyoung Timofte, Radu Lee, Kyoung Mu Chen, Liangyu Zhang, Jie Lu, Xin Chu, Xiaojie Chen, Chengpeng Xiong, Zhiwei Xu, Ruikang Xiao, Zeyu Huang, Jie Zhang, Yueyi Xi, Si Wei, Jia Bai, Haoran Cheng, Songsheng Wei, Hao Sun, Long Tang, Jinhui Pan, Jinshan Lee, Donghyeon Lee, Chulhee Kim, Taesung Wang, Xiaobing Zhang, Dafeng Pan, Zhihong Lin, Tianwei Wu, Wenhao He, Dongliang Li, Baopu Li, Boyun Xi, Teng Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Tao, Guangpin Chu, Wenqing Cao, Yun Luo, Donghao Tai, Ying Lu, Tong Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Hanting Chen, Shuaijun Guo, Tianyu Wang, Yunhe Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zuo, Yushen Ou, Yimin Chai, Yuanjun Shi, Lei Liu, Shuai Lei, Lei Feng, Chaoyu Zeng, Kai Yao, Yuying Liu, Xinran Zhang, Zhizhou Huang, Huacheng Zhang, Yunchen Jiang, Mingchao Zou, Wenbin Miao, Si Kim, Yangwoo Sun, Yuejin Deng, Senyou Ren, Wenqi Cao, Xiaochun Wang, Tao Suin, Maitreya Rajagopalan, A.N. Duong, Vinh Van Nguyen, Thuc Huu Yim, Jonghoon Jeon, Byeungwoo Li, Ru Xie, Junwei Han, Jong-Wook Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Zhang, Jiaxin Peng, Fan Svitov, David Pakulich, Dmitry Kim, Jaeyeob Jeong, Jechang Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland University of Science and Technology of China China Megvii China Fudan University China Peking University China Netease Games AI Lab Nanjing University of Science and Technology China Guilin University of Electronic Technology China Samsung Electronics Co. Ltd Sunmoon University Asan Korea Republic of Samsung Research China Beijing China Baidu Research United States Department of Computer Vision Technology Baidu Inc China Fudan University China Megvii China Nanjing University China Tencent Noah's Ark Lab Huawei Technologies Co. Ltd Inception Institute of Artificial Intelligence Tsinghua University Beijing China North China University of Technology China Xiaomi South China University of Technology China Lab of Image Science and Technology Southeast University China China Design Group Co. Ltd China JOYY AI GROUP Fujian Normal University China Shanghai Advanced Research Institute Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Huawei Noah's Ark Lab Indian Institute of Technology Madras India Department of ECE Sungkyunkwan University Korea Republic of Fuzhou University China Imperial Vision Co. Ltd School of Integrated Technology Yonsei University Korea Republic of Expasoft LLC Institute of Automation and Electrometry The SB RAS Image Communication & Signal Processing Laboratory Hanyang University Korea Republic of

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. © 2021, CC BY-NC-ND.

关键词： Image enhancement

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Experimental Combined Grouping Analysis Approach for Robust Battery pack design for Electric Vehicles with Higher Performance

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IOP Conference Series: Earth and Environmental science 2019年第1期268卷

作者： Liu Yun Li Shui Liang Gao Zhun Fan C. Ruhatiya Chin-Tsan Wang Akhil Garg Intelligent Manufacturing Key Laboratory of Ministry of Education Shantou University Guangdong China State Key Lab of Digital Manufacturing Equipment & Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shantou 515063 China School of Engineering Sciences Mahindra École Centrale Hyderabad Telangana 500043 India Department of Mechanical and Electro-Mechanical Engineering National I Lan University ILan Taiwan

Battery consistency is an important factor for battery pack performance. Excellent battery consistency can make battery packs more energy efficient and electric vehicles can have longer mileage and higher safety. Thus, in this study a comprehensive intelligent clustering methodology for the design of Li-ion battery pack on the basis of uniformity and equalization criteria of the cell was proposed. Firstly, multiple parameters (capacity, voltage, temperature and resistance) test of single cell performance was performed. Secondly, a clustering method combine with self-organizing map neural network (SOM) was proposed. Furthermore, a validation experiment (pack level) was carried out to verify the accuracy of proposed clustering algorithm. It can be concluded that the battery pack formed from SOM sorting results perform better than the battery pack having random cells combination as well as the pack originally purchased from the manufacturer.

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The Solution Cosmological Constant Problem. Quantum Field Theory in Fractal Space-Time with Negative Hausdorff-Colombeau Dimensions and Dark Matter Nature

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Journal of Physics: Conference Series 2019年第1期1391卷

作者： Jaykov Foukzon Elena R Men'kova Alexander A Potapov Department of mathematics Israel Institute of Technology Haifa 3200003 Israel All-Russian Research Institute for Optical and Physical Measurements Moscow 119361 Russia Kotelínikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences Moscow 125009 Russia JNU-IREE Joint Laboratory of Fractal Method and Signal Processing Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou People's Republic of China

