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Boosted convolutional neural networks for motor imagery EEG decoding with multiwavelet-based time-frequency conditional granger causality analysis

arXiv

引用

arXiv 2018年

作者： Li, Yang Lei, Mengying Zhang, Xianrui Cui, Weigang Guo, Yuzhu Huang, Ting-Wen Wei, Hua-Liang Department of Automation Sciences and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Advanced Innovation Center for Big Date-based Precision Medicine Beihang University Beijing China Department of Automation Sciences and Electrical Engineering Beihang University Beijing China Department of Mathematics Texas A&M University at Qatar Doha23874 Qatar Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3DJ United Kingdom

Decoding EEG signals of different mental states is a challenging task for brain-computer interfaces (BCIs) due to non-stationarity of perceptual decision processes. This paper presents a novel boosted convolutional neural networks (ConvNets) decoding scheme for motor imagery (MI) EEG signals assisted by the multiwavelet-based time-frequency (TF) causality analysis. Specifically, multiwavelet basis functions are first combined with Geweke spectral measure to obtain high-resolution TF-conditional Granger causality (CGC) representations, where a regularized orthogonal forward regression (ROFR) algorithm is adopted to detect a parsimonious model with good generalization performance. The causality images for network input preserving time, frequency and location information of connectivity are then designed based on the TF-CGC distributions of alpha band multichannel EEG signals. Further constructed boosted ConvNets by using spatio-temporal convolutions as well as advances in deep learning including cropping and boosting methods, to extract discriminative causality features and classify MI tasks. Our proposed approach outperforms the competition winner algorithm with 12.15% increase in average accuracy and 74.02% decrease in associated inter subject standard deviation for the same binary classification on BCI competition-IV dataset-IIa. Experiment results indicate that the boosted ConvNets with causality images works well in decoding MI-EEG signals and provides a promising framework for developing MI-BCI systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistical tests

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P2P Over MANET: A Review and Its Conceptual Framework

P2P Over MANET: A Review and Its Conceptual Framework

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International Conference on Parallel Distributed and Grid computing (PDGC)

作者： Farkhana Muchtar Pradeep Kumar Singh Siti Nor Zawani Ahmmad Radzi Ambar Hafizul Fahri Hanafi Suzi Iryanti Fadilah Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia Department of CSE &IT JayPee University of IT Waknaghat Solan Himachal Pradesh India Instrumentation & Control Engineering MITEC Johor Malaysia Faculty of Electronic & Electrical Engineering Universiti Tun Hussein Onn Batu Pahat Malaysia Faculty of Art Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Perak Malaysia School of Computer Sciences Universiti Sains Malaysia Gelugor Pulau Pinang Malaysia

ISBN: (纸本)9781728106489

Lately, Peer to Peer (P2P) over Mobile Ad hoc Network have become a hot topics. Thus, the main motivation of this paper is to review various techniques that have been used in previously reported research of Peer to Peer (P2P) over Mobile Ad hoc Network (MANET). From the said document, it is identified that all previously techniques used in P2P over MANET have one similarity; namely maintaining the use of TCP/IP stack as network solution, in which the source for high energy resources for their proposed solution. Hence, this paper proposed a new solution beyond the TCP/IP stack paradigms in order to improve energy efficiency in P2P over MANET, whereby through the use of Content Centric Networking (CCN). In detail, this paper has included conceptual framework of our suggested solution. It is hoped that this paper achieves its two objectives: (i) to present a literature review on the topic of P2P over MANET, and (ii) to provide conceptual framework of our suggested solution.

关键词： Mobile ad hoc networks Peer-to-peer computing TCPIP Overlay networks Internet Protocols

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Relation Model of Burden Operation and State Variables of Blast Furnace Based on Low Frequency Feature Extraction*

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IFAC-PapersOnLine 2017年第1期50卷 13796-13801页

作者： Zhang, Kexin Wu, Min An, Jianqi Cao, Weihua Liu, Zhentao Ning, Fulong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Faculty of Engineering China University of Geosciences wuhan430074 China Department of Computer Science School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan

In the blast furnace ironmaking process, the running state of the blast furnace can be directly adjusted by the burden operation. Therefore, the explicit relation between the burden operation and the running state variables is vital for the blast furnace operators. However, the state variables of the blast furnace are affected by many factors and the burden matrix is complicated. In this paper, a support vector regression(SVR) predicting model based on low frequency feature extraction is proposed to solve these problems. First, the low frequency components of state variables time series which are mostly affected by the burden operation are extracted. Then, the dimensions of the burden matrix are reduced by a devised simple expression method. Finally, the relation model based on SVR is proposed to predict the values of the expected state variables according to the simplified burden matrix and the value of initial state variables. The simulation on real factory data indicates that the model reflects the accurate quantitative relation between the burden operation and the state variables. © 2017

