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Enabling Temporal-Spectral Decoding in Pre-movement Detection

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arXiv 2022年

作者： Jia, Hao Caiafa, Cesar F. Duan, Feng Zhang, Yu Sun, Zhe Solé-Casals, Jordi The Data and Signal Processing Research Group University of Vic-Central University of Catalonia Catalonia Vic Spain The Instituto Argentino de Radioastronomía CONICET CCT La Plata/CIC-PBA/UNLP V. Elisa Argentina The College of Artificial Intelligence Nankai University Tianjin China The College of Artificial Intelligence Nankai University Tianjin China The Department of Bioengineering The Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States The Computational Engineering Applications Unit Head Office for Information Systems and Cybersecurity RIKEN Saitama Japan The Department of Psychiatry University of Cambridge United Kingdom

Non-invasive brain-computer interfaces help the subjects to control external devices by brain intentions. The multi-class classification of upper limb movements can provide external devices with more control commands. The onsets of the upper limb movements are located by the external limb trajectory to eliminate the delay and bias among the trials. However, the trajectories are not recorded due to the limitation of experiments. The delay cannot be avoided in the analysis of signals. The delay negatively influences the classification performance, which limits the further application of upper limb movements in the brain-computer interface. This work focuses on multi-channel brain signals analysis in the temporal-frequency approach. It proposes the two-stage-training temporal-spectral neural network (TTSNet) to decode patterns from brain signals. The TTSNet first divides the signals into various filter banks. In each filter bank, task-related component analysis is used to reduce the dimension and reject the noise of the brain. A convolutional neural network (CNN) is then used to optimize the temporal characteristic of signals and extract class-related features. Finally, these class-related features from all filter banks are fused by concatenation and classified by the fully connected layer of the CNN. The proposed method is evaluated in two public datasets. The results show that TTSNet has an improved accuracy of 0.7456±0.1205 compared to the EEGNet of 0.6506±0.1275 (p Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

Asymptotic analysis of max-min weighted SINR for IRS-assisted MISO systems with hardware impairments

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arXiv 2021年

作者： Papazafeiropoulos, Anastasios Pan, Cunhua Elbir, Ahmet Nguyen, Van Kourtessis, Pandelis Chatzinotas, Symeon Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom SnT University of Luxembourg Luxembourg Luxembourg School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical and Electronics Engineering Koc University Istanbul Turkey Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom

We focus on the realistic maximization of the uplink minimum signal-to-interference-plus-noise ratio (SINR) of a general multiple-input single-output (MISO) system assisted by an intelligent reflecting surface (IRS) in the large system limit accounting for HIs. In particular, we introduce the HIs at both the IRS (IRS-HIs) and the transceiver HIs (AT-HIs), usually neglected despite their inevitable impact. Specifically, the deterministic equivalent analysis enables the derivation of the asymptotic weighted maximum-minimum SINR with HIs by jointly optimizing the HIs-aware receiver, the transmit power, and the reflect beamforming matrix (RBM). Notably, we obtain the optimal power allocation and reflect beamforming matrix with low overhead instead of their frequent necessary computation in conventional MIMO systems based on the instantaneous channel information. Monte Carlo simulations verify the analytical results which show the insightful interplay among the key parameters and the degradation of the performance due to HIs. © 2021, CC BY.

关键词： 5G mobile communication systems

Performance-oriented data-driven controller tuning for smooth and precise tray indexing 19

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Performance-oriented data-driven controller tuning for smoot...

19th International Conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2019

作者： Li, Xiaocong Cheng, Zilong Ma, Jun Teo, Chek Sing Zhu, Haiyue Tan, Kok Kiong Mechatronics Group Singapore institute of Manufacturing Technology Singapore Singapore NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore SIMTech-NUS Joint Lab on Precision Motion Systems Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)9780995775145

The feedback controller design and tuning play an important role in mechatronics and robotic systems for robust and high precision motion control. For instance, in a typical tray indexing application, when the controller is not appropriately tuned, large tracking error, dislodged chips and humming sound could occur. Traditional feedback controller design and tuning is based on system modelling, but it faces challenges when modelling is difficult or less accurate. Hence, in recent years, more and more data-driven approaches are developed, which utilize the actual motion data to optimize the controller parameters iteratively, without relying upon a detailed system model. Such control design is also performance-oriented as the design objective is directly taken into account during the optimization as the cost function. In our work, a data-driven tuning procedure for tray indexing applications is proposed. Different with existing data-driven approaches, the cost function of the proposed method takes into account both the tracking error and jerk minimization as it is particularly suitable for the tray indexing application. Subsequently, the gradient and Hessian matrix of the cost function is estimated solely based on the motion data collect from the encoder without any attempt to build a system model. At last, Gauss-Newton optimization algorithm is executed based on the estimated gradient and Hessian matrix in order to provide the next set of controller parameters to reduce the pre-defined cost function. The effectiveness of the proposed method is shown on an industrial tray indexing setup with significantly improved performance in terms of tracking and jerk reduction. © 2019 European Society for Precision engineering and Nanotechnology, Conference Proceedings - 19th International Conference and Exhibition, EUSPEN 2019. All rights reserved.

