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Engineering 2024年第2期 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua University Department of Cognitive Robotics Faculty of Mechanical Maritime and Materials Engineering (3mE) Delft University of Technology Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Xi'an Institute of High-Technology

This study presents a general optimal trajectory planning(GOTP) framework for autonomous vehicles(AVs) that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers' behavior and summarizing their manipulation characteristics of "seeking benefits and avoiding losses." Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework's feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multisource interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers' manipulation.

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Bebop 2 Quadrotor as a Platform for Research and Education in robotics and Control Engineering

Bebop 2 Quadrotor as a Platform for Research and Education i...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Wojciech Giernacki Piotr Kozierski Jacek Michalski Marek Retinger Rafal Madonski Pascual Campoy Institute of Robotics and Machine Intelligence Faculty of Control Robotics and Electrical Engineering of Poznan University of Technology Poznań Poland Institute of Computing Science (Faculty of Computing and Telecommunications) and with the Institute of Robotics and Machine Intelligence (Faculty of Control Robotics and Electrical Engineering) Poznan University of Technology Poznań Poland International Energy College Energy Electricity Research Center Jinan University Zhuhai China Computer Vision and Aerial Robotics (CVAR) Centre for Automation and Robotics Technical University of Madrid (UPM) Madrid Spain

ISBN: (数字)9781728142784

ISBN: (纸本)9781728142791

In conducting research and teaching in fields related to unmanned aerial vehicles (UAVs), it is particularly important to select a universal, safe, open research platform and tools for rapid prototyping. Ready-to-use, low-cost micro-class UAVs such as Bebop 2 are successfully used in that regard. This article presents how to use the potential of this flying robot with Robot Operating System (ROS). The most important software solutions for the developed experimental testbed FlyBebop are characterized here. Their capabilities in research and education are exemplified using three distinct cases: 1) research results on the method of optimal, in-flight, iterative self-tuning of UAV position controller parameters (based only on current measurements), 2) the use of the reinforcement learning method in the autonomous landing of a single drone on a moving vehicle, 3) planning the movement of UAVs for autonomous video recording along the planned path in the arrangement: "cameraman drone" and "lighting technician drones".

关键词： Drones Robots Education Cameras Sensors Tools

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Deep Learning for Brain Age Estimation: A Systematic Review

arXiv

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arXiv 2022年

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Ser, Javier Del Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Assam Silchar788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Retinal Disorder Diagnosis Based on Hybrid Deep Learning Models

SSRN

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SSRN 2022年

作者： Sedik, Ahmed El-Hag, Noha A. El-Hoseny, Heba M. El-Banby, Ghada M. Khalaf, Ashraf A.M. Abd El-Samie, Fathi E. El-Shafai, Walid Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Egypt Menoufia University Egypt Department of Electronics and Electrical Communication Engineering Al-Obour High Institute for Engineering and Technology Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Deptartment of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtainedwith several modalities such as magnetic resonance (MR) and confocal microscopy need to be classified for anomaly *** MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, braintumors are represented as areas composed of abnormal cells that need to be detected. In addition, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify © 2022, The Authors. All rights reserved.

关键词： Ophthalmology

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The conceptual approach of system for automatic vehicle accident detection and searching for life signs of casualties 5th

The conceptual approach of system for automatic vehicle acci...

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5th International Doctoral Symposium on Applied Computation and Security Systems, ACSS 2018

作者： Lupinska-Dubicka, Anna Tabedzki, Marek Adamski, Marcin Rybnik, Mariusz Omieljanowicz, Miroslaw Omieljanowicz, Andrzej Szymkowski, Maciej Gruszewski, Marek Klimowicz, Adam Rubin, Grzegorz Saeed, Khalid Faculty of Computer Science Bialystok University of Technology Bialystok Poland Faculty of Mathematics and Informatics University of Bialystok Bialystok Poland Faculty of Mechanical Engineering Department of Automatic Control and Robotics Bialystok University of Technology Bialystok Poland Faculty of Computer and Food Science Lomza State University of Applied Sciences Lomza Poland