关键词： Blast furnaces Blast furnace practice Extraction Feature extraction Support vector regression Blast furnace iron making Blast furnace operators burden operation Initial state variables Low frequency components Relation models State variables Support vector regression (SVR)

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A Big Data based Cost Prediction Method for Remanufacturing End-of-Life Products

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Procedia CIRP 2018年 72卷 1362-1367页

作者： Zhouyang Ding Zhigang Jiang Ying Liu Yan Wang Congbo Li Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science & Technology Wuhan 430081 China Key Laboratory of Metallurgical Equipment and Control Technology Wuhan University of Science & Technology Wuhan 430081 China Institute of mechanical and manufacturing engineering School of Engineering Cardiff University Cardiff CF24 3AA Department of Computing Engineering and Mathematics University of Brighton Brighton BN2 4GJ United Kingdom State Key Laboratory of Mechanical Transmission Chongqing University Chongqing 400030 China

Remanufacturing is considered as an important industrial process to restore the performance and function of End-of-Life (EOL) products to a like-new state. In order to help enterprises effectively and precisely predict the cost of remanufacturing processes, a remanufacturing cost prediction model based on big data is developed. In this paper, a cost analysis framework is established by applying big data technologies to interpret the obtained data, identify the intricate relationship of obtained sensor data and its corresponding remanufacturing processes and associated costs. Then big data mining and particle swarm optimization Back Propagation (BP) neural network algorithm are utilized to implement the cost prediction. The application of presented model is verified by a case study, and the results demonstrates that the developed model can predict the cost of the remanufacturing accurately allowing early decision making for remanufacturability of the EOL products.

关键词： Remanufacturing Cost Prediction End-of-Life Products Big Data BP Neural Network

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Adaptive segmentation of multimodal polysomnography data for sleep stages detection 22

Adaptive segmentation of multimodal polysomnography data for...

引用

2017 22nd International Conference on Digital Signal Processing, DSP 2017

作者： Procházka, Aleš Kuchyňka, Jiri Yadollahi, Mohammadreza Araujo, Carmen Paz Suarez Vyšata, Oldřich University of Chemistry and Technology Department of Computing and Control Engineering Czech Republic Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Republic Charles University Department of Neurology Czech Republic Universidad de Las Palmas de Gran Canaria Spain

ISBN: (纸本)9781538618950

The paper presents a new algorithm for adaptive classification of sleep stages using multimodal data recorded in the sleep laboratory during overnight polysomnography records. The proposed method includes the learning process applied for the set of individuals with their sleep stages classified by an experienced neurologist. Features evaluated for time windows 30 s long and selected multimodal signals are used for construction and optimization of the proposed two-layer neural network model. Resulting computational system based upon breathing EEG and EOG features is used for analysis of new individuals to detect their sleep stages. Results include classification accuracy higher than 80% and 90% for Wake and REM stages, respectively. The proposed method can adaptively modify model coefficients to detect sleep stages and sleeping disorders using man-machine interaction. © 2017 IEEE.

关键词： Network layers

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Characterising acute and chronic care needs: insights from the Global Burden of Disease Study 2019

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Nature communications 2025年第1期16卷 4235-4235页