关键词： Cost functions

Correction to: development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

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Journal of Cloud Computing 2023年第1期12卷 1-1页

作者： Kumar, Ankit Khan, Surbhi Bhatia Pandey, Saroj Kumar Shankar, Achyut Maple, Carsten Mashat, Arwa Malibari, Areej A. Department of Computer Engineering & Applications GLA University Mathura India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Data Science School of Science Engineering and Environment University of Salford Manchester Byblos UK WMG University of Warwick Coventry UK Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK Faculty of Computing and Information Science King Abdulaziz University Rabigh Saudi Arabia Department of Industrial and Systems Engineering College of Engineering Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

A Hybrid Approach for Weather-based Power Interruption Forecasting using Multilayer Perceptrons and Parametric Models 12

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A Hybrid Approach for Weather-based Power Interruption Forec...

12th IEEE PES Asia-Pacific Power and Energy engineering Conference, APPEEC 2020

作者： Wei, Longfei Yang, Xiaolei Liu, Guangyi Dai, Renchang Sundararajan, Aditya Sarwat, Arif I. Global Energy Interconnection Research Institute North America San JoseCA United States State Grid Sichuan Electric Power Company Chengdu Sichuan China Oak Ridge National Laboratory Power and Energy Systems Group Oak RidgeTN United States Florida International University Department of Electrical Computer Engineering MiamiFL United States

ISBN: (纸本)9781728157481

This paper investigates the impact of various weather conditions on the reliability performance of power distribution networks. Especially, a hybrid approach based on multilayer perceptrons (MLPs) and parametric models is proposed to forecast the daily numbers of sustained and momentary power interruptions in the distribution management area using chronological weather data. First, the parametric regression models are implemented to analyze the relationship between power interruptions and different weather characteristics including temperature, wind speed, rain precipitation, air pressure, and lightning. The selected weather characteristics and corresponding parametric models are then integrated as inputs to formulate a MLP neural network model to forecast the daily numbers of power interruptions. In addition, a modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced for training the formulated forecasting model. Finally, the real power interruption data collected from a Florida electric utility is used to verify the applicability and effectiveness of the proposed hybrid approach. © 2020 IEEE.

关键词： Electric utilities

A micromagnetic study of the switching dynamics of the BiFeO3/CoFe heterojunction

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arXiv 2020年

作者： Liao, Yu-Ching Nikonov, Dmitri E. Dutta, Sourav Chang, Sou-Chi Hsu, Chia-Sheng Young, Ian A. Naeemi, Azad School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Components Research Technology & Manufacturing Group Intel Corporation HillsboroOR97124 United States Department of Electrical Engineering University of Notre Dame Notre DameIN46556 United States

The switching dynamics of a single-domain BiFeO3/CoFe heterojunction is modeled and key parameters such as interface exchange coupling coefficient are extracted from experimental results. The lower limit of the magnetic order response time of CoFe in the BiFeO3/CoFe heterojunction is theoretically quantified to be on to the order of 100 ps. Our results indicate that the switching behavior of CoFe in the BiFeO3/CoFe heterojunction is dominated by the rotation of the Neel vector in BiFeO3 rather than the unidirectional exchange bias at the interface. We also quantify the magnitude of the interface exchange coupling coefficient Jint to be 0.32 pJ/m by comparing our simulation results with the giant magnetoresistance (GMR) curves and the magnetic hysteresis loop in the experiments. To the best of our knowledge, this is the first time that Jint is extracted quantitatively from experiments. Furthermore, we demonstrate that the switching success rate and the thermal stability of the BiFeO3/CoFe heterojunction can be improved by reducing the thickness of CoFe and increasing the length to width aspect ratio of the BiFeO3/CoFe heterojunction. Our theoretical model provides a comprehensive framework to study the magnetoelectric properties and the manipulation of the magnetic order of CoFe in the BiFeO3/CoFe heterojunction. Copyright © 2020, The Authors. All rights reserved.

关键词： Binary alloys

Reconstruction of photospheric velocity fields from highly corrupted data

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arXiv 2022年

作者： Rempel, Erico L. Chertovskih, Roman Davletshina, Kamilla R. Silva, Suzana S.A. Welsch, Brian T. Chian, Abraham C.-L. SP São José dos Campos12228–900 Brazil P.O. Box 515 SP São José dos Campos12227-010 Brazil Electrical and Computer Engineering Department Faculty of Engineering University of Porto Rua Dr. Roberto Frias s/n Porto4200-465 Portugal Yandex Lva Tolstogo street 16 Moscow119021 Russia Plasma Dynamics Group Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom University of Wisconsin Green BayWI54311 United States University of Adelaide School of Mathematical Sciences AdelaideSA5005 Australia

The analysis of the photospheric velocity field is essential for understanding plasma turbulence in the solar surface, which may be responsible for driving processes such as magnetic reconnection, flares, wave propagation, particle acceleration, and coronal heating. Currently, the only available methods to estimate velocities at the solar photosphere transverse to an observer’s line of sight infer flows from differences in image structure in successive observations. Due to data noise, algorithms such as local correlation tracking (LCT) may lead to a vector field with wide gaps where no velocity vectors are provided. In this letter, a novel method for image inpainting of highly corrupted data is proposed and applied to the restoration of horizontal velocity fields in the solar photosphere. The restored velocity field preserves all the vector field components present in the original field. The method shows robustness when applied to both simulated and observational data. Copyright © 2022, The Authors. All rights reserved.

关键词： Velocity

Modelling of variable-speed refrigeration for fast-frequency control in low-inertia systems

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arXiv 2020年

作者： Vorwerk, Johanna Markovic, Uros Aristidou, Petros Vrettos, Evangelos Hug, Gabriela Power Systems Laboratory ETH ZÃijrich Physikstrasse 3 ZÃijrich8092 Switzerland Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Grid Integration Group Lawrence Berkeley National Lab

In modern power systems, shiftable loads contribute to the flexibility needed to increase robustness and ensure security. Thermal loads are among the most promising candidates for providing such service due to the large thermal storage time constants. This paper demonstrates the use of Variable-Speed Refrigeration (VSR) technology, based on brushless DC motors, for fast-frequency response. First, we derive a detailed dynamic model of a single-phase VSR unit suitable for time-domain and small-signal stability analysis in low-inertia systems. For analysing dynamic interactions with the grid, we consider the aggregated response of multiple devices. However, the high computational cost involved in analysing large-scale systems leads to the need for reduced-order models. Thus, a set of reduced-order models is derived though transfer function fitting of data obtained from time-domain simulations of the detailed model. The modelling requirements and the accuracy versus computational complexity trade-off are discussed. Finally, the time-domain performance and frequency-domain analyses reveal substantial equivalence between the full- and suitable reduced-order models, allowing the application of simplified models in large-scale system studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Large scale systems

MONAI: An open-source framework for deep learning in healthcare

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arXiv 2022年

作者： Cardoso, M. Jorge Li, Wenqi Brown, Richard Ma, Nic Kerfoot, Eric Wang, Yiheng Murrey, Benjamin Myronenko, Andriy Zhao, Can Yang, Dong Nath, Vishwesh He, Yufan Xu, Ziyue Hatamizadeh, Ali Zhu, Wentao Liu, Yun Zheng, Mingxin Tang, Yucheng Yang, Isaac Zephyr, Michael Hashemian, Behrooz Alle, Sachidanand Darestani, Mohammad Zalbagi Budd, Charlie Modat, Marc Vercauteren, Tom Wang, Guotai Li, Yiwen Hu, Yipeng Fu, Yunguan Gorman, Benjamin Johnson, Hans Genereaux, Brad Erdal, Barbaros S. Gupta, Vikash Diaz-Pinto, Andres Dourson, Andre Maier-Hein, Lena Jaeger, Paul F. Baumgartner, Michael Kalpathy-Cramer, Jayashree Flores, Mona Kirby, Justin Cooper, Lee A.D. Roth, Holger R. Xu, Daguang Bericat, David Floca, Ralf Zhou, S. Kevin Shuaib, Haris Farahani, Keyvan Maier-Hein, Klaus H. Aylward, Stephen Dogra, Prerna Ourselin, Sebastien Feng, Andrew School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom NVIDIA Corporation Santa Clara and Bethesda United States School of Electrical and Computer Engineering Rice University Houston United States Department of Engineering Science University of Oxford Oxford United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Department of BioAI InstaDeep Ltd London United Kingdom Department of Electrical & Computer Engineering University of Iowa Iowa City United States Mayo Clinic Jacksonville United States Mars Incorporated United States Div. Intelligent Medical Systems German Cancer Research Center Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Medical Physics Guy’s & St Thomas’ NHS Foundation Trust London United Kingdom National Cancer Institute Bethesda United States Kitware Inc. Clifton Park United States Department of Ophthalmology University of Colorado Aurora United States Frederick National Laboratory for Cancer Research Frederick United States Department of Pathology Northwestern University Chicago United States

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare. © 2022, CC BY.

关键词： Clinical research