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Blind source separation algorithm for biomedical signal based on lie group manifold

MCB Molecular and Cellular Biomechanics 2024年第3期21卷

作者： Cheng, Daguang Zheng, Mingliang School of Mechanical and Electrical Engineering Huainan Normal University Huainan232038 China Human-computer collaborative robot Joint Laboratory of Anhui Province Huainan232038 China

Independent Component Analysis (ICA) is a powerful tool for solving blind source separation problem in biomedical engineering. The traditional ICA algorithm ignores the Lie group structure of constrained matrix manifold. In this paper, a gradient descent algorithm on Lie group manifold is proposed based on the geometric framework of optimization algorithm on Riemann manifold. Firstly, the orthogonal constraint separation matrices are regarded as a Lie group manifold, and the gradient of ICA objective function on the Lie group manifold is given by using Riemann metric;Secondly, the geodesic equation of the current iteration point along the gradient descent direction is calculated;Finally, a new iteration point is obtained by moving a certain step along the geodesic line, meanwhile, the step length can be adjusted adaptively. Simulation results show that the gradient algorithm on Lie group manifold is feasible for blind Source Separation, and its performance (convergence speed, stability and error) is better than other algorithms. Copyright © 2024 by author(s).

关键词： Independent component analysis

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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao robot: A Closed-Loop Approach

Real-Time Imitation of Human Head Motions, Blinks and Emotio...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Keyhan Rayati Amirhossein Feizi Alireza Beigy Pourya Shahverdi Mehdi Tale Masouleh Ahmad Kalhor Wing-Yue Geoffrey Louie Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran Intelligent Robotics Laboratory Oakland University Michigan USA

This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot’s responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot’s imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs.

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Model-Free Dynamic Control of a 3-DoF Delta Parallel robot for Pick-and-Place Application based on Deep Reinforcement Learning

Model-Free Dynamic Control of a 3-DoF Delta Parallel Robot f...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Hasan Jalali Saba Samadi Ahmad Kalhor Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665454537

This article investigates the performance of the Delta robot using model-free controller. Since it is difficult to obtain an accurate dynamic model of the robot and identification methods are complex and challenging. Therefore, in this article, a new approach is presented which checks the performance of the Delta robot in pick-and-place operation using model-free dynamic controllers. For the purposes of this paper, Deep Reinforcement Learning (DRL) and also the Deep Deterministic Policy Gradient (DDPG) network is used. The DDPG algorithm is implemented for the first time on the Delta robot, and according to the obtained results, it reveals that it can provide a favorable approach for the pick-and-place operation. In this method, based on the science of DRL, a reward function based on the pick-and-place operation is also defined, which gives negative reward (punishment) or a positive reward according to the way the robot performs. It is noteworthy that according to the obtained results, it is possible to generalize other points for the pick-and-place operation. It should be noted that the robot was able to learn the target after a long episode and its graph converged after approximately 18,000 episodes.

关键词： Training Deep learning Parallel robots Actuators Mechatronics Heuristic algorithms Reinforcement learning

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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An Integrated Bifunctional Pressure‒Temperature Sensing System Fabricated on a Breathable Nanofiber and Powered by Rechargeable Zinc–Air Battery for Long‑Term Comfortable Health Care Monitoring

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Advanced Fiber Materials 2024年第4期6卷 1037-1052页

作者： Peng Wang Gengsheng Liu Guifen Sun Chuizhou Meng Guozhen Shen Yang Li School of Mechanical Engineering University of JinanJinan 250022China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China School of Microelectronics Shandong UniversityJinan 250101China

Bulky external power supplies largely limit the continuous long-term application and miniaturization development of smart sensing ***,we fabricate a flexible and wearable integrated sensing system on an electrospun all-nanofiber *** three parts of the sensing system are all obtained by a facile ink-based direct writing *** resistive pressure sensor is realized by decorating MXene sheets on TPU ***,the resistive temperature sensor is prepared by compositing MXene sheets into poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS).The thin-film zinc–air battery(ZAB)includes an interdigital zinc–air electrode that is bonded with a gel polymer *** can supply a high open-circuit voltage of 1.39 V and a large areal capacity of 18.2 mAh cm^(-2) for stable and reliable power-supplying sensing parts *** to the hydrophobic nature of TPU and open-ended micropores in the TPU nanofiber,the sensing system is waterproof,self-cleaning,and air and moisture *** application,the above-mentioned functional components are seamlessly integrated into an intelligent electronic wristband,which is comfortably worn on a human wrist to monitor pulse and body temperature in real time with continuous operation of up to 4 *** the novel design and remarkable performance,the proposed integrated all-nanofiber sensing system presents a promising solution for developing advanced multifunctional wearable electronics.

关键词： Pressure‒temperature sensor Rechargeable zinc–air battery Electrospun nanofibers Permeable electronic Integrated sensing system

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Natural human Skin‑Inspired Wearable and Breathable Nanofiber‑based Sensors with Excellent Thermal Management Functionality

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Advanced Fiber Materials 2024年第6期6卷 1955-1968页

作者： Peng Wang Xiaodan Li Guifen Sun Guoqing Wang Qing Han Chuizhou Meng Zhonghe Wei Yang Li School of Mechanical Engineering University of JinanJinan 250022China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China Shandong Provincial Key Laboratory of Network‑Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China Network Information Center Department of Computer ScienceQilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Integrated Circuits Shandong UniversityJinan 250101China

Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently need to be ***,we develop a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating *** multifunctional device consists of a micropatterned carbon nanotube(CNT)/thermoplastic polyurethane(TPU)nanofiber electrode,a microporous ionic aerogel electrolyte and a microstructured Ag/TPU nanofiber *** to the presence of a supercapacitive sensing mechanism and the appli-cation of microstructuration,the sensor shows excellent sensing performance,with a sensitivity of 24.62 ***,due to the overall porous structure and hydrophobicity of TPU,the sensor shows good breathability(62 mm/s)and water repellency,with a water contact angle of 151.2°.In addition,effective passive heat preservation is achieved by combining CNTs with high solar absorption rates(85%)as the top layer facing the outside,aerogel with a low thermal conductivity(0.063 W m-1 k-1)as the middle layer for thermal insulation,and Ag with a high infrared reflectance rate as the bottom layer facing the *** warming,this material yields a higher temperature than ***,the active Joule heat-ing effect is realized by applying current through the bottom resistive electrode,which can quickly increase the temperature to supply controlled warming on *** proposed wearable and breathable sensor with tunable thermal properties is promising for monitoring and heat therapy applications in cold environments.

关键词： Wearable sensor Breathable Personal thermal management Electrospun nanofibers Joule heating

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Kinematic analysis of an under-constrained cable-driven robot using neural networks 28

Kinematic analysis of an under-constrained cable-driven robo...

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28th Iranian Conference on electrical engineering, ICEE 2020

作者： Zare, Soroush Haghighi, Morteza Shahamiri Hairi Yazdi, Mohammad Reza Kalhor, Ahmad Masouleh, Mehdi Tale School of Mechanical Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran School of Electrical and Computer Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran School of Mechanical Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781728172965

In this paper, three types of neural networks including, multilayer perceptron, radial basis function, and Local Linear Model Trees, are considered based on adaptability with the significant behavior of data to solve the forward and inverse kinematics of the under-constrained cable-driven parallel robot. The practical workspace of the robot is a rectangular cube with dimensions of 360cm × 220c× 160cm. Forward kinematics problem of the under-constrained cable-driven parallel robot is challenging to solve by reason of complexity and nonlinearity of the robot kinematic equations. The typical approach utilizes a numerical method to solve these equations, which are computationally expensive and very time-consuming. Resorting to the neural network approaches results in the feasible orientation, which confirms that all the cables are in tension. The trained neural network gives a feasible inverse kinematic problem solution and the running time of the optimal trained neural network in the control loop, obtained about 12 microseconds, pales in comparison to the sampling time of the control loop that facilitates the real-time control of the robot. Obtained results reveal that the multilayer perceptron has the mean average error order of about 2 millimeters for the position and 0.78 degrees for orientation, which makes a case for precise control in the high-speed motion of the robot. © 2020 IEEE.

关键词： Multilayer neural networks

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Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks

arXiv

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arXiv 2022年

作者： Haghpanah, Mohammad Amin Saeedizade, Ehsan Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Computer Science and Engineering University of Nevada Reno United States

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Design and Evaluation of Adaptive and Sliding Mode Control for a 3-DOF Delta Parallel robot 28

Design and Evaluation of Adaptive and Sliding Mode Control f...

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28th Iranian Conference on electrical engineering, ICEE 2020

作者： Azad, Faraz Abed Rahimi, Saeed Hairi Yazdi, Mohammad Reza Masouleh, Mehdi Tale Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781728172965

In this paper, simulation, kinematics analysis, and control of a 3-DOF Delta parallel robot have been investigated. Delta parallel robots are one of the most popular manipulators for industrial applications. The kinematic equations of the robot have been established by the screw theory, in which, the position and velocity analyses have been carried out. In SimScape simulation environment, the kinematic model of the robot has been verified and subsequently, an inverse kinematic model was adopted for control purposes. In order to improve the tracking ability of the robot, three important control approaches, consisting in PID, adaptive control, and a Sliding Mode Control (SMC) have been implemented. Afterwards, the effectiveness of each approach has been investigated by employing the root mean square error (RMSE) index. By comparing the obtained index values, the SMC demonstrates the best performance with the RMSE value of 0.4188 in comparison to other methods. Simulation results were illustrated to show the ability of the controllers to reject white noise disturbance added to the system's input signals. © 2020 IEEE.

关键词： Three term control systems

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Experimental Study on the Imitation of the human Head-and-Eye Pose Using the 3-DOF Agile Eye Parallel robot with ROS and Mediapipe Framework

Experimental Study on the Imitation of the Human Head-and-Ey...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Amirmohammad Radmehr Milad Asgari Mehdi Tale Masouleh School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Electrical and Computer Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran

ISBN: (纸本)9781665420952

In this paper, a method to mimic a human face and eyes is proposed which can be regarded as a combination of computer vision techniques and neural network concepts. From a mechanical standpoint, a 3-DOF spherical parallel robot is used which imitates the human head movement. In what concerns eye movement, a 2-DOF mechanism is attached to the end-effector of the 3-DOF spherical parallel mechanism. In order to have robust and reliable results for the imitation, meaningful information should be extracted from the face mesh for obtaining the pose of a face, i.e., the roll, yaw, and pitch angles. To this end, two methods are proposed where each of them has its own pros and cons. The first method consists in resorting to the so-called Mediapipe library which is a machine learning solution for high-fidelity body pose tracking, introduced by Google. As the second method, a model is trained by a linear regression model for a gathered dataset of face pictures in different poses. In addition, a 3-DOF Agile Eye parallel robot is utilized to show the ability of this robot to be used as a system which is similar to a human head for performing a 3-DOF rotational motion pattern. Furthermore, a 3D printed face and a 2-DOF eye mechanism are fabricated to display the whole system more stylish way. Experimental tests, which are done based on a ROS platform, demonstrate the effectiveness of the proposed methods for tracking the human head and eye movement.

关键词： Parallel robots Three-dimensional displays Tracking Linear regression Psychology Magnetic heads Libraries

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