The cosmological constant problem arises because the magnitude of vacuum energy density predicted by quantum field theory is about 120 orders of magnitude larger than the value implied by cosmological observations of accelerating cosmic expansion. We pointed out that the fractal nature of the quantum space-time with negative Hausdorff- Colombeau dimensions can resolve this tension. The canonical Quantum Field Theory is widely believed to break down at some fundamental high-energy cutoff Λ* and therefore the quantum fluctuations in the vacuum can be treated classically seriously only up to this high-energy cutoff. In this paper we argue that Quantum Field Theory in fractal space-time with negative Hausdorff-Colombeau dimensions gives high-energy cutoff on natural way. We argue that there exists hidden physical mechanism which cancel divergences in canonical QED 4, QCD 4, Higher-Derivative - Quantum-Gravity, etc. In fact we argue that corresponding supermassive Pauli-Villars ghost fields really exists. It means that there exists the ghost-driven acceleration of the univers hidden in cosmological constant. In order to obtain desired physical result we apply the canonical Pauli-Villars regularization up to Λ*. This would fit in the observed value of the dark energy needed to explain the accelerated expansion of the universe if we choose highly symmetric masses distribution between standard matter and ghost matter below that scale Λ*,i.e., fs.m (µ) ≈ −fg.m (µ), µ = mc, µ ≤ µ eff, µ eff < Λ*/c. The small value of the cosmological constant explaned by tiny violation of the symmetry between standard matter and ghost matter. Dark matter nature also explaned using a common origin of the dark energy and dark matter phenomena.

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The EPR-B Paradox Resolution. Bell inequalities revisited.

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Journal of Physics: Conference Series 2019年第1期1391卷

One of the Bell's assumptions in the original derivation of his inequalities was the hypothesis of locality, i.e., the absence of the influence of two remote measuring instruments on one another. That is why violations of these inequalities observed in experiments are often interpreted as a manifestation of the nonlocal nature of quantum mechanics, or a refutation of a local realism. It is well known that the Bell's inequality was derived in its traditional form, without resorting to the hypothesis of locality and without the introduction of hidden variables, the only assumption being that the probability distributions are nonnegative. This can therefore be regarded as a rigorous proof that the hypothesis of locality and the hypothesis of existence of the hidden variables not relevant to violations of Bell's inequalities. The physical meaning of the obtained results is examined. Physical nature of the violation of the Bell inequalities is explained under new EPR-B nonlocality postulate. We show that the correlations of the observables involved in the Bohm–Bell type experiments can be expressed as correlations of classical random variables. The revisited Bell type inequality in canonical notatons reads 〈AB〉 + 〈A'B〉 + 〈AB'〉 − 〈A'B'〉 ≤ 6.

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Novel explanation of the Active Galactic Nuclei. The Power Source of Quasars as a result of vacuum polarization by the gravitational singularities on the distributional BHs horizon

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Journal of Physics: Conference Series 2021年第1期1730卷

作者： Jaykov Foukzon Elena R Men'kova Alexander A Potapov Department of mathematics Israel Institute of Technology Haifa 3200003 Israel All-Russian Research Institute for Optical and Physical Measurements Moscow 119361 Russia Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences Moscow 125009 Russia JNU-IREE Joint Laboratory of Fractal Method and Signal Processing Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou People's Republic of China

In this paper we argue that the current paradigm for AGN and quasars is essentially incomplete and a rivision is needed. Remind that the current paradigm for AGN and quasars is that their radio emission is explained by synchrotron radiation from relativistic electrons that are Doppler boosted through bulk motion. In this model, the intrinsic brightness temperatures cannot exceed 1011 to 1012 K. Typical Doppler boosting is expected to be able to raise this temperature by a factor of 10. The observed brightness temperature of the most compact structures in BL Lac, constrained by baselines longer than 5.3G, must indeed exceed 2 × 1013K and can reach as high as ˜ 3 × 1014K. This is difficult to reconcile with current incoherent synchrotron emission models from relativistic electrons, requiring alternative models such as emission from relativistic protons. However the proton, as we know, is 1836 times heavier than an electron and absolutely huge energy is required to accelerated it to sublight speed. These alternative models such as emission from relativistic protons can be suported by semiclassical gravity effect finds its roots in the singular behavior of quantum fields on curved distributional space- times presented by rotating gravitational singularities.

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Corrigendum to “A user-specific and selective multimodal biometric fusion strategy by ranking subjects” [Pattern Recognition 46 (2013) 3341–3357]

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Pattern Recognition 2014年第1期47卷 493-493页

作者： Norman Poh Arun Ross Weifeng Li Josef Kittler Department of Computing University of Surrey Guildford GU27XH UK West Virginia University Morgantown WV 26506 USA Shenzhen Key Laboratory of Information Science & Technology Shenzhen Engineering Laboratory of IS&DRM Department of Electronic Engineering/Graduate School at Shenzhen Tsinghua University China Centre for Vision Speech and Signal Processing (CVSSP) FEPS University of Surrey Guildford GU27XH UK

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Mo1503 Narrow Band Imaging and High-Magnification Endoscopy (NBI-Hme) for Gastric Lesions Differentiation and Histology Prediction

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Gastrointestinal Endoscopy 2014年第5期79卷 AB461-AB461页

作者： Roman Kuvaev Evgeny Nikonov Sergey V. Kashin Herbert Edelsbrunner Viktor Kapranov Michael Machin OLGA a. Dunaeva Endoscopy Yaroslavl Regional Cancer Hospital Yaroslavl Russian Federation Administration Polyclinic No.1 of the Buisness Administration for the President of the Russian Federation Moscow Russian Federation Institute of Science and Technology Austria (IST Austria) Klosterneuburg Austria Department of the University Internet Centre Demidov Yaroslavl State University Yaroslavl Russian Federation Digital Signal and Image Processing Laboratory Demidov Yaroslavl State University Yaroslavl Russian Federation

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