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An Algebraic Approach for a Stability Analysis Methodology for Multiple Time-delay Systems

IFAC-PapersOnLine 2018年第14期51卷 324-329页

作者： Alikoc, Baran Ergenc, Ali Fuat Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánu 1580/3 160 00 Praha 6 Czech Republic Department of Control and Automation Eng. Faculty of Electrical and Electronics Eng. Istanbul Technical University Istanbul34469 Turkey

This paper presents an improvement on Cluster Treatment of Characteristic Roots (CTCR), which is a well-known methodology for delay-dependent stability analysis of multiple time-delay systems (MTDS). We propose an algebraic approach to extract the stability switching hypersurfaces in spectral delay space, instead of a numerical procedure in CTCR with Extended Kronecker Sum (EKS) operation. The proposed algebraic approach is based on an efficient zero location test, and the deployment of this test to an auxiliary characteristic polynomial whose unique properties have recently been revealed. The achieved improvement is demonstrated by applying the new CTCR procedure to a system with three delays. © 2018

关键词： System stability Algebra Convergence of numerical methods Delay control systems Time delay Timing circuits Bistritz Tabulation Characteristic polynomials Cluster treatment of characteristic roots Delay dependent stability analysis Kronecker Multiple time delay system Numerical procedures Time delay systems

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Recognition of patient groups with sleep related disorders using bio-signal processing and deep learning

arXiv

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arXiv 2021年

作者： Jarchi, Delaram Andreu-Perez, Javier Kiani, Mehrin Vysata, Oldrich Kuchynka, Jiri Prochazka, Ales Sanei, Saeid Smart Health Technologies Group School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Embedded and Intelligent Systems Laboratory School of Computer Science and Electronics University of Essex ColchesterCO4 3SQ United Kingdom Department of Computing and Control Engineering University of Chemistry and Technology in Prague Prague 6166 28 Czech Republic Department of Neurology Faculty of Medicine in Hradec Králové Charles University Hradec Králové500 05 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6160 00 Czech Republic School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem. © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

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AUV geophysical navigation using magnetic data — The MEDUSA GN system

AUV geophysical navigation using magnetic data — The MEDUSA...

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IEEE Symposium on Position Location and Navigation (PLANS)

作者： João Quintas Francisco Curado Teixeira António Pascoal Institute for Systems and Robotics Univ. Lisboa Lisbon Portugal Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Aveiro Portugal

This paper reports recent advances on the development of geophysical navigation (GN) algorithms for small, affordable autonomous underwater vehicles. The successful results obtained by the authors during prior work on magnetic-based GN algorithms (MAGNAV), tested in computer simulations, motivated the implementation of a GN module to be incorporated in the Robotic Operating System (ROS) installed on an autonomous marine vehicle of the MEDUSA class. The development work included: acquisition of prior magnetic maps in an offshore area of Lisbon; execution of data-acquisition tests using a MEDUSA vehicle equipped with a towed magnetometer; development of a medusa_gn package and testing of its capabilities as a GN module of the ROS system installed on-board MEDUSA. A realistic assessment of the performance of the MAGNAV method was performed in simulations by exploiting, for the first time, the ROS capability of playing back the recorded data as if the vehicle were operating in real-time. The results obtained in the present work confirm the good performance of the MAGNAV filter observed in previous tests in terms of vehicle position estimation and pave the way for a new series of tests aimed at the validation of GN algorithms in real-time navigation experiments.

关键词： Navigation Magnetometers Noise measurement Robots Sea measurements Atmospheric measurements

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Information Technologies in Science in Solving Some Environmental Problems of Natural Landscapes Based on the Synthesis of Accumulated Empirical Experience

Information Technologies in Science in Solving Some Environm...

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IEEE Conference on Quality Management, Transport and Information Security, Information Technologies (IT&MQ&IS)

作者： Tatyana Yu. Khashirova Elizaveta K. Edgulova Zamir G. Lamerdonov Salih A. Zhaboev Madina A. Enaldieva Institute of Informatics Electronics and Robotics Kabardino-Balkarian State University. H.M. Berbekova Nalchik Russia College of Information Technology and Economics Kabardino-Balkarian State University. H.M. Berbekova Nalchik Russia Kabardino-Balkarian State Agrarian University Nalchik Russia North Caucasian Mining and Metallurgical Institute Nalchik Russia

ISBN: (纸本)9781728125961

The developed method of constructing generalized models of management and environmental forecasting processes on complex modified techno-natural systems is proposed. The construction of generalized models is based on accumulated empirical experience of well-known science schools and prominent scientists using the capabilities of modern computer technology. As a rule, leading scientists' accumulated experience is generalized, and it is presented as empirical formulas. The construction of generalized models is performed on the basis of computational experiment using mathematical theory of experiment planning. The objects of the research are the subsystems of natural landscapes as a complex modified geosystem, they are: slope subsystem; ravine and gully subsystem; river subsystem and water management complex. Conceptual models and algorithms of construction and solution of the scientific problem of complex techno-natural systems' management such as the mountain and foothill landscape are presented in the report developed by Russian scientific schools.

