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arXiv 2019年

作者： Struckmeier, Oliver Tiwari, Kshitij Dora, Shirin Pearson, Martin J. Bohte, Sander M. Pennartz, Cyriel M.A. Kyrki, Ville Department of Electrical Engineering and Automation Aalto University Espoo02150 Finland Cognitive and Systems Neuroscience Group University of Amsterdam Centrum Wiskunde & Informatica Amsterdam Netherlands Bristol Robotics Laboratory BristolBS16 1QY United Kingdom

Extracting and binding salient information from different sensory modalities to determine common features in the environment is a significant challenge in robotics. Here we present MuPNet (Multi-modal Predictive Coding Network), a biologically plausible network architecture for extracting joint latent features from visuo-tactile sensory data gathered from a biomimetic mobile robot. In this study we evaluate MuPNet applied to place recognition as a simulated biomimetic robot platform explores visually aliased environments. The F1 scores demonstrate that its performance over prior handcrafted sensory feature extraction techniques is equivalent under controlled conditions, with significant improvement when operating in novel environments. Copyright © 2019, The Authors. All rights reserved.

关键词： Network coding

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Cover Image, Volume 6, Issue 10

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Health Science Reports 2023年第10期6卷

作者： Mohammad Faranoush Pooya Faranoush Iraj Heydari Mohammad Reza Foroughi-Gilvaee Azita Azarkeivan Ali Parsai Kia Negin Sadighnia Ali Elahinia Afsoon Zandi Mohammad Reza Rezvany Nahid Hashemi-Madani Amir Ziaee Reza Nekouian Farzaneh Rohani Pediatric Growth and Development Research Center Institute of Endocrinology Iran University of Medical Sciences Tehran Iran Cardio-Oncology Research Center Rajaie Cardiovascular Medical & Research Center Iran University of Medical Sciences Tehran Iran Nano Bio Electronic Devices Lab Cancer Electronics Research Group School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Blood Transfusion Research Center High Institute for Research and Education in Transfusion Medicine Tehran Iran Robotics Research Laboratory School of Mechanical Engineering Iran University of Science and Technology Tehran Iran Department of Otolaryngology Head & Neck Surgery Taleghani Hospital Shahid Beheshti University of Medical Sciences Tehran Iran

The cover image is based on the Research ArticleComplications in patients with transfusion dependent thalassemia: A descriptive cross‐sectional studyby Mohammad Faranoush et al.,https://***/10.1002/hsr2.1624

The cover image is based on the Research Article Complications in patients with transfusion dependent thalassemia: A descriptive cross‐sectional study by Mohammad Faranoush et al., https://***/10.1002/hsr2.1624

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Variational autoencoded regression: High dimensional regression of visual data on complex manifold

arXiv

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arXiv 2019年

作者： Yoo, YoungJoon Yun, Sangdoo Chang, Hyung Jin Demiris, Yiannis Choi, Jin Young Graduate School of Convergence Science and Technology Seoul National University Korea Republic of ASRI Dept. of Electrical and Computer Eng. Seoul National University Korea Republic of Personal Robotics Laboratory Department of Electrical and Electronic Engineering Imperial College London United Kingdom

This paper proposes a new high dimensional regression method by merging Gaussian process regression into a variational autoencoder framework. In contrast to other regression methods, the proposed method focuses on the case where output responses are on a complex high dimensional manifold, such as images. Our contributions are summarized as follows: (i) A new regression method estimating high dimensional image responses, which is not handled by existing regression algorithms, is proposed. (ii) The proposed regression method introduces a strategy to learn the latent space as well as the encoder and decoder so that the result of the regressed response in the latent space coincide with the corresponding response in the data space. (iii) The proposed regression is embedded into a generative model, and the whole procedure is developed by the variational autoencoder framework. We demonstrate the robustness and effectiveness of our method through a number of experiments on various visual data regression problems. Copyright © 2019, The Authors. All rights reserved.

关键词： Regression analysis

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Robust Semi-Global Leaderless Consensus and Containment Control of Identical Linear Systems with Imperfect Actuators

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Journal of Systems Science & Complexity 2018年第1期31卷 69-86页

作者： SHI Liangren ZHAO Zhiyun LIN Zongli Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240 China Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology) Ministry of Education Shanghai 200237 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia P.O. Box 400743 Charlottesville VA 22904-4743 USA

This paper investigates both the robust semi-global leaderless consensus problem and the robust semi-global containment control problem for a group of identical linear systems with imperfect actuators. The imperfect actuators are characterized by nonlinearities such as saturation and dead zone and there input output relationships are not precisely known. The dynamics of follower agents are also affected by the input additive disturbances. Low-and-high gain feedback consensus protocols are constructed to solve these problems. More specifically, it is shown that robust semi-global leaderless consensus can be achieved over a connected undirected graph and robust semi-global containment control can be achieved when each follower agent has access to the information of at least one leader agent. Numerical simulation illustrates the theoretical results.

关键词： Consensus containment control dead zone leaderless consensus multi-agent systems saturation

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Vehicular Multi-Camera Sensor System for Automated Visual Inspection of Electric Power Distribution Equipment

Vehicular Multi-Camera Sensor System for Automated Visual In...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jinsun Park Ukcheol Shin Gyumin Shim Kyungdon Joo Francois Rameau Junhyeok Kim Dong-Geol Choi In So Kweon Robotics and Computer Vision Laboratory School of Electrical Engineering KAIST Daejeon Republic of Korea Korea Electric Power Corporation Korea Electric Power Research Institute Daejeon Republic of Korea Department of Information and Communication Engineering Hanbat National University Daejeon Republic of Korea

In this paper, we present a multi-camera sensor system along with its control algorithm for automated visual inspection from a moving vehicle. To accomplish this task, we propose a unique hardware configuration consisting of a frontal stereo vision system, six lateral cameras motorized to tilt, and a GPS/IMU sensor mounted on the roof of a car. From the frontal stereo system, we detect electric poles and estimate their corresponding 3D positions. Based on this 3D estimation, the tilt angles of the motorized lateral cameras are controlled in real-time to capture high resolution images of the equipment - typically installed a few meters above the road surface. In addition, inertial odometry information from the GPS/IMU module is utilized for pose estimation, object localization, and re-identification among cameras. Experimental results demonstrate the efficiency and robustness of our system for automated electric equipment maintenance, which can reduce human effort significantly.

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A New Approach of Dynamic Fuzzy Cognitive Knowledge Networks in Modelling Diagnosing Process of Meniscus Injury

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IFAC-PapersOnLine 2017年第1期50卷 5861-5866页

作者： Anninou A.P. Groumpos P.P. Poulios P. Gkliatis I. Laboratory of Automation and Robotics Department of Electrical and Computer Engineering University of Patras Greece Orthopaedic Clinic Department of Medicine University of Patras Greece

A new approach of Dynamic Fuzzy Cognitive Knowledge Networks is presented. This is an evolutionary type of Fuzzy Cognitive Maps (FCM) that arose from the need for updating classic methodology in order to overcome its drawbacks, concerning the single calculation rule, stability and real time problems and expand its use in a variety of applications. This new approach is being tested for its accuracy in Decision Support Systems in medicine, trying to model knee injuries by using 17 real cases of patients. The new proposed model is able to diagnose meniscus injuries and to distinguish between acute and degenerative injury. Subsequently we observe the evolution of the injury by administering a proposed treatment by the physician. Results of this new method, which are presented in detail, are very satisfactory for both two levels and treatment stage, and in total agreement with Magnetic Resonance Imaging outcomes. The whole methodology is the outcome of a close collaboration between engineers and medical doctors and is significant because it is a promising tool which sets aside the main disadvantages of Fuzzy Cognitive Maps and allows us a wide use in many real time problems. © 2017

关键词： Dynamic Fuzzy Cognitive Knowledge Networks Fuzzy Control Meniscus Injuries

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Bio-Syncretic Light-gated Field-Effect Transistor: Fabrication and Characterization

Bio-Syncretic Light-gated Field-Effect Transistor: Fabricati...

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IEEE Conference on Nanotechnology

作者： Jia Yang Wenxue Wang Gongxin Li Guangyong Li Ning Xi Lianqing Liu State Key Laboratory of Robotics Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China State Key Laboratory of Robotics Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Jiangnan University Wuxi China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States Emerging Technologies Institute University of Hong Kong Pokfulam Hong Kong

Biological visual system has many intrinsic functional superiorities, such as small size, high sensitivity and no heat-sink cooling, which are hard to be completely replicated by electromechanical systems. The bio-syncretic design of sensing device with living materials may provide some functional superiorities to those conventional sensing devices, and will have great potential benefits in both biomedical and engineering applications. In this study, we developed a novel bio-syncretic light-gated field-effect transistor (FET), in which optogenetically engineered cells were used as the gate substitute for conventional gate electrode, and the exposure of the cells to light irradiation results in the variation of the transistor's channel current. The bio-syncretic transistor was fabricated by virtue of graphene film and optogenetically engineered cells, and its basic photo-responsive performance was then tested and characterized. This reference-less bio-syncretic transistor has a wide range of potential applications in photodetection and biosensing with good biocompatibility and stability.

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Robust Lane Marking Detection Algorithm Using Drivable Area Segmentation and Extended SLT

Robust Lane Marking Detection Algorithm Using Drivable Area ...

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IEEE International Conference on robotics and Biomimetics

作者： Umar Ozgunalp Rui Fan Shanshan Cheng Yuxiang Sun Weixun Zuo Yilong Zhu Bohuan Xue Linwei Zheng Qing Liang Ming Liu Electrical and Electronics Engineering Cyprus International University North Cyprus Turkey Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong SAR China Guolu Gaoke Engineering Technology Institute Co. Ltd. Beijing China Robotics and Multiperception Laboratory Robotics Institute the Hong Kong University of Science and Technology Hong Kong SAR China

In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented. Since the lane markings are on the road area and any feature point above the ground will be a noise source for the lane detection, a mask is created for the road area to remove some of the noise for lane detection. The estimated mask is multiplied by the lane feature map in a bird's eye view (BEV). The lane feature points are extracted by using an extended version of symmetrical local threshold (SLT), which not only considers dark light dark transition (DLD) of the lane markings, like (SLT), but also considers parallelism on the lane marking borders. The segmentation then uses only the feature points that are on the road area. A maximum of two linear lane markings are detected using an efficient 1D Hough transform. Then, the detected linear lane markings are used to create a region of interest (ROI) for parabolic lane detection. Finally, based on the estimated region of interest, parabolic lane models are fitted using robust fitting. Due to the robust lane feature extraction and road area segmentation, the proposed algorithm robustly detects lane markings and achieves lane marking detection with an accuracy of 91% when tested on a sequence from the KITTI dataset.

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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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Energetic electron precipitation driven by electromagnetic ion cyclotron waves from ELFIN’s low altitude perspective

arXiv

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arXiv 2022年

作者： Angelopoulos, V. Zhang, X.-J. Artemyev, A.V. Mourenas, D. Tsai, E. Wilkins, C. Runov, A. Liu, J. Turner, D.L. Li, W. Khurana, K. Wirz, R.E. Sergeev, V.A. Meng, X. Wu, J. Hartinger, M.D. Raita, T. Shen, Y. An, X. Shi, X. Bashir, M.F. Shen, X. Gan, L. Qin, M. Capannolo, L. Ma, Q. Russell, C.L. Masongsong, E.V. Caron, R. He, I. Iglesias, L. Jha, S. King, J. Kumar, S. Le, K. Mao, J. McDermott, A. Nguyen, K. Norris, A. Palla, A. Roosnovo Tam, J. Xie, E. Yap, R.C. Ye, S. Young, C. Adair, L.A. Shaffer, C. Chung, M. Cruce, P. Lawson, M. Leneman, D. Allen, M. Anderson, M. Arreola-Zamora, M. Artinger, J. Asher, J. Branchevsky, D. Cliffe, M. Colton, K. Costello, C. Depe, D. Domae, B.W. Eldin, S. Fitzgibbon, L. Flemming, A. Frederick, D.M. Gilbert, A. Hesford, B. Krieger, R. Lian, K. McKinney, E. Miller, J.P. Pedersen, C. Qu, Z. Rozario, R. Rubly, M. Seaton, R. Subramanian, A. Sundin, S.R. Tan, A. Thomlinson, D. Turner, W. Wing, G. Wong, C. Zarifian, A. Earth Planetary and Space Sciences Department Institute of Geophysics and Planetary Physics University of California Los Angeles Los AngelesCA90095 United States University of Texas at Dallas RichardsonTX75080 United States CEA DAM DIF Arpajon France Atmospheric and Oceanic Sciences Departments University of California Los AngelesCA United States Johns Hopkins University Applied Physics Laboratory LaurelMD United States Department of Astronomy Center for Space Physics Boston University BostonMA United States Mechanical and Aerospace Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States School of Mechanical Industrial Manufacturing Engineering Oregon State University CorvallisOR97331 United States University of St. Petersburg St. Petersburg Russia Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Space Science Institute BoulderCO80301 United States Sodankylä Geophysical Observatory University of Oulu Sodankylä Finland Materials Science and Engineering Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Deloitte Consulting New YorkNY10112 United States Computer Science Department Henry Samueli School of Engineering University of California Los AngelesCA90095 United States Microsoft RedmondWA98052 United States Physics and Astronomy Department University of California Los AngelesCA90095 United States Department of Astronomy and Astrophysics The University of Chicago ChicagoIL60637 United States Raybeam Inc. Mountain ViewCA94041 United States SpaceX HawthorneCA90250 United States Reliable Robotics Corporation Mountain ViewCA94043 United States Los Alamos National Laboratory Los AlamosNM87545 United States Mathematics Department University of California Los AngelesCA90095 United States Planet Labs PBC San FranciscoCA94107 United States KSAT Inc. De

We review comprehensive observations of electromagnetic ion cyclotron (EMIC) wave-driven energetic electron precipitation using data from the energetic electron detector on the Electron Losses and Fields InvestigatioN (ELFIN) mission, two polar-orbiting low-altitude spinning CubeSats, measuring 50-5000 keV electrons with good pitch-angle and energy resolution. EMIC wave-driven precipitation exhibits a distinct signature in energy-spectrograms of the precipitating-to-trapped flux ratio: peaks at >0.5 MeV which are abrupt (bursty) (lasting ∼17s, or ∆L ∼ 0.56) with significant substructure (occasionally down to sub-second timescale). We attribute the bursty nature of the precipitation to the spatial extent and structuredness of the wave field at the equator. Multiple ELFIN passes over the same MLT sector allow us to study the spatial and temporal evolution of the EMIC wave - electron interaction region. Case studies employing conjugate ground-based or equatorial observations of the EMIC waves reveal that the energy of moderate and strong precipitation at ELFIN approximately agrees with theoretical expectations for cyclotron resonant interactions in a cold plasma. Using 2 years of ELFIN data uniformly distributed in local time, we assemble a statistical database of ∼50 events of strong EMIC wave-driven precipitation. Most reside at L ∼ 5 − 7 at dusk, while a smaller subset exists at L ∼ 8 − 12 at post-midnight. The energies of the peak-precipitation ratio and of the half-peak precipitation ratio (our proxy for the minimum resonance energy) exhibit an L-shell dependence in good agreement with theoretical estimates based on prior statistical observations of EMIC wave power spectra. The precipitation ratio’s spectral shape for the most intense events has an exponential falloff away from the peak (i.e., on either side of ∼ 1.45 MeV). It too agrees well with quasi-linear diffusion theory based on prior statistics of wave spectra. It should be noted though that this diffusive

关键词： Radiation belts

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