ISBN: (纸本)9789811337017

The European eSafety initiative aims to improve the safety and efficiency of road transport. The main element of eSafety is the pan-European eCall project—an in-vehicle system which idea is to inform about road collisions or serious accidents. As estimated by the European Commission, the implemented system will reduce services’ response time by 40%. This will save 2,500 people a year. In 2015, the European Parliament adopted the legislation that from the end of March 2018 all new cars from the EU should be equipped with the eCall system. The limitation of this idea is that only small part of cars driven in UE are brand new (about 3.7% brand new cars were sold in 2015). This paper presents the first concept of an onboard eCall device which can be installed at the owners’ request in used vehicles. The proposed system will be able to detect a road accident, indicate the number of vehicle’s occupants, report their vital functions, and send that information to dedicated emergency services via duplex communication channel. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Emergency services

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Switched-capacitor finite impulse response rotator filter

Switched-capacitor finite impulse response rotator filter

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Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Paweł Pawłowski Rafał Długosz Adam Dąbrowski Division of Signal Processing and Electronic Systems Jana Pawł a II 24 Poznan University of Technology Faculty of Control Robotics and Electrical Engineering and Institute of Automation and Robotics Poznań Poland Faculty of Telecommunication Computer Science and Electrical Engineering Kaliskiego 7 UTP University of Science and Technology Bydgoszcz Poland

ISBN: (数字)9788362065394

ISBN: (纸本)9781728177465

Design of the topology and layout for an analog finite impulse response (FIR) filter realized in the CMOS technology using the switched capacitor (SC) technique, is presented. The filter is based on the so called “rotator structure” supplemented with the programmable capacitor array. This array makes the on-line adjusting of the frequency response of the filter possible in a very wide range, thus, enlarges the area of applications of this circuit, e.g., to multi-standard wireless communication, software-defined radio, adaptive systems, etc. The presented circuit realizes filters of orders up to 15 (16 filter coefficients). The maximum frequency range approaches ca. 2 MHz. The circuit dissipates ca. 8 mW from the power supply.

关键词： Finite impulse response filters Capacitors IIR filters Clocks Operational amplifiers Switches Capacitance

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Accurate, interpretable, and fast animation: An iterative, sparse, and nonconvex approach

arXiv

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arXiv 2021年

作者： Racković, Stevo Soares, Cláudia Jakovetić, Dušan Desnica, Zoranka Institute for Systems and Robotics Instituto Superior Técnico Lisbon1049-001 Portugal Computer Science Department NOVA School of Science and Technology Caparica2825-149 Portugal Department of Mathematics Faculty of Sciences University of Novi Sad Novi Sad21000 Serbia 3Lateral Animation Studio Epic Games Company

Digital human animation relies on high-quality 3D models of the human face-rigs. A face rig must be accurate and, at the same time, fast to compute. One of the most common rigging models is the blendshape model. We propose a novel algorithm for solving the nonconvex inverse rig problem in facial animation. Our approach is model-based, but in contrast with previous model-based approaches, we use a quadratic instead of the linear approximation to the higher order rig model. This increases the accuracy of the solution by 8% (0:26mm) on average and, confirmed by the empirical results, increases the sparsity of the resulting parameter vector-an important feature for interpretability by animation artists. The proposed solution is based on a Levenberg-Marquardt (LM) algorithm, applied to a nonconvex constrained problem with sparsity regularization. In order to reduce the complexity of the iterates, a paradigm of Majorization Minimization (MM) is further invoked, which leads to an easy to solve problem that is separable in the parameters at each algorithm iteration. The algorithm is evaluated on a number of animation datasets, proprietary and open-source, and the results indicate the superiority of our method compared to the standard approach based on the linear rig approximation. Although our algorithm targets the specific problem, it might have additional signal processing applications. © 2021, CC BY-NC-SA.