作者： Cecilia Anza-Ramirez J. Jaime Miranda Benedetta Armocida Jorge César Correia Harriette Gillian Christine Van Spall David Beran Amirali Aali Kalkidan Hassen Abate Semagn Mekonnen Abate Cristiana Abbafati Mohsen Abbasi-Kangevari Zeinab Abbasi-Kangevari Zahra Aryan Sina Azadnajafabad Mohammadreza Azangou-Khyavy Seyyed-Hadi Ghamari Mohammad Keykhaei Javad Khanali Sara Momtazmanesh Fateme Montazeri Shayan Rahmani Mohammad-Mahdi Rashidi Nazila Rezaei Negar Rezaei Sahar Saeedi Moghaddam Hossein Hassanian-Moghaddam Ali-Asghar Kolahi Ali Nikoobar Hedayat Abbastabar Ahmed M. Abdel-Azeem Michael Abdelmasseh Juan Sanabria Sherief Abd-Elsalam Ahmed Abdelwahab Gholamreza Abdoli Mohammad Abdollahi Meriem Abdoun Deldar Morad Abdulah Abu Yousuf Md Abdullah Ame Mehadi Abdurehman Getachew Abebe Aidin Abedi Chirag P. Doshi Vida Abedi Hassan Abidi Richard Gyan Aboagye Hassan Abolhassani Nima Rezaei Lucas Guimarães Abreu Michael R. M. Abrigo Yonas Derso Abtew Hiwa Abubaker Ali Eman Abu-Gharbieh Hiba Jawdat Barqawi Rabih Halwani Maha Mohamed Saber-Ayad Iman M. Talaat Ahmed Abu-Zaid Manfred Mario Kokou Accrombessi Juan Manuel Acuna Denberu Eshetie Adane Tigist Demssew Adane Isaac Yeboah Addo Giovanni Addolorato Oluwafemi Atanda Adeagbo Victor Adekanmbi Miracle Ayomikun Adesina Isaac Iyinoluwa Olufadewa Olatunji O. Adetokunboh Daniel Adedayo Adeyinka Qorinah Estiningtyas Sakilah Adnani Aanuoluwapo Adeyimika Afolabi Olayinka Stephen Ilesanmi Muhammad Sohail Afzal Saira Afzal Gina Agarwal Prerna Agarwal Pradyumna Agasthi Anurag Agrawal Marcela Agudelo-Botero Adolfo Martinez-Valle Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Sohail Ahmad Tauseef Ahmad Ali Ahmadi Siamak Sabour Keivan Ahmadi Sanjay Basu Sepideh Ahmadi Ali Ahmed Shaun Wen Huey Lee Ayman Ahmed Haroon Ahmed Jivan Qasim Ahmed Gahin Abdulraheem Tayib Yahya Yahya Luai A. Ahmed Iffat Elbarazi Syed Mahboob Shah Janardhana P. Aithala Marjan Ajami Budi Aji Hossein Akbarialiabad Yibeltal Akelew Meisam Akhlaghdoust Elham Jamshidi Addis Aklilu Azene Tesfaye Melat Woldemariam Maxwell Ak CRONICAS Center of Excellence in Chronic Diseases Universidad Peruana Cayetano Heredia Lima Peru School of Medicine Universidad Peruana Cayetano Heredia Lima Peru Sydney School of Public Health Faculty of Medicine and Health University of Sydney Sydney NSW Australia Faculty of Epidemiology and Population Health London School of Hygiene & Tropical Medicine London UK Division of Tropical and Humanitarian Medicine University of Geneva Geneva Switzerland Department of Cardiovascular Endocrine-metabolic Diseases and Aging Istituto Superiore di Sanità Rome Italy Division of Endocrinology Diabetology Nutrition and Therapeutic Patient Education Department of Medicine Geneva University Hospitals Geneva Switzerland Department of Medicine and Department of Health Research Evidence and Impact McMaster University Hamilton ON Canada Research Institute of St. Joe’s Hamilton ON Canada Population Health Research Institute Hamilton Canada Geneva University Hospitals Geneva Switzerland Faculty of Medicine Mashhad University of Medical Sciences Mashhad Iran Department of Nutrition and Dietetics Jimma University Jimma Ethiopia Anesthesiology Department Dilla University Addis Ababa Ethiopia Department of Juridical and Economic Studies La Sapienza University Rome Italy Non-Communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Brigham and Women’s Hospital Harvard University Boston MA USA Students’ Scientific Research Center (SSRC) Tehran University of Medical Sciences Tehran Iran School of Medicine Tehran University of Medical Sciences Tehran Iran School of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Institute Tehran University of Medical Sciences Tehran Iran Chapter of Addiction Medicine University of Sydney Sydney NSW Australia Advanced Diagnostic and Intervent

Chronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly. © 2025. The Author(s).

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Fast Computation of the Discrete Pascal Transform

Fast Computation of the Discrete Pascal Transform

引用

International Symposium on Multiple-Valued Logic

作者： Dušan B. Gajić Radomir S. Stanković Department of Computing and Control Engineering University of Novi Sad Novi Sad Serbia Department of Computer Science University of Niš Niš Serbia

