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An Application of Pixel Interval Down-sampling (PID) for Dense Tiny Microorganism Counting on Environmental Microorganism Images

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arXiv 2022年

作者： Zhang, Jiawei Zhao, Xin Jiang, Tao Rahaman, Md Mamunur Yao, Yudong Lin, Yu-Hao Zhang, Jinghua Pan, Ao Grzegorzek, Marcin Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China School of Resources and Civil Engineering Northeastern University Shenyang China Stevens Institute of Technology United States Department of Environmental Engineering National Chung Hsing University 145 Xingda Rd. Taichung402 Taiwan School of Control Engineering Chengdu University of Information Technology China Institute of Medical Informatics University of Luebeck Germany School of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China

This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural network (CNN) model with an encoder-decoder architecture. The pixel interval down-sampling operations are concatenated with max-pooling operations to combine the sparse and dense features. This addresses the limitation of contour conglutination of dense objects while counting. The evaluation was conducted using classical segmentation metrics (the Dice, Jaccard and Hausdorff distance) as well as counting metrics. The experimental results show that the proposed PID-Net had the best performance and potential for dense tiny object counting tasks, which achieved 96.97% counting accuracy on the dataset with 2448 yeast cell images. By comparing with the state-of-the-art approaches, such as Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can segment dense tiny objects with clearer boundaries and fewer incorrect debris, which shows the great potential of PID-Net in the task of accurate counting. © 2022, CC0.

关键词： Pixels

Parameter estimation and control of an unmanned aircraft-based transportation system for variable-mass payloads

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Parameter estimation and control of an unmanned aircraft-bas...

作者： Alatorre, Armando Espinoza, Eduardo S. Sánchez, Bonifacio Ordaz, Patricio Muñoz, Filiberto García Carrillo, Luis Rodolfo French-Mexican Laboratory of Informatics and Automatics Center of Research and Advanced Studies of the National Polytechnic Institute México City Mexico Automatic Control Department Center of Research and Advanced Studies of the National Polytechnic Institute México City Mexico Research Center of Information Technologies and Systems Autonomous University of the State of Hidalgo Hidalgo Mexico Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM United States

This paper deals with the development of an adaptive robust controller for an Unmanned Aircraft System with variable mass. The goal is to improve trajectory tracking and energy performance while the aircraft mass is gradually reducing due to payload release. To improve the trajectory tracking performance, we propose an algorithm that merges the concepts of least squares method and sliding mode observer for the estimation of the vehicle mass. The nonlinear observer estimates the linear velocities and accelerations, which are further used to obtain the vehicle mass. A robust adaptive pole placement controller based on the Attractive Ellipsoid Method uses this estimation to update the controllers gains due to the mass variation. To validate the effectiveness of the proposed algorithm for performing aerial transportation and deployment of payloads, we present a numerical example comparing the performance of the proposed method with respect to using Proportional Derivative controllers as well as Robust controllers. © 2021 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： controllers

Learning compliant grasping and manipulation by teleoperation with adaptive force control

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arXiv 2021年

作者： Zeng, Chao Li, Shuang Jiang, Yiming Li, Qiang Chen, Zhaopeng Yang, Chenguang Zhang, Jianwei Department of Informatics Universiẗat Hamburg Germany National Engineering Laboratory for Robot Visual Perception and Control Hunan University China Center for Cognitive Interaction Technology Bielefeld University Germany Bristol Robotics Laboratory University of the West of England United Kingdom

In this work, we focus on improving the robot's dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framework, is exploited to map human hand postures to a multifingered robot hand. We augment TeachNet, which is originally based on an imprecise kinematic mapping and position-only servoing, with a biomimetic learning-based compliance control algorithm for dexterous manipulation tasks. This compliance controller takes the mapped robotic joint angles from TeachNet as the desired goal, computes the desired joint torques. It is derived from a computational model of the biomimetic control strategy in human motor learning, which allows adapting the control variables (impedance and feedforward force) online during the execution of the reference joint angle trajectories. The simultaneous adaptation of the impedance and feedforward profiles enables the robot to interact with the environment in a compliant manner. Our approach has been verified in multiple tasks in physics simulation, i.e., grasping, opening-a-door, turning-a-cap, and touching-a-mouse, and has shown more reliable performances than the existing position control and the fixed-gain-based force control approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Remote control

On the Energy Self-Sustainability of IoT via Distributed Compressed Sensing

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China Communications 2020年第12期17卷 37-51页

作者： Wei Chen Nikos Deligiannis Yiannis Andreopoulos Ian JWassell State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China Electronics and Informatics department Vrije Universiteit BrusselBrussel Pleinlaan 21050Belgium Department of Electronic and Electrical Engineering University College LondonLondon WC1E 6BTU.K. Computer Laboratory University of CambridgeCambridge CB30FDU.K imec Kapeldreef 75B-3001 LeuvenBelgium

This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy *** consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit *** provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH ***,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)*** performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability *** results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.

关键词： distributed compressed sensing energy harvesting internet of things energy self-sustainability

Higher order automatic differentiation with dual numbers

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Periodica polytechnica Electrical engineering and computer s...

Periodica polytechnica Electrical engineering and computer science 2020年第1期65卷 1-10页

作者： Szirmay-Kalos, László Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics P. O. B. 91 BudapestH-1521 Hungary

In engineering applications, we often need the derivatives of functions defined by a program. The approach chosen for derivative computation must be algebraic to allow computer implementation. A particular solution to obtain first derivatives is the application of dual numbers. This paper proposes simple and compact generalizations of this idea to obtain derivatives of arbitrary order for single or multi-variate functions and the automatic handling of 0/0 ambiguities in the calculations. We also provide the C++ code that takes advantage of operator overloading and recursion. The method is demonstrated by path animation, Gaussian curvature computation, and curve fairing. © 2020 Budapest University of Technology and Economics. All rights reserved.