关键词： Animation

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Efficient Visual Perception of Human-Robot Walking Environments Using Semi-Supervised Learning

Efficient Visual Perception of Human-Robot Walking Environme...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Dmytro Kuzmenko Oleksii Tsepa Andrew Garrett Kurbis Alex Mihailidis Brokoslaw Laschowski Department of Mathematics National University of Kyiv-Mohyla Academy Kyiv Ukraine Department of Mathematical Methods of System Analysis Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Department of Computer Science University of Toronto Toronto Canada Department of Systems Design Engineering University of Waterloo Waterloo Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Institute of Biomedical Engineering University of Toronto Toronto Canada Toronto Rehabilitation Institute Toronto Canada Department of Mechanical and Industrial Engineering Robotics Institute University of Toronto Toronto Canada

Convolutional neural networks trained using supervised learning can improve visual perception for human-robot walking. These advances have been possible due to largescale datasets like ExoNet and StairNet - the largest open-source image datasets of real-world walking environments. However, these datasets require vast amounts of manually annotated data, the development of which is time consuming and labor intensive. Here we present a novel semi-supervised learning system (ExoNet-SSL) that uses over 1.2 million unlabelled images from ExoNet to improve training efficiency. We developed a deep learning model based on mobile vision transformers and trained the model using semi-supervised learning for image classification. Compared to standard supervised learning (98.4%), our ExoNet-SSL system was able to maintain high prediction accuracy (98.8%) when tested on previously unseen environments, while requiring 35% fewer labelled images during training. These results show that semi-supervised learning can improve training efficiency by leveraging large amounts of unlabelled data and minimize the size requirements for manually annotated images. Future research $\text{will}$ focus on model deployment for onboard real-time inference and control of human-robot walking.

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Performance Evaluation of Nonlinear Effects in Frequency-Splitting SWIPT Signals

Performance Evaluation of Nonlinear Effects in Frequency-Spl...

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IEEE Conference on Vehicular Technology (VTC)

作者： Akashkumar Rajaram Rui Dinis João Madeira Dushantha Nalin K. Jayakody Marko Beko Faculty of Science and Technology New University of Lisbon Portugal School of Computer Science and Robotics National Research Tomsk Polytechnic University Russia Copelabs Universidade Lusòfona Lisbon Portugal

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

Simultaneous wireless information and power transmission (SWIPT) is one of the popular technique used in radio frequency energy harvesting (EH). Frequency splitting based SWIPT (FS-SWIPT) is a novel SWIPT technique that can be adopted with frequency domain multiple access signal transmission protocol for the near field EH application in biomedical sensors. In this paper, we implement FS-SWIPT in orthogonal frequency division multiplexed (OFDM) signal, the OFDM signal has separate sub carriers for the data symbols and the energy carrying symbols. At the receiver, the energy and information symbols are based on their sub carriers by using FS-SWIPT technique. The amplification of high energy OFDM signal at transmitter by using a high power amplifier are susceptible to non linear distortion (NLD), which degrades the bit error rate (BER) performance. We analyze the impact of NLD on the BER performance of FS-SWIPT and validate the analytical results by using simulations.

关键词： OFDM Frequency-domain analysis Wireless communication Energy harvesting Nonlinear distortion Bit error rate

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Attention state of children during Schulte tables task

Attention state of children during Schulte tables task

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Nonlinearity, Information and robotics, (NIR) International Conference

作者： Vadim Grubov Vladimir Maksimenko Semen Kurkin Marina Khramova Natalya Aleksandrova Alexander Hramov Neuroscience and Cognitive Technology Lab Center for Technologies in Robotics and Mechatronics Components Innopolis University Russia Faculty of Computer Science Saratov State University Saratov Russia

ISBN: (数字)9781728187631

ISBN: (纸本)9781728187648

We have analyzed EEG signals of children during cognitive load of specific type in order to estimate level of attention during this task. EEG signals were recorded in accordance with proposed design of experiment. For obtained EEG data we have analyzed behavioral characteristics as well as EEG-related characteristics. We have found that behavioural characteristic changes through the task, so may the attention level. We have also found that cognitive task completion is accompanied by appearance of spatial structure - specific distribution of wavelet energy across cortex, which partially changes during completion of the task as well. We believe that changes in attention level are tied to changes in energy distribution, so we suggest this distribution to be used as the marker for attention state estimation.

关键词： Time-frequency analysis Education Wavelet analysis Electroencephalography Task analysis State estimation Robots

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