The discrete Pascal transform (DPT) is a relatively recently introduced spectral transform based on the concept of the Pascal triangle which has been known for centuries. It is used in digital image processing, digital filtering, pattern recognition, watermarking, and related areas. Its applicability is limited by the O(N 2 ) asymptotical time complexity of best current algorithms for its computation, where N is the size of the function to be processed. In this paper, we propose a method for the efficient computation of the DPT in O(N log N) time, based on the factorization of its transform matrix into a product of three matrices with special structure - two diagonal matrices and a Toeplitz matrix. The Toeplitz matrix is further embedded into a circulant matrix of order 2N. The diagonalization of the circulant matrix by the Fourier matrix permits the use of the fast Fourier transform (FFT) for performing the computations, leading to an algorithm with the overall computational complexity of O(N log N). Since the entries in the Toeplitz matrix have very different magnitudes, the numerical stability of this algorithm is also discussed. We also consider the issues in implementing the proposed algorithm for highly-parallel computation on graphics processing units (GPUs). The experiments show that computing the DPT using the proposed algorithm processed on GPUs is orders of magnitude faster than the best current approach. As a result, the proposed method can significantly extend the practical applicability of the discrete Pascal transform.

关键词： Transforms Matrix decomposition Matrix converters Time complexity Numerical stability Symmetric matrices Nickel

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Multi-antenna channel interpolation via Tucker decomposed extreme learning machine

arXiv

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arXiv 2018年

作者： Zhang, Han Ai, Bo Xu, Wenjun Xu, Li Cui, Shuguang Department of Electrical and Computer Engineering University of California DavisCA95616 United States State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Key Lab of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Institute of Computing Technology Chinese Academy of Science Beijing100190 China

Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information (CSI) for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms. In this letter, a modified extreme learning machine (ELM) that can process tensorial data, or ELM model with tensorial inputs (TELM), is proposed to handle the channel interpolation task. The TELM inherits many good properties from ELMs. Based on the TELM, the Tucker decomposed extreme learning machine (TDELM) is proposed for further improving the performance. Furthermore, we establish a theoretical argument to measure the interpolation capability of the proposed learning machines. Experimental results verify that our proposed learning machines can achieve comparable mean squared error (MSE) performance against the traditional ELMs but with 15% shorter running time, and outperform the other methods for a 20% margin measured in MSE for channel interpolation. Copyright © 2018, The Authors. All rights reserved.

关键词： Interpolation

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Traffic parameters prediction using a three-channel convolutional neural network 2nd

Traffic parameters prediction using a three-channel convolut...

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2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017

作者： Zang, Di Wang, Dehai Cheng, Jiujun Tang, Keshuang Li, Xin Department of Computer Science and Technology Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Transportation Information and Control Engineering Tongji University Shanghai China Shanghai Lujie Electronic Technology Co. Ltd. PudongShanghai China

ISBN: (纸本)9783319681207

Traffic three elements consisting of flow, speed and occupancy are very important parameters representing the traffic information. Prediction of them is a fundamental problem of Intelligent Transportation Systems (ITS). Convolutional Neural Network (CNN) has been proved to be an effective deep learning method for extracting hierarchical features from data with local correlations such as image, video. In this paper, in consideration of the spatiotemporal correlations of traffic data, we propose a CNN-based method to forecast flow, speed and occupancy simultaneously by converting raw flow, speed and occupancy (FSO) data to FSO color images. We evaluate the performance of this method and compare it with other prevailing methods for traffic prediction. Experimental results show that our method has superior performance. © IFIP International Federation for Information Processing 2017.

关键词： Forecasting

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Statistical shape model generation using diffeomorphic surface registration 17

Statistical shape model generation using diffeomorphic surfa...

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2nd International Conference on Biomedical Signal and Image Processing, ICBIP 2017

作者： Wu, J. Li, G. Lu, H. Kim, H. Ogunbona, P.O. School of Electronics and Information Engineering Tianjin Polytechnic University No. 399 Binshui Xi Road Xiqing District Tianjin300387 China Department of Control Engineering Kyushu Institute of Technology 1-1 Sensuicho Tobata Kitakyushu Fukuoka Japan School of Computing and Information Technology University of Wollongong Northfield Avenue WollongongNSW2522 Australia

ISBN: (纸本)9781450352680

Statistical shape modelling is an efficient and robust method for medical image segmentation in computer-aided diagnosis. The key step in building a statistical shape model is to find corresponding landmarks in each instance of a training set. In this paper, a novel landmark correspondence estimation method that uses edge collapse surface simplification and the sphere registration is proposed. All the landmarks are selected and transformed by spherical conformal mapping from the instances of the training set and the associated correspondence are automatically found on the spheres. We applied our method on 21 cases of 3-D right lung shapes. The results of image segmentation experiment indicate that our method has a positive influence on the accuracy of segmentation result. © 2017 Association for computing Machinery.

关键词： Spheres

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