关键词： Application programs

control law and actuator capacity effect on the dynamic performance of a hybrid mass damper;the case of rottweil tower 11

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Control law and actuator capacity effect on the dynamic perf...

11th International Conference on Structural Dynamics, EURODYN 2020

作者： Koutsoloukas, Lefteris Nikitas, Nikolaos Aristidou, Petros Meinhardt, Christian School of Civil Engineering University of Leeds LeedsLS2 9JT United Kingdom School of Civil Engineering University of Leeds LeedsLS2 9JT United Kingdom Department of Electrical Engineering & Computer Engineering and Informatics Cyprus University of Technology Limassol Limassol3036 Cyprus GERB Vibration Control Systems Roedernallee 174-176 Berlin13407 Germany

ISBN: (纸本)9786188507203

This paper considers different vibration control options for a real high-rise tower subjected to real wind loading. To mitigate excessive responses, the tower utilizes a hybrid passive - active control system with a relatively small actuator capacity. Firstly, the methodology for establishing a reduced order numerical model of the tower from a finite element realization is presented. Subsequently, to deal with physical constraints, a Model Predictive control (MPC) algorithm is employed and compared to a more conventional Linear Quadratic Regulator (LQR). In both cases, the algorithms managed to keep the dynamic displacements within the set desired limit. It was concluded that, the MPC had a better performance when compared to the LQR in terms of peak amplitudes at the expense though of energy. © 2020 European Association for Structural Dynamics. All rights reserved.

关键词： Model predictive control

EigenMPC: An Eigenmanifold-Inspired Model-Predictive control Framework for Exciting Efficient Oscillations in Mechanical Systems

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EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control...

IEEE Conference on Decision and control

作者： Andre Coelho Alin Albu-Schaeffer Arne Sachtler Hrishik Mishra Davide Bicego Christian Ott Antonio Franchi Institute of Robotics and Mechatronics of the German Aerospace Center (DLR) Oberpfaffenhofen Germany Dextrous Robotics Inc Memphis TN USA Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Netherlands Department of Informatics Technical University of Munich Germany Automation and Control Institute (ACIN) Technical University of Vienna Austria LAAS-CNRS University of Toulouse France

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper proposes a Nonlinear Model-Predictive control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation.

关键词： Analytical models Aerospace electronics Energy efficiency Trajectory Mechanical systems Oscillators Robots

EMDS-7: Environmental Microorganism Image Dataset Seventh Version for multiple object detection evaluation

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arXiv 2021年

作者： Yang, Hechen Li, Chen Zhao, Xin Cai, Bencheng Zhang, Jiawei Ma, Pingli Zhao, Peng Chen, Ao Jiang, Tao Sun, Hongzan Teng, Yueyang Qi, Shouliang Huang, Xinyu Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group MBIE College Northeastern University Shenyang110169 China Department of Environmental Engineering School of Control Engineering Chengdu University of Information Technology Chengdu610225 China China Medical University China Institute of Medical Informatics University of Luebeck Luebeck Germany

The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set, including the original Environmental Microorganism images (EMs) and the corresponding object labeling files in ".XML" format file. The EMDS-7 data set consists of 41 types of EMs, which has a total of 2365 images and 13216 labeled objects. The EMDS-7 database mainly focuses on the object detection. In order to prove the effectiveness of EMDS-7, we select the most commonly used deep learning methods (Faster-RCNN, YOLOv3, YOLOv4, SSD and RetinaNet) and evaluation indices for testing and evaluation. EMDS-7 is freely published for non-commercial purpose at: Https://***/yanghechen/EMDS-7. © 2021, CC BY.

关键词： Microorganisms

Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework

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arXiv 2020年

作者： Nouri, Mahbod Moradi, Faraz Ghaemi, Hafez Nasrabadi, Ali Motie School of Informatics The University of Edinburgh United Kingdom Faculty of Engineering University of Ottawa Canada Department of Control and Computer Engineering Politecnico di Torino Italy Department of Biomedical Engineering Shahed University Tehran Iran

A conventional brain-computer interface (BCI) requires a complete data gathering, training, and calibration phase for each user before it can be used. In recent years, a number of subject-independent (SI) BCIs have been developed. Many of these methods yield a weaker performance compared to the subject-dependent (SD) approach, and some are computationally expensive. A potential real-world application would greatly benefit from a more accurate, compact, and computationally efficient subject-independent BCI. In this work, we propose a novel subject-independent BCI framework, named CCSPNet (Convolutional Common Spatial Pattern Network) that is trained on the motor imagery (MI) paradigm of a large-scale electroencephalography (EEG) signals database consisting of 400 trials for every 54 subjects who perform two-class hand-movement MI tasks. The proposed framework applies a wavelet kernel convolutional neural network (WKCNN) and a temporal convolutional neural network (TCNN) in order to represent and extract the spectral features of EEG signals. A common spatial pattern (CSP) algorithm is implemented for spatial feature extraction, and the number of CSP features is reduced by a dense neural network. Finally, the class label is determined by a linear discriminant analysis (LDA) classifier. The CCSPNet evaluation results show that it is possible to have a compact BCI that achieves both SD and SI state-of-the-art performance comparable to complex and computationally expensive models. © 2020, CC BY-NC-SA.

关键词： Electroencephalography