关键词： Mathematical model Biological system modeling Computational modeling Information technology Rivers Environmental factors

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Exploring human-robot interfaces for service mobile robots 1

Exploring human-robot interfaces for service mobile robots

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1st IEEE World Symposium on Digital Intelligence for Systems and Machines, DISA 2018

作者： Chivarov, Nayden Chikurtev, Denis Pleva, Matus Ondas, Stanislav European Polytechnical University Service Robotics Lab Pernik Bulgaria Institute of Information and Communication Technologies Bulgarian Academy of Sciences BAS Sofia Bulgaria Dept. of Electronics and Multimedia Communications Faculty of Electrical Engineering and Informatics Technical University of Kosice Letna 9 Košice04120 Slovakia

ISBN: (纸本)9781538651025

this article describes the developed human-robot interfaces, designed to provide user-friendly interaction between the elderly or disabled and the robot ROBCO 17. There are four possible methods for controlling the robot presented: joystick control, gesture recognition control, speech command control, and tele-control via Web user interface. Julius Open source speech recognizer based system as an alternative solution for a local automatic speech recognition system is proposed. Experiments with elderly were conducted with developed interfaces and the results are presented. © 2018 IEEE.

关键词： Open systems

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Spoken dialogue-based remote control of educational mobile robot with mecanum wheels 17

Spoken dialogue-based remote control of educational mobile r...

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17th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2019

作者： Chivarov, Nayden Chikurtev, Denis Pleva, Matus Ondas, Stanislav Juhar, Jozef Liao, Yuan-Fu Institute of Information and Communication Technologies Bulgarian Academy of Sciences BAS Acad. G. Bonchev St. Sofia1113 Bulgaria Robotics Lab European Polytechnical University 23 Sv. Sv. Kiril i Metodiy Str. Pernik2300 Bulgaria Department of Electronics and Multimedia Communications Faculty of Electrical Engineering and Informatics Technical University of Kosice Letna 9 Kosice04200 Slovakia Department of Electronic Engineering College of Electrical Engineering &computer Science National Taipei University of Technology Zhongxiao E. Rd. 406 Taipei10608 Taiwan

ISBN: (纸本)9781728149677

This paper describes a new platform of educational mobile robot with Mecanum wheels and voice enabled dialogue-based human robot interaction model was established. Moreover, new trends in using deep neural networks for cloud-and edge-based speech recognition was described in detail. Finally, the progress of both the cloudand edge-based speech recognition was reported. Especially, promising usability test result on resisting room reverberation and background noise had been achieved by the proposed convolutional neural network (CNN)-based wake-up word (WUW) spotting system. © 2019 IEEE.

关键词： Mobile robots

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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Algorithms for joint operation of service robotic platform and set of UAVs in agriculture tasks 5

Algorithms for joint operation of service robotic platform a...

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5th IEEE Workshop on Advances in Information, Electronic and Electrical Engineering, AIEEE 2017

作者： Vu, Quyen Nguyen, Vinh Solenaya, Oksana Ronzhin, Andrey Mehmet, H. Department of Electromechanics and Robotics Saint Petersburg State University of Aerospace Instrumentation SUAI St. Petersburg Russia Laboratory of Autonomous Robotic Systems St. Petersburg Institute for Informatics and Automation Russian Academy of Sciences SPIIRAS St. Petersburg Russia Guzey Department of Electrical and Electronics Engineering Erzurum Technical University Erzurum Turkey

ISBN: (纸本)9781538641378

The problems of designing a mobile robotic platform carrying a set of unmanned aerial vehicles and containing a container for a payload are considered. The issues of transportation of robots and joint activity of robots with various functions in solving agriculture problems are discussed. The conceptual model of a mobile platform design is given. The main stages of the algorithmic model of the joint operation of the mobile platform and the set of unmanned aerial vehicles brought to the working area are described. © 2017 IEEE.

关键词： Manipulators

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Time-constrained detection of colored objects on raw Bayer data 5th

Time-constrained detection of colored objects on raw Bayer d...

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5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VipIMAGE 2015

作者： Neves, António J. R. Trifan, Alina Azevedo, José Luís Department of Electronics and Telecommunications Engineering Intelligent Systems and Intelligent Robotics Laboratory Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal

ISBN: (纸本)9781138029262

In most image processing applications, the raw data acquired by an image sensor passes through a demosaicing algorithm in order to obtain a full color image. The demosaicing step can take time and resources that are of great importance when developing real-time applications. Most of the times, this is done with single purpose of rendering the images into a viewable format for humans. However, most of nowadays sensors allow the retrieval of images in raw format and processing the data as is. In this paper we present a study on the direct usage of raw Bayer data for real-time color object detection, in the scenario of autonomous robot soccer. The experimental results that we provide prove that the efficiency of the chosen object detection algorithm is not lowered and that several improvements of the autonomous robotic vision system used in this study can be noted: the bandwidth needed to transmit the data between the digital camera and the PC is reduced, allowing, in some models, to increase the maximum frame rate. Moreover, a decrease in the delay between the perception and action of the autonomous robot was measured, when using raw image data for processing the environment. © 2016 Taylor & Francis Group, London.

关键词： Object detection

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