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IEEE International Conference on Automation Science and engineering (CASE)

作者： Ehtisham Lodhi Zeeshan Lodhi Zhu Fenghua Gang Xiong Feiyue Wang Waheed ur Rehman Tariku Sinshaw The SKL for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering COMSATS University Islamabad Abbottabad Campus Pakistan Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan Guangdong China Beijing Engineering Research Center of Intellectual System and Technology Chinese Academy of Sciences Beijing China College of Mechanical Engineering and Applied Electronics Technologies Beijing University of Technology Beijing China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

In modern era, one of the rising power quality (PQ) concern is harmonics distortion (HD). The HD is usually produced by increase in the utilization of nonlinear user loads at domestic level. It not only spoils the PQ but also have harmful effects on electrical appliances. So, this paper presents a new approach to mitigate the HD in home appliances rapidly by utilizing the active band pass filter (ABPF). The fast Fourier transform (FFT) technique is employed for the detection of harmonics in appliances. The band pass filter dramatically affects the response time of active filter. The suggested control methodology is capable to eliminate harmonics from distorted input voltage waveform and provides a pure sinusoidal waveform. The prototype of this model is also implemented. Experimental results along with simulation show that the proposed model is quite effective in eliminating HD despite load variations and also useful to compensate the reactive power in electronic home appliances. Hence, this technique is cost effective for domestic users with high quality performance in all aspects.

关键词： Power harmonic filters Harmonic analysis Band-pass filters Harmonic distortion Home appliances

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Multivariate Models for Decoding Hearing Impairment using EEG Gamma-Band Power Spectral Density

Multivariate Models for Decoding Hearing Impairment using EE...

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International Joint Conference on Neural Networks (IJCNN)

作者： Md Sultan Mahmud Faruk Ahmed Mohammed Yeasin Claude Alain Gavin M. Bidelman Department of Electrical and Computer Engineering University of Memphis TN USA Rotman Research Institute-Baycrest Centre for Geriatric Care Toronto Canada Department of Psychology University of Toronto Toronto Canada Institute of Medical Sciences University of Toronto Toronto Canada School of Communication Sciences & Disorders University of Memphis Memphis TN USA Institute for Intelligent Systems University of Memphis Memphis TN USA Department of Anatomy and Neurobiology University of Tennessee Health Sciences Center Memphis TN USA

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Speech-in-noise (SIN) comprehension decreases with age, and these declines have been related to social isolation, depression, and dementia in the elderly. In this work, we build models to distinguish the normal hearing (NH) or mild hearing impairment (HI) using the different genres of machine learning. We compute band wise power spectral density (PSD) of source- derived EEGs as features in building models using support vector machine (SVM), k-nearest neighbors (KNN), and AdaBoost classifiers and compare their performance while listeners perceived clear or noise-degraded sounds. Combining all frequency bands features obtained from the whole-brain, the SVM registered the best performance. The group classification accuracy was found to be 94.90% [area under the curve (AUC) 94.75%; F1-score 95.00%] perceived the clear speech, and for noise- degraded speech perception, accuracy was found to be 92.52% (AUC 91.12%, and F1-score 93.00%). Remarkably, individual frequency band analysis on whole-brain data showed that γ frequency band segregated groups with a best accuracy of 96.78%, AUC 96.79% for clear speech data and noise-degraded speech data yielded slightly less accuracy of 93.62% with AUC 93.17% by using SVM. A separate analysis using the left hemisphere (LH) and right hemisphere (RH) data showed that the LH activity is a better predictor of groups compared to RH. These results are consistent with the dominance of LH in auditory-linguistic processing. Our results demonstrate that spectral features of the γ-band frequency could be used to differentiate NH and HI older adults in terms of their ability to process speech sounds. These findings would be useful to model attentional and listening assistive devices to amplify a more specific pitch than others.

关键词： Auditory system Brain modeling Electroencephalography Support vector machines Diseases Machine learning Standards

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Text summarization in the biomedical domain

arXiv

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arXiv 2019年

作者： Moradi, Milad Ghadiri, Nasser Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence and Decision Support Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

This chapter gives an overview of recent advances in the field of biomedical text summarization. Different types of challenges are introduced, and methods are discussed concerning the type of challenge that they address. Biomedical literature summarization is explored as a leading trend in the field, and some future lines of work are pointed out. Underlying methods of recent summarization systems are briefly explained and the most significant evaluation results are mentioned. The primary purpose of this chapter is to review the most significant research efforts made in the current decade toward new methods of biomedical text summarization. As the main parts of this chapter, current trends are discussed and new challenges are introduced. Copyright © 2019, The Authors. All rights reserved.

关键词： Text processing

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Defend Jamming Attacks: How to Make Enemies Become Friends

Defend Jamming Attacks: How to Make Enemies Become Friends

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Thai Hoang Dinh Mohammad Abu Alsheikh Shimin Gong Dusit Niyato Zhu Han Ying-Chang Liang University of Technology Sydney Sydney NSW Australia Faculty of Science & Technology University of Canberra Australia School of Intelligent Systems Engineering Sun Yat-sen University China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Electrical and Computer Engineering University of Houston USA Center for Intelligent Networking & Communications University of Electronic Science and Technology China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In this paper, we consider a smart jammer that only attacks the channel if it detects activities of legitimate devices on that channel. To cope with smart jamming attacks, we propose an intelligent deception strategy in which the legitimate device will send fake transmissions to lure the jammer. Then, if the jammer launches attacks to the channel, the legitimate device can either backscatter the jamming signals to transmit data or harvest energy from the jamming signals for future active transmission. In this way, we can not only undermine the attack ability of the jammer, but also leverage jamming attacks as means to enhance system performance. In addition, to find an optimal defense strategy for the legitimate device under uncertainty of wireless environment as well as incomplete information from the jammer, we develop Q-learning and deep Q-learning algorithms based on the Markov decision process. Through simulation results, we demonstrate that our proposed solution is able to not only deal with smart jamming attacks, but also successfully leverage jamming attacks to improve the system performance.

关键词： Jamming Performance evaluation Backscatter Transmitters Logic gates Receivers System performance

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Nonlinear Model Predictive Control for Distributed Motion Planning in Road Intersections Using PANOC

Nonlinear Model Predictive Control for Distributed Motion Pl...

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IEEE Annual Conference on Decision and Control

作者： Alexander Katriniok Pantelis Sopasakis Mathijs Schuurmans Panagiotis Patrinos Ford Research & Innovation Center Aachen Germany Centre For Intelligent Autonomous Manufacturing Systems (i-AMS) Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science Belfast Northern Ireland UK Department of Electrical Engineering (ESAT-STADIUS) KU Leuven Leuven Belgium

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

The coordination of highly automated vehicles (or agents) in road intersections is an inherently nonconvex and challenging problem. In this paper, we propose a distributed motion planning scheme under reasonable vehicle-to-vehicle communication requirements. Each agent solves a nonlinear model predictive control problem in real time and transmits its planned trajectory to other agents, which may have conflicting objectives. The problem formulation is augmented with conditional constraints that enable the agents to decide whether to wait at a stopping line, if safe crossing is not possible. The involved nonconvex problems are solved very efficiently using the proximal averaged Newton method for optimal control (PANOC). We demonstrate the efficiency of the proposed approach in a realistic intersection crossing scenario.

关键词： Planning Collision avoidance Splines (mathematics) Trajectory Acceleration Roads Optimal control

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Design and Implementation of RFID based Smart Shopping Booth

Design and Implementation of RFID based Smart Shopping Booth

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International Conference on Information Science and Control engineering (ICISCE)

作者： Ehtisham Lodhi Fenghua Zhu Zeeshan Lodhi Qasim Saleem Gang Xiong FeiYue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering COMSATS University Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Beijing China The Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728157122

ISBN: (纸本)9781728157139

In this modern era, technology has advanced to such an extent to make our daily life more proficient and convenient. This paper presents the design and implementation of vending machine (VM) for students, which operates on radio frequency identification technology (RFID). Hence, it turns the VM into smart shopping booth (SSB). The SSB gives the access only through RFID card and vend different products according to the desire of student. The SSB is equipped with RFID reader and student identity card that holds the RFID tag. When the student identity card is scanned for verification and then after selecting the required products, will be dispensed off if a student has available balance in his/her identity card and SMS will be sent to students about remaining balance in their cards by GSM technology. The programming for controller is done by using proton IDE language. The SSB is the customized idea of vending machine and it attempts to resolve various issues that appeared in coin/note based VM. The student identity card RFID based technology is a breakthrough towards a paperless and cashless environment. Moreover, it is very beneficial in saving the precious time of students due to fast paced life and also user friendly. RFID based SSB also improved the communication and sensing capabilities of VM. Hence, our suggested approach outperforms in all aspects.

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Power system state estimation via feasible point pursuit: Algorithms and Cramér-rao bound

Power system state estimation via feasible point pursuit: Al...

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作者： Wang, Gang Zamzam, Ahmed S. Giannakis, Georgios B. Sidiropoulos, Nicholas D. State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Digital Technology Center Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States

Accurately monitoring the system's operating point is central to the reliable and economic operation of an autonomous energy grid. Power system state estimation (PSSE) aims to obtain complete voltage magnitude and angle information at each bus given a number of system variables at selected buses and lines. Power flow analysis amounts to solving a set of noise-free power flow equations, and is cast as a special case of PSSE. Physical laws dictate quadratic relationships between available quantities and unknown voltages, rendering general instances of power flow and PSSE nonconvex and NP-hard. Past approaches are largely based on gradient-type iterative procedures or semidefinite relaxation (SDR). Due to nonconvexity, the solution obtained via gradient-type schemes depends on initialization, while SDR methods do not perform as desired in challenging scenarios. This paper puts forth novel feasible point pursuit (FPP)-based solvers for power flow analysis and PSSE, which iteratively seek feasible solutions for a nonconvex quadratically constrained quadratic programming reformulation of the weighted least-squares (WLS). Relative to the prior art, the developed solvers offer superior numerical performance at the cost of higher computational complexity. Furthermore, they converge to a stationary point of the WLS problem. As a baseline for comparing different estimators, the Cramér-Rao lower bound is derived for the fundamental PSSE problem in this paper. Judicious numerical tests on several IEEE benchmark systems showcase markedly improved performance of our FPP-based solvers for both power flow and PSSE tasks over popular WLS-based Gauss-Newton iterations and SDR approaches. © 1991-2012 IEEE.

关键词： State estimation

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CrowdWatcher: an open-source platform to catch the eye of the crowd

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Quality and User Experience 2019年第1期4卷 1-17页

作者： Lebreton, Pierre Hupont, Isabelle Hirth, Matthias Mäki, Toni Skodras, Evangelos Schubert, Anton Raake, Alexander Group of Networked Sensing and Control (NeSC) Zhejiang University Hangzhou China Institute of Intelligent Systems and Robotics Sorbonne University Paris France User-Centric Analysis of Multimedia Data Group Technische Universität Ilmenau Ilmenau Germany Department of Computer Science Aalto University Espoo Finland Department of Electrical and Computer Engineering University of Patras Patras Greece Audio Visual Technology Group Technische Universität Ilmenau Ilmenau Germany

This paper presents the open-source eye tracking platform CrowdWatcher. It enables researchers to measure gaze location and user engagement in a crowdsourcing context through traditional RGB webcams. The proposed platform particularly advances the field of Quality of Experience (QoE) research, as it allows the experimenter to collect remotely and with very limited effort novel information from crowds of participants, such as their commitment towards a task, attention and decision-making processes. Two different experiments are described that were conducted to demonstrate the platform’s potential. The first experiment addresses the measurement of participants’ behavior while performing a movie selection task. Results show that the platform provides complementary information to traditional self-reported data by taking gaze analysis into account. This is of particular relevance, since in a crowdsourcing context decision processes and attention are difficult to assess, and there is often limited control over the engagement of the test user with the task. A second experiment is conducted in the scenario of a multimedia QoE test. Prediction accuracy is compared to a professional infrared eye tracker. While CrowdWatcher performs less well than the professional eye tracker, it is still able to collect valuable gaze information in the far more challenging environment of crowdsourcing. As an outlook to further application domains, the usage of the platform to measure user engagement allows participants who do not pay attention to the task to be identified.

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From FAIR to CURE: Guidelines for Computational Models of Biological systems

arXiv

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arXiv 2025年

作者： Sauro, Herbert M. Agmon, Eran Blinov, Michael L. Gennari, John H. Hellerstein, Joe Heydarabadipour, Adel Hunter, Peter Jardine, Bartholomew E. May, Elebeoba Nickerson, David P. Smith, Lucian P. Bader, Gary D. Bergmann, Frank Boyle, Patrick M. Dräger, Andreas Faeder, James R. Feng, Song Freire, Juliana Fröhlich, Fabian Glazier, James A. Gorochowski, Thomas E. Helikar, Tomas Hoops, Stefan Imoukhuede, Princess Keating, Sarah M. Konig, Matthias Laubenbacher, Reinhard Loew, Leslie M. Lopez, Carlos F. Lytton, William W. McCulloch, Andrew Mendes, Pedro Myers, Chris J. Myers, Jerry G. Mulugeta, Lealem Niarakis, Anna van Niekerk, David D. Olivier, Brett G. Patrie, Alexander A. Quardokus, Ellen M. Radde, Nicole Rohwer, Johann M. Sahle, Sven Schaff, James C. Sego, T.J. Shin, Janis Snoep, Jacky L. Vadigepalli, Rajanikanth Wiley, H. Steve Waltemath, Dagmar Moraru, Ion Department of Bioengineering University of Washington SeattleWA98195-5061 United States Intelligent Systems Engineering and Biocomplexity Institute Indiana University Street BloomingtonIN47408 United States Center for Cardiovascular Biology University of Washington SeattleWA98195-5061 United States eScience Institute University of Washington SeattleWA98195-5061 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98195-5061 United States School of Biological Sciences University of Bristol 24 Tyndall Avenue BristolBS8 1TQ United Kingdom Departments of Physiology & Pharmacology Neurology Downstate Health Science University BrooklynNY11203 United States Department of Neurology Kings County Hospital BrooklynNY11203 United States NASA-John H. Glenn Research Center MS 110-3 21000 Brookpark Road ClevelandOH44135 United States Center for Cell Analysis and Modeling UConn Health 263 Farmington Avenue FarmingtonCT06030-6406 United States Amsterdam Institute for Life and Environment Vrije Universiteit Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Department of Pathology and Genomic Medicine Thomas Jefferson University 1020 Locust St PhiladelphiaPA19107 United States Biological Sciences Division Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Auckland Bioengineering Institute University of Auckland Auckland1010 New Zealand Center of Integrative Biology University of Toulouse III-Paul Sabatier 165 Rue Marianne Grunberg-Manago Toulouse31400 France Lifeware Group Inria Building Alan Turing 1 Rue Honoré d’Estienne d’Orves Palaiseau91120 France Multiscale Modeling Group Altos Labs Redwood CityCA94065 United States partner site Tübingen Tübingen Germany Ottfried-Müller-Str. 37 Tübingen72076 Germany 06120 Germany Department of Computer Science Center for Data Science New York University New YorkNY11201 United States Department of Bi

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility;the clarity of model descriptions and annotations for understandability;adherence to standards and open science practices for reproducibility;and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible. © 2025, CC